Queue protection using a shared global memory reserve

Information

  • Patent Grant
  • 11563695
  • Patent Number
    11,563,695
  • Date Filed
    Monday, August 29, 2016
    7 years ago
  • Date Issued
    Tuesday, January 24, 2023
    a year ago
Abstract
The subject technology relates to the management of a shared buffer memory in a network switch. Systems, methods, and machine readable media are provided for receiving a data packet at a first network queue from among a plurality of network queues, determining if a fill level of a queue in a shared buffer of the network switch exceeds a dynamic queue threshold, and in an event that the fill level of the shared buffer exceeds the dynamic queue threshold, determining if a fill level of the first network queue is less than a static queue minimum threshold.
Description
TECHNICAL FIELD

The subject technology pertains to managing memory resources in a network switch and in particular, for managing a shared buffer memory amongst multiple queues in a shared memory network switch.


BACKGROUND

Several different architectures are commonly used to build packet switches (e.g., IP routers, ATM switches and Ethernet switches). One architecture is the output queue (OQ) switch, which places received packets in various queues that are dedicated to outgoing ports. The packets are stored in their respective queues until it is their turn to depart (e.g. to be “popped”). While various types of OQ switches have different pros and cons, a shared memory architecture is one of the simplest techniques for building an OQ switch. In some implementations, a shared memory switch functions by storing packets that arrive at various input ports of the switch into a centralized shared buffer memory. When the time arrives for the packets to depart, they are read from the shared buffer memory and sent to an egress line.


There are various techniques for managing a shared memory buffer. In some memory management solutions, the network switch prevents any single output queue from taking more than a specified share of the buffer memory when the buffer is oversubscribed, and permits a single queue to take more than its share to handle incoming packet bursts if the buffer is undersubscribed.





BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features of the disclosure can be obtained, a more particular description of the principles briefly described above will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments of the disclosure and are not therefore to be considered to be limiting of its scope, the principles herein are described and explained with additional specificity and detail through the use of the accompanying drawings in which:



FIG. 1A graphically illustrates an example of queue occupancy relative to free buffer memory in a shared memory network switch.



FIGS. 1B and 1C illustrate examples of memory allocation tables that indicate occupancy for various queues, as well as a total available free memory resource for a shared buffer memory.



FIG. 2 illustrates an example flow chart for implementing a shared buffer memory allocation algorithm utilizing a global shared reserve, according to some aspects of the technology.



FIG. 3A illustrates an example table of queue occupancy levels for multiple queues implementing a global shared reserve memory management technique, according to some aspects of the technology.



FIG. 3B graphically illustrates an example of memory allocated to a shared buffer memory by various queues using a global shared reserve memory management technique, according to some aspects of the technology.



FIG. 4 illustrates an example network device.



FIGS. 5A and 5B illustrate example system embodiments.





DESCRIPTION OF EXAMPLE EMBODIMENTS

Various embodiments of the disclosure are discussed in detail below. While specific implementations are discussed, it should be understood that this is done for illustration purposes only. A person skilled in the relevant art will recognize that other components and configurations can be used without parting from the spirit and scope of the disclosure.


Overview


One problem with managing shared memory space amongst multiple queues is to ensure that active queues (i.e., “aggressor queues”) do not occupy the entire memory and thereby impede buffer access by other queues. Queues that are prevented from enqueue due to limited buffer space are referred to herein as “victim queues.” In a shared memory switch, an algorithm is required to prevent any single queue from taking more than its fair allocation of shared memory. In some memory management solutions, the algorithm calculates a dynamic maximum threshold by multiplying the amount of unallocated/free memory in the shared memory by a parameter (e.g., “alpha”). Typically values of alpha range between 0.5 and 2.0.


With alpha set to 1.0 consider a single oversubscribed queue: the system stabilizes with the queue and the free memory both being the same size, i.e., the queue can consume only half of memory. With 2 oversubscribed queues the queues can each have ⅓rd of the memory and ⅓rd remains unallocated, and so on up to N oversubscribed queues, where each queue will have 1/(N+1) of the memory and 1/(N+1) will remain unallocated. An example of the relative memory allocation amongst multiple queues is discussed in further detail with respect to FIG. 1A, below.


In some data center deployments, the buffer is required to be able to absorb large data bursts into a single queue (e.g., incast burst absorption). So the “alpha” parameter (which is programmable), is usually set to greater than 1, typically 9 (e.g., 90% of the buffer). With this setting, few aggressor queues/concurrent bursts could consume the entire buffer, and any new incoming traffic is dropped (e.g. a tail—drop), affecting throughput for victim queues.


Another solution is to provide a dedicated memory allocation for each queue (e.g., a minimum reserve), and reduce the total shareable buffer space by the sum of all minimum reserves. Depending on implementation, this can result in carving out a section of the buffer memory that isn't efficiently used. Additionally, the amount of reserved buffer space is a function of the number of ports and classes of service required, so as the number of ports/services scale, dedicated memory allocations become increasingly likely to deplete available memory.


Description


Aspects of the subject technology address the foregoing problem by providing memory management systems, methods and computer-executable instructions to facilitate packet storage using a shared buffer memory. In particular, the disclosed technology provides a packet enqueuing method which requires certain preconditions before a received packet can be enqueued. In some aspects, the decision of whether or not to enqueue a packet is first based on a fill level of the shared buffer memory. That is, if an occupancy of the queue in the shared buffer memory is below a pre-determined dynamic queue threshold (e.g., a “dynamic queue maximum” or “dynamic queue MAX”), then the packet is enqueued.


Alternatively, in instances where the queue occupancy in the shared buffer exceeds the dynamic queue max threshold, then further conditions may be verified before the packet is enqueued (or dropped). As discussed in further detail below, if the fill level of the queue in the shared buffer memory exceeds the dynamic queue max threshold, then an occupancy of the referring queue may be compared to static queue threshold (e.g., a “static queue minimum” or “static queue MIN”), to determine if the packet can still be enqueued.


As used herein, the dynamic queue maximum refers to a measure of shared buffer occupancy for the entire shared buffer memory. Thus, the dynamic queue max can be understood as a function of total free/available memory in the buffer. As discussed in further detail below, the static queue minimum threshold is a threshold that relates to a minimum amount of memory in the shared buffer that is allocated for use by victim queues.



FIG. 1A graphically illustrates an example of queue occupancy levels relative to a free shared buffer memory allocation in a network switch. For example, shared buffer 102 illustrates an example in which a shared buffer occupancy is maximally allocated at ½ of the total memory capacity, i.e., for a single queue wherein alpha=1.0. Shared buffer 104 illustrates an example of a total buffer allocation for two total queues, wherein the respective shared buffer memory allocation for each is ⅓rd of the of the total buffer size. Similarly, shared buffer 106 illustrates an example of a shared buffer allocation amongst N total queues.



FIG. 1B illustrates an example memory allocation table 108 that indicates occupancy for multiple queues, as well as a total free memory for a shared buffer.


In particular, the example of FIG. 1B illustrates a memory management scheme in which any queue is permitted to occupy the entirety of shared buffer memory. As table 108 illustrates, this memory management method can be problematic due to the fact that aggressor queues can rapidly utilize the entirety of shared memory space, consequently halting the ability for victim queues to enqueue additional incoming packets.


By way of example, table 108 illustrates various occupancy levels for multiple queues (i.e., Q0, Q1, and Q2), such that any individual queue is permitted to utilize all available free memory. This scenario is demonstrated, for example, at time=T5 where Q0, and Q1 occupy 90% and 9% of the total memory, respectively (leaving a total free memory of 1%).



FIG. 1C illustrates an example memory allocation table 110 that indicates occupancy levels for multiple queues using a memory management technique that employs a dedicated “minimum reserve” for each respective queue. As discussed above, such solutions can also be sub-optimal due to the fact that some amount of shared buffer memory can be persistently reserved for inactive queues, even when memory resources are needed elsewhere. For example, using a per-queue memory reservation technique depicted by FIG. 1C (e.g., with alpha=9) a total of 25% of the total buffer memory is reserved for various queues.


By way of example, table 110 illustrates this scenario at time=T7, where Q0 occupancy is at 69 (e.g., 69% of the shared buffer size), and Q1 occupancy is at 8 (e.g., 8% of the shared buffer size), however, dynamic queue max=0, indicating that free memory (e.g., total free=23) is no longer available to other aggressor queues. Therefore, in this scenario, a total of 23% of the shared buffer memory is unallocated if all victim queues are unutilized.


As discussed above, aspects of the subject technology address the foregoing limitations of conventional buffer memory management techniques, by providing a shared buffer memory in which packet enqueuing is dependent upon the verification of various conditions, for example, relating to a fill level of the shared buffer (e.g., a dynamic queue max threshold), as well as comparisons between a fill level of a referring queue and a threshold related to a reserved apportionment of buffer resources (e.g., a static queue min threshold).



FIG. 2 illustrates an example flow chart for implementing a method 200 for allocating shared buffer memory in a network switch. Method 200 begins at step 202, in which a determination is made as to whether there is any available (unallocated) memory in the shared buffer of a network switch. If it is determined that no free memory is available, method 200 proceeds to step 204, and any newly arriving packets are dropped.


Alternatively, if it is determined that the shared buffer memory contains unallocated space, method 200 proceeds to step 206, in which a determination is made as to whether any shared buffer space is available in the shared buffer memory.


If it is determined in step 206 that no memory in the shared buffer is available, then method 200 proceeds to step 208, in which a determination is made as to whether or not the occupancy of the referring queue is below a predetermined static queue minimum, e.g., a “static queue MIN” threshold, as discussed above. In some aspects, the static queue MIN threshold is a predetermined threshold used to define a minimum threshold, above which the received data/packets from a referring queue cannot be accepted into the shared buffer memory. As such, if in step 208 it is determined that the referring queue occupancy is not less than the static queue minimum, then method 200 proceeds to step 204 and incoming packet/s are dropped.


Alternatively, if in step 208 it is determined that the referring queue occupancy is less than the static queue MIN threshold, method 200 proceeds to step 212, and data from the referring queue is stored in a “reserved portion” of the shared buffer memory. It is understood herein that the reserved portion of buffer memory (or “global reserve”) refers to a logical allotment of memory space in the shared buffer. However, it is not necessary that the global reserve portions of memory be physically distinct memory spaces that are separate, for example, from various other regions in the shared memory buffer.


Referring back to step 206, if it is determined that shared memory space is available, then method 200 proceeds to step 210 in which a determination is made as to whether the queue occupancy is less than a dynamic queue threshold (e.g., “dynamic queue MAX”). As used herein, the dynamic queue max is a threshold that defines a cutoff, above which data from an aggressor queue cannot be admitted into the shared buffer memory. Because the dynamic queue max is a function of unallocated memory space in the shared buffer memory, in some aspects the dynamic queue max threshold may be conceptualized as a function of queue activity for each associated queue in the network switch.


If in step 210 it is determined that the queue occupancy is less than the dynamic queue max, then method 200 proceeds to step 212 and the packet/s are stored in the buffer memory. Alternatively, if it is determined that the queue occupancy (e.g., queue allocation) is greater than the dynamic queue max threshold, then method 200 proceeds to step 208, where it is determined if the referring queue occupancy is less than the static queue minimum (see above).


By providing a global reserve buffer available to any referring queue that has less than a specified occupancy level, the subject memory management techniques permit data storage in the shared buffer by less active (victim) queues, even in instances where the majority of buffer storage space has been filed by aggressor queues.



FIG. 3A illustrates an example table 301 of queue occupancy levels for multiple queues implementing a shared memory management technique, as discussed above. By implementing the memory management scheme discussed with respect to method 200 above, victim queues with relatively low throughput (as compared to aggressor queues) can access portions of shared buffer memory that would otherwise be unavailable in other shared memory schemes. As illustrated in table 301, for example, at time=T5, victim queue (Q1) is able to occupy some amount of the total buffer memory (e.g., 9%), although aggressor queue Q1 has occupied most of the shared buffer. A similar example is graphically illustrated with respect to FIG. 3B.


Specifically, FIG. 3B illustrates an example of the apportionment of a memory 303 amongst multiple queues (e.g., first queue 309, second queue 311, and third queue 313), using a global shared reserve management technique, according to aspects of the subject technology. As illustrated, memory 303 is logically apportioned into a dynamic allocation 305 and a global reserve 307. As discussed above, dynamic allocation 305 can be a shared resource available to any aggressor queue until an occupancy level of that queue has reached a pre-determined threshold (e.g., a dynamic queue max). However, the global reserve 307 of memory 303 remains reserved for low volume or victim queues, so long as the occupancy of the referring queue does not exceed a pre-determined threshold governing storage to the global reserve (e.g., a static queue min threshold), as discussed above with respect to step 208 of method 200.


By way of example, the occupancy of buffer memory 303 in the example of FIG. 3B illustrates storage by three different queues. The storage of data pertaining to first queue 309 is managed solely within dynamic allocation 305. The storage of data pertaining to second queue 311 is shared amongst dynamic allocation 305 and global reserve 307, and data associated with third queue 313 is stored exclusively into global reserve 307.


As discussed above, storage of data from second queue 311 first began by storing data to dynamic allocation 305, until occupancy of dynamic allocation 305 was complete. After dynamic allocation 305 reached capacity, a determination was made as to whether the remaining data in second queue 311 was smaller than a static queue threshold, necessary to admit the data into the global reserve. Lastly, data from third queue 313, which could not have been stored to dynamic allocation 305 (due to its fill state), was exclusively stored into global reserve 307.


By maintaining global reserve 307 portion of buffer memory 303, the disclosed memory management technique provides for a minimal apportionment of shared buffer space that is continuously available to victim queues.


Example Devices



FIG. 4 illustrates an example network device 410 suitable for high availability and failover. Network device 410 includes a master central processing unit (CPU) 462, interfaces 468, and a bus 415 (e.g., a PCI bus). When acting under the control of appropriate software or firmware, the CPU 462 is responsible for executing packet management, error detection, and/or routing functions. The CPU 462 preferably accomplishes all these functions under the control of software including an operating system and any appropriate applications software. CPU 462 may include one or more processors 463 such as a processor from the Motorola family of microprocessors or the MIPS family of microprocessors. In an alternative embodiment, processor 463 is specially designed hardware for controlling the operations of router 410. In a specific embodiment, a memory 461 (such as non-volatile RANI and/or ROM) also forms part of CPU 462. However, there are many different ways in which memory could be coupled to the system.


The interfaces 468 are typically provided as interface cards (sometimes referred to as “line cards”). Generally, they control the sending and receiving of data packets over the network and sometimes support other peripherals used with the router 410. Among the interfaces that may be provided are Ethernet interfaces, frame relay interfaces, cable interfaces, DSL interfaces, token ring interfaces, and the like. In addition, various very high-speed interfaces may be provided such as fast token ring interfaces, wireless interfaces, Ethernet interfaces, Gigabit Ethernet interfaces, ATM interfaces, HSSI interfaces, POS interfaces, FDDI interfaces and the like. Generally, these interfaces may include ports appropriate for communication with the appropriate media. In some cases, they may also include an independent processor and, in some instances, volatile RAM. The independent processors may control such communications intensive tasks as packet switching, media control and management. By providing separate processors for the communications intensive tasks, these interfaces allow the master microprocessor 462 to efficiently perform routing computations, network diagnostics, security functions, etc.


Although the system shown in FIG. 4 is one specific network device of the present invention, it is by no means the only network device architecture on which the present invention can be implemented. For example, an architecture having a single processor that handles communications as well as routing computations, etc. is often used. Further, other types of interfaces and media could also be used with the router.


Regardless of the network device's configuration, it may employ one or more memories or memory modules (including memory 461) configured to store program instructions for the general-purpose network operations and mechanisms for roaming, route optimization and routing functions described herein. The program instructions may control the operation of an operating system and/or one or more applications, for example. The memory or memories may also be configured to store tables such as mobility binding, registration, and association tables, etc.



FIG. 5A and FIG. 5B illustrate example system embodiments. The more appropriate embodiment will be apparent to those of ordinary skill in the art when practicing the present technology. Persons of ordinary skill in the art will also readily appreciate that other system embodiments are possible.



FIG. 5A illustrates a conventional system bus computing system architecture 500 wherein the components of the system are in electrical communication with each other using a bus 505. Exemplary system 500 includes a processing unit (CPU or processor) 510 and a system bus 505 that couples various system components including the system memory 515, such as read only memory (ROM) 520 and random access memory (RAM) 525, to the processor 510. The system 500 can include a cache of high-speed memory connected directly with, in close proximity to, or integrated as part of the processor 510. The system 500 can copy data from the memory 515 and/or the storage device 530 to the cache 512 for quick access by the processor 510. In this way, the cache can provide a performance boost that avoids processor 510 delays while waiting for data. These and other modules can control or be configured to control the processor 510 to perform various actions. Other system memory 515 may be available for use as well. The memory 515 can include multiple different types of memory with different performance characteristics. The processor 510 can include any general purpose processor and a hardware module or software module, such as module 1532, module 2534, and module 3536 stored in storage device 530, configured to control the processor 510 as well as a special-purpose processor where software instructions are incorporated into the actual processor design. The processor 510 may essentially be a completely self-contained computing system, containing multiple cores or processors, a bus, memory controller, cache, etc. A multi-core processor may be symmetric or asymmetric.


To enable user interaction with the computing device 500, an input device 545 can represent any number of input mechanisms, such as a microphone for speech, a touch-sensitive screen for gesture or graphical input, keyboard, mouse, motion input, speech and so forth. An output device 535 can also be one or more of a number of output mechanisms known to those of skill in the art. In some instances, multimodal systems can enable a user to provide multiple types of input to communicate with the computing device 500. The communications interface 540 can generally govern and manage the user input and system output. There is no restriction on operating on any particular hardware arrangement and therefore the basic features here may easily be substituted for improved hardware or firmware arrangements as they are developed.


Storage device 530 is a non-volatile memory and can be a hard disk or other types of computer readable media which can store data that are accessible by a computer, such as magnetic cassettes, flash memory cards, solid state memory devices, digital versatile disks, cartridges, random access memories (RAMs) 525, read only memory (ROM) 520, and hybrids thereof.


The storage device 530 can include software modules 532, 534, 536 for controlling the processor 510. Other hardware or software modules are contemplated. The storage device 530 can be connected to the system bus 505. In one aspect, a hardware module that performs a particular function can include the software component stored in a computer-readable medium in connection with the necessary hardware components, such as the processor 510, bus 505, display 535, and so forth, to carry out the function.



FIG. 5B illustrates an example computer system 550 having a chipset architecture that can be used in executing the described method and generating and displaying a graphical user interface (GUI). Computer system 550 is an example of computer hardware, software, and firmware that can be used to implement the disclosed technology. System 550 can include a processor 555, representative of any number of physically and/or logically distinct resources capable of executing software, firmware, and hardware configured to perform identified computations. Processor 555 can communicate with a chipset 560 that can control input to and output from processor 555.


In this example, chipset 560 outputs information to output device 565, such as a display, and can read and write information to storage device 570, which can include magnetic media, and solid state media, for example. Chipset 560 can also read data from and write data to RAM 575. A bridge 580 for interfacing with a variety of user interface components 585 can be provided for interfacing with chipset 560. Such user interface components 585 can include a keyboard, a microphone, touch detection and processing circuitry, a pointing device, such as a mouse, and so on. In general, inputs to system 550 can come from any of a variety of sources, machine generated and/or human generated.


Chipset 560 can also interface with one or more communication interfaces 590 that can have different physical interfaces. Such communication interfaces can include interfaces for wired and wireless local area networks, for broadband wireless networks, as well as personal area networks. Some applications of the methods for generating, displaying, and using the GUI disclosed herein can include receiving ordered datasets over the physical interface or be generated by the machine itself by processor 555 analyzing data stored in storage 570 or 575. Further, the machine can receive inputs from a user via user interface components 585 and execute appropriate functions, such as browsing functions by interpreting these inputs using processor 555.


It can be appreciated that example systems 500 and 550 can have more than one processor 510 or be part of a group or cluster of computing devices networked together to provide greater processing capability.


For clarity of explanation, in some instances the present technology may be presented as including individual functional blocks including functional blocks comprising devices, device components, steps or routines in a method embodied in software, or combinations of hardware and software.


In some embodiments the computer-readable storage devices, mediums, and memories can include a cable or wireless signal containing a bit stream and the like. However, when mentioned, non-transitory computer-readable storage media expressly exclude media such as energy, carrier signals, electromagnetic waves, and signals per se.


Methods according to the above-described examples can be implemented using computer-executable instructions that are stored or otherwise available from computer readable media. Such instructions can comprise, for example, instructions and data which cause or otherwise configure a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Portions of computer resources used can be accessible over a network. The computer executable instructions may be, for example, binaries, intermediate format instructions such as assembly language, firmware, or source code. Examples of computer-readable media that may be used to store instructions, information used, and/or information created during methods according to described examples include magnetic or optical disks, flash memory, USB devices provided with non-volatile memory, networked storage devices, and so on.


Devices implementing methods according to these disclosures can comprise hardware, firmware and/or software, and can take any of a variety of form factors. Typical examples of such form factors include laptops, smart phones, small form factor personal computers, personal digital assistants, rackmount devices, standalone devices, and so on. Functionality described herein also can be embodied in peripherals or add-in cards. Such functionality can also be implemented on a circuit board among different chips or different processes executing in a single device, by way of further example.


The instructions, media for conveying such instructions, computing resources for executing them, and other structures for supporting such computing resources are means for providing the functions described in these disclosures.


Although a variety of examples and other information was used to explain aspects within the scope of the appended claims, no limitation of the claims should be implied based on particular features or arrangements in such examples, as one of ordinary skill would be able to use these examples to derive a wide variety of implementations. Further and although some subject matter may have been described in language specific to examples of structural features and/or method steps, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to these described features or acts. For example, such functionality can be distributed differently or performed in components other than those identified herein. Rather, the described features and steps are disclosed as examples of components of systems and methods within the scope of the appended claims. Moreover, claim language reciting “at least one of” a set indicates that one member of the set or multiple members of the set satisfy the claim.

Claims
  • 1. A method of managing memory in a network switch, the method comprising: receiving a data packet at a first network queue from among a plurality of network queues;determining if a fill level of the first network queue in a shared buffer of the network switch exceeds a dynamic queue threshold, the dynamic queue threshold being reconfigurable;and in response to the fill level of the first network queue exceeding the dynamic queue threshold, determining if the fill level of the first network queue is less than a static queue minimum threshold, the static queue minimum threshold being a minimum amount of memory reserved for the first network queue.
  • 2. The method of claim 1, further comprising: |enqueuing the data packet in the shared buffer when the fill level of the first network queue is less than the static queue minimum threshold.
  • 3. The method of claim 1, further comprising: |dropping the data packet when the fill level of the first network queue is greater than the static queue minimum threshold.
  • 4. The method of claim 1, further comprising: |dropping the data packet when the fill level of the first network queue exceeds the dynamic queue threshold and the static queue minimum threshold.
  • 5. The method of claim 1, wherein the dynamic queue threshold is based on an amount of unallocated memory in the shared buffer.
  • 6. The method of claim 1, wherein the dynamic queue threshold is a function of a fill level for each respective one of the plurality of network queues.
  • 7. The method of claim 1, wherein the static queue minimum threshold is a user defined value.
  • 8. A shared memory network switch comprising: at least one processor;a shared buffer memory, the shared buffer memory comprising a dynamic memory allocation and a reserve memory allocation;and a memory device storing instructions that, when executed by the at least one processor, cause the at least one processor to perform operations comprising:receiving a data packet at a first network queue from among a plurality of network queues;determining if a fill level of the first network queue in the shared buffer of the network switch exceeds a dynamic queue threshold, the dynamic queue threshold being reconfigurable;and when the fill level of the first network queue in the shared buffer exceeds the dynamic queue threshold, determining if the fill level of the first network queue is less than a static queue minimum threshold, the static queue minimum threshold being a minimum amount of memory reserved for the first network queue.
  • 9. The shared memory network switch of claim 8, further comprising: enqueuing the data packet in the shared buffer when the fill level of the first network queue is less than the static queue minimum threshold.
  • 10. The shared memory network switch of claim 8, further comprising: dropping the data packet when the fill level of the first network queue is greater than the static queue minimum threshold.
  • 11. The shared memory network switch of claim 8, further comprising: dropping the data packet when the fill level of the first network queue exceeds the dynamic queue threshold and the static queue minimum threshold.
  • 12. The shared memory network switch of claim 8, wherein the dynamic queue threshold is based on an amount of unallocated memory in the shared buffer.
  • 13. The shared memory network switch of claim 8, wherein the dynamic queue threshold is a function of a fill level for each respective one of the plurality of network queues.
  • 14. The shared memory network switch of claim 8, wherein the static queue minimum threshold is a user defined value.
  • 15. A non-transitory computer-readable storage medium comprising instructions stored therein, which when executed by one or more processors, cause the processors to perform operations comprising: receiving a data packet at a first network queue from among a plurality of network queues; determining if a fill level of the first network queue in a shared buffer exceeds a dynamic queue threshold, the dynamic queue threshold being reconfigurable;and when the fill level of the first network queue in the shared buffer exceeds the dynamic queue threshold, determining if the fill level of the first network queue is less than a static queue minimum threshold, the static queue minimum threshold being a minimum amount of memory reserved for the first network queue.
  • 16. The non-transitory computer-readable storage medium of claim 15, further comprising: enqueuing the data packet in the shared buffer when the fill level of the first network queue is less than the static queue minimum threshold.
  • 17. The non-transitory computer-readable storage medium of claim 15, further comprising: dropping the data packet when the fill level of the first network queue is greater than the static queue minimum threshold.
  • 18. The non-transitory computer-readable storage medium of claim 15, further comprising: dropping the data packet when the fill level of the fill level of the first network queue exceeds the dynamic queue threshold and the static queue minimum threshold.
  • 19. The non-transitory computer-readable storage medium of claim 15, wherein the dynamic queue threshold is based on an amount of unallocated memory in the shared buffer.
  • 20. The non-transitory computer-readable storage medium of claim 15, wherein the dynamic queue threshold is a function of a fill level for each respective one of the plurality of network queues.
US Referenced Citations (576)
Number Name Date Kind
4688695 Hirohata Aug 1987 A
5263003 Cowles et al. Nov 1993 A
5339445 Gasztonyi Aug 1994 A
5430859 Norman et al. Jul 1995 A
5457746 Dolphin Oct 1995 A
5535336 Smith et al. Jul 1996 A
5588012 Oizumi Dec 1996 A
5617421 Chin et al. Apr 1997 A
5680579 Young et al. Oct 1997 A
5690194 Parker et al. Nov 1997 A
5740171 Mazzola et al. Apr 1998 A
5742604 Edsall et al. Apr 1998 A
5764636 Edsall Jun 1998 A
5809285 Hilland Sep 1998 A
5812814 Sukegawa Sep 1998 A
5812950 Tom Sep 1998 A
5838970 Thomas Nov 1998 A
5999930 Wolff Dec 1999 A
6035105 McCloghrie et al. Mar 2000 A
6043777 Bergman et al. Mar 2000 A
6101497 Ofek Aug 2000 A
6148414 Brown et al. Nov 2000 A
6185203 Berman Feb 2001 B1
6188694 Fine et al. Feb 2001 B1
6202135 Kedem et al. Mar 2001 B1
6208649 Kloth Mar 2001 B1
6209059 Ofer et al. Mar 2001 B1
6219699 McCloghrie et al. Apr 2001 B1
6219753 Richardson Apr 2001 B1
6223250 Yokono Apr 2001 B1
6226771 Hilla et al. May 2001 B1
6260120 Blumenau et al. Jul 2001 B1
6266705 Ullum et al. Jul 2001 B1
6269381 St. Pierre et al. Jul 2001 B1
6269431 Dunham Jul 2001 B1
6295575 Blumenau et al. Sep 2001 B1
6400730 Latif et al. Jun 2002 B1
6408406 Parris Jun 2002 B1
6539024 Janoska Mar 2003 B1
6542909 Tamer et al. Apr 2003 B1
6542961 Matsunami et al. Apr 2003 B1
6553390 Gross et al. Apr 2003 B1
6564252 Hickman et al. May 2003 B1
6647474 Yanai et al. Nov 2003 B2
6675258 Bramhall et al. Jan 2004 B1
6683883 Czeiger et al. Jan 2004 B1
6694413 Mimatsu et al. Feb 2004 B1
6708227 Cabrera et al. Mar 2004 B1
6715007 Williams Mar 2004 B1
6728791 Young Apr 2004 B1
6772231 Reuter et al. Aug 2004 B2
6820099 Huber et al. Nov 2004 B1
6847647 Wrenn Jan 2005 B1
6848759 Doornbos et al. Feb 2005 B2
6850955 Sonoda et al. Feb 2005 B2
6876656 Brewer et al. Apr 2005 B2
6880062 Ibrahim et al. Apr 2005 B1
6898670 Nahum May 2005 B2
6907419 Pesola et al. Jun 2005 B1
6912668 Brown et al. Jun 2005 B1
6952734 Gunlock et al. Oct 2005 B1
6976090 Ben-Shaul et al. Dec 2005 B2
6978300 Beukema et al. Dec 2005 B1
6983303 Pellegrino et al. Jan 2006 B2
6986015 Testardi Jan 2006 B2
6986069 Oehler et al. Jan 2006 B2
7051056 Rodriguez-Rivera et al. May 2006 B2
7069465 Chu et al. Jun 2006 B2
7073017 Yamamoto Jul 2006 B2
7108339 Berger Sep 2006 B2
7149858 Kiselev Dec 2006 B1
7171514 Coronado et al. Jan 2007 B2
7171668 Molloy et al. Jan 2007 B2
7174354 Andreasson Feb 2007 B2
7200144 Terrell et al. Apr 2007 B2
7222255 Claessens et al. May 2007 B1
7237045 Beckmann et al. Jun 2007 B2
7240188 Takata et al. Jul 2007 B2
7246260 Brown et al. Jul 2007 B2
7266718 Idei et al. Sep 2007 B2
7269168 Roy et al. Sep 2007 B2
7277431 Walter et al. Oct 2007 B2
7277948 Igarashi et al. Oct 2007 B2
7305658 Hamilton et al. Dec 2007 B1
7328434 Swanson et al. Feb 2008 B2
7340555 Ashmore et al. Mar 2008 B2
7346751 Prahlad et al. Mar 2008 B2
7352706 Klotz et al. Apr 2008 B2
7353305 Pangal et al. Apr 2008 B2
7359321 Sindhu et al. Apr 2008 B1
7383381 Faulkner et al. Jun 2008 B1
7403987 Marinelli et al. Jul 2008 B1
7433326 Desai et al. Oct 2008 B2
7433948 Edsall Oct 2008 B2
7434105 Rodriguez-Rivera et al. Oct 2008 B1
7441154 Klotz et al. Oct 2008 B2
7447839 Uppala Nov 2008 B2
7487321 Muthiah et al. Feb 2009 B2
7500053 Kavuri et al. Mar 2009 B1
7512744 Banga et al. Mar 2009 B2
7542681 Cornell et al. Jun 2009 B2
7558872 Senevirathne et al. Jul 2009 B1
7587570 Sarkar et al. Sep 2009 B2
7631023 Kaiser et al. Dec 2009 B1
7643505 Colloff Jan 2010 B1
7654625 Amann et al. Feb 2010 B2
7657796 Kaiser et al. Feb 2010 B1
7668981 Nagineni et al. Feb 2010 B1
7669071 Cochran et al. Feb 2010 B2
7689384 Becker Mar 2010 B1
7694092 Mizuno Apr 2010 B2
7697554 Ofer et al. Apr 2010 B1
7706303 Bose et al. Apr 2010 B2
7707481 Kirschner et al. Apr 2010 B2
7716648 Vaidyanathan et al. May 2010 B2
7752360 Galles Jul 2010 B2
7757059 Ofer et al. Jul 2010 B1
7774329 Peddy et al. Aug 2010 B1
7774839 Nazzal Aug 2010 B2
7793138 Rastogi et al. Sep 2010 B2
7840730 D'Amato et al. Nov 2010 B2
7843906 Chidambaram et al. Nov 2010 B1
7895428 Boland, IV et al. Feb 2011 B2
7904599 Bennett Mar 2011 B1
7930494 Goheer et al. Apr 2011 B1
7975175 Votta et al. Jul 2011 B2
7979670 Saliba et al. Jul 2011 B2
7984259 English Jul 2011 B1
8031703 Gottumukkula et al. Oct 2011 B2
8032621 Upalekar et al. Oct 2011 B1
8051197 Mullendore et al. Nov 2011 B2
8086755 Duffy, IV et al. Dec 2011 B2
8161134 Mishra et al. Apr 2012 B2
8196018 Forhan et al. Jun 2012 B2
8205951 Boks Jun 2012 B2
8218538 Chidambaram et al. Jul 2012 B1
8230066 Heil Jul 2012 B2
8234377 Cohn Jul 2012 B2
8266238 Zimmer et al. Sep 2012 B2
8272104 Chen et al. Sep 2012 B2
8274993 Sharma et al. Sep 2012 B2
8290919 Kelly et al. Oct 2012 B1
8297722 Chambers et al. Oct 2012 B2
8301746 Head et al. Oct 2012 B2
8335231 Kloth et al. Dec 2012 B2
8341121 Claudatos et al. Dec 2012 B1
8345692 Smith Jan 2013 B2
8352941 Protopopov et al. Jan 2013 B1
8392760 Kandula et al. Mar 2013 B2
8442059 de la Iglesia et al. May 2013 B1
8479211 Marshall et al. Jul 2013 B1
8495356 Ashok et al. Jul 2013 B2
8514868 Hill Aug 2013 B2
8532108 Li et al. Sep 2013 B2
8560663 Baucke et al. Oct 2013 B2
8619599 Even Dec 2013 B1
8626891 Guru et al. Jan 2014 B2
8630983 Sengupta et al. Jan 2014 B2
8660129 Brendel et al. Feb 2014 B1
8661299 Ip Feb 2014 B1
8677485 Sharma et al. Mar 2014 B2
8683296 Anderson et al. Mar 2014 B2
8706772 Hartig et al. Apr 2014 B2
8719804 Jain May 2014 B2
8725854 Edsall May 2014 B2
8768981 Milne et al. Jul 2014 B1
8775773 Acharya et al. Jul 2014 B2
8793372 Ashok et al. Jul 2014 B2
8805918 Chandrasekaran et al. Aug 2014 B1
8805951 Faibish et al. Aug 2014 B1
8832330 Lancaster Sep 2014 B1
8855116 Rosset et al. Oct 2014 B2
8856339 Mestery et al. Oct 2014 B2
8868474 Leung et al. Oct 2014 B2
8887286 Dupont et al. Nov 2014 B2
8898385 Jayaraman et al. Nov 2014 B2
8909928 Ahmad et al. Dec 2014 B2
8918510 Gmach et al. Dec 2014 B2
8918586 Todd et al. Dec 2014 B1
8924720 Raghuram et al. Dec 2014 B2
8930747 Levijarvi et al. Jan 2015 B2
8935500 Gulati et al. Jan 2015 B1
8949677 Brundage et al. Feb 2015 B1
8996837 Bono et al. Mar 2015 B1
9003086 Schuller et al. Apr 2015 B1
9007922 Mittal et al. Apr 2015 B1
9009427 Sharma et al. Apr 2015 B2
9009704 McGrath et al. Apr 2015 B2
9075638 Barnett et al. Jul 2015 B2
9141554 Candelaria Sep 2015 B1
9141785 Mukkara et al. Sep 2015 B2
9164795 Vincent Oct 2015 B1
9176677 Fradkin et al. Nov 2015 B1
9201704 Chang et al. Dec 2015 B2
9203784 Chang et al. Dec 2015 B2
9207882 Rosset et al. Dec 2015 B2
9207929 Katsura Dec 2015 B2
9213612 Candelaria Dec 2015 B2
9223564 Munireddy et al. Dec 2015 B2
9223634 Chang et al. Dec 2015 B2
9244761 Yekhanin et al. Jan 2016 B2
9250969 Lager-Cavilla et al. Feb 2016 B2
9264494 Factor et al. Feb 2016 B2
9270754 Iyengar et al. Feb 2016 B2
9280487 Candelaria Mar 2016 B2
9304815 Vasanth et al. Apr 2016 B1
9313048 Chang et al. Apr 2016 B2
9374270 Nakil et al. Jun 2016 B2
9378060 Jansson et al. Jun 2016 B2
9396251 Boudreau et al. Jul 2016 B1
9448877 Candelaria Sep 2016 B2
9471348 Zuo et al. Oct 2016 B2
9501473 Kong et al. Nov 2016 B1
9503523 Rosset et al. Nov 2016 B2
9565110 Mullendore et al. Feb 2017 B2
9575828 Agarwal et al. Feb 2017 B2
9582377 Dhoolam et al. Feb 2017 B1
9614763 Dong et al. Apr 2017 B2
9658868 Hill May 2017 B2
9658876 Chang et al. May 2017 B2
9686209 Arad Jun 2017 B1
9733868 Chandrasekaran et al. Aug 2017 B2
9763518 Charest et al. Sep 2017 B2
9830240 George et al. Nov 2017 B2
9853873 Dasu et al. Dec 2017 B2
20020049980 Hoang Apr 2002 A1
20020053009 Selkirk et al. May 2002 A1
20020073276 Howard et al. Jun 2002 A1
20020083120 Soltis Jun 2002 A1
20020095547 Watanabe et al. Jul 2002 A1
20020103889 Markson et al. Aug 2002 A1
20020103943 Lo et al. Aug 2002 A1
20020112113 Karpoff et al. Aug 2002 A1
20020120741 Webb et al. Aug 2002 A1
20020138675 Mann Sep 2002 A1
20020156971 Jones et al. Oct 2002 A1
20030023885 Potter et al. Jan 2003 A1
20030026267 Oberman et al. Feb 2003 A1
20030055933 Ishizaki et al. Mar 2003 A1
20030056126 O'Connor et al. Mar 2003 A1
20030065986 Fraenkel et al. Apr 2003 A1
20030084359 Bresniker et al. May 2003 A1
20030118053 Edsall et al. Jun 2003 A1
20030131105 Czeiger et al. Jul 2003 A1
20030131165 Asano et al. Jul 2003 A1
20030131182 Kumar et al. Jul 2003 A1
20030140134 Swanson et al. Jul 2003 A1
20030140210 Testardi Jul 2003 A1
20030149763 Heitman et al. Aug 2003 A1
20030154271 Baldwin et al. Aug 2003 A1
20030159058 Eguchi et al. Aug 2003 A1
20030174725 Shankar Sep 2003 A1
20030189395 Doornbos et al. Oct 2003 A1
20030210686 Terrell et al. Nov 2003 A1
20040024961 Cochran et al. Feb 2004 A1
20040030857 Krakirian et al. Feb 2004 A1
20040039939 Cox et al. Feb 2004 A1
20040054776 Klotz et al. Mar 2004 A1
20040057389 Klotz et al. Mar 2004 A1
20040059807 Klotz et al. Mar 2004 A1
20040088574 Walter et al. May 2004 A1
20040117438 Considine et al. Jun 2004 A1
20040123029 Dalai et al. Jun 2004 A1
20040128470 Hetzler et al. Jul 2004 A1
20040128540 Roskind Jul 2004 A1
20040153863 Klotz et al. Aug 2004 A1
20040190901 Fang Sep 2004 A1
20040215749 Tsao Oct 2004 A1
20040230848 Mayo et al. Nov 2004 A1
20040250034 Yagawa et al. Dec 2004 A1
20050033936 Nakano et al. Feb 2005 A1
20050036499 Dutt et al. Feb 2005 A1
20050050211 Kaul et al. Mar 2005 A1
20050050270 Horn et al. Mar 2005 A1
20050053073 Kloth et al. Mar 2005 A1
20050055428 Terai et al. Mar 2005 A1
20050060574 Klotz et al. Mar 2005 A1
20050060598 Klotz et al. Mar 2005 A1
20050071851 Opheim Mar 2005 A1
20050076113 Klotz et al. Apr 2005 A1
20050091426 Horn et al. Apr 2005 A1
20050114611 Durham et al. May 2005 A1
20050114615 Ogasawara et al. May 2005 A1
20050117522 Basavaiah et al. Jun 2005 A1
20050117562 Wrenn Jun 2005 A1
20050138287 Ogasawara et al. Jun 2005 A1
20050169188 Cometto et al. Aug 2005 A1
20050185597 Le et al. Aug 2005 A1
20050188170 Yamamoto Aug 2005 A1
20050198523 Shanbhag et al. Sep 2005 A1
20050235072 Smith et al. Oct 2005 A1
20050283658 Clark et al. Dec 2005 A1
20060015861 Takata et al. Jan 2006 A1
20060015928 Setty et al. Jan 2006 A1
20060034302 Peterson Feb 2006 A1
20060045021 Deragon et al. Mar 2006 A1
20060075191 Lolayekar et al. Apr 2006 A1
20060098672 Schzukin May 2006 A1
20060117099 Mogul Jun 2006 A1
20060136684 Le et al. Jun 2006 A1
20060184287 Belady et al. Aug 2006 A1
20060198319 Schondelmayer et al. Sep 2006 A1
20060215297 Kikuchi Sep 2006 A1
20060230227 Ogasawara et al. Oct 2006 A1
20060242332 Johnsen et al. Oct 2006 A1
20060251111 Kloth et al. Nov 2006 A1
20070005297 Beresniewicz et al. Jan 2007 A1
20070067593 Satoyama et al. Mar 2007 A1
20070079068 Draggon Apr 2007 A1
20070091903 Atkinson Apr 2007 A1
20070094465 Sharma et al. Apr 2007 A1
20070101202 Garbow May 2007 A1
20070121519 Cuni et al. May 2007 A1
20070136541 Herz et al. Jun 2007 A1
20070162969 Becker Jul 2007 A1
20070211640 Palacharla et al. Sep 2007 A1
20070214316 Kim Sep 2007 A1
20070250838 Belady et al. Oct 2007 A1
20070258380 Chamdani et al. Nov 2007 A1
20070263545 Foster et al. Nov 2007 A1
20070276884 Hara et al. Nov 2007 A1
20070283059 Ho et al. Dec 2007 A1
20080016412 White et al. Jan 2008 A1
20080034149 Sheen Feb 2008 A1
20080052459 Chang et al. Feb 2008 A1
20080059698 Kabir et al. Mar 2008 A1
20080114933 Ogasawara et al. May 2008 A1
20080126509 Subrannanian et al. May 2008 A1
20080126734 Murase May 2008 A1
20080168304 Flynn et al. Jul 2008 A1
20080201616 Ashmore Aug 2008 A1
20080244184 Lewis et al. Oct 2008 A1
20080256082 Davies et al. Oct 2008 A1
20080267217 Colville et al. Oct 2008 A1
20080288661 Galles Nov 2008 A1
20080294888 Ando et al. Nov 2008 A1
20090063766 Matsumura et al. Mar 2009 A1
20090083484 Basham et al. Mar 2009 A1
20090089567 Boland, IV et al. Apr 2009 A1
20090094380 Qiu et al. Apr 2009 A1
20090094664 Butler et al. Apr 2009 A1
20090125694 Innan et al. May 2009 A1
20090193223 Saliba et al. Jul 2009 A1
20090201926 Kagan et al. Aug 2009 A1
20090222733 Basham et al. Sep 2009 A1
20090240873 Yu et al. Sep 2009 A1
20090282471 Green et al. Nov 2009 A1
20090323706 Germain et al. Dec 2009 A1
20100011365 Gerovac et al. Jan 2010 A1
20100030995 Wang et al. Feb 2010 A1
20100046378 Knapp et al. Feb 2010 A1
20100083055 Ozonat Apr 2010 A1
20100174968 Charles et al. Jul 2010 A1
20100318609 Lahiri et al. Dec 2010 A1
20100318837 Murphy et al. Dec 2010 A1
20110010394 Carew et al. Jan 2011 A1
20110022691 Banerjee et al. Jan 2011 A1
20110029824 Schöler et al. Feb 2011 A1
20110035494 Pandey et al. Feb 2011 A1
20110075667 Li et al. Mar 2011 A1
20110087848 Trent Apr 2011 A1
20110119556 de Buen May 2011 A1
20110142053 Van Der Merwe et al. Jun 2011 A1
20110161496 Nicklin Jun 2011 A1
20110173303 Rider Jul 2011 A1
20110185117 Beeston Jul 2011 A1
20110228679 Varma et al. Sep 2011 A1
20110231899 Pulier et al. Sep 2011 A1
20110239039 Dieffenbach et al. Sep 2011 A1
20110252274 Kawaguchi et al. Oct 2011 A1
20110255540 Mizrahi et al. Oct 2011 A1
20110276584 Cotner et al. Nov 2011 A1
20110276675 Singh et al. Nov 2011 A1
20110276951 Jain Nov 2011 A1
20110299539 Rajagopal et al. Dec 2011 A1
20110307450 Hahn et al. Dec 2011 A1
20110313973 Srivas et al. Dec 2011 A1
20120023319 Chin et al. Jan 2012 A1
20120030401 Cowan et al. Feb 2012 A1
20120054367 Ramakrishnan et al. Mar 2012 A1
20120072578 Alam Mar 2012 A1
20120072985 Davne et al. Mar 2012 A1
20120075999 Ko et al. Mar 2012 A1
20120084445 Brock et al. Apr 2012 A1
20120084782 Chou et al. Apr 2012 A1
20120096134 Suit Apr 2012 A1
20120130874 Mane et al. May 2012 A1
20120131174 Ferris et al. May 2012 A1
20120134672 Banerjee May 2012 A1
20120144014 Natham et al. Jun 2012 A1
20120159112 Tokusho et al. Jun 2012 A1
20120167094 Suit Jun 2012 A1
20120173581 Hartig et al. Jul 2012 A1
20120173589 Kwon et al. Jul 2012 A1
20120177039 Berman Jul 2012 A1
20120177041 Berman Jul 2012 A1
20120177042 Berman Jul 2012 A1
20120177043 Berman Jul 2012 A1
20120177044 Berman Jul 2012 A1
20120177045 Berman Jul 2012 A1
20120177370 Berman Jul 2012 A1
20120179909 Sagi et al. Jul 2012 A1
20120201138 Yu et al. Aug 2012 A1
20120210041 Flynn et al. Aug 2012 A1
20120254440 Wang Oct 2012 A1
20120257501 Kucharczyk Oct 2012 A1
20120265976 Spiers et al. Oct 2012 A1
20120281706 Agarwal et al. Nov 2012 A1
20120297088 Wang et al. Nov 2012 A1
20120303618 Dutta et al. Nov 2012 A1
20120311106 Morgan Dec 2012 A1
20120311568 Jansen Dec 2012 A1
20120320788 Venkataramanan et al. Dec 2012 A1
20120324114 Dutta et al. Dec 2012 A1
20120331119 Bose et al. Dec 2012 A1
20130003737 Sinicrope Jan 2013 A1
20130013664 Baird et al. Jan 2013 A1
20130028135 Berman Jan 2013 A1
20130036212 Jibbe et al. Feb 2013 A1
20130036213 Hasan et al. Feb 2013 A1
20130036449 Mukkara et al. Feb 2013 A1
20130054888 Bhat et al. Feb 2013 A1
20130061089 Valiyaparambil et al. Mar 2013 A1
20130067162 Jayaraman et al. Mar 2013 A1
20130080823 Roth et al. Mar 2013 A1
20130086340 Fleming et al. Apr 2013 A1
20130100858 Kamath et al. Apr 2013 A1
20130111540 Sabin May 2013 A1
20130138816 Kuo et al. May 2013 A1
20130138836 Cohen et al. May 2013 A1
20130139138 Kakos May 2013 A1
20130144933 Hinni et al. Jun 2013 A1
20130152076 Patel Jun 2013 A1
20130152175 Hromoko et al. Jun 2013 A1
20130163426 Beliveau et al. Jun 2013 A1
20130163606 Bagepalli et al. Jun 2013 A1
20130179941 McGloin et al. Jul 2013 A1
20130182712 Aguayo et al. Jul 2013 A1
20130185433 Zhu et al. Jul 2013 A1
20130191106 Kephart et al. Jul 2013 A1
20130198730 Munireddy et al. Aug 2013 A1
20130208888 Agrawal et al. Aug 2013 A1
20130212130 Rahnama Aug 2013 A1
20130223236 Dickey Aug 2013 A1
20130238641 Mandelstein et al. Sep 2013 A1
20130266307 Garg et al. Oct 2013 A1
20130268922 Tiwari et al. Oct 2013 A1
20130275470 Cao et al. Oct 2013 A1
20130297655 Narasayya et al. Nov 2013 A1
20130297769 Chang et al. Nov 2013 A1
20130318134 Bolik et al. Nov 2013 A1
20130318288 Khan et al. Nov 2013 A1
20140006708 Huynh et al. Jan 2014 A1
20140016493 Johnsson et al. Jan 2014 A1
20140019684 Wei et al. Jan 2014 A1
20140025770 Warfield et al. Jan 2014 A1
20140029441 Nydell Jan 2014 A1
20140029442 Wallman Jan 2014 A1
20140039683 Zimmermann et al. Feb 2014 A1
20140040473 Ho et al. Feb 2014 A1
20140040883 Tompkins Feb 2014 A1
20140047201 Mehta Feb 2014 A1
20140053264 Dubrovsky et al. Feb 2014 A1
20140059187 Rosset et al. Feb 2014 A1
20140059266 Ben-Michael et al. Feb 2014 A1
20140086253 Yong Mar 2014 A1
20140089273 Borshack et al. Mar 2014 A1
20140095556 Lee et al. Apr 2014 A1
20140096249 Dupont et al. Apr 2014 A1
20140105009 Vos et al. Apr 2014 A1
20140108474 David et al. Apr 2014 A1
20140109071 Ding et al. Apr 2014 A1
20140112122 Kapadia et al. Apr 2014 A1
20140123207 Agarwal et al. May 2014 A1
20140156557 Zeng et al. Jun 2014 A1
20140164666 Yand Jun 2014 A1
20140164866 Bolotov et al. Jun 2014 A1
20140172371 Zhu et al. Jun 2014 A1
20140173060 Jubran et al. Jun 2014 A1
20140173195 Rosset et al. Jun 2014 A1
20140173579 McDonald et al. Jun 2014 A1
20140189278 Peng Jul 2014 A1
20140198794 Mehta et al. Jul 2014 A1
20140211661 Gorkemli et al. Jul 2014 A1
20140215265 Mohanta et al. Jul 2014 A1
20140215590 Brand Jul 2014 A1
20140219086 Cantu′ et al. Aug 2014 A1
20140222953 Karve et al. Aug 2014 A1
20140229790 Goss et al. Aug 2014 A1
20140244585 Sivasubramanian et al. Aug 2014 A1
20140244897 Goss et al. Aug 2014 A1
20140245435 Belenky Aug 2014 A1
20140269390 Ciodaru et al. Sep 2014 A1
20140281700 Nagesharao et al. Sep 2014 A1
20140297941 Rajani et al. Oct 2014 A1
20140307578 DeSanti Oct 2014 A1
20140317206 Lomelino et al. Oct 2014 A1
20140324862 Bingham et al. Oct 2014 A1
20140325208 Resch et al. Oct 2014 A1
20140331276 Frascadore et al. Nov 2014 A1
20140348166 Yang et al. Nov 2014 A1
20140355450 Bhikkaji et al. Dec 2014 A1
20140366155 Chang et al. Dec 2014 A1
20140376550 Khan et al. Dec 2014 A1
20150003450 Salam et al. Jan 2015 A1
20150003458 Li et al. Jan 2015 A1
20150003463 Li et al. Jan 2015 A1
20150010001 Duda et al. Jan 2015 A1
20150016461 Qiang Jan 2015 A1
20150030024 Venkataswami et al. Jan 2015 A1
20150046123 Kato Feb 2015 A1
20150063353 Kapadia et al. Mar 2015 A1
20150067001 Koltsidas Mar 2015 A1
20150082432 Eaton et al. Mar 2015 A1
20150092824 Wicker, Jr. et al. Apr 2015 A1
20150120907 Niestemski et al. Apr 2015 A1
20150121131 Kiselev et al. Apr 2015 A1
20150127979 Doppalapudi May 2015 A1
20150142840 Baldwin et al. May 2015 A1
20150169313 Katsura Jun 2015 A1
20150180672 Kuwata Jun 2015 A1
20150207763 Bertran Ortiz et al. Jun 2015 A1
20150205974 Talley et al. Jul 2015 A1
20150222444 Sarkar Aug 2015 A1
20150229546 Somaiya et al. Aug 2015 A1
20150248366 Bergsten et al. Sep 2015 A1
20150248418 Bhardwaj et al. Sep 2015 A1
20150254003 Lee et al. Sep 2015 A1
20150254088 Chou et al. Sep 2015 A1
20150261446 Lee Sep 2015 A1
20150263993 Kuch et al. Sep 2015 A1
20150269048 Marr et al. Sep 2015 A1
20150277804 Arnold et al. Oct 2015 A1
20150281067 Wu Oct 2015 A1
20150303949 Jafarkhani et al. Oct 2015 A1
20150341237 Cuni et al. Nov 2015 A1
20150341239 Bertran Ortiz et al. Nov 2015 A1
20150358136 Medard Dec 2015 A1
20150379150 Duda Dec 2015 A1
20160004611 Lakshman et al. Jan 2016 A1
20160011936 Luby Jan 2016 A1
20160011942 Golbourn et al. Jan 2016 A1
20160054922 Awasthi et al. Feb 2016 A1
20160062820 Jones et al. Mar 2016 A1
20160070652 Sundararaman et al. Mar 2016 A1
20160087885 Tripathi et al. Mar 2016 A1
20160088083 Bharadwaj et al. Mar 2016 A1
20160119159 Zhao et al. Apr 2016 A1
20160119421 Semke et al. Apr 2016 A1
20160139820 Fluman et al. May 2016 A1
20160149639 Pham et al. May 2016 A1
20160205189 Mopur et al. Jul 2016 A1
20160210161 Rosset et al. Jul 2016 A1
20160231928 Lewis et al. Aug 2016 A1
20160274926 Narasimhamurthy et al. Sep 2016 A1
20160285760 Dong Sep 2016 A1
20160292359 Tellis et al. Oct 2016 A1
20160294983 Kliteynik et al. Oct 2016 A1
20160334998 George et al. Nov 2016 A1
20160366094 Mason et al. Dec 2016 A1
20160378624 Jenkins, Jr. et al. Dec 2016 A1
20160380694 Guduru Dec 2016 A1
20170010874 Rosset Jan 2017 A1
20170010930 Dutta et al. Jan 2017 A1
20170019475 Metz et al. Jan 2017 A1
20170068630 Iskandar et al. Mar 2017 A1
20170168970 Sajeepa et al. Jun 2017 A1
20170177860 Suarez et al. Jun 2017 A1
20170212858 Chu et al. Jul 2017 A1
20170273019 Park et al. Sep 2017 A1
20170277655 Das et al. Sep 2017 A1
20170337010 Kriss Nov 2017 A1
20170337097 Sipos et al. Nov 2017 A1
20170340113 Charest et al. Nov 2017 A1
20170371558 George et al. Dec 2017 A1
20180097707 Wright et al. Apr 2018 A1
Foreign Referenced Citations (9)
Number Date Country
2228719 Sep 2010 EP
2439637 Apr 2012 EP
2680155 Jan 2014 EP
2350028 May 2001 GB
2000-242434 Sep 2000 JP
1566104 Jan 2017 TW
W0 2004077214 Sep 2004 WO
WO 2016003408 Jan 2016 WO
WO 2016003489 Jan 2016 WO
Non-Patent Literature Citations (83)
Entry
International Search Report and Written Opinion from the International Searching Authority for the corresponding International Application No. PCT/US2017/043463, dated Oct. 16, 2017, 11 pages.
Aweya, James, et al., “Multi-level active queue management with dynamic thresholds,” Elsevier, Computer Communications 25 (2002) pp. 756-771.
Author Unknown, “5 Benefits of a Storage Gateway in the Cloud,” Blog, TwinStrata, Inc., posted Jul. 10, 2012, 4 pages, https://web.archive.org/web/20120725092613/http://blog.twinstrata.com/2012/07/10//5-benetits-of-a-storage-gateway-in-the-cloud.
Author Unknown, “Configuration Interface for IBM System Storage DS5000, IBM DS4000, and IBM DS3000 Systems,” IBM SAN Volume Controller Version 7.1, IBM® System Storage® SAN Volume Controller Information Center, Jun. 16, 2013, 3 pages.
Author Unknown, “Coraid EtherCloud, Software-Defined Storage with Scale-Out Infrastructure,” Solution Brief, 2013, 2 pages, Coraid, Redwood City, California, U.S.A.
Author Unknown, “Coraid Virtual DAS (VDAS) Technology: Eliminate Tradeoffs between DAS and Networked Storage,” Coraid Technology Brief, © 2013 Cora id, Inc., Published on orabout Mar. 20, 2013, 2 pages.
Author Unknown, “Creating Performance-based SAN SLAs Using Finisar's NetWisdom” May 2006, 7 pages, Finisar Corporation, Sunnyvale, California, U.S.A.
Author Unknown, “Data Center, Metro Cloud Connectivity: Integrated Metro SAN Connectivity in 16 Gbps Switches,” Brocade Communication Systems, Inc., Apr. 2011, 14 pages.
Author Unknown, “Data Center, SAN Fabric Administration Best Practices Guide, Support Perspective,” Brocade Communication Systems, Inc., May 2013, 21 pages.
Author Unknown, “delphi—Save a CRC value in a file, without altering the actual CRC Checksum?” Stack Overflow, stackoverflow.com, Dec. 23, 2011, XP055130879, 3 pages http://stackoverflow.com/questions/8608219/save-a-crc-value-in-a-file-wihout-altering-the-actual-crc-checksum.
Author Unknown, “EMC Unisphere: Innovative Approach to Managing Low-End and Midrange Storage; Redefining Simplicity in the Entry-Level and Midrange Storage Markets,” Data Sheet, EMC Corporation; published on or about Jan. 4, 2013 [Retrieved and printed Sep. 12, 2013] 6 pages http://www.emc.com/storage/vnx/unisphere.htm.
Author Unknown, “HP XP Array Manager Software—Overview & Features,” Storage Device Management Software; Hewlett-Packard Development Company, 3 pages; © 2013 Hewlett-Packard Development Company, L.P.
Author Unknown, “Joint Cisco and VMWare Solution for Optimizing Virtual Desktop Delivery: Data Center 3.0: Solutions to Accelerate Data Center Virtualization,” Cisco Systems, Inc. and VMware, Inc., Sep. 2008, 10 pages.
Author Unknown, “Network Transformation with Software-Defined Networking and Ethernet Fabrics,” Positioning Paper, 2012, 6 pages, Brocade Communications Systems.
Author Unknown, “Recreating Real Application Traffic in Junosphere Lab,” Solution Brief, Juniper Networks, Dec. 2011, 3 pages.
Author Unknown, “Shunra for HP Softwarer, Enabiling Confidence in Application Performance Before Deployment,” 2010, 2 pages.
Author Unknown, “Software Defined Networking: The New Norm for Networks,” White Paper, Open Networking Foundation, Apr. 13, 2012, 12 pages.
Author Unknown, “Software Defined Storage Networks an Introduction,” White Paper, Doc # 01-000030-001 Rev. A, Dec. 12, 2012, 8 pages; Jeda Networks, Newport Beach, California, U.S.A.
Author Unknown, “Standard RAID Levels,” Wikipedia, the Free Encyclopedia, last updated Jul. 18, 2014, 7 pages; http://en.wikipedia.org/wiki/Standard_RAID_levels.
Author Unknown, “Storage Infrastructure for the Cloud,” Solution Brief, © 2012, 3 pages; coraid, Redwood City, California, U.S.A.
Author Unknown, “Storage Area Network—NPIV: Emulex Virtual HBA and Brocade, Proven Interoperability and Proven Solution,” Technical Brief, Apr. 2008, 4 pages, Emulex and Brocade Communications Systems.
Author Unknown, “The Fundamentals of Software-Defined Storage, Simplicity at Scale for Cloud-Architectures” Solution Brief, 2013, 3 pages; Coraid, Redwood City, California, U.S.A.
Author Unknown, “VirtualWisdom® SAN Performance Probe Family Models: Probe FC8, HD, and HD48,” Virtual Instruments Data Sheet, Apr. 2014 Virtual Instruments. All Rights Reserved; 4 pages.
Author Unknown, “Xgig Analyzer: Quick Start Feature Guide 4.0,” Feb. 2008, 24 pages, Finisar Corporation, Sunnyvale, California, U.S.A.
Author Unknown, “Sun Storage Common Array Manager Installation and Setup Guide,” Software Installation and Setup Guide Version 6.7.x 821-1362-10, Appendix D: Configuring In-Band Management, Sun Oracle; retrieved and printed Sep. 12, 2013, 15 pages.
Author Unknown, “Vblock Solution for SAP: Simplified Provisioning for Operation Efficiency,” VCE White Paper, VCE—The Virtual Computing Environment Company, Aug. 2011, 11 pages.
Berman, Stuart, et al., “Start-Up Jeda Networks in Software Defined Storage Network Technology,” Press Release, Feb. 25, 2013, 2 pages, http://www.storagenewsletter.com/news/startups/ieda-. networks.
Borovick, Lucinda, et al., “White Paper, Architecting the Network for the Cloud,” IDC Analyze the Future, Jan. 2011, pp. 1-8.
Chakrabarti, Kaushik, et al., “Locally Adaptive Dimensionality Reduction for Indexing Large Time Series Databases,” ACM Transactions on Database Systems, vol. 27, No. 2, Jun. 2009, pp. 188-228.
Chandola, Varun, et al., “A Gaussian Process Based Online Change Detection Algorithm for Monitoring Periodic Time Series,” Proceedings of the Eleventh SIAM International Conference on Data Mining, SDM 2011, Apr. 28-30, 2011, 12 pages.
Cisco Systems, Inc. “N-Port Virtualization in the Data Center,” Cisco White Paper, Cisco Systems, Inc., Mar. 2008, 7 pages.
Cisco Systems, Inc., “Best Practices in Deploying Cisco Nexus 1000V Series Switches on Cisco UCS B and C Series Cisco UCS Manager Servers,” White Paper, Cisco Systems, Inc., Apr. 2011, 36 pages.
Cisco Systems, Inc., “Cisco Prime Data Center Network Manager 6.1,” At-a-Glance, © 2012, 3 pages.
Cisco Systems, Inc., “Cisco Prime Data Center Network Manager,” Release 6.1 Datasheet, © 2012, 10 pages.
Cisco Systems, Inc., “Cisco Unified Network Services: Overcome Obstacles to Cloud-Ready Deployments,” White Paper, Cisco Systems, Inc., Jan. 2011, 6 pages.
Clarke, Alan, et al., “Open Data Center Alliance Usage: Virtual Machine (VM) Interoperability in a Hybrid Cloud Environment Rev. 1.2,” Open Data Center Alliance, Inc., 2013, pp. 1-18.
Cummings, Roger, et al., Fibre Channel—Fabric Generic Requirements (FC-FG), Dec. 4, 1996, 33 pages, American National Standards Institute, Inc., New York, New York, U.S.A.
Farber, Franz, et al. “An In-Memory Database System for Multi-Tenant Applications,” Proceedings of 14th Business, Technology and Web (BTW) Conference on Database Systems for Business, Technology, and Web, Feb. 28-Mar. 4, 2011, 17 pages, University of Kaiserslautern, Germany.
Guo, Chang Jie, et al., “IBM Resarch Report: Data Integration and Composite Business Services, Part 3, Building a Multi-Tenant Data Tier with with [sic] Access Control and Security,” RC24426 (C0711-037), Nov. 19, 2007, 20 pages, IBM.
Hatzieleftheriou, Andromachi, et al., “Host-side Filesystem Journaling for Durable Shared Storage,” 13th USENIX Conference on File and Storage Technologies (FAST '15), Feb. 16-19, 2015, 9 pages; https://www.usenix.org/system/files/conference/fast15/fast15-paper-hatzieleftheriou.pdf.
Hedayat, K., et al., “A Two-Way Active Measurement Protocol (TWAMP),” Network Working Group, RFC 5357, Oct. 2008, 26 pages.
Horn, C., et al., “Online anomaly detection with expert system feedback in social networks,” 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), May 22-27, 2011, 2 pages, Prague; [Abstract only].
Hosterman, Cody, et al., “Using EMC Symmetrix Storage inVMware vSph ere Environments,” Version 8.0, EMC2Techbooks, EMC Corporation; published on or about Jul. 8, 2008, 314 pages; [Retrieved and printed Sep. 12, 2013].
Hu, Yuchong, et al., “Cooperative Recovery of Distributed Storage Systems from Multiple Losses with Network Coding,” University of Science & Technology of China, Feb. 2010, 9 pages.
Keogh, Eamonn, et al., “Dimensionality Reduction for Fast Similarity Search in Large Time Series Databases,” KAIS Long Paper submitted May 16, 2000; 19 pages.
Kolyshkin, Kirill, “Virtualization in Linux,” Sep. 1, 2006, pp. 1-5.
Kovar, Joseph F., “Startup Jeda Networks Takes SDN Approach to Storage Networks,” CRN Press Release, Feb. 22, 2013, 1 page, http://www.crn.com/240149244/printablearticle.htm.
Lampson, Butler, W., et al., “Crash Recovery in a Distributed Data Storage System,” Jun. 1, 1979, 28 pages.
Lewis, Michael E., et al., “Design of an Advanced Development Model Optical Disk-Based Redundant Array of Independent Disks (RAID) High Speed Mass Storage Subsystem,” Final Technical Report, Oct. 1997, pp. 1-211.
Lin, Jessica, “Finding Motifs in Time Series,” SIGKDD'Jul. 23-26, 2002, 11 pages, Edmonton, Alberta, Canada.
Linthicum, David, “VM Import could be a game changer for hybrid clouds”, InfoWorld, Dec. 23, 2010, 4 pages.
Long, Abraham Jr., “Modeling the Reliability of RAID Sets,” Dell Power Solutions, May 2008, 4 pages.
Ma, Ao, et al., “RAIDShield: Characterizing, Monitoring, and Proactively Protecting Against Disk Failures,” FAST '15, 13th USENIX Conference on File and Storage Technologies, Feb. 16-19, 2015, 17 pages, Santa Clara, California, U.S.A.
Mahalingam, M., et al., “Virtual extensible Local Area Network (VXLAN): A Framework for Overlaying Virtualized Layer 2 Networks over Layer 3 Networks,” Independent Submission, RFC 7348, Aug. 2014, 22 pages; http://www.hip.at/doc/rfc/rfc7348.html.
McQuerry, Steve, “Cisco UCS M-Series Modular Servers for Cloud-Scale Workloads,” White Paper, Cisco Systems, Inc., Sep. 2014, 11 pages.
Monia, Charles, et al., IFCP—A Protocol for Internet Fibre Channel Networking, draft-monia-ips-ifcp-00.txt, Dec. 12, 2000, 6 pages.
Mueen, Abdullah, et al., “Online Discovery and Maintenance of Time Series Motifs,” KDD'10 the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Jul. 25-28, 2010, 10 pages, Washington, DC, U.S.A.
Muglia, Bob, “Decoding SDN,” Jan. 14, 2013, Juniper Networks, pp. 1-7, http://forums.juniper.net/t5/The-New-Network/Decoding-SDN/ba-p/174651.
Murray, Joseph F., et al., “Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application,” Journal of Machine Learning Research 6 (2005), pp. 783-816; May 2005, 34 pages.
Nelson, Mark, “File Verification Using CRC,” Dr. Dobb's Journal, May 1, 1992, pp. 1-18, XP055130883.
Pace, Alberto, “Technologies for Large Data Management in Scientific Computing,” International Journal of Modern Physics C., vol. 25, No. 2, Feb. 2014, 72 pages.
Petersen, Chris, “Introducing Lightning: A flexible NVMe JBOF,” Mar. 9, 2016, 6 pages.
Pinheiro, Eduardo, et al., “Failure Trends in a Large Disk Drive Population,” FAST '07, 5th USENIX Conference on File and Storage Technologies, Feb. 13-16, 2007, 13 pages, San Jose, California, U.S.A.
Raginsky, Maxim, et al., “Sequential Anomaly Detection in the Presence of Noise and Limited Feedback,” arXiv:0911.2904v4 [cs.LG] Mar. 13, 2012, 19 pages.
Saidi, Ali G., et al., “Performance Validation of Network-Intensive Workloads on a Full-System Simulator,” Interaction between Operating System and Computer Architecture Workshop, (IOSCA 2005), Austin, Texas, Oct. 2005, 10 pages.
Sajassi, A., et al., “BGP MPLS Based Ethernet VPN,” Network Working Group, Oct. 18, 2014, 52 pages.
Sajassi, Ali, et al., “A Network Virtualization Overlay Solution using EVPN,” L2VPN Workgroup, Nov. 10, 2014, 24 pages; http://tools.ietf.org/pdf/draft-ietf-bess-evpn-overlay-00.pdf.
Sajassi, Ali, et al., “Integrated Routing and Bridging in EVPN,” L2VPN Workgroup, Nov. 11, 2014, 26 pages; http://tools.ietf.org/pdf/draft-ietf-bess-evpn-inter-subnet-forwarding-00.pdf.
Schroeder, Bianca, et al., “Disk failures in the real world: What does an MTTF of 1,000,000 hours mean to you?” FAST '07: 5th USENIX Conference on File and Storage Technologies, Feb. 13-16, 2007, 16 pages, San Jose, California, U.S.A.
Shue, David, et al., “Performance Isolation and Fairness for Multi-Tenant Cloud Storage,” USENIX Association, 10th USENIX Symposium on Operating Systems Design Implementation (OSDI '12), 2012, 14 pages; https://www.usenix.org/system/files/conference/osdi12/osdi12-finai-215.pdf.
Staimer, Marc, “Inside Cisco Systems' Unified Computing System,” Dragon Slayer Consulting, Jul. 2009, 5 pages.
Swami, Vijay, “Simplifying SAN Management for VMWare Boot from SAN, Utilizing Cisco UCS and Palo,” posted May 31, 2011, 6 pages.
Tate, Jon, et al., “Introduction to Storage Area Networks and System Networking,” Dec. 2017, 302 pages, ibm.com/redbooks.
Vuppala, Vibhavasu, et al., “Layer-3 Switching Using Virtual Network Ports,” Computer Communications and Networks, 1999, Proceedings, Eight International Conference in Boston, MA, USA, Oct. 11-13, 1999, Piscataway, NJ, USA, IEEE, ISBN: 0-7803-5794-9, pp. 642-648.
Wang, Feng, et al. “Obfs: A File System for Object-Based Storage Devices,” Storage System Research Center, MSST. vol. 4., Apr. 2004, 18 pages.
Weil, Sage A., “Ceph: Reliable, Scalable, and High-Performance Distributed Storage,” Dec. 2007, 239 pages, University of California, Santa Cruz.
Weil, Sage A., et al. “CRUSH: Controlled, Scalable, Decentralized Placement of Replicated Data.” Proceedings of the 2006 ACM/IEEE conference on Supercomputing. ACM, Nov. 11, 2006, 12 pages.
Weil, Sage A., et al. “Ceph: A Scalable, High-performance Distributed File System,” Proceedings of the 7th symposium on Operating systems design and implementation. USENIX Association, Nov. 6, 2006, 14 pages.
Wu, Joel, et al., “The Design, and Implementation of AQuA: An Adaptive Quality of Service Aware Object-Based Storage Device,” Department of Computer Science, MSST, May 17, 2006, 25 pages; hitp://storgeconference.us/2006/Presentations/30Wu.pdf.
Xue, Chendi, et al. “A Standard framework for Ceph performance profiling with latency breakdown,” CEPH, Jun. 30, 2015, 3 pages.
Zhou, Zihan, et al., “Stable Principal Component Pursuit,” arXiv:1001.2363v1 [cs.IT], Jan. 14, 2010, 5 pages.
Zhu, Yunfeng, et al., “A Cost-based Heterogeneous Recovery Scheme for Distributed Storage Systems with RAID-6 Codes,” University of Science & Technology of China, 2012, 12 pages.
Stamey, John, et al., “Client-Side Dynamic Metadata in Web 2.0,” SIGDOC '07, Oct. 22-24, 2007, pp. 155-161.
Related Publications (1)
Number Date Country
20180063030 A1 Mar 2018 US