Patent Application
20240160482
The present invention relates generally to the field of data management and more particularly to techniques for allocating resources dynamically.
Many businesses have found it to be more cost effective to purchase computer resources as part of a service. Using a service provider often involves the computer resources that may be purchased to reside remotely. These services may also involve remote program execution, request processing by virtual machines and remote data storage. Many customer needs may be provided in form of software containers. A container may be a software package that also includes everything the software requires in order to run a particular program on a particular platform. This includes the executable program as well as system tools, libraries, and settings.
Containers may not be installed like traditional software programs so as to allows them to be isolated from one another and the operating system itself. Customers, also called tenants, may be responsible for selecting a container size suitable for their workloads which they can change to leverage cloud's elasticity. However, automating this task may be daunting for most tenants and service providers because predicting workloads and resources to process them can be complex and challenging as the resource requirements can vary significantly within minutes to hours.
Embodiments of the present invention disclose a method, computer system, and a computer program product for dynamic allocation of resources. In one embodiment, a database access pattern is determined by analyzing and monitoring traffic pattern between a pool of resources and a plurality of clients during performance of at least one task, wherein the traffic pattern includes accessing at least one database. The relationship between each of the resources in the pool is also determined. Access is enabled to a plurality of resources in the pool based on the database access and resource relationships, so as to enable the plurality of resources to become accessible for use without being allocated unless there is a processing request. A consumption model is then generated that can predict resource need for a processing request based on resource relationships, traffic pattern and resources availability. Upon receipt of a subsequent request for processing, this consumption model is used to predict resource needs and to dynamically allocate, reallocate and release the plurality of resources in a cascading manner until the subsequent request for processing is completed.
These and other objects, features and advantages of the present invention will become apparent from the following detailed description of illustrative embodiments thereof, which may be to be read in connection with the accompanying drawings. The various features of the drawings are not to scale as the illustrations are for clarity in facilitating one skilled in the art in understanding the invention in conjunction with the detailed description. In the drawings:
Detailed embodiments of the claimed structures and methods may be disclosed herein; however, it can be understood that the disclosed embodiments may be merely illustrative of the claimed structures and methods that may be embodied in various forms. This invention may, however, be embodied in many different forms and should not be construed as limited to the exemplary embodiments set forth herein. Rather, these exemplary embodiments may be provided so that this disclosure will be thorough and complete and will fully convey the scope of this invention to those skilled in the art. In the description, details of well-known features and techniques may be omitted to avoid unnecessarily obscuring the presented embodiments.
Various aspects of the present disclosure are described by narrative text, flowcharts, block diagrams of computer systems and/or block diagrams of the machine logic included in computer program product (CPP) embodiments. With respect to any flowcharts, depending upon the technology involved, the operations can be performed in a different order than what is shown in a given flowchart. For example, again depending upon the technology involved, two operations shown in successive flowchart blocks may be performed in reverse order, as a single integrated step, concurrently, or in a manner at least partially overlapping in time.
A computer program product embodiment (“CPP embodiment” or “CPP”) is a term used in the present disclosure to describe any set of one, or more, storage media (also called “mediums”) collectively included in a set of one, or more, storage devices that collectively include machine readable code corresponding to instructions and/or data for performing computer operations specified in a given CPP claim. A “storage device” is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, the computer readable storage medium may be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the foregoing. Some known types of storage devices that include these mediums include: diskette, hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), compact disc read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded device (such as punch cards or pits/lands formed in a major surface of a disc) or any suitable combination of the foregoing. A computer readable storage medium, as that term is used in the present disclosure, is not to be construed as storage in the form of transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through a fiber optic cable, electrical signals communicated through a wire, and/or other transmission media. As will be understood by those of skill in the art, data is typically moved at some occasional points in time during normal operations of a storage device, such as during access, de-fragmentation or garbage collection, but this does not render the storage device as transitory because the data is not transitory while it is stored.
COMPUTER 101 of
PROCESSOR SET 110 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips. Processing circuitry 120 may implement multiple processor threads and/or multiple processor cores. Cache 121 is memory that is located in the processor chip package(s) and is typically used for data or code that should be available for rapid access by the threads or cores running on processor set 110. Cache memories are typically organized into multiple levels depending upon relative proximity to the processing circuitry. Alternatively, some, or all, of the cache for the processor set may be located “off chip.” In some computing environments, processor set 110 may be designed for working with qubits and performing quantum computing.
Computer readable program instructions are typically loaded onto computer 101 to cause a series of operational steps to be performed by processor set 110 of computer 101 and thereby effect a computer-implemented method, such that the instructions thus executed will instantiate the methods specified in flowcharts and/or narrative descriptions of computer-implemented methods included in this document (collectively referred to as “the inventive methods”). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 121 and the other storage media discussed below. The program instructions, and associated data, are accessed by processor set 110 to control and direct performance of the inventive methods. In computing environment 100, at least some of the instructions for performing the inventive methods may be stored in block 1200 in persistent storage 113.
COMMUNICATION FABRIC 111 is the signal conduction paths that allow the various components of computer 101 to communicate with each other. Typically, this fabric is made of switches and electrically conductive paths, such as the switches and electrically conductive paths that make up busses, bridges, physical input/output ports and the like. Other types of signal communication paths may be used, such as fiber optic communication paths and/or wireless communication paths.
VOLATILE MEMORY 112 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, the volatile memory is characterized by random access, but this is not required unless affirmatively indicated. In computer 101, the volatile memory 112 is located in a single package and is internal to computer 101, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer 101.
PERSISTENT STORAGE 113 is any form of non-volatile storage for computers that is now known or to be developed in the future. The non-volatility of this storage means that the stored data is maintained regardless of whether power is being supplied to computer 101 and/or directly to persistent storage 113. Persistent storage 113 may be a read only memory (ROM), but typically at least a portion of the persistent storage allows writing of data, deletion of data and rewriting of data. Some familiar forms of persistent storage include magnetic disks and solid state storage devices. Operating system 122 may take several forms, such as various known proprietary operating systems or open source Portable Operating System Interface type operating systems that employ a kernel. The code included in block 1200 typically includes at least some of the computer code involved in performing the inventive methods.
PERIPHERAL DEVICE SET 114 includes the set of peripheral devices of computer 101. Data communication connections between the peripheral devices and the other components of computer 101 may be implemented in various ways, such as Bluetooth connections, Near-Field Communication (NFC) connections, connections made by cables (such as universal serial bus (USB) type cables), insertion type connections (for example, secure digital (SD) card), connections made though local area communication networks and even connections made through wide area networks such as the internet. In various embodiments, UI device set 123 may include components such as a display screen, speaker, microphone, wearable devices (such as goggles and smart watches), keyboard, mouse, printer, touchpad, game controllers, and haptic devices. Storage 124 is external storage, such as an external hard drive, or insertable storage, such as an SD card. Storage 124 may be persistent and/or volatile. In some embodiments, storage 124 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 101 is required to have a large amount of storage (for example, where computer 101 locally stores and manages a large database) then this storage may be provided by peripheral storage devices designed for storing very large amounts of data, such as a storage area network (SAN) that is shared by multiple, geographically distributed computers. IoT sensor set 125 is made up of sensors that can be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.
NETWORK MODULE 115 is the collection of computer software, hardware, and firmware that allows computer 101 to communicate with other computers through WAN 102. Network module 115 may include hardware, such as modems or Wi-Fi signal transceivers, software for packetizing and/or de-packetizing data for communication network transmission, and/or web browser software for communicating data over the internet. In some embodiments, network control functions and network forwarding functions of network module 115 are performed on the same physical hardware device. In other embodiments (for example, embodiments that utilize software-defined networking (SDN)), the control functions and the forwarding functions of network module 115 are performed on physically separate devices, such that the control functions manage several different network hardware devices. Computer readable program instructions for performing the inventive methods can typically be downloaded to computer 101 from an external computer or external storage device through a network adapter card or network interface included in network module 115.
WAN 102 is any wide area network (for example, the internet) capable of communicating computer data over non-local distances by any technology for communicating computer data, now known or to be developed in the future. In some embodiments, the WAN may be replaced and/or supplemented by local area networks (LANs) designed to communicate data between devices located in a local area, such as a Wi-Fi network. The WAN and/or LANs typically include computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and edge servers.
END USER DEVICE (EUD) 103 is any computer system that is used and controlled by an end user (for example, a customer of an enterprise that operates computer 101), and may take any of the forms discussed above in connection with computer 101. EUD 103 typically receives helpful and useful data from the operations of computer 101. For example, in a hypothetical case where computer 101 is designed to provide a recommendation to an end user, this recommendation would typically be communicated from network module 115 of computer 101 through WAN 102 to EUD 103. In this way, EUD 103 can display, or otherwise present, the recommendation to an end user. In some embodiments, EUD 103 may be a client device, such as thin client, heavy client, mainframe computer, desktop computer and so on.
REMOTE SERVER 104 is any computer system that serves at least some data and/or functionality to computer 101. Remote server 104 may be controlled and used by the same entity that operates computer 101. Remote server 104 represents the machine(s) that collect and store helpful and useful data for use by other computers, such as computer 101. For example, in a hypothetical case where computer 101 is designed and programmed to provide a recommendation based on historical data, then this historical data may be provided to computer 101 from remote database 130 of remote server 104.
PUBLIC CLOUD 105 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and/or other computer capabilities, especially data storage (cloud storage) and computing power, without direct active management by the user. Cloud computing typically leverages sharing of resources to achieve coherence and economies of scale. The direct and active management of the computing resources of public cloud 105 is performed by the computer hardware and/or software of cloud orchestration module 141. The computing resources provided by public cloud 105 are typically implemented by virtual computing environments that run on various computers making up the computers of host physical machine set 142, which is the universe of physical computers in and/or available to public cloud 105. The virtual computing environments (VCEs) typically take the form of virtual machines from virtual machine set 143 and/or containers from container set 144. It is understood that these VCEs may be stored as images and may be transferred among and between the various physical machine hosts, either as images or after instantiation of the VCE. Cloud orchestration module 141 manages the transfer and storage of images, deploys new instantiations of VCEs and manages active instantiations of VCE deployments. Gateway 140 is the collection of computer software, hardware, and firmware that allows public cloud 105 to communicate through WAN 102.
Some further explanation of virtualized computing environments (VCEs) will now be provided. VCEs can be stored as “images.” A new active instance of the VCE can be instantiated from the image. Two familiar types of VCEs are virtual machines and containers. A container is a VCE that uses operating-system-level virtualization. This refers to an operating system feature in which the kernel allows the existence of multiple isolated user-space instances, called containers. These isolated user-space instances typically behave as real computers from the point of view of programs running in them. A computer program running on an ordinary operating system can utilize all resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running inside a container can only use the contents of the container and devices assigned to the container, a feature which is known as containerization.
PRIVATE CLOUD 106 is similar to public cloud 105, except that the computing resources are only available for use by a single enterprise. While private cloud 106 is depicted as being in communication with WAN 102, in other embodiments a private cloud may be disconnected from the internet entirely and only accessible through a local/private network. A hybrid cloud is a composition of multiple clouds of different types (for example, private, community or public cloud types), often respectively implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technology that enables orchestration, management, and/or data/application portability between the multiple constituent clouds. In this embodiment, public cloud 105 and private cloud 106 are both part of a larger hybrid cloud.
Database-as-a-Service (DaaS) platforms today support the abstraction of a resource container that guarantees a fixed number of resources. Customers, sometimes referred to as tenants are responsible for selecting a container size suitable for their workloads, which they can change to leverage the cloud's elasticity. However, automating this task is daunting for most tenants since estimating resource demands for arbitrary SQL workloads in DBaaS is complex and challenging. In addition, workloads and resource requirements can vary significantly within minutes to hours, and container sizes vary by orders of magnitude both in the amount of resources as well as monetary cost. Consequently, it may become necessary to allocate database resources in the proper time with the proper amount, including connection pool, Buffer Pool, Queue, etc, especially during cascade situation or across databases. The process 200 of
In Step 210, database access pattern may be established. This may involve collecting and analyzing traffic patterns. In one embodiment, the traffic patterns are monitored for both external and internal clients. In addition, a Client Traffic Monitor module may be used to collect database traffic from these external and internal clients. This may include periodically extracting and analyzing transactional traffic such as by using a Client Traffic Analyzer (See
In one embodiment, the traffic can be monitored and controlled. In one embodiment, a Traffic Control Monitor (not illustrated) may be used as a part of the Client Traffic Analyzer to analyze and collect client traffic data from internal client and external client against database. A variety of means can be used to accomplish this task. Some examples are server-based agent software, in-line network collectors and out-of-band network collectors.
In one embodiment, count accesses to database and database indexes are monitored and analyzed both in a disk-block and individual terms. This can be shown as an example in
Referring back to
In Step 230, it is determined whether the resources in the pool may be sufficient for each request processing. In one embodiment, this can be performed by providing a consumption model by using a Multi Resource Usage Predictor module. Using this module may establish a resource consumption model to predict resource consumption that will in turn allow for determining and selecting whether the existing resource pool will be sufficient for each request processing.
In Step 240, when it has been determined that a resource pool may be sufficient for request processing, existing resources may be selected and fetched from the existing resource pool. Alternatively, as provided in step 242, when the pool may not be sufficient, additional resources can be added or alternatively an alert can be provided.
In Step 250, resources will be allocated (new or existing resources) in a cascading manner. In one embodiment, a Multi Resource Manger module allocates these new resources and adds them when appropriate into the resource pool. In one embodiment, this includes determining when to allocate new resources in a cascading manner. It also includes releasing resources dynamically when the task has been completed.
In one embodiment, the methodology of
In one embodiment, an application 310 acquires a data resource or a connection factory object 322/323 from a resource adapter 320 (residing in an application server 301 in communication with a driver 350). The data source/connection factory 322/323 delegates the connection allocation request to a connection manager 330. The connection manager 330 retrieves a free connection from the database (DB) connection pool 340 or creates a new one if none may be available. The retrieved or created connection may then be returned to the application via the database server 360.
In
In
Now looking at the provided output of the Predictor 820, the first output provides an estimated workload and determine some characteristics and metrics using the feature extractions as seen at 840. Some examples of such metrics may be provided below but many others can be provided in alternate embodiments:
The predictor can also provide a case based reasoning (CBR) which can also be used for machine learning and by Artificial Intelligence (AI) modules and agents as seen at 842. This will allow new cases to be adapted without requiring retraining of data.
In one embodiment, CBR in turn can use the information to check if an identical training pool allocation case exists. If one can be found, an accompanying solution to the case can be returned. If no identical case can be found, then the CBR will search for training cases, having components that may be similar to those of any new cases (similar to database access model). In one embodiment, the cases may be represented by graphs. In such a case the search involves looking for subgraphs that may be similar to the one of a new request/case.
In one embodiment, if the CBR tries to combine the solutions of the neighboring training cases to propose a solution for the new case. In this case, if compatibilities arise with the individual solutions, then a backtracking tool will search for other solutions, as appropriate and necessary.
Another output provided has to do with Resource Adaptation as shown at 844. This aims at determining the execution of order of queries. Again, an AI module or a machine learning based component can provide a reasoning-based structure.
The descriptions of the various embodiments of the present invention have been presented for purposes of illustration but may be not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
