Patent Application
20080195447
1. Technical Field
The present invention relates to efficient computer capacity control and more particularly to systems and methods for sizing computer systems, which include a plurality of procedures that determine capacity sizes based on user performance requirements for batch, transactional and other applications.
2. Description of the Related Art
E-business hosting centers often include hundreds or thousands of computer systems. Management of amounts of resources allocated to these large numbers of systems is a challenging task. One particularly important problem is capacity sizing which is needed to determine the appropriate capacity size allocations for each server. It would be beneficial to determine the appropriate capacity sizes so as to guarantee that the server capacity is sufficient to handle an arrival load (requests, transactions, etc.) within prescribed user/customers performance requirements.
The capacity problem is particularly important for systems with resource virtualization capacities, such as IBM®'s mainframe computers, p-Series with POWERS™ technology and x-Series with virtualized infrastructure such as VMware™ or Hypervisor™. These systems permit the logical partitioning of physical hardware system resources, such as processors and memory, into fractional units, thereby permitting more flexible and more accurate allocation of system resources to individual customers and/or applications.
Existing capacity sizing algorithms are mostly ad-hoc methods based on statistical parameters such as the observed peak or a percentile of the system utilization data. Such algorithms are very sensitive to noise or other sources of jitter in the system measurements. These algorithms often over-estimate the capacity requirement and lead to waste of system resources.
A system and method for capacity sizing in a computer device or system includes determining one or more classes of operations based on at least one of historical computational usage and predicted usage for a system, Based on the one or more classes of operations, at least one capacity target is set based on the computational usage for each class such that computational capacity is maintained at a set level over a given time period and the set level satisfies at least one usage criterion over the given time period.
In alternate embodiments, the one or more classes includes a batch class and at least one capacity target includes a capacity, m, set to indicate an amount of work that can be completed in a given time period. The capacity, m may include determining residual work above the capacity and queuing the residual work in a next time period. The at least one usage criterion may include an amount of residual work, and the method may include limiting the residual work such that the residual work can be completed in a defined period after a period where the residual work has occurred. The capacity, m, may be manually set to guarantee a level of performance.
In other embodiments, the one or more classes may include a transactional class and at least one capacity target may be set to provide less than a maximum amount of lost work in a given time period. This may include determining lost work by computing a utilization area under a utilization curve above a set capacity. The at least one usage criterion may include a maximum percentage of lost work, and the method may include limiting the utilization area to an amount corresponding to less than the maximum percentage of lost work. The capacity, m, may be manually set to guarantee a level of performance.
The methods further include optimizing the computational capacity based on the at least one target objective using a binary search method.
Another method for capacity sizing in a computer device or system includes determining one or more classes of operations based on at least one of historical computational usage and predicted usage for a system. For batch processing, the method includes setting a first capacity target to indicate an amount of work capacity that can be completed in a given time period; and determining residual work above the first capacity target while meeting a first usage criteria such that queuing the residual work in a next time period maintains usage at the first capacity target. For transactional processing, the method includes setting a second capacity target that provides less than a maximum amount of lost work in a given time period by computing a utilization area under a utilization curve above the second capacity target.
These and other features and advantages will become apparent from the following detailed description of illustrative embodiments thereof, which is to be read in connection with the accompanying drawings.
The disclosure will provide details in the following description of preferred embodiments with reference to the following figures wherein:
Embodiments in accordance with present principles provide for sizing computer systems. In particularly useful embodiments, a plurality of procedures are employed to determine the capacity sizes based on user performance requirements for different applications, e.g., batch applications, transactional applications, combinations of these, etc. Present embodiments consider, as input, past observed system utilization data as measured on a current resource allocation or predicted utilization data based on estimates or models. This data may be suboptimal, but still useful for determining future utilization information and provide an initial capacity guess. Observed systems exhibit different types of behavior depending on whether the applications are of, e.g., batch or transactional nature. The difference between these two types of applications is mainly due to how they can respond to conditions during which the allocated resources are fully utilized. Embodiments will illustratively be described in terms of batch and transactional processes; however, other types of tasks may also be employed using similar methods.
For batch applications, the excess or unfinished work in a time interval can be queued and carried over to be processed in the next time interval. User provided performance requirements for batch applications can specify the maximum amount of extra time the busy period can last. For example, the requirement of an extra busy time of one time period implies that a capacity m is sufficient if, whenever the arriving workload is more than m, then all the residual work will be finished in the next time period.
For transactional applications, it is assumed that the arriving work is time sensitive, so any unfinished work during a time interval of full resource utilization is deemed lost. The user specified performance requirements for the transactional applications can be described as the maximum percentage of lost work over all time windows of a given length. For example, a requirement of maximum lost work percentage of 0.1% over a time window, e.g., size x, implies that a capacity m is sufficient if, the percentage of lost work is smaller than 0.1% for every time window of length x.
In accordance with the present embodiments, the systems and methods described herein provide many advantages in evaluating and capacity sizing computer Systems. For example, the embodiments include parameters that can provide level of performance guarantees. The embodiments can effectively filter outliers and/or noise in measurement data. For example, an isolated spike in a past utilization observation will have limited effect on the sizing decisions. The embodiments are efficient in running time as well, and can account for a wide range of different application classes. The type of application can be explicitly provided by the user/customer or inferred indirectly by observation of system parameters.
Embodiments of the present invention can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment including both hardware and software elements. In a preferred embodiment, the present invention is implemented in software, which includes but is not limited to firmware, resident software, microcode, etc.
Furthermore, the invention can take the form of a computer program product accessible from a computer-usable or computer-readable medium providing program code for use by or in connection with a computer or any instruction execution system. For the purposes of this description, a computer-usable or computer readable medium can be any apparatus that may include, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. The medium can be an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system (or apparatus or device) or a propagation medium. Examples of a computer-readable medium include a semiconductor or solid state memory, magnetic tape, a removable computer diskette, a random access memory (RAM), a read-only memory (ROM), a rigid magnetic disk and an optical disk. Current examples of optical disks include compact disk—read only memory (CD-ROM), compact disk—read/write (CD-R/W) and DVD.
A data processing system suitable for storing and/or executing program code may include at least one processor coupled directly or indirectly to memory elements through a system bus. The memory elements can include local memory employed during actual execution of the program code, bulk storage, and cache memories which provide temporary storage of at least some program code to reduce the number of times code is retrieved from bulk storage during execution. Input/output or I/O devices (including but not limited to keyboards, displays, pointing devices, etc.) may be coupled to the system either directly or through intervening I/O controllers.
Network adapters may also be coupled to the system to enable the data processing system to become coupled to other data processing systems or remote printers or storage devices through intervening private or public networks. Modems, cable modem and Ethernet cards are just a few of the currently available types of network adapters.
Referring now to the drawings in which like numerals represent the same or similar elements and initially to
For a specified capacity, m, the workload w(t) at time t may include residual (or remaining) work r(t) at time t. This may be expressed mathematically as:
w(t)=max(w(t−1)+r(t)−m,0) (1).
Equation (1) includes that w(t) equals the workload from a previous period w(t−1) plus the residual workload r(t) of this time period minus the base load or capacity load m. w(t) represents the maximum of (w(t−1)+r(t)−m) or zero. As mentioned, m may initially be user set or system determined.
In block 25, based on the historic or predicted information collected, check the target capacity against criteria. For example, the residual work is determined for a given time period based upon an initial guess or the target objective capacity. Capacity m is determined to be sufficient if a set criterion is met. In one embodiment, m is sufficient if, whenever the arrival work is more than m, then all the residual work will be finished in the next time period (or in the next k time periods, depending on the user setting). The parameters may be user determined and adjusted. For example, in block 24, a maximum permitted residual (r(t)) may be specified as a criterion to batch processing. In another embodiment, the residual can be defined as anything above the current capacity setting (m) that can be completed in a predefined time. The predefined time may include one or more cycles or periods. k may also be specified or changed.
A smallest sufficient capacity m is preferable to conserve resources. Therefore, m may be determined using a method to find an optimal CPU usage. For illustration purposes, assume size capacity is determined based on residual work being performed in a next time period. The size capacity (m) is adjusted according to the ability to perform all the tasks in a given time period in the next time period.
In block 26, to find a better capacity, the method may employ a binary search to determine the appropriate size capacity. Using the target capacity guessed or computed in block 22, a test of whether the excess or unfinished work (r(t)) in one time interval can be queued and carried over to be processed in the next time interval is performed. If this residual work can be completed, an optimal solution may have been determined (e.g., the initial guess was correct). However, if the residual was too high (could not complete the work in the next cycle or period) or too low (the actual processing percent was below the target capacity), an adjustment to the capacity may be needed. From the initial guess for capacity, say e.g., 80% usage, the usage is split in half to 40% usage in accordance with the binary search method. 40% usage is then checked against the criteria (e.g., will the residual work be handled in the next period). Next, the midpoint between 40% and 80% (or 60%) is checked to further improve the result. If better results are achieved by a larger usage than the midpoint between 60% and 80% is (70%) checked. This continues until the best capacity can be determined. Binary search methods are known in the art. Other methods may also be employed to optimize the results.
The user may provide a factor of safety or make special adjustments based upon known anomalies in the system. For example, if the system usage is high on New Year's Eve at midnight, etc. Performance requirements for batch applications can specify the maximum amount of extra time the busy period can last before additional capacity is needed and added. Other criteria for determining when and how work will be queued are also contemplated.
Referring to
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As described above, the 80% usage meets the criteria (e.g., residual work performed in a next cycle); however, this capacity may not be optimal. Hence, a binary search may be employed to optimize the capacity.
Referring to
Referring again to
In addition, a maximum permitted utilization area that is permitted to exceed the target objective may also be provided in block 34. This may also be user set or system determined. The utilization area considers not only the percentage of samples above the target but also the area exceeding the target. Larger longer “spikes” impact sizing more than smaller shorter duration spikes even though the shorter duration spikes may have a larger peak magnitude. (See
In block 36, the criterion/criteria are checked. For example, lost work is determined, or a percent area exceeding the utilization area is computed for any usage that exceeds the target objective based on the historical (or predicted) data and the initial guess. If the criteria are met, for example, the utilization area is less than the maximum allowed and/or the lost work is below the threshold set, then the capacity is sufficient. In block 38, a method is employed to find an optimal CPU usage. In one embodiment, the method includes a binary search method. The binary method may be applied as described above, taking the midpoint between the initial guess and A the initial guess and then taking the midpoint on the next segment to converge on an optimal capacity.
Block 40 provides for other classes of work tasks, e.g., hybrid combinations of batch and transactional tasks, long running tasks, constant tasks, etc. These tasks may have other sets of criteria for optimizing capacity.
Hybrids combination may be handled in different ways. For example: A) Fixed capacity assignment for batch and transactional workloads. This means the assignment does not change over time. This is a somewhat easy case. The problem can be decomposed into batch and transactional processes, and the problem for batch and transactional workloads can be handled separately. Then, the solutions can be combined. B) Variable capacity assignment. This means the capacity assigned to the two workloads can be different at different times. For this case, the capacity assignment is determined for each time segment. This problem can be formulated as a linear program.
In block 50, the capacity is set for further system processing. This capacity may be an overall system capacity based on the capacities optimized for one or more of the batch tasks, transactional tasks or other tasks. The overall capacity may be based on a combination of the optimized capacities or the overall capacity may consider all tasks at once.
Referring to
The present principles provide parameters that can guarantee a certain level of performance. For example, by setting m, a performance level may be specified in terms of CPU usage. The present principles effectively filter outliers and/or noise in the measurement data. For example, an isolated spike in past utilization observations will have limited effect on the sizing decisions, which is preferable to capacity planners. The methods are efficient in running time as well, e.g., the complexity is O(data_length*ABS(log(accuracy))), where data_length is the number of input data points and accuracy is the acceptable error, for example, within 0.1 percent for CPU utilization. Since accuracy is smaller than 1, log(accuracy) is negative and the absolute value of log(accuracy) is used.
The algorithms can account for a wide range of different application classes and may be able to handle combinations of classes. For example, transactional and batch processes can be handled simultaneously. The type of application can be explicitly provided by the user/customer or inferred indirectly by observation of system parameters.
It should be understood that the present principles are applicable to a single computer, a single processor, a system of computers, a system of processors, a distributed network of computers, etc. The percent usage may include an overall system usage or the usage of a single CPU. In a system, overload computational tasks may be assigned to the same CPU or another CPU within the system. A CPU may be partitioned, and portions may be used to determine capacity, or portions may be included to provide additional capacity.
Referring to
In block 608, computational loads are allocated and/or optimized to time periods based upon the historic observed or predicted loads. A binary search method or other method may be employed to optimize the CPU usage in block 610, CPU loads are sized and maintained as a result of the allocation for better performance.
Having described preferred embodiments of a system and method for capacity sizing for computer systems (which are intended to be illustrative and not limiting), it is noted that modifications and variations can be made by persons skilled in the art in light of the above teachings. It is therefore to be understood that changes may be made in the particular embodiments disclosed which are within the scope and spirit of the invention as outlined by the appended claims. Having thus described aspects of the invention, with the details and particularity required by the patent laws, what is claimed and desired protected by Letters Patent is set forth in the appended claims.
