Patent Application
20150278066
Enterprises and other companies may reduce information technology (“IT”) costs by externalizing hardware computing costs, hardware maintenance and administration costs, and software costs. One option to externalize IT costs is by purchasing cloud computing processing and hosting from a third party cloud computing provider. Cloud computing providers purchase and maintain computer servers typically in server farms, and act as a utility company by reselling their computing capacity to customers. Some customers may be value added resellers (“VARs”), that are software companies who host their software applications on computing capacity from cloud providers. These VARs then make money by selling access to their software applications to customers. In this way, cloud computing providers directly externalize hardware computing costs and hardware maintenance costs, and indirectly externalize software costs by providing a hosting platform for VARs.
Cloud computing providers typically add infrastructure services, that provide common services for the cloud provider. Some infrastructure services are operating system-like services that control allocation of services of the cloud. For example, physical servers in server farms are typically disaggregated and resold in unitary blocks of service in the form of processing power, memory, and storage. Specifically, a unitary block is some unit to inform a customer of the volume of computing capacity purchased from a cloud provider. Consider a customer that purchases a unitary block of denoted, for example, one “virtual processor”. That customer may in fact be purchasing processing power where the virtual process is provided by different cores on a processor, different processors on the same physical server, or potential processing cores on different physical servers. The unitary block measuring computer service is proffered by the vendor, rather than a third party operating at arm's length.
Other infrastructure services provide services that support the cloud provider business model. For example, cloud providers typically provide different billing options based on metering a customer's usage on the cloud. A billing infrastructure is an example of an infrastructure service that supports the cloud provider business model. However, metering, service level agreements, and ultimately billing are often provided in terms of a vendor's chosen unitary measure.
Accordingly, customers are obliged to independently verify vendor claims about the unitary measure, or alternatively simply take the vendor at their word. Thus customers are faced with evaluating cloud provider claims without a ready point of reference.
Verification of claims about unitary services is not trivial. Cloud providers use infrastructure services as competitive differentiators to attract customers and VARs. For example, yet other infrastructure services provide abstractions that facilitate application development and hosting on the cloud. Well known examples include Platform-as-a-Service (“PAAS”), Infrastructure-as-a-Service (“IAAS”) and Software-as-a-Service (“SAAS”) hosting and development infrastructure.
Thus additionally, customers who seek to compare cloud providers are faced with evaluating different hardware configurations, different software configurations, and different infrastructure services, often without transparency to the operation of different cloud providers.
The Detailed Description is set forth with reference to the accompanying figures.
The present disclosure describes benchmarking from the perspective of benchmarking cloud computing. Before discussing benchmarking cloud computing, the present disclosure will describe some preliminaries regarding benchmarking.
Benchmarking is the selection of one or more indicia that are used to compare one item to another or one item to an idealized version of that item. In the case of computer science, common comparative indicia may include software performance, hardware performance, overall system performance. For example volume of data processed, number of faults, and memory usage may be candidate metrics for benchmarking software performance. A particular software implementation may be compared to a competing implementation. Alternatively, the software implementation might be compared to the theoretical optimum values of those metrics. Regardless of what metrics are chosen, the aggregating of those chosen metrics constitutes benchmarking.
Since the indicia chosen to constitute a benchmark are used for comparisons, the indicia chosen are to be based on a measure. A measure is sometimes called a distance function that is a value based on a comparison. Measure can be categorized by their behavior upon comparing measure values, called measurements, against each other. Measures may come in the following four categories.
i. Different Categories
Indicia may be placed in different categories. Here, the indicia indicates what kind of item, something is. It does not indicate whether something is better or worse than another item. Rather it simply indicates that it is different and should be treated and/or evaluated differently. For example, a cloud infrastructure service might be classified as PAAS, IAAS, or SAAS. None of the three options are necessarily better or worse, rather just in different categories.
ii. Ordered Categories
Indicia may be placed in ordered categories. Here, the categories have a clear order as to which categories is more desirable. Typically the categories are ordered in monotonically increasing order, such as from worst to best. For example, customer satisfaction with a cloud vendor might be classified from “bad”, “average”, “good” and “excellent.” Therefore, a cloud vendor classified as “excellent” might be considered better than another classified as “average.” However, there is no indication of degree of how much better an “excellent” vendor is over another that is merely “average.”
iii. Additive Categories
Indicia may be additive. Additive indicia allow multiple measurements to be aggregated into a single measurement, where order is preserved. For example, number of processors on a server for parallel processing is additive. Two processors generally are able to do more processing than one processor. However, two processors are not necessarily able to do twice as much processing as one processor, due to communications overhead and/or the possibility of the processors being heterogeneous. So additive indicia do not scale.
iv. Scalable Measurements
Indicia may be scalable. Not only are scalable indicia additive, scalable indicia support all arithmetic operations including multiplication and division. For example, megaflops per second (“MFLOPS”) is an indicia that is a scalable measure. A processor that can perform 2,500 MFLOPS is two and half times as powerful as a processor that can perform 1,000 MFLOPS.
Additive and scalable measures are sometimes called metrics, because the distance function comprising the measure satisfies the mathematical properties of separation, coincidence, symmetry and the triangle inequality. Regarding the latter, a measure satisfies the triangle inequality if the measurements between A and C is greater than or equal to the measurement between A and B added to the measurement between B and C. Expressed mathematically, F(x, y) satisfies the triangle inequality if:
F(A,C)≦F(A,B)+F(B,C).
Metrics provide the basis for performing statistical functions, many of which are based on arithmetic operations. Accordingly, metrics are desirable measures, because they enable statistical techniques to be brought to bear during analysis. For example, consider the function for a standard deviation:
The standard deviation function is comprised of square roots and exponents which use multiplication, summations which use addition, averages which use division, and the like. Thus the standard deviation function is mathematically and statistically meaningful where a metric is used as a measurement.
Turning to the application of benchmarking to cloud computing, there are several potential cloud provider evaluation goals that are driven by business operations. The evaluation goals may include a potential business decisions to:
These evaluation goals may be achieved by identifying and selecting indicia to comprise a benchmark. The indicia may support simple difference comparisons, between one or more systems. Alternatively, the indicia may provide the basis to define a measure in terms of one or more normalized units to make baseline measurements. Defining a normalized unit that supports a metric enables bringing not only direct comparisons, but also statistical techniques to support a comprehensive evaluation.
The selected indicia are chosen on the basis of either being an indicia of a cloud provider's performance, functionality, or characteristics, known collectively as a PFC. Performance indicia are artifacts that indicate how a cloud provider performs under a work load, for example processor usage percentage. Functionality includes computing features that are available from the cloud provider, for example a maximum of 4 GB memory available to a virtual server instance. Characteristics differentiate categories for cloud providers, such as type of billing model. The selected indicia may be measured with varying frequency. In some situations, a single measurement may be made over the lifetime of a benchmarking cycle. In others, multiple measurements are made either periodically, according to a predetermined schedule, or upon detecting an event or condition.
Cloud computing benchmarks may comprise indicia that allow for the aggregation of measurements over time. Specifically indicia may be selected to continuously, periodically, or at selected intervals measure and track the overall performance capability over time. This enables the development of complex algorithms which may include for example the overall performance capabilities across systems; the impact of multiple demands on a system; impact to the system's capabilities; and their respective trend over time. A specific benchmark may be to capture the processor maximum performance over time, to capture the network throughput over time and to combine these measures based on a workload demand to generate a predictive model of what the maximum processor capability is given a variable network throughput. While this benchmark example outlines two indicia, by definition, the overall performance capability will be impacted by all of the demand on the cloud provider. Thus, the measurement of indicia is enhanced by the temporal view that enables adaptive and predictive modeling based on customer defined indicia.
Potential indicia include indicia in the following categories.
i. Compute
The compute category covers information about the physical and/or virtual processor cores used by servers in a cloud provider. In general, computing processors are known as computing processing units (“CPUs”). The following table lists potential indicia in the compute category.
ii. Memory
The memory category covers information about the physical and/or virtual (swap) random access memory (“RAM”) used by servers in a cloud provider. The following table lists potential indicia in the memory category.
iii. Disk
The disk category covers information about the storage media available via the operating system or disk drives used by servers in a cloud provider. The following table lists potential indicia in the disk category.
iv. Operating System
The operating system (“OS”) category covers information about the operating system used by servers in a cloud provider. The following table lists potential indicia in the operating system category.
v. Network
The network category covers information about the server's connection to its local area network (“LAN”) and to the Internet for servers in a cloud provider. The following table lists potential indicia in the network category.
vi. Database
The database (“DB”) category covers information about a structured query language (“SQL”) or noSQL database management system (“DBMS”) application running on servers in a cloud provider. The following table lists potential indicia in the database category.
vii. Cloud Provider
The cloud category covers information about the cloud provider in which the server is instantiated. In some cases, the indicia may be in terms of a normalized work load unit. The following table lists potential indicia in the cloud provider category.
Selection of indicia for a benchmark may be driven by the consumer of the benchmark. A basis for a benchmark to be accepted by a consumer is that the consumer trusts the measurement. There are several factors that may affect the trust of a measurement.
i. The Observation Problem aka Heisenberg
The act of observing a system will affect a system. When a measurement consumes computing resources as to affect the observable accuracy of a measurement, the measurement will not be trusted. This problem is also known as the “Heisenberg” problem. In the case of cloud computing, a benchmarking application running within a cloud instance will use processing, memory, and network resources. In particular, since cloud communications are typically geographically disparate, network latency during measurement may have a significant adverse impact on measurement accuracy. Furthermore, cloud infrastructure services often have sophisticated “adaptive” algorithms that modify resource allocation based on their own observations. In such situations, it is very possible that a benchmarking application may become deadlocked.
One approach is to guarantee performance overhead of a benchmarking application to be less than some level of load/processing core overhead. Measurements would be compared only on like systems. For example a Windows™ based platform would not necessarily be compared to a Linux platform. Also, memory and network overhead could be managed by carefully controlling collected data is transferred. For example, benchmark data may be cached on a local disk drive and will transfer upon an event trigger such as meeting a predetermined threshold to limit disk load. Since data transfer potentially creates network load, data may be transferred upon receiving a transfer command from a remote central controller.
Another approach may be to understand the statistical behavior of the system to be benchmarked. If an accurate statistics model is developed, then a statistically small amount of benchmarking data may be collected, and the measurement projected by extrapolation based on the statistics model. For example, a workload over time model may be developed where an initial measurement is made at the beginning of benchmarking. Since the initial measurement theoretically occurs before any additional workload, that initial measurement may be used as a theoretical processing maximum to compare subsequent measurements against.
Statistical models may be comprised where a cloud provider has infrastructure services that are adaptive. For example, a measurement at time T0 may not be comparable at time Tn if the cloud provider silently reconfigured between the two times. However, properly designed normalized unit should continue to be a normalized unit. Thus even if measurements may not be consistently comparable, the performance changes may be detected over time. Thus the adaptations of the cloud infrastructure and the triggers for those adaptations may be detected, and the benchmarking application may be configured to avoid those triggers or to compensate.
Yet another approach is to limit benchmarking under predetermined conditions. Some conditions are detected prior to benchmarking, and other conditions are detected during benchmarking. Regarding the former, given that the benchmarking application can negatively impact its environment, the central controller may have an “emergency stop” button customer that halts at least some of the benchmarking on at least some cloud provider instances under test. For example, a configuration file received by the benchmarking application may contain a “permit to run” flag. Before starting benchmarking, the benchmarking application may poll the central controller for the most recent configuration file. If there have been no changes the benchmarking application may receive a message indicating that the configuration file has not changed along with a set “permit to run” flag, and that the benchmarking application is permitted to start benchmarking. In this case, the benchmarking application will use the present configuration file and commence benchmarking. If the “permit to run” flag is not set, then the benchmarking application will not commence testing. In case where the benchmarking application cannot communicate with the central controller, the benchmarking application may default to not benchmarking and will assume the “permit to run” flag is not set. Regarding the detecting of conditions during benchmarking, the benchmarking application may gather at least some environment data for the cloud provider instance under test. If the benchmarking application detects that the environment data satisfies some predetermined condition, such as some or all of the current environment data being in excess of a predetermined level, then the benchmarking application may prevent benchmarking from starting.
Note that the benchmarking application under operation would only effect performance data collection, if at all. Thus functionality and characteristic data may continue to be collected without compromising the cloud performance instance under test.
ii. Meaningful Statistics
Books have been written about how to characterize statistics. For some, the risk is that the consumer is overly credulous when confronted with statistics, and may conflate the reception of statistics with a full analysis in making a business decision. For others, the risk is that the consumer has been exposed to shoddy statistical analysis, and may be overly suspicious of all statistics. Benchmarking trustworthiness may be based on some of the following factors: the results are verifiable, the methodology is transparent and verifiably accurate, and the methodology is repeatable.
Consumer trust may be engendered by methodology transparency. For example, reporting may clearly indicate that a statistically significant amount of data has not yet been collected when reporting a benchmark. One way to ensure statistical significance is to take an initial measurement at the beginning of benchmarking and to track frequency/periodicity and timing of data sampling. Alternatively, reporting may indicate a confidence level, potentially calculated by the sampling frequency/periodicity and timing data. In this way, the consumer's desire for immediate data may be balanced against potential inaccuracies.
In addition to transparency, benchmarking may be performed by trusted third parties. Past benchmarks have been “gamed” by vendors, where the vendor implemented features specifically to optimize benchmark reports, without commensurate genuine improvements. While vendors may continue to game benchmarks, having a trusted third party owning the benchmarking infrastructure allows that third party to independently verify results, and modify the benchmarks as vendor gaming is detected.
Benchmarking is ideally repeatable. In other words, the performance reported by a benchmark should be similar to a separate test under similar test conditions. In general, samplings of indicia or benchmarking may be time/stamped. Accordingly, arbitrary time sets may be compared to each other in order to determine whether the benchmarking results were repeatable.
iii. Security
Benchmarking data and performance data are inherently sensitive. Cloud providers and VARs will not like poor performance results to be publicized. Furthermore, the integrity of the benchmarking system has to be protected from hackers, lest the collected results be compromised.
Security is to be balanced against processing overhead giving rise to a Heisenberg observation problem. For example, cryptography key exchange with remote key servers gives rise to network load. Such measurements may render at least network measurements inaccurate. However, sensitive data is ideally encrypted. Encryption overhead may be minimized by selectively encrypting only the most sensitive data and/or by encrypting portions of the data.
By way of an example, a benchmarking application may include a configuration file that may define the behavior of that benchmarking application. Therefore, the configuration file is to be delivered securely so that it is not a point of insertion for rogue instructions that would put the benchmarking operation at risk. The configuration file may be encrypted and/or make use of message digests to detect tampering. Hash algorithms and/or security certificates may be used to allow the benchmarking application to validate the configuration file prior to any benchmarking. For example, a configuration file may be identified as work only with a specified target cloud provider instance identifier, a version identifier, a time stamp, and a security identifier. The benchmarking application may be configured to only load and/or execute the configuration file only if some predetermined subset of these identifiers, or if all of these identifiers are validated and authorized.
Since the benchmarking application has not begun benchmarking prior to receiving and validating the configuration file, any network load from accessing key servers is not measured, and therefore will not cause a Heisenberg observation problem.
Note that the security of benchmarking is not the same as testing the security of the cloud provider. However, security testing of the cloud provider may be a function of the benchmarking application. Part of benchmarking applications capabilities may be to adapt its measurements based on an understanding of the relationship between both latency and security service checks. An initial benchmark measurement and can be validated across a number of clouds to identify the difference between the latency for a non-secure transaction and the latency for a security impacted latency for secure transactions. This difference may then be factored into the ongoing tests to confirm consistent performance.
The cloud computing benchmarking infrastructure 100 may comprise a central controller 102. The central controller 102 may be local or remote to the cloud provider. For example, where the central controller 102 may be guaranteed to be in the same server cluster as the cloud provider instance under test, it may be desirable to host the central controller 102 locally as to reduce network latency. However, the central controller 102 may be located on a remote computer to provide a single point of control where multiple cloud provider instances are to be tested.
Central controller 102 may comprise a controller application 104 a data store 108 to store benchmarks, benchmarking results, configuration files, and other related data for cloud computing benchmarking. For example, in addition to storing benchmarking results and collected raw indicia data, the central controller 102 may perform comparative reporting and statistics, or other automated analysis, and store that analysis on data store 108.
The cloud computing benchmarking infrastructure 100 may benchmark enterprise servers 110 on a local area network (“LAN”). Alternatively, cloud computing benchmarking infrastructure 100 may benchmark one or more clouds 112, 114. Note that clouds 112, 114 need not be the same type of cloud. For example, cloud 112 may be a PAAS infrastructure and cloud 114 may be a SAAS infrastructure. Communications connections between the central controller 102 and enterprise servers 110 and clouds 112 and 114 may be effected via network connections 116, 118, 120 respectively.
Network connections 116, 118, 120 may be used to send/install a benchmarking application 122 on enterprise servers 110 and/or clouds 112, 114.
Once benchmarking application 122 is installed, the benchmarking application 122 may request a configuration file 124 indicating which PFC are to be collected may be sent to enterprise servers 110 and/or clouds 112 from central controller 102. Accordingly, the benchmarking application 122 may operate on a pull basis. Alternatively, central controller 102 may push a configuration file 124 to enterprise servers 110 and/or clouds 112.
Periodically, benchmarking application 122 may send benchmarking data results 126 back to the central controller 102 for storage in data store 108. The sending may be based on a predetermined condition being detected, such as benchmarking completing. Alternatively, the central controller 102 may affirmatively request some or all of the benchmarking data results 126.
The central controller 102 may affirmatively send commands 130 to the benchmarking application 122. For example, it may send a “permit to run” flag set to “on” or “off” In the latter case, the benchmarking application may stop upon reception of command 130.
Client device 202 is any computing device. A client device 202 may have a processor 204 and a memory 206. Client device 202's memory 206 is any computer-readable media which may store several programs including an application 208 and/or an operating system 210.
Computer-readable media includes, at least, two types of computer-readable media, namely computer storage media and communications media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules, or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium that can be used to store information for access by a computing device. In contrast, communication media may embody computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave, or other transmission mechanism. As defined herein, computer storage media does not include communication media.
To participate in a communications environment, client device 202 may have a network interface 212. The network interface 212 may be one or more network interfaces including Ethernet, Wi-Fi, or any number of other physical and data link standard interfaces. In the case where the programming language transformations are to be done on a single machine, the network interface 212 is optional.
Client device 202 may use the network interface 212 to communicate to remote storage 214. Remote storage 214 may include network aware storage (“NAS”) or may be removable storage such as a thumb drive or memory stick.
Client device 202 may communicate to a server 216. Server 216 is any computing device that may participate in a network. Client network interface 212 may ultimate connect to server 216 via server network interface 218. Server network interface 218 may be one or more network interfaces as described with respect to client network interface 212.
Server 216 also has a processor 220 and memory 222. As per the preceding discussion regarding client device 202, memory 222 is any computer-readable media including both computer storage media and communication media.
In particular, memory 222 stores software which may include an application 224 and/or an operating system 226. Memory 222 may also store applications 224 that may include a database management system. Accordingly, server 216 may include data store 228. Data store 228 may be configured as a relational database, an object-oriented database, and/or a columnar database, or any configuration to support policy storage.
Server 216 need not be on site or operated by the client enterprise. Server 216 may be hosted in a cloud 230. Cloud 230 may represent a plurality of disaggregated servers which provide virtual web application server 232 functionality and virtual database 234 functionality. Cloud 230 services 232, 234 may be made accessible via cloud infrastructure 236. Cloud infrastructure 236 not only provides access to cloud services 232, 234 but also billing services. Cloud infrastructure 236 may provide additional service abstractions such as Platform as a Service (“PAAS”), Infrastructure as a Service (“IAAS”), and Software as a Service (“SAAS”).
Central controller 302 comprises a computer 304 hosting a controller application (not shown) and data store 306. In the present example, central controller 302 is to benchmark enterprise server 308 on a LAN, Cloud A 310 and Cloud B 312.
Clouds A and B 310, 312 may include disaggregated application servers 314 and disaggregated data storage 316 either exposed via a file system or database management system. Cloud A 310 and Cloud B 312 each expose cloud functionality through their respective infrastructure services 318 and 320.
Central controller 302 may communicate with enterprise server 308, Cloud A 310, or Cloud B 312 via communications connections 322, 324, 326 respectively. Over communications connections 322, 324, 326, executables, configuration files, results, commands, and generally arbitrary data 328, 330, 332 may be transmitted and received without loss of generality.
In block 402 of
While dispatching benchmarking applications 334, 336, 338, in 406 of
Upon arrival, benchmarking applications 334, 336, 338 will instantiate. In block 408 of
The configuration file 340, 342, 344 provides for separation data and metadata, which enable versioning. This enables for measurements based on a data point to be collected and tied to a particular version and a particular set of applicable predictive models. For each new version, the benchmarking application 334, 336, 338 may then validate data for backwards compatibility, and adapts the metadata based on usability. At this point the metadata is assigned and maintained by the central controller 102 and serialized such that the configuration file 340, 342, 344 carries the metadata tag through benchmarking operations to ensure that the data sets are collected and stored with the metadata version for tracking, auditability and certification.
The data is also keyed and/or serialized to a given cloud provider instance where its respective benchmarking application 334, 336, 338 is executing, since cloud provider instances are both temporal in location and existence. Several services are activated by benchmarking measurements over time. An example of such a service will be for a cloud provider to use the benchmarking measurements to move workloads between cloud provider instances as to ensure minimize impact to the overall workload. Another example may be the ability to enable hibernation of cloud instances, such as development and test instances, that are only needed sporadically, but may be restarted quickly while ensuring that the restarted instances meet the same benchmarking measurements before. Over time, the benchmarking measurements may enable analyzing service performance trends across interruptions in service,
Additionally, tracking metadata and the cloud computing instance, enables cross correlation of benchmarking measurements both within the same cloud provider and between different cloud providers. For example, two very different customers may select a similar application profile comprised of one or more PFCs and/or indicia. Comparison is only possible if the PCFs and/or indicia are of a common specific test methodology and serialized for analysis against consistent benchmarking algorithms.
The benchmarking applications 334, 336, 338 will perform several checks prior to initiating benchmarking. First the benchmarking applications 334, 336, 338 authenticate and validate the configuration files 340, 342, 344 as described previously. The benchmarking applications 334, 336, 338 will then affirmatively poll for a new version from the central controller 302. If there is a new version, then the new version is retrieved. Otherwise, a command indicating that the benchmarking is “permitted to run” is dispatched by the central controller 302. Furthermore, the benchmarking applications 334, 336, 338 will determine if its local environment has sufficient capacity to perform benchmarking. The benchmarking may be in the form of measuring known PFCs. If there is sufficient capacity, then the benchmarking applications 334, 336, 338 may instantiate other executables or scripts (not shown) to aid in benchmarking.
Benchmarking applications 334, 336, 338 then make an initial PFC and time stamp measurement. This initial PFC measurement provides a baseline for comparing future measurements. During the benchmarking cycle, the benchmarking applications 334, 336, 338 may periodically or upon detecting an event take PFC measurements. The measurements are persisted to local storage. When the central controller 302 requests the results, or when a predetermined condition is satisfied, the benchmarking applications 334, 336, 338 transmit at least some of the persisted measurements as results 346, 348, 350 back to central control 302 for storage in data store 306.
In block 410 of
Proceeding to block 412 of
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
