# Understanding Cloud Computing: Experimentation and Capacity Planning

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7 5 (At the master node) π 4 n i=1 NumPointsInQuadrant i/m We implemented the distributed algorithm in PlanetLab with a number of nodes n varying from 1 to 10 The value of m was set to 10 9 (ie, 1 billion) and each node executes steps 3 of the distributed algorithm m/n times In our experiments, all nodes have the following characteristics: 2 Intel(R) Core(TM)2 Duo E6550 processors at 233 GHz with 344 GB of main memory The ten nodes that participated in the experiments were spread around the country according to Table 1 The last column in the table shows the participation of a node in the experiments For example, node UP1 participated in all experiments in which n varied from 1 to 10 Node GMU4 participated in the experiments in which n varied from 6 to 10 Node VT was only included in the experiment with 10 nodes So, when we ran experiments with 5 nodes, the nodes that participated were UP1, UP2, GT1, GT2, and GMU3 Execution Time (msec) ExecTime= * (No Nodes) R 2 = Node Name Location Participation UP1 Univ Pennsylvania 1-10 UP2 Univ Pennsylvania 2-10 GT1 Georgetown University 3-10 GT2 Georgetown University 4-10 GMU3 George Mason University 5-10 GMU4 George Mason University 6-10 CT1 Caltech 7-10 CT2 Caltech 8-10 CN4 Cornell University 9-10 VT Virginia Tech 10 Table 1: Distribution of the 10 nodes for the experiment For each value of the number of nodes n, 100 runs were made and the execution time E of the algorithm was measured as the time needed for all n nodes to complete their execution Figure 3 shows the variation of the average execution time for the 100 runs as a function of the number of nodes used The figure also shows 95% confidence intervals for the average A regression analysis shows that a power curve approximates reasonably well (ie, R 2 = 09055) the variation of the execution time E with the number of nodes n The regression curve is E = 124, 828 n (1) The execution time decreases as n increases according to a power relationship For example, with a single node, the computation of π takes 2 minutes on average while with ten nodes it takes 20 seconds on average Figure 4 shows the variation of the speedup, defined as the ratio between the average execution time with one node E 1 and the execution time E n with n nodes For example, when 10 nodes are used, the application runs No of Nodes Figure 3: Execution time (in msec) vs number of nodes Figure 4: Speedup vs number of nodes

8 times faster than if a single node were used The speedup increases with n A linear regression shows that the relationship between the speedup S and the number of nodes n can be reasonably approximated as S = n (2) with a coefficient of determination R 2 equal to While the example above is relatively simple, it illustrates how computations can be performed on top of a cloud platform However, many organizations are moving significantly complex applications to the cloud This requires that capacity planning issues usually handled within the confines of an organization be tackled from an external perspective 5 Capacity Planning for the Cloud How does the traditional notion of capacity planning change when using cloud computing? There are two points of view to consider: one from the user of cloud services and the other from the provider 51 From the Cloud User s Point of View When services are executed on demand using cloud resources, the burden of capacity planning shifts to the provider of the cloud services However, cloud users have to be able to negotiate SLAs with cloud service providers Since there may be SLAs for different QoS metrics, cloud users should consider using the notion of utility function to determine the combined usefulness of cloud services as a function of the various SLAs Utility functions are used quite often in economics and have been extensively used by the first author of this paper in autonomic computing [1, 8, 9, 10, 13] However, the use of utility functions to determine the optimal mix of SLAs in cloud computing as presented here is novel We introduce the following notation to formalize the problem of optimal selection of SLAs to be negotiated with the provider of cloud services Let, SLA r : SLA (in seconds) on the average response time per transaction SLA x : SLA (in tps) on the transaction throughput SLA a : SLA on the cloud availability C r (SLA r ): per transaction cost (in cents) when the negotiated response time SLA is SLA r C x (SLA x ): per transaction cost (in cents) when the negotiated throughput SLA is SLA x C a (SLA a ): per transaction cost (in cents) when the negotiated availability SLA is SLA a U: global utility The global utility function is composed of terms that represent the utility for various metrics such as response time, throughput, and availability The utility is a dimensionless number in the [0,1] range w r, w x, w a : weights associated to response time, throughput, and availability, respectively, used to compute the global utility w r + w x + w a = 1 The cost functions used in the following example are: βr SLAr C r (SLA r ) = α r e C x (SLA x ) = α x SLA x C a (SLA a ) = e βa SLAa e 09βa, SLA a 09 (3) As it can be seen, the response time cost decreases exponentially as the SLA for response time increases The throughput cost increases linearly with the throughput SLA and the availability cost increases exponentially as the availability SLA goes from 09 to 10 We used the following utility function: U = w r 20 e SLAr 1 + e SLAr + w x (1 e 01 SLAx ) + w a (10 SLA a 9) (4) The first term in Eq (4) is the response time utility function, the second term is the throughput utility function, and the third is the availability utility function (see Fig 5) Because each of these individual utility functions have their values in the [0, 1] range and because w r + w x + w a = 1, U [0, 1] A cloud user is now faced with the problem of selecting the optimal values of the SLAs that maximize the utility function subject to SLA and cost constraints This can be cast as the (generally) non-linear constraint optimization problem shown below maximize U = f(sla r, SLA x, SLA a ) subject to γr min γx min γa min SLA r γr max SLA x γx max SLA a γa max C r (SLA r ) + C x (SLA x ) + C a (SLA a ) C max (5) The optimization problem above indicates that values of the SLAs for response time, throughput, and availability have to obtained so that the utility function is maximized The SLAs have constraints (minimum and maximum values), which may be inherent to what the cloud provider can offer There is also a maximum cost constraint C max We provide here a numeric solution for this problem with the following set of parameters: α r = 04, β r = 01, α x =

9 U_r Response time Utility Function U_x Throughput Utility Function SLA_r (sec) SLA_x (tps) U_a Availability Utility Function SLA_a Figure 5: Utility functions for response time, throughput, and availability 003, β a = 08, γ min r γ max x =, γ min a = 1 sec, γr max = 092, and γ max = 4 sec, γ min x = 5 tps, a = 0999 Table 2 shows the results of solving the non-linear constrained optimization problem for various values of the maximum cost C max The values of the weights w r, w x, w a used are 04, 03, and 03, respectively The maximum pertransaction cost decreases from row 4 to row 1 When the user is willing to spend 070 cents per transaction, the response time SLA is the best possible (ie, 1 second), the throughput SLA is slightly above the worst possible value of 5 tps, and the availability is the best possible (ie, 0999) As the user is less willing to spend more money per transaction, the SLAs to be negotiated with the cloud change For example, for C max = 060 cents, the user will need to settle for a response time SLA of 3543 sec The throughput SLA goes down to its worst value (ie, 50 tps) As the C max is reduced, worse SLAs need to be negotiated as shown by the table As seen in the table, the cloud utility value decreases as C max decreases Many optimization solvers can be used to solve the type of non-linear optimization problem discussed above The NEOS Server for Optimization (see provides many such options MS Excel s Solver (under the Tools menu) can also be used to solve medium scale optimization problems This is the solver that was used in the numerical example discussed in this section C max Utility SLA r SLA x SLA a Table 2: Numeric results for optimal SLA selection Cost is in cents, SLA r in sec, and SLA x in tps 52 From the Point of View of the Cloud Provider From the point of view of the cloud infrastructure provider, the workload is very unpredictable, exhibits a high time variability, and tends to be very heterogeneous The cloud provider s infrastructure tends to be very large and very complex with a myriad of parameter settings that can influence performance in a significant way Therefore, it is generally very difficult for system administrators to manually change these parameters at run time to keep pace with the variability of the workload in a way that meets customer s SLAs The solution is for cloud providers to use autonomic computing techniques [7] Proposed autonomic techniques for data centers are based on three types of methods: control theory [5], machine learning [30], and combinatorial

10 search methods combined with queuing network models [1, 8, 9, 10, 13] The latter set of methods have been successfully applied by Menascé and his students and colleagues in a variety of settings including e-commerce, virtualized environments, and Internet data centers A platform that provides cloud computing services must be able to dynamically provision its various resources (eg, computing, storage, software licenses, and networking) to its various users according to their instantaneous needs and in compliance with negotiated SLAs Autonomic computing techniques allow for resources to be dynamically shifted without human intervention in a way that optimizes a certain objective function Once again, utility functions are very useful as an objective function to be maximized by an autonomic controller such as the one in [1] One can establish a global utility function for the cloud computing service provider This utility function takes into account the negotiated SLAs for each customer as well as their relative importance The objective then is to design an autonomic controller that determines the allocation of resources that maximizes the global utility function of the cloud computing service provider The controller should run its algorithm periodically to dynamically track customer s constant changes in resource demands 6 Concluding Remarks This paper has discussed the concept of cloud computing as well as its advantages and disadvantages Some examples of cloud computing infrastructures were presented The list is by no means exhaustive nor is there an implication that the examples discussed are representative of best practices in the industry We tried though to provide a diverse range of examples by including cloud platforms widely used in academia and research as well as those provided by companies In order to provide a more concrete example of the benefits of cloud computing, we developed a parallel application on top of PlanetLab and provided results on execution time and speedup as a function of the number of nodes involved The paper then discussed the important issue of how cloud users optimally select the values of SLAs to be negotiated with cloud providers in order to maximize their utility subject to cost constraints A numeric example was thoroughly discussed Acknowledgments The work of Daniel Menascé is partially supported by award no CCF from the National Science Foundation References [1] MN Bennani and DA Menascé, Resource Allocation for Autonomic Data Centers Using Analytic Performance Models, Proc 2005 IEEE International Conference on Autonomic Computing, Seattle, WA, June 13-16, 2005 [2] F Chang, J Dean, S Ghemawat, W Hsieh, D Wallach, M Burrows, T Chandra, A Fikes, R Gruber Bigtable: A Distributed Storage System for Structured Data [3] Cloud Computing Basics wwwwebguildorg/2008/07/cloud-computingbasicsphp [4] Cloud Computing: Anything as a Service wwwcioinsightcom/c/a/strategic-tech/cloud- Computing-Anything-as-a-Service/ [5] Y Diao, N Gandhi, J L Hellerstein, S Parekh, and D M Tilbury, Using MIMO Feedback Control to Enforce Policies for Interrelated Metrics With Application to the Apache Web Server, Proc IEEE/IFIP Network Operations and Management Symp, Florence, Italy, April 15-19, 2002 [6] F Douglis, Staring at Clouds, IEEE Internet Computing, Vol 13, No 3, 2009 [7] DA Menascé and J Kephart, Guest Editor s Introduction, Special Issue on Autonomic Computing, IEEE Internet Computing, January 2007 [8] DA Menascé and MN Bennani, Autonomic Virtualized Environments, Proc IEEE International Conference on Autonomic and Autonomous Systems, July 19-21, 2006, Silicon Valley, CA, USA [9] DA Menascé and MN Bennani, Dynamic Server Allocation for Autonomic Service Centers in the Presence of Failures, in Autonomic Computing: Concepts, Infrastructure, and Applications, eds S Hariri and M Parashar, CRC Press, 2006 [10] DA Menascé, MN Bennani, and H Ruan, On the Use of Online Analytic Performance Models in Self- Managing and Self-Organizing Computer Systems, in the book Self-Star Properties in Complex Information Systems, O Babaoglu, et al, eds, Lecture Notes in Computer Science, Vol 3460, Springer Verlag, 2005 [11] DA Menascé and E Casalicchio, Quality of Service Aspects and Metrics in Grid Computing, Proc 2004 Computer Measurement Group Conference, Las Vegas, NV, December 5-10, 2004 [12] DA Menascé and E Casalicchio, A Framework for Resource Allocation in Grid Computing, Proc 12th Annual Meeting of the IEEE/ACM International

11 Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS), Volendam, The Netherlands, October 5-7, 2004 [13] DA Menascé and MN Bennani, On the Use of Performance Models to Design Self-Managing Computer Systems, Proc 2003 Computer Measurement Group Conference, Dallas, TX, Dec 7-12, 2003 [14] I Foster and C Kesselman, The grid: Blueprint for a new Computing Infrastructure, Morgan- Kaufman,1999 [15] Google, What Is Google App Engine [28] PlanetLab Consortium [29] PlanetLab Central API api [30] G Tesauro, NK Jong, R Das, and MN Bennani, A Hybrid Reinforcement Learning Approach to Autonomic Resource Allocation, Proc IEEE Intl Conf Autonomic Computing (ICAC 06), Dublin, Ireland, June 13-16, 2006 [31] D Thain, T Tannenbaum, and M Livny, Distributed Computing in Practice: The Condor Experience, Concurrency and Computation: Practice and Experience, Vol 17, No 2-4, pages , February- April, 2005 [16] Google App Engine App Engine [17] Microsoft, Windows Azure wwwmicrosoftcom/azure/windowsazuremspx [18] Microsoft, What is the Azure Service Platform wwwmicrosoftcom/azure/whatisazuremspx [19] Microsoft, Pricing & Licensing Overview wwwmicrosoftcom/azure/pricingmspx [20] Microsoft, SQL Services wwwmicrosoftcom/azure/sqlmspx [21] Microsoft, SQL Data Services wwwmicrosoftcom/azure/datamspx [22] Microsoft, NET Services wwwmicrosoftcom/azure/netservicesmspx [23] National Science Foundation, Cloud Computing Research wwwnsfgov/news/news summjsp?cntn id= [24] L Peterson, A Bavier, M Fiuczynski, and S Muir, Experiences Implementing PlanetLab, OSDI 2006, Seattle, WA, November 2006 [25] PlanetLab Sites https://wwwplanetlaborg/db/pub/sitesphp Last accessed on May 29, 2009 [26] PlanetLab Nodes https://wwwplanet-laborg/db/nodes/indexphp Last accessed on May 29, 2009 [27] PlanetLab Slides sliceshtml Last accessed on May 29, 2009

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