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using Firefox and Selenium. However, these proprietary products can only be used in private clouds or dedicated test environments. BenchLab is fully open and can be deployed on any device that has a Web browser. By deploying browsers on home desktops or cell phones, BenchLab can be used to analyze last mile latencies. Server-side monitors and log analysis tools have been used previously to try to model the dependencies between file accesses and predict the full page load times observed by clients [10][18]. BenchLab’s use of real Web browsers allows it to accurately capture the behavior of loading Web pages composed of multiple files, and could be used by Web service providers to monitor the performance of their applications. When deployed on a large number of machines (home desktops, cell phones, cloud data centers…), BenchLab can be used to reproduce large scale flash crowds. When client browsers are geographically dispersed, BenchLab can be used to evaluate Content Delivery Network (CDN) performance or failover mechanisms for highly available Web applications. BenchLab is designed to be easily deployed across wide geographic areas by utilizing public clouds. The impact of wide area latency on Web application performance has been studied in a number of scenarios [5]; Bench- Lab provides a standardized architecture to allow application developers and researchers to measure how their systems perform in real WAN environments. We believe that BenchLab can be used to measure the effectiveness of WAN accelerators (CDNs or proxy caches) as well as validate distributions modeling WAN load patterns. 7. Conclusion We have demonstrated the need to capture the behavior of real Web browsers to benchmark real Web 2.0 applications. We have presented BenchLab, an open testbed for realistic benchmarking of modern Web applications using real Web browsers. BenchLab employs a modular architecture that is designed to support different backend server applications. Our evaluation has illustrated the need for 1) updated Web applications with realistically sized datasets to use as benchmarks, 2) real browser based load injection tools that authentically reproduce user interactions and 3) wide area benchmark client deployments to match the network behavior seen by real applications. We believe that BenchLab meets these needs, and we hope that it will help the research community improve the realism and accuracy of Web application benchmarking. We are making all the BenchLab software (runtime, tools, Web applications…) available to the community under an open source license on our Web site [3]. 8. Acknowledgement The authors would like to thank the anonymous reviewers and our shepherd Geoffrey Voelker for their valuable feeback. We would also like to thank Fabien Mottet and Vivien Quema from INRIA Rhône-Alpes, Guillaume Pierre from Vrije University and Vimal Mathew from UMass, for their contributions to BenchLab. This research was supported in part by grants from Amazon AWS, UMass President’s Science & Technology fund, and NSF grants CNS-0916972, CNS- 083243, CNS-0720616 and CNS-0855128. 9. References [1] Apache HttpComponents – http://hc.apache.org/ [2] A. Beitch, B. Liu, T. Yung, R. Griffith, A. Fox and D. Patterson – Rain: A Workload Generation Toolkit for Cloud Computing Applications – Technical Report UCB/EECS-2010-14, February 10, 2010. [3] BenchLab - http://lass.cs.umass.edu/projects/benchlab/ [4] BrowserMob - http://browsermob.com/performancetesting [5] S. Chen, K.R. Joshi, M.A. Hiltunen, W.H. Sanders and R.D. Schlichting – Link Gradients: Predicting the Impact of Network Latency on Multitier Applications – IN- FOCOM 2009, pp.2258-2266, 19-25 April 2009. [6] HP - TruClient technology: Accelerating the path to testing modern applications – Business white paper, 4AA3-0172ENW, November 2010. [7] R. Hughes and K. Vodicka – Why Real Browsers Matter – Keynote white paper, http://www.keynote.com/docs/ whitepapers/why_real_browers_matter.pdf. [8] D. Krishnamurthy, J. A. Rolia and Shikharesh Majumdar – A Synthetic Workload Generation Technique for Stress Testing Session-Based Systems – IEEE Transaction on Software Engineering. 32, 11 - November 2006. [9] HTTP Archive specification (HAR) v1.2 - http://www.softwareishard.com/blog/har-12-spec/. [10] Z. Li, M. Zhang, Z. Zhu, Y. Chen, A.G. Greenberg, and Y. Wang - WebProphet: Automating Performance Prediction for Web Services – NSDI, 2010, pp.143-158. [11] E. M. Nahum, M.C. Rosu, S. Seshan and J. Almeida – The effects of wide-area conditions on WWW server performance – SIGMETRICS 2001. [12] Olio – http://incubator.apache.org/olio/ [13] RUBiS Web site – http://rubis.ow2.org. [14] Selenium - http://seleniumhq.org/ [15] W. Sobel, S. Subramanyam, A. Sucharitakul, J. Nguyen, H. Wong, A. Klepchukov, S. Patil, A. Fox and D. Patterson – Cloudstone: Multi-platform, multi-language benchmark and measurement tools for Web 2.0 – Cloud Computing and its Applications CCA-08, 2008. [16] TPC-W Benchmark, ObjectWeb implementation, http://jmob.objectWeb.org/tpcw.html [17] G. Urdaneta, G. Pierre and M. van Steen – Wikipedia Workload Analysis for Decentralized Hosting – Elsevier Computer Networks, vol.53, July 2009. [18] J. Wei, C.Z. Xu - Measuring Client-Perceived Pageview Response Time of Internet Services – IEEE Transactions on Parallel and Distributed Systems, 2010. [19] WikiBench - http://www.wikibench.eu/ [20] Wikibooks – http://www.wikibooks.org [21] Wikipedia – http://www.wikipedia.org 48 WebApps ’11: <strong>2nd</strong> <strong>USENIX</strong> <strong>Conference</strong> on Web Application Development <strong>USENIX</strong> Association
Resource Provisioning of Web Applications in Heterogeneous Clouds Jiang Dejun VU University Amsterdam Tsinghua University Beijing Abstract Cloud computing platforms provide very little guarantees regarding the performance of seemingly identical virtual machine instances. Such instances have been shown to exhibit significantly different performance from each other. This heterogeneity creates two challenges when hosting multi-tier Web applications in the Cloud. First, different machine instances have different processing capacity so balancing equal amounts of load to different instances leads to poor performance. Second, when an application must be reprovisioned, depending on the performance characteristics of the new machine instance it may be more beneficial to add the instance to one tier or another. This paper shows how we can efficiently benchmark the individual performance profile of each individual virtual machine instance when we obtain it from the Cloud. These performance profiles allow us to balance the request load more efficiently than standard load balancers, leading to better performance at lower costs. The performance profiles also allow us to predict the performance that the overall application would have if the new machine instance would be added to any of the application tiers, and therefore to decide how to make best use of newly acquired machine instances. We demonstrate the effectiveness of our techniques by provisioning the TPC-W e-commerce benchmark in the Amazon EC2 platform. 1 Introduction Cloud computing is an attractive platform to host Web applications. Besides the advantages of outsourcing machine ownership and system management, Clouds offer the possibility to dynamically provision resources according to an application’s workload — and to pay only for the resources that are actually being used. Given a service-level objective (SLO) which states the response time guarantees an application provider wants to main- Guillaume Pierre VU University Amsterdam Chi-Hung Chi Tsinghua University Beijing tain, dynamic resource provisioning continuously adjusts the number of computing resources used to host the application. Additional capacity can be added dynamically when the load of user requests increases, and later released when the extra power is no longer necessary. Resource provisioning for Web applications in the Cloud faces two important challenges. First, Web applications are not monolithic. At the very least a Web application is composed of application servers and database servers, which both can benefit from dynamic resource provisioning. However, the effect of reprovisioning an extra machine varies from tier to tier. When adding or removing capacity, one needs to decide which tier must be (de-)provisioned such that the performance remains within the SLO at the lowest cost. Second, computing resources in the Cloud are not homogeneous. This is obvious when considering the many different instance types in a platform such as Amazon’s EC2 [1]. However, even an application which would decide to use a single instance type would not experience homogeneous performance. Figure 1(a) illustrates the performance of 30 ’identical’ EC2 instances when running the same application server or database server workloads [2]. Clearly, some instances are more suitable than others for efficiently running CPU-intensive application servers, but are less suitable for I/O intensive workloads. Other instances have faster I/O but slower CPU, and may be better used as database servers. Finally, we have fast machines which can be used for either of both, and slow machines which should either be given a modest task such as load balancing or de-provisioned altogether. On the other hand, Figure 1(b) shows the response time of individual instance running application server workload on EC2 over a period of 24-hours, measured at a 1-minute granularity [2]. Performance spikes occasionally occur with an average duration of 1 to 3 minutes, but overall the performance of individual instances is consistent over time. The performance spikes of an individual instance are presumably caused by the launch/shutdown opera- <strong>USENIX</strong> Association WebApps ’11: <strong>2nd</strong> <strong>USENIX</strong> <strong>Conference</strong> on Web Application Development 49
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