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Multi-Tier Internet Service Management MULTI-TIER INTERNET SERVICE MANAGEMENT : STATISTICAL LEARNING APPROACHES by SIREESHA MUPPALA B.E., Nagarjuna University, India, 1992 A dissertation submitted to the Graduate Faculty of the University of Colorado at Colorado Springs in partial fulfillment of the requirements for the degree of Doctor of Philosophy Department of Computer Science 2013 This dissertation for the Doctor of Philosophy degree by Sireesha Muppala has been approved for the Department of Computer Science by Xiaobo Zhou, Chair Edward Chow Chaun Yue Rory Lewis Jia Rao Liqiang Zhang Date ii Muppala, Sireesha (Ph.D., Computer Science) Multi-tier Internet Service Management: Statistical Learning Approaches Dissertation directed by Associate Professor, Chair Xiaobo Zhou Modern Internet services are multi-tiered and are typically hosted in virtualized shared platforms. While facilitating flexible service deployment, multi-tier architecture introduces significant challenges for Quality of Service (QoS) provisioning in hosted Internet services. Complex inter-tier dependencies and dynamic bottleneck tier shift are challenges inherent to tiered architectures. Hard-to-predict and bursty session-based Internet workloads further magnify this complexity. Virtualization of shared platforms adds yet another layer of complication in managing the hosted multi-tier Internet services. We consider three critical aspects of Internet service management for improved performance and qual- ity of service provisioning : admission control, dynamic resource provisioning and service differentiation. This thesis concentrates on statistical learning based approaches for multi-tier Internet service management to achieve efficient, balanced and scalable services. Statistical learning techniques are capable of solving complex dynamic problems through learning and adaptation with no priori domain-specific knowledge. We explore the effectiveness of supervised and unsupervised learning in managing multi-tier Internet services. First, we develop a session based admission control strategy to improve session throughput of multi- tier Internet services. Using a supervised bayesian network, it achieves coordination among multiple tiers resulting in a balanced service. Second, we promote session-slowdown, a novel session-oriented metric for user perceived performance. We develop a regression based dynamic resource provisioning strategy, which utilizes a combination of offline training and online monitoring, for session slowdown guarantees in multi-tier systems. Third, we develop a reinforcement learning based coordinated combination of admission control and adaptive resource management for multi-tier Internet service differentiation and performance improvement in a shared virtualized platform. It addresses limitations of supervised learning by integrating model-independence of reinforcement learning and self-learning of neural networks for system scalability iii and agility. Finally, we develop an user interface based Monitoring and Management Console, intended for an administrator to monitor and fine tune the performance of hosted multi-tier Internet services. We evaluate the developed management approaches using an e-commerce simulator and an implementa- tion testbed on a virtualized blade server system hosting multi-tier RUBiS benchmark applications. Results demonstrate the effectiveness and efficiency of statistical learning approaches for QoS provisioning and per- formance improvement in virtualized multi-tier Internet services. iv Dedication This thesis is dedicated to my father, Dasaraju Chandrashekhar Raju, for his constant support, encouragement and for teaching me the love of hard work. v Acknowledgements I would like to first and foremost thank my advisor, Dr. Xiaobo Zhou, for his amazing support and guidance throughout my Ph.D. program. His continuous demand for the best possible work and his enthusiastic will- ingness to help his students reach their goals were invaluable to me. I would also like to thank my graduate committee members, Dr. Chow, Dr. Yue, Dr. Lewis, Dr. Rao and Dr. Zhang, for their help and encour- agement throughout my student life at UCCS. Their critique and feedback at the research proposal stage tremendously helped improve the quality of this thesis. Many thanks to my fellow DISCO lab members, Palden Lama and Yanfei Guo for their help over the past few years. I am incredibly fortunate to have the support of my family. My husband, Damu and kids, Anurag and Amritha never let me quit, even in the toughest of situations. I am forever grateful for their support and sacrifices they made so I could reach my goal. I am also thankful for my parents and mother-in-law for their experience, advice and encouragement. Without the flexibility extended by my manager, Robert Holstine, I could not have justified being a part- time Ph.D. student while holding a full-time job. I am thankful for the privilege of working with my lead Ben Kirk and the rest of the most talented and generous team at Blackhawk Network. Thanks to Yishiuan Chin and many, many other friends for their strength and love. Late night work sessions were bearable and even fun because of their companionship and humor. The research and dissertation were supported in part by the US National Science Foundation CAREER Award CNS-0844983 and research grant CNS-0720524. I thank the NISSC for providing blade servers for conducting the experiments. vi Contents 1 Introduction 1 1.1 Typical Multi-tier Internet Service Architecture . 2 1.2 Virtualized Data Centers and Cloud Computing . 4 1.3 Multi-tier Internet Service Management and Challenges . 6 1.4 Statistical Learning for Multi-tier Internet Service Management . 12 1.5 Research Objectives . 13 1.6 Research Contributions . 17 1.7 Thesis Roadmap . 18 2 Related Work 20 2.1 Multi-tier Systems : Analytical Models . 20 2.2 Admission Control for Internet Services . 22 2.3 Dynamic Resource Provisioning of Internet Services . 24 2.4 Service Differentiation in Internet Services . 26 2.5 Related Issues in Internet Service Management . 29 2.5.1 Network Virtualization . 29 2.5.2 Server Consolidation, VM Placement and VMMigration . 30 2.5.3 Storage Virtualization . 30 2.5.4 Security . 31 2.5.5 Distributed Data Centers . 31 vii 2.6 Statistical Learning Techniques for Internet Service Management . 32 2.7 Summary . 33 3 Coordinated Session Based Admission Control In Multi-tier Internet Services 36 3.1 Introduction . 36 3.2 Session Based Admission Control : Algorithms . 38 3.2.1 A Blackbox Approach . 38 3.2.2 The MBAC Approach . 38 3.3 Coordinated Session Based Admission Control : A Statistical Learning Approach . 42 3.3.1 Bayesian Networks - Background . 43 3.3.2 Multi-tier Internet Service : A Bayesian Network Model . 44 3.3.3 Conditional Probability Tables (CPTs) and Bayesian Network Training . 45 3.3.4 CoSAC : Operation . 46 3.4 Performance Evaluation . 48 3.4.1 Experimental Setup . 48 3.4.2 Impact of Multi-tier Architecture on Session based Admission Control . 49 3.4.3 Why not accept Ordering sessions only . 51 3.4.4 Impact of CoSAC on Throughput . 53 3.4.5 Choosing the Admission Control Interval . 56 3.5 Summary And Discussion . 58 4 Dynamic Server Provisioning for Multi-tier Internet Service Performance Guarantees 60 4.1 Introduction . 60 4.2 Statistical Regression Analysis : Background . 62 4.3 Terms and Definitions . 63 4.3.1 Session Slowdown . 63 4.3.2 Tier Session Slowdown Ratio . 64 4.3.3 Resource Utilization . 65 viii 4.3.4 A Behavior Model . 66 4.4 Dynamic Resource Provisioning : A Statistical Regression Approach . 66 4.4.1 The Training Phase : Learning Behavior Models . 67 4.4.2 The Online Phase : Dynamic Server Provisioning . 71 4.5 Performance Evaluation . 74 4.5.1 Experimental Setup . 74 4.5.2 Session Slowdown Guarantee . 74 4.5.3 Efficiency in Per-Tier Resource Allocation . 77 4.5.4 Impact of Session Slowdown Threshold on Performance . 78 4.5.5 Impact of Resource Utilization Threshold on Performance . 79 4.5.6 Impact of Online Monitoring Interval . 81 4.5.7 Validation of Regression Models and Comparison with an Analytical Model . 82 4.5.8 Impact of TPC-W Workload Burstiness on Performance . 87 4.6 Summary and Discussion . 89 5 Multi-tier Internet Service Differentiation 90 5.1 Introduction . 90 5.2 Reinforcement Learning and Neural Networks : Background . 92 5.3 Multi-tier Service Differentiation and Performance Improvement . 95 5.3.1 Problem Statement . 95 5.3.2 Problem Formulation . 96 5.4 The System Design for Scalability and Agility . 98 5.5 Algorithms . 100 5.5.1 Basic Reinforcement Learning for VM Auto-Configuration . 100 5.5.2 Basic Reinforcement Learning for Session-Based Admission Control . 102 5.5.3 Cascade Neural Network Enhancements . 103 5.6 Testbed Implementation . 104 5.7 Performance Evaluation . 106 ix 5.7.1 Effectiveness of VM Auto-configuration - Stationary Workloads . 106 5.7.2 Effectiveness of VM Auto-configuration - Dynamic Workloads . 107 5.7.3 Effectiveness of Coordinated VM Auto-configuration and Admission Control . 110 5.7.4 Impact of Bursty Workloads . 111 5.7.5 Sensitivity Analysis of the Learning Algorithms . 113 5.7.6 Agility Analysis of the Learning Algorithms . 115 5.7.7 Scalability Analysis of the Learning Algorithms . 116 5.7.8 Comparison with Statistical Regression Based Resource Provisioning . 117 5.8 Summary and Discussion . 122 6 Multi-tier Internet Service Management and Monitoring Console 124 6.1 MISMC Features . 125 6.1.1 Dashboard . 125 6.1.2 Applications . 127 6.1.3 Virtual Machines . 127 6.1.4 Notifications . 131 6.1.5 Reports . 131 6.2 Future Enhancements . 133 7 Conclusion 136 7.1 Contributions.
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