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SLA-Oriented Resource Provisioning for Cloud Computing

SLA-Oriented Resource Provisioning for Cloud Computing. Challenges, Architecture , and Solutions. Author. Content. Abstract Introduction Challenges and Requirements SLA-Oriented Cloud Computing Vision State-of-the-art System Architecture SLA Provisioning in Aneka

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SLA-Oriented Resource Provisioning for Cloud Computing

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  1. SLA-Oriented Resource Provisioning for Cloud Computing Challenges, Architecture, and Solutions

  2. Author

  3. Content • Abstract • Introduction • Challenges and Requirements • SLA-Oriented Cloud Computing Vision • State-of-the-art • System Architecture • SLA Provisioning in Aneka • Performance Evaluation • Future Directions

  4. Abstract • Need to offer differentiated services to users and meet their quality expectations. • Existing resource management systems are yet to support SLA-oriented resource allocation. • No work has been done to collectively incorporate customer-driven service management, computational risk management, and autonomic resource management into a market-based resource management system to target the rapidly changing enterprise requirements of Cloud computing. • This paper presents vision, challenges, and architectural elements of SLA-oriented resource management.

  5. Introduction • There are dramatic differences between developing software for millions to use as a service versus distributing software for millions to run their PCs --Professor David Patterson • New Computing Paradigms • Cloud Computing • Grid Computing • P2P Computing • Utility Computing

  6. Challenges and Requirements - 1

  7. Challenges and Requirements - 2 • Customer-driven Service Management • Computational Risk Management • Autonomic Resource Management • SLA-oriented Resource Allocation Through Virtualization • Service Benchmarking and Measurement • System Modeling and Repeatable Evaluation

  8. SLA-Oriented Cloud Computing Vision The resource provisioning will be driven by market-oriented principles for efficient resource allocation depending on user QoS targets and workload demand patterns. • Support for customer-driven service management based on customer profiles and QoS requirements; • Definition of computational risk management tactics to identify, assess, and manage risks involved in the execution of applications; • Derivation of appropriate market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; • Incorporation of autonomic resource management models; • Leverage of Virtual Machine technology to dynamically assign resource shares; • Implementation of the developed resource management strategies and models into a real computing server;

  9. State-of-the-art • Traditional Resource Management Systems(Condor, LoadLeveler, Load Sharing Facility, Portable Batch System) • adopt system-centric resource allocation approaches that focus on optimizing overall cluster performance • Increase processor throughput and utilization for the cluster • Reduce the average waiting time and response time for jobs • Assume that all job requests are of equal user importance and neglect actual levels of service required by different users. • Virtual Machine management platform solutions(Eucalyptus, OpenStack, Apache VCL, Citrix Essentials) • Main goal is to provide automatic configuration and maintenance of the centers • Market-based resource management • Not considered and incorporated customer-driven service management, computational risk management, and autonomic resource management into market-driven resource management

  10. System Architecture High-level system architectural framework

  11. SLA Provisioning in Aneka -1 Aneka architecture

  12. SLA Provisioning in Aneka - 2

  13. SLA Provisioning in Aneka - 3

  14. SLA Provisioning in Aneka - 4

  15. Performance Evaluation - 1 • Static resource • 1 Aneka master - m1.large(7.5GB memory, 4 EC2 compute units, 850GB instance storage, 64bit platform, US0.48 per instance per hour) Windows-based VM • 4 Aneka workers – m1.small(1.7GB memory, 1 EC2 compute unit, 160GB instance storage, 32bit platform, US0.085 per instance per hour) Linux-based VM • Dynamic resources • m1.small Linux-based instances

  16. Performance Evaluation - 2 • CPU-intensive application • SLA is defined in terms of user-defined deadline • execution time of each task was set to 2 minutes • Each job consists of 120 tasks

  17. Conclusions and Future Directions • The need for a deeper investigation in SLA-oriented resource allocation strategies that encompass: • Customer-driven service management • Computational risk management • Autonomic management of Clouds In order to: • Improve the system efficiency • Minimize violation of SLAs • Improve profitability of service providers

  18. Thanks !

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