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

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Presentation on theme: "SLA-Oriented Resource Provisioning for Cloud Computing Challenges, Architecture, and Solutions 1."— Presentation transcript:

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

2 Author 2

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 3

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. 4

5 Introduction 5  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 6

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 7

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; 8

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 9

10 System Architecture 10 High-level system architectural framework

11 SLA Provisioning in Aneka Aneka architecture

12 SLA Provisioning in Aneka

13 SLA Provisioning in Aneka

14 SLA Provisioning in Aneka

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 15

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 16

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 17

18 Thanks ! 18


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