Sla oriented resource provisioning for cloud computing
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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

SLA-Oriented Resource Provisioning for Cloud Computing

Challenges, Architecture, and Solutions



Content
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


Abstract
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.


Introduction
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


  • Challenges and requirements 1
    Challenges and Requirements - 1


    Challenges and requirements 2
    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


    Sla oriented cloud computing vision
    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;


    State of the art
    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


    System architecture
    System Architecture

    High-level system architectural framework


    Sla provisioning in aneka 1
    SLA Provisioning in Aneka -1

    Aneka architecture





    Performance evaluation 1
    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


    Performance evaluation 2
    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


    Conclusions and future directions
    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



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