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Composite SaaS Placement and Resource Optimization in Cloud Computing

This paper discusses the placement and resource optimization of composite SaaS applications in cloud computing using evolutionary algorithms. It explores the initial placement to optimize performance and the maintenance phase to optimize resource usage.

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Composite SaaS Placement and Resource Optimization in Cloud Computing

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  1. Composite SaaS Placement and ResourceOptimization in Cloud Computing usingEvolutionary Algorithms 徐猛 2013年12月13日

  2. Zeratul Izzah Mohd Yusoh and Maolin Tang Science and Engineering Faculty Queensland University of Technology Brisbane, Australia 2012 IEEE Fifth International Conference on Cloud Computing

  3. composite application - a group of loosely-coupled individual applications that communicate with each other in order to form a higher-level functional system or application data components that are shared between the services. Delivering the SaaS in such approach allows flexibility of the SaaS functionalities where components can be combined and recombined as needed composite SaaS - a number of application components and data components where each component is deployed at Cloud physical servers and later executes insides virtual machines (VM). Introduction

  4. 1) the initial placement of the composite SaaS onto Cloud physical servers to optimize its performance 2) the maintenance phase where the initial placement is reconfigured in order to optimize its resource usage SaaS resource management system

  5. 1) A Cloud’s data centre with its compute servers and storage servers. The compute servers may consist of at least one VM 2) Cloud’s data centre network topology with its links between compute servers and storage servers 3) Multiple composite SaaS with their resource requirements and constraints according to their SLA, and the current placement of the components in Cloud data centre. Input

  6. 1) Resource constraints: The total resource requirements for SaaS components must not exceed the machines’ resources capacity. 2) Execution time constraints: the TET must not exceed the maximum response time of a SaaS as agreed in users’ SLA 3) Sequence of migration constraints: Constraints

  7. a) Composite SaaS initial placement problem: To place the SaaS application components, AC and data component, DC onto Cloud servers such that the requirements are satisfied and the SaaS performance is optimal based on its estimated execution time, TET.(Cooperative Coevolutionary GA (CCGA)) b) Composite SaaS resource optimization problem: To find a new placement of S onto VM by clustering the application components AC, such that the placement will minimize the resources’ costs while satisfying the SaaS constraints. (Repair-based Grouping GA (RGGA)) Problem

  8. 谢谢！ 山东大学计算机学院

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