QoS Optimization for Cloud Service Composition Based on Economic Model

Abstract

Abstract. Cloud service composition is usually long term based and economically driven. Services in cloud computing can be categorized into two groups: Application services and Computing Services. Compositions in the application level are similar to the Web service compositions in Service-Oriented Computing. Compositions in the computing level are similar to the task matching and scheduling in grid computing. We consider cloud service composition from end users perspective. We propose Genetic Algorithm-based approach to model the cloud service composition problem. A comparison is given between the proposed composition approach and other existing algorithms such as Integer Linear Programming. The experiment results proved the efficiency of the proposed approach.

Minimum Unsatisfiability based QoS Web Service Composition over the Cloud Computing

“…In [24] the composition problem is solved, using linear integer programming, in a way that maxi mizes user satisfaction expressed as utility functions over QoS attributes. Other approaches [25]- [27] use specific algorithms to select a Web Services to provide a composition with the best QoS.…”

A new service composition method in the cloud‐based Internet of things environment using a grey wolf optimization algorithm and MapReduce framework

“…Kholidy 31 described the cloud service composition issue as a multi‐objective optimization issue, intending to meet the user's QoS criteria. Besides, they proposed a method for addressing the multi‐objective issue by converting it to a single‐objective form.…”

Multicloud service composition: A survey of current approaches and issues

“…Another classification was realized in Navimipour and Vakili . Cloud service composition approaches, in this work, are regrouped into frameworks‐based approaches, agents‐based approaches, and heuristics‐based approaches …”

“…14 Cloud service composition approaches, in this work, are regrouped into frameworks-based approaches, [42][43][44] agents-based approaches, 2,4,45 and heuristics-based approaches. [10][11][12][46][47][48] Although the above 2 studies have shed the light on the main cloud service composition issues, they are limited to classify the single-cloud service composition approaches. Three points make our survey different from the 2 existing ones.…”

“…Several techniques and methods have been applied by researchers to solve the service composition problem in the cloud. Existing approaches can be regrouped into agents‐based, graph‐based, combinatorial optimization‐based, machine learning‐based, heuristics‐based, etc.…”