Abstract
Cloud computing has a great ability to store and manage remote access to services in a term of software as a service (SaaS). Recently, many organizations have moved to use outsourcing over the cloud to reduce the local resource burden. The stored services over the cloud are too scalable and complex, so an optimization method is more desirable to select appropriate services that satisfy the clients' request. To do so, the quality of service (QoS) parameters that associated with each service are the best resources for choosing and optimizing the appropriate services over the cloud. Therefore, the cloud service composition aims to select and integrate services over the cloud to satisfy the clients' request. In this work, a hybrid algorithm is introduced, which combines ant colony optimization (ACO) and genetic algorithm (GA) to efficiently compose the services over the cloud. The GA is used to tune the ACO's parameters automatically and the ACO adapts its performance based on the parameters tuning. The main contribution of this work is to help the ACO algorithm to avoid stagnation problem and enhance the performance of the ACO where this performance is affected by the value of the ACO's parameters. The experimental results on 15 different real datasets have shown the effectiveness of the proposed algorithm to search comparable solutions compared to five competitors.
Highlights
Cloud computing provides elasticity and scalability platform for IT providers to deliver their IT software to the clients as services, which supported by the service oriented computing paradigm [1]
The service collaboration was facilitated because the cloud computing and service selection as well, which motivates working with the Cloud services composition (CSC) problem
HYBRID METAHEURISTIC ALGORITHM In this work, we propose a hybrid algorithm, GACSCSC, where we utilize genetic algorithm (GA) on top of Algorithm 2 to automatically tune the parameters
Summary
Cloud computing provides elasticity and scalability platform for IT providers to deliver their IT software to the clients as services, which supported by the service oriented computing paradigm [1]. The proliferation of cloud computing encourages the IT providers to provide services that have similar functionalities with different values of quality of service (QoS) parameters [2], [3] to be available over the web. The challenge of the cloud computing is to build a composition workflow that satisfies the clients’ request. The aim of CSC is to satisfy the client’s request by finding a superior service or a combination of services that matches their QoS parameters. In CSC, the client’s request is decomposed into a set of tasks which named workflow [5], [6]. The services from different providers are retrieved from the cloud pool which functionally equivalent to the client’s request, but with different QoS parameters for each task which form a set of services called candidate services list
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