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1.INTRODUCTIONThe cloud computing system is a distributed system which offers the utility computing vision with some attractive qualities such as sharing the many dynamic resources by virtualization technology in order to meet the requirements of widely varying Requests.Generally, task scheduling process is an important issue in distributed systems because the requests should be mapped on resources in an efficient manner by considering the environment properties. Because of heterogeneous resources and many numbers of tasks with different characteristics in these systems, this issue is known as a NP-hard (Non-deterministic Polynomialtime hard) problem. Since a good scheduling method would impress on the system performance and there is no direct method to find an optimal solution in polynomial time, the scheduling process must rely on finding the best solution within possibilities. A number of meta-heuristic based methods were presented to solve NP problems such as: particle swarm optimization (PSO) [5], tabu search (TS) [6], simulated annealing (SA) [7], genetic algorithm (GA) etc. In contrast, GA by [8] [9] [10] are known to give good results in several

In heterogeneous distributed systems like grid and cloud computing infrastructures, the major problem is the task scheduling which can have much impact on system performance. For some reasons, such as heterogeneous and dynamic features and the dependencies among the requests, this issue is known as a NP-hard problem. In this article a hybrid meta-heuristic method based on Genetic Algorithm (GMSW) is being proposed in order to find a suitable solution for mapping the requests on resources. The proposed method tries to obtain the response quickly, with some goal-oriented operations. It begins, through making a good initial population by merging some features of the Best-Fit and Round Robin methods and a bi-directional tasks prioritization in unbalanced-structured workflow, considering their impact on each other, based on graph topology. Some other operations control and lead the algorithm steps in order to obtain the solution by using efficient parameters in the mentioned systems. Here the focus is on optimizing the makespan and reliability, by considering a good distribution of workload on resources. The experiments here indicate that the GMSW improves the results, with the increasing number of tasks in application graph, for the mentioned objectives. The results are compared with other studied algorithms.

The subject of providing mobile users with an optimized service based on the Service Level Agreement (SLA) in cloud computing environment is one of the controversial issues, because there are a lot of challenging features in this environment such as the heterogeneity of cloud resources and also processing power of mobile phones.In this article, a framework called CSRAM is proposed for optimizing the response quality of services (QoS) in mobile cloud computing systems that tries to increase the precision and speed of the best service selection by offloading part of the computations to the cloud as well as using the context information of service provider in request adaptation process.In the proposed framework, it was tried to design a modular system and also to consider an appropriate algorithm for using in service request adaptation process. Finally, with regarding to seven effective environmental parameters as the inputs and also, with comparison between CSRAM framework and another applied framework, more flexibility was achieved in changing the environmental parameters of the problem, Reduction in the imposed computational load on user's mobile phone and also, increase in solution precision based on the reality.

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