Presentation an Approach for Placement Phase in Mapping Algorithm

The data requirements of both scientific and commercial applications have been increasing drastically in recent years. Just a couple of years ago, the data requirements for an average scientific application were measured in terabytes, whereas today we use petabytes to measure them. Moreover, these data requirements continue to increase rapidly every year, and in less than a decade they are expected to reach the exabyte (1 million terabytes) scale.. In this work, the data duplication technique has not been used by us. That’s because of increase in costs and expenses of using a cloud system.In this paper, an approach to mapping workflow tasks and data between cloud system data centers has been presented. This approach encompasses 2 phases: both of which both have been given enough input to appropriately map tasks and data between data centers in such a way that the total time for task execution and data movement becomes minimal. In other words, the goal of mentioned approach is to present a trade-off between these two Goals. Simulations have demonstrated that the said approach can fulfill stated goals effectively. Keywords:Distributed system, scientific application, application, data requirement

Simulated Annealing Algorithm Combined with Chaos for Task Allocation in Real-Time Distributed Systems

This paper addresses the problem of task allocation in real-time distributed systems with the goal of maximizing the system reliability, which has been shown to be NP-hard. We take account of the deadline constraint to formulate this problem and then propose an algorithm called chaotic adaptive simulated annealing (XASA) to solve the problem. Firstly, XASA begins with chaotic optimization which takes a chaotic walk in the solution space and generates several local minima; secondly XASA improves SA algorithm via several adaptive schemes and continues to search the optimal based on the results of chaotic optimization. The effectiveness of XASA is evaluated by comparing with traditional SA algorithm and improved SA algorithm. The results show that XASA can achieve a satisfactory performance of speedup without loss of solution quality