云环境下成本感知任务分配算法研究

Manish Gupta, Anurag Jain
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引用次数: 2

摘要

云计算是大型计算密集型或数据密集型任务的可靠计算平台。这已经被许多软件业的工业巨头所接受,他们的软件解决方案,像微软,埃森哲,爱立信等公司已经采用云计算作为他们廉价可靠的计算的首选。但是,随着采用这种方法的客户端数量的增加,需要更高的成本效益和高性能计算,以提高客户端和服务提供之间的信任和可靠性,以保证廉价和更有效的解决方案。因此,需要以有效的方式分配云中的任务,以提供高资源利用率和最少的执行时间来实现高性能,同时提供最少的计算成本,因为云遵循按使用付费模型。为了提高性能,人们提出了许多资源算法,但这些算法的成本效率都不高。遗传算法、粒子群算法和蚁群算法是有效的解决方案,但成本效益不高。因此,本文首先对现有的各种算法进行了研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A survey on cost aware task allocation algorithm for cloud environment
Cloud computing is a reliable computing platform for large computational intensive or data intensive tasks. This has been accepted by many industrial giants of software industry for their software solutions, companies like Microsoft, Accenture, Ericson etc has adopted cloud computing as their first choice for cheap and reliable computing. But which increase in number of clients adopting this there is requirement of much more cost efficient and high performance computing for more trust and reliability among the client and the service provide to guarantee cheap and more efficient solutions. So the tasks in cloud need to be allocated in an efficient manner to provide high resource utilization and least execution time for high performance, at the same time provide least computational cost as cloud follows pay-per use model. Many resource algorithms are been proposed to improve the performance, but are not cost efficient at same time. Algorithms like genetic, particle swarm and ant colony algorithm are efficient solutions but not cost efficient. So this paper presets an study of various existing algorithms.
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