A Unique Multi-Agent-Based Approach for Enhanced QoS Resource Allocation in Multi Cloud Environment while Maintaining Minimized Energy and Maximize Revenue

Umamageswaran Jambulingam, K. Balasubadra
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引用次数: 1

Abstract

The use of the multi-cloud data storage in one heterogeneous service is a polynimbus cloud strategy. Cloud computing uses a pay-as-you-go model to deliver services to a variety of end users. Customers can outsource daunting tasks to cloud data centres for processing and producing results, thanks to cloud computing. Cloud computing becomes the popular IT brand that provides various on-demand services over the internet. This technology is devoted to distributing computer and software resources. The proven usefulness of workflows to enforce relevant scientific achievements is the availability of data from advanced scientific tools. Scheduling algorithms are essential in order to automate these strenuous workflows efficiently. A number of new heuristics based on a Cloud resource model have been developed. The majority of these heuristic - based address QoS issues in one or two dimensions. The cloud computing technology offers a decentralised pool of services and resources with various models that are provided to the customers across the Internet in an on-demand, continuously distributed, and pay-per-use model. The key challenge we address in this paper is to maximise revenue while maintaining a minimum consumption of energy with an enhanced QoS for resource allocation. The obtained results from proposed method when compared with the existing state of art methods observed to be novel and better.
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一种独特的基于多agent的多云环境下增强QoS资源分配的方法,同时保持能量最小化和收益最大化
在一个异构服务中使用多云数据存储是一种多云策略。云计算使用现收现付模式向各种终端用户交付服务。由于云计算,客户可以将艰巨的任务外包给云数据中心来处理和产生结果。云计算成为流行的IT品牌,在互联网上提供各种按需服务。该技术致力于分配计算机和软件资源。工作流程在加强相关科学成果方面已被证明有用的是来自先进科学工具的数据可用性。为了有效地自动化这些繁重的工作流程,调度算法是必不可少的。许多基于云资源模型的新启发式方法已经被开发出来。大多数基于启发式的方法在一个或两个维度上解决QoS问题。云计算技术提供了一个分散的服务和资源池,这些服务和资源具有各种模型,通过Internet以按需、连续分布和按使用付费的模式提供给客户。我们在本文中解决的关键挑战是,在保持最低能源消耗的同时,通过增强的资源分配QoS来实现收入最大化。通过与现有方法的比较,发现该方法的结果新颖且更好。
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