Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry

2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing Pub Date : 2015-11-03 DOI:10.1109/CSCloud.2015.73

Keke Gai, Z. Du, Meikang Qiu, Hui Zhao

{"title":"Efficiency-Aware Workload Optimizations of Heterogeneous Cloud Computing for Capacity Planning in Financial Industry","authors":"Keke Gai, Z. Du, Meikang Qiu, Hui Zhao","doi":"10.1109/CSCloud.2015.73","DOIUrl":null,"url":null,"abstract":"The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. This trend has enabled global enterprises to apply distributed computing techniques to reach many benefits. An effective risk management approach is required for service deliveries and a capacity planning is considered one of the convincing methods for financial industry. However, executing a capacity planning is still encountering a great challenge from bottlenecks of the Web server capacities. The unstable service demands often result in service delays, which embarrasses the competitivenesses of the enterprises. This paper addresses this issue and proposes an approach, named Efficiency-aware Cloud-based Workload Optimization (ECWO) Model, using greedy programming to predict server workloads of heterogeneous cloud computing in financial industry. The main algorithms used in the proposed model are Task Mapping Algorithm (TMA) and Efficiency-Aware Task Assignment (EATA) Algorithm. Our experimental evaluations have examined the performance of the proposed scheme.","PeriodicalId":278090,"journal":{"name":"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing","volume":"5 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"78","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CSCloud.2015.73","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}

引用次数: 78

Abstract

The broad implementation of cloud computing has brought a dramatic change to multiple industries, which derives from the development of the Internet-related technologies. This trend has enabled global enterprises to apply distributed computing techniques to reach many benefits. An effective risk management approach is required for service deliveries and a capacity planning is considered one of the convincing methods for financial industry. However, executing a capacity planning is still encountering a great challenge from bottlenecks of the Web server capacities. The unstable service demands often result in service delays, which embarrasses the competitivenesses of the enterprises. This paper addresses this issue and proposes an approach, named Efficiency-aware Cloud-based Workload Optimization (ECWO) Model, using greedy programming to predict server workloads of heterogeneous cloud computing in financial industry. The main algorithms used in the proposed model are Task Mapping Algorithm (TMA) and Efficiency-Aware Task Assignment (EATA) Algorithm. Our experimental evaluations have examined the performance of the proposed scheme.

查看原文本刊更多论文

面向金融行业容量规划的异构云计算工作负载效率优化

云计算的广泛实施给多个行业带来了巨大的变化，这源于互联网相关技术的发展。这种趋势使全球企业能够应用分布式计算技术来获得许多好处。提供服务需要有效的风险管理方法，能力规划被认为是金融行业令人信服的方法之一。但是，执行容量规划仍然面临着Web服务器容量瓶颈的巨大挑战。服务需求的不稳定往往会导致服务延迟，从而影响企业的竞争力。本文针对这一问题，提出了一种基于效率感知的基于云的工作负载优化(ECWO)模型，利用贪心编程对金融行业异构云计算服务器工作负载进行预测。该模型使用的主要算法是任务映射算法(TMA)和效率感知任务分配算法(EATA)。我们的实验评估检验了所提出方案的性能。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文求助全文

来源期刊

2015 IEEE 2nd International Conference on Cyber Security and Cloud Computing

自引率

0.00%

发文量