Multi-Cloud Provisioning and Load Distribution for Three-Tier Applications

IF 2.2 4区 计算机科学 Q3 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE ACM Transactions on Autonomous and Adaptive Systems Pub Date : 2014-10-07 DOI:10.1145/2662112
N. Grozev, R. Buyya
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引用次数: 80

Abstract

Cloud data centers are becoming the preferred deployment environment for a wide range of business applications because they provide many benefits compared to private in-house infrastructure. However, the traditional approach of using a single cloud has several limitations in terms of availability, avoiding vendor lock-in, and providing legislation-compliant services with suitable Quality of Experience (QoE) to users worldwide. One way for cloud clients to mitigate these issues is to use multiple clouds (i.e., a Multi-Cloud). In this article, we introduce an approach for deploying three-tier applications across multiple clouds in order to satisfy their key nonfunctional requirements. We propose adaptive, dynamic, and reactive resource provisioning and load distribution algorithms that heuristically optimize overall cost and response delays without violating essential legislative and regulatory requirements. Our simulation with realistic workload, network, and cloud characteristics shows that our method improves the state of the art in terms of availability, regulatory compliance, and QoE with acceptable sacrifice in cost and latency.
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面向三层应用的多云配置和负载分配
云数据中心正在成为各种业务应用程序的首选部署环境,因为与私有内部基础设施相比,云数据中心提供了许多好处。然而,使用单一云的传统方法在可用性、避免供应商锁定以及向全球用户提供具有适当体验质量(QoE)的符合法规的服务方面存在一些限制。云客户端缓解这些问题的一种方法是使用多个云(即多云)。在本文中,我们将介绍一种跨多个云部署三层应用程序的方法,以满足其关键的非功能需求。我们提出自适应、动态和被动的资源配置和负载分配算法,在不违反基本立法和监管要求的情况下,启发式地优化总体成本和响应延迟。我们对实际工作负载、网络和云特征进行的模拟表明,我们的方法在可用性、法规遵从性和QoE方面提高了技术水平,并在成本和延迟方面做出了可接受的牺牲。
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来源期刊
ACM Transactions on Autonomous and Adaptive Systems
ACM Transactions on Autonomous and Adaptive Systems 工程技术-计算机:理论方法
CiteScore
4.80
自引率
7.40%
发文量
9
审稿时长
>12 weeks
期刊介绍: TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community -- and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. TAAS addresses research on autonomous and adaptive systems being undertaken by an increasingly interdisciplinary research community - and provides a common platform under which this work can be published and disseminated. TAAS encourages contributions aimed at supporting the understanding, development, and control of such systems and of their behaviors. Contributions are expected to be based on sound and innovative theoretical models, algorithms, engineering and programming techniques, infrastructures and systems, or technological and application experiences.
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