维护云中由用户提供的数据流通知的实时部署的近似技术

James R. Edmondson, A. Gokhale, D. Schmidt
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引用次数: 4

摘要

分布式应用程序越来越多地通过组合许多参与者(如服务、组件和对象)来开发。在将分布式应用程序部署到移动自组织云中时,相互通信的应用程序参与者的位置可能会影响延迟、电源/电池使用、吞吐量以及云提供商是否能够满足服务水平协议(SLA)。在处理部署在分布式实时和嵌入式(DRE)场景中的关键任务应用程序时,优化分布式应用程序中的重要通信链接尤为重要,因为违反sla可能会导致财产、网络基础设施或生命损失。使优化过程复杂化的是,底层云环境可能在操作期间发生变化,分布式应用程序的最佳部署可能会随着时间的推移而降级,原因是硬件故障、主机过载以及分布式应用程序开发人员无法控制的其他问题。因此,为了在动态环境中优化分布式应用程序的性能,参与者的部署可能需要根据应用程序开发人员的需求和底层云环境中可用的资源进行调整和修改。本文对云中用户提供的部署的动态优化研究做出了两项贡献。首先,我们提出了一种数据流描述语言,允许开发人员在其分布式应用程序中指定参与者之间的关键通信路径。其次,我们描述了使用这种数据流表示来确定云中的初始部署和/或后续重新部署的最佳配置的启发式方法。给出了一个实验来验证启发式方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Approximation Techniques for Maintaining Real-Time Deployments Informed by User-Provided Dataflows within a Cloud
Distributed applications are increasingly developed by composing many participants, such as services, components, and objects. When deploying distributed applications into a mobile ad hoc cloud, the locality of application participants that communicate with each other can affect latency, power/\-battery usage, throughput, and whether or not a cloud provider can meet service-level agreements (SLA). Optimization of important communication links within a distributed application is particularly important when dealing with mission-critical applications deployed in a distributed real-time and embedded (DRE) scenario, where violation of SLAs may result in loss of property, cyber infrastructure, or lives. To complicate the optimization process, the underlying cloud environment can change during operation and an optimal deployment of the distributed application may degrade over time due to hardware failures, overloaded hosts, and other issues that are beyond the control of distributed application developers. To optimize performance of distributed applications in dynamic environments, therefore, the deployment of participants may need adapting and revising according to the requirements of application developers and the resources available in the underlying cloud environment. This paper present two contributions to the study of dynamic optimizations of user-provided deployments within a cloud. First, we present a dataflow description language that allows developers to designate key communication paths between participants within their distributed applications. Second, we describe heuristics that use this dataflow representation to identify optimal configurations for initial deployments and/or subsequent redeployments within a cloud. An experiment is presented to validate the heuristic approaches.
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