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Workflow-based Resource Allocation To Optimize Overall Performance Of Composite Services

机译：基于工作流的资源分配，以优化组合服务的整体性能

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摘要

In software service provision, the overall performance of a composite service is often the ultimate focus of concern rather than those of its individual components. This opens new opportunities for resource allocation because with its service workflow definition, more accurate prediction of its individual components' dynamic workload is possible, thus resulting in better utilization of resources. In this paper, we propose to improve resource allocation through tracing and prediction of workload dynamics of component services as requests traverse and pipeline through the workflow. Factors affecting service workload such as service time, transition probability, replication overhead for additional service etc. as well as the uncertainty in request arrival time are all taken into consideration in our model. The goal is to maximize the number of requests completed under the constraints of limited available resources. Experimental study on TPC-W and synthetic workflow shows that our dynamic workflow-based resource allocation scheme is much more efficient in enhancing the overall performance of composite services than current resource allocation schemes do.
机译：在软件服务提供中，复合服务的整体性能通常是关注的最终焦点，而不是其各个组件的关注焦点。这为资源分配开辟了新机会，因为借助其服务工作流程定义，可以更准确地预测其各个组件的动态工作负载，从而可以更好地利用资源。在本文中，我们提出通过跟踪和预测组件服务在工作流程中遍历和流水线时的工作负载动态来改善资源分配。影响服务工作量的因素，例如服务时间，转换概率，附加服务的复制开销等，以及请求到达时间的不确定性，都在我们的模型中考虑在内。目标是在可用资源有限的情况下最大化已完成请求的数量。对TPC-W和合成工作流的实验研究表明，基于动态工作流的资源分配方案在增强复合服务的整体性能方面比当前的资源分配方案有效得多。

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