Predicting temporal violations for parallel business cloud workflows

Abstract

SummaryWorkflow temporal violations, namely, intermediate workflow runtime delays, often occur and have a serious impact on the on‐time completion of massive concurrent requests. Therefore, accurate prediction of cloud workflow temporal violations is critical as its result can serve as an essential reference for temporal violation prevention and handling strategies. Conventional studies mainly focus on the time delays of a single workflow activity or a single workflow instance but overlook the propagation of time delays among them. This is a serious problem as time delays can propagate in cloud workflow system due to resource sharing and the dependencies among workflow activities. This paper first proposes a novel temporal violation transmission model inspired by an epidemic model to model the dynamics of time delay propagation. Afterward, a novel temporal violation prediction strategy is presented to estimate the number of temporal violations that may occur and determine the number of violations that must be handled to achieve the target service‐level agreement, namely, the on‐time completion rate. To the best of our knowledge, this is the first attempt to predict cloud workflow temporal violations at the workflow build‐time stage by analyzing the propagation of temporal violations. Experimental results demonstrate that our strategy can make highly accurate predictions and is scalable for a large batch of parallel workflows running in the cloud.