Abstract
Abstract In this paper, we study the scheduling decisions for handling deadline-constrained workflows in the context of planning customized virtual infrastructures in the cloud. We specifically focus on the effects of using different types of greediness in selecting cost-effective virtual machines for the tasks in an application’s workflow graph. The profiling procedure followed demonstrates that for the widely used approach of the partial critical path algorithm a greedy version is preferred to a more stringent version under different stress conditions, from tight to loose deadlines. Representative topologies of workflow applications are used to generate sets of task graph scheduling problems. Monitoring the performance of the partial critical path algorithm with different types of greediness reveals which of the topologies tested are difficult to solve under various stress conditions. It turns out that an invalid outcome of a greedy version of the partial critical path algorithm is more susceptible to become valid via a final refinement cycle than a less greedy version. The procedure outlined in this paper will allow for a systematic study of a specific heuristic in a workflow scheduling method to increase its success in infrastructure planning under different deadline conditions and is proposed to be part of a general profiling framework.
Highlights
In many scientific and industrial applications, e.g., climate modeling [1], disaster early warning [2], or IoT systems [3], workflows composed of many interdependent processing components are present
Monitoring the performance of the partial critical path algorithm with different types of greediness reveals which of the topologies tested are difficult to solve under various stress conditions
Other meta-heuristics applied in a global search to find the best solution of a workflow are a Particle Swarm optimization-based heuristic (PSO) applied by Pandey et al [20], and an Ant Colony optimization-based heuristic applied by Xue et al [21]
Summary
In many scientific and industrial applications, e.g., climate modeling [1], disaster early warning [2], or IoT systems [3], workflows composed of many interdependent processing components are present These workflows are usually complex due to the strict data dependencies among the different processing components, and the required time constraints or deadlines for finishing execution. The framework should be equipped with different sets of workflow examples to discover the kind of workflows the approach has difficulty with; Knowledge does not increase from a series of confirmatory observations, disconfirming instances do [8] These difficult to solve workflow examples may be found by studying the performance under high-stress conditions, e.g. tight deadline conditions.
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