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MER trades the minimal makespan increase for the maximal resource usage reduction by consolidating tasks with the exploitation of resource inefficiency in the original workflow schedule.
To this end, we present the Maximum Effective Reduction (MER) algorithm, a resource efficiency solution that optimizes the resource usage of a workflow schedule generated by any particular scheduling algorithm.
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As a validation of the introduced functionality, we simulate the impact of instability on scientific workflow scheduling by assessing and comparing the performance of four schedulers in the course of several experiments both in simulation and on real cloud infrastructure.
Our algorithm showed noticeable enhancements over the classical workflow scheduling algorithms.
In this paper, we address the problem of resource-efficient workflow scheduling.
Research on workflow scheduling has largely concentrated on temporal constraints, which specify correct ordering of tasks.
Thus, to ensure efficient execution of the workflow, Grid workflow scheduling is required to manage the allocation of Grid resources.
However, it also challenges traditional best-effort workflow scheduling strategies, that only focus on minimizing schedule length.
In this paper, taxonomies of cloud workflow scheduling problem and techniques are proposed based on analytical review.
The proposed adaptive workflow scheduling (AWS) approach involves initial static scheduling, resource monitoring and rescheduling with the aim to achieve the minimum execution time for workflow application.
We identify and explain the aspects and classifications unique to workflow scheduling in the cloud environment in three categories, namely, scheduling process, task and resource.
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