A Cost Effective Reliability Aware Scheduler for Task Graphs in Multi-Cloud System
Atharva Tekawade, Suman Banerjee

TL;DR
This paper introduces a cost-effective, reliability-aware scheduling algorithm for task graphs in multi-cloud systems, optimizing workflow execution considering cost, time, and reliability.
Contribution
It presents a novel heuristic scheduler that accounts for practical factors like pricing, discounts, and reliability, outperforming existing methods.
Findings
Outperforms state-of-the-art in cost by up to 12%
Reduces makespan by up to 11%
Improves reliability by approximately 1.1%
Abstract
Many scientific workflows can be represented by a Directed Acyclic Graph (DAG) where each node represents a task, and there will be a directed edge between two tasks if and only if there is a dependency relationship between the two i.e. the second one can not be started unless the first one is finished. Due to the increasing computational requirements of these workflows, they are deployed on cloud computing systems. Scheduling of workflows on such systems to achieve certain goals(e.g. minimization of makespan, cost, or maximization of reliability, etc.) remains an active area of research. In this paper, we propose a scheduling algorithm for allocating the nodes of our task graph in a heterogeneous multi-cloud system. The proposed scheduler considers many practical concerns such as pricing mechanisms, discounting schemes, and reliability analysis for task execution. This is a list-based…
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Taxonomy
TopicsCloud Computing and Resource Management · Distributed and Parallel Computing Systems · IoT and Edge/Fog Computing
