Abstract
More and more application workflows are computed in cloud and most of them can be expressed by Directed Acyclic Graph (DAG). As Cloud resource providers, they should guarantee as many as possible DAGs be accomplished within their deadline when they face the overstep request of computer resource. In this paper, we define the urgency of DAG and introduce the MTMD (Maximize Throughput of Multi-DAG with Deadline) algorithm to improve the ratio of DAGs which can be accomplished within deadline. The urgency of DAG is changing among execution and determine the execution order of tasks. We can detect DAGs which will exceed the deadline by this algorithm and abandon these DAGs timely. Based on the MTMD algorithm, we put forward the CFS (Cost Fairness Scheduling) algorithm to reduce the unfairness of cost between different DAGs. The simulation results show that the MTMD algorithm outperforms three other algorithms and the CFS algorithm reduces the cost of all DAGs by 12.1 % on average and reduces the unfairness among DAGs by 54.5 % on average.
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Wang, W., Wu, Q., Tan, Y., Wu, F. (2015). Maximize Throughput Scheduling and Cost-Fairness Optimization for Multiple DAGs with Deadline Constraint. In: Wang, G., Zomaya, A., Martinez, G., Li, K. (eds) Algorithms and Architectures for Parallel Processing. ICA3PP 2015. Lecture Notes in Computer Science(), vol 9529. Springer, Cham. https://doi.org/10.1007/978-3-319-27122-4_43
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DOI: https://doi.org/10.1007/978-3-319-27122-4_43
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