Journal
IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS
Volume 24, Issue 9, Pages 1874-1883Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPDS.2012.262
Keywords
Cloud computing; parallel computing; parallel job scheduling; resource consolidation; parallel discrete event simulation
Funding
- National Natural Science Foundation of China [91024030, 61074108, 61172014, 60702037]
- Australian Research Council [DP1097111]
- Australian Research Council [DP1097111] Funding Source: Australian Research Council
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The cloud computing paradigm is attracting an increased number of complex applications to run in remote data centers. Many complex applications require parallel processing capabilities. Parallel applications of certain nature often show a decreasing utilization of CPU resources as parallelism grows, mainly because of the communication and synchronization among parallel processes. It is challenging but important for a data center to achieve a certain level of utilization of its nodes while maintaining the level of responsiveness of parallel jobs. Existing parallel scheduling mechanisms normally take responsiveness as the top priority and need nontrivial effort to make them work for data centers in the cloud era. In this paper, we propose a priority-based method to consolidate parallel workloads in the cloud. We leverage virtualization technologies to partition the computing capacity of each node into two tiers, the foreground virtual machine (VM) tier (with high CPU priority) and the background VM tier (with low CPU priority). We provide scheduling algorithms for parallel jobs to make efficient use of the two tier VMs to improve the responsiveness of these jobs. Our extensive experiments show that our parallel scheduling algorithm significantly outperforms commonly used algorithms such as extensible argonne scheduling system in a data center setting. The method is practical and effective for consolidating parallel workload in data centers.
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