Exploiting Redundancy and Application Scalability for Cost-Effective, Time-Constrained Execution of HPC Applications on Amazon EC2

Journal Article · Thu Dec 17 00:00:00 EST 2015 · IEEE Transactions on Parallel and Distributed Systems

DOI:https://doi.org/10.1109/TPDS.2015.2508457· OSTI ID:1399726

Marathe, Aniruddha P. ^[1]; Harris, Rachel A. ^[2]; Lowenthal, David K. ^[3]; de Supinski, Bronis R. ^[1]; Rountree, Barry L. ^[1]; Schulz, Martin ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Google Corp., Mountain View, CA (United States)
3. The Univ. of Arizona, Tucson, AZ (United States)

The use of clouds to execute high-performance computing (HPC) applications has greatly increased recently. Clouds provide several potential advantages over traditional supercomputers and in-house clusters. The most popular cloud is currently Amazon EC2, which provides fixed-cost and variable-cost, auction-based options. The auction market trades lower cost for potential interruptions that necessitate checkpointing; if the market price exceeds the bid price, a node is taken away from the user without warning. We explore techniques to maximize performance per dollar given a time constraint within which an application must complete. Specifically, we design and implement multiple techniques to reduce expected cost by exploiting redundancy in the EC2 auction market. We then design an adaptive algorithm that selects a scheduling algorithm and determines the bid price. We show that our adaptive algorithm executes programs up to seven times cheaper than using the on-demand market and up to 44 percent cheaper than the best non-redundant, auction-market algorithm. We extend our adaptive algorithm to incorporate application scalability characteristics for further cost savings. In conclusion, we show that the adaptive algorithm informed with scalability characteristics of applications achieves up to 56 percent cost savings compared to the expected cost for the base adaptive algorithm run at a fixed, user-defined scale.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1399726
• Report Number(s): LLNL-JRNL-676899; TRN: US1702972
• Journal Information: IEEE Transactions on Parallel and Distributed Systems, Vol. 27, Issue 9; ISSN 1045-9219
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
fault tolerance
reliability
cloud computing
resource provisioning
cost optimization