Ra2: Predicting simulation execution time for cloud-based design space explorations

• Main Authors: TA, Nguyen Binh Duong, CAI, Wentong, LI, Zengxiang, ZHOU, Suiping
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2016
• Subjects: neural network; prediction; cloud-based simulations; resource-aware; OS and Networks; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/4768; https://ink.library.smu.edu.sg/context/sis_research/article/5771/viewcontent/151209476.pdf
• Institution: Singapore Management University

Design space exploration refers to the evaluation of implementation alternatives for many engineering and design problems. A popular exploration approach is to run a large number of simulations of the actual system with varying sets of configuration parameters to search for the optimal ones. Due to the potentially huge resource requirements, cloud-based simulation execution strategies should be considered in many cases. In this paper, we look at the issue of running largescale simulation-based design space exploration problems on commercial Infrastructure-as-a-Service clouds, namely Amazon EC2, Microsoft Azure and Google Compute Engine. To efficiently manage cloud resources used for execution, the key problem would be to accurately predict the running time for each simulation instance in advance. This is not trivial due to the currently wide range of cloud resource types which offer varying levels of performance. In addition, the widespread use of virtualization techniques in most cloud providers often introduces unpredictable performance interference. In this paper, we propose a resource and application-aware (RA2 ) prediction approach to combat performance variability on clouds. In particular, we employ neural network based techniques coupled with non-intrusive monitoring of resource availability to obtain more accurate predictions. We conducted extensive experiments on commercial cloud platforms using an evacuation planning design problem over a month-long period. The results demonstrate that it is possible to predict simulation execution times in most cases with high accuracy. The experiments also provide some interesting insights on how we should run similar simulation problems on various commercially available clouds.