A constraint programming-based resource management technique for processing mapreduce jobs with SLAs on clouds
Clouds that are rapidly gaining in popularity require an effective resource manager that can harness the power of the underlying resource pool, and provide resources on demand to its users. This paper focuses on resource management on clouds for workflow requests characterized by Service Level Agreements (SLAs). Specifically, we devise a novel MapReduce constraint programming based resource manager (MRCP-RM) that can effectively perform matchmaking and scheduling of MapReduce jobs, each characterized by an SLA comprising an earliest start time, execution time, and an end-to-end deadline. Using discrete event simulation a performance evaluation of MRCP-RM is conducted for an open system subjected to a stream of job arrivals. The simulation results demonstrate the effectiveness of the resource manager and provide insights into system behaviour and performance.
|Keywords||MapReduce with deadlines, MapReduce with SLAs, Matchmaking and scheduling on clouds, Resource management on clouds, SLAs on clouds|
|Conference||43rd International Conference on Parallel Processing, ICPP 2014|
Lim, N. (Norman), Majumdar, S, & Ashwood-Smith, P. (Peter). (2014). A constraint programming-based resource management technique for processing mapreduce jobs with SLAs on clouds. Presented at the 43rd International Conference on Parallel Processing, ICPP 2014. doi:10.1109/ICPP.2014.50