Format of Original
Institute of Electrical and Electronics Engineers (IEEE)
41st International Conference on Parallel Processing Workshops (ICPPW)
Original Item ID
Data-intensive workloads demand a large portion of data center resources and consume massive amounts of energy. Energy conservation for data-intensive computing requires enabling technology to provide detailed and systemic energy information and to identify the energy inefficiencies in the underlying system hardware and software. In this work, we address this need and present eTune, a fine-grained, scalable power analysis framework for data-intensive computing on large-scale distributed systems. eTune leverages the fine-grained component level power measurement and the hardware performance monitoring counters (PMCs) on modern computer components and statistically builds power-performance correlation models. Using the learned models, eTune implements a software-based power estimator that runs on computer nodes and reports power at multiple levels including node, core, memory, and disks with a high accuracy. The conducted case studies with MapReduce applications reveal detailed energy behaviors of typical execution phases and data movements and provide insights on energy optimization via algorithm designs and resource allocations.