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ISTC-CC Abstract
Purlieus: Locality-aware Resource Allocation for
MapReduce in a Cloud
ACM/IEEE International Conference on SuperComputing (SC2011), Seattle WA, Nov. 12-18, 2011.
Balaji Palanisamy, Aameek Singh†, Ling Liu, Bhushan Jain‡
Georgia Institute of Technology
†IBM Research - Almaden
‡IBM India Software Lab
The large amount of energy consumed by Internet services represents significant and fast-growing financial and environmental costs. Increasingly, services are exploring dynamic methods to minimize energy costs while respecting their service-level agreements (SLAs). Furthermore, it will soon be important for these services to manage their usage of "brown energy" (produced via carbon-intensive means) relative to renewable or "green" energy. This paper introduces a general, optimization-based framework for enabling multi-data-center services to manage their brown energy consumption and leverage green energy, while respecting their SLAs and minimizing energy costs. Based on the framework, we propose a policy for request distribution across the data centers. Our policy can be used to abide by caps on brown energy consumption, such as those that might arise from Kyoto-style carbon limits, from corporate pledges on carbon-neutrality, or from limits imposed on services to encourage brown energy conservation. We evaluate our framework and policy extensively through simulations and real experiments. Our results show how our policy allows a service to trade off consumption and cost. For example, using our policy, the service can reduce brown energy consumption by 24% for only a 10% increase in cost, while still abiding by SLAs.
FULL PAPER: pdf