科研之友
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摘要
With the growth in computing needs, energy cost includes a large portion of operating cost of cloud data centers. Electricity prices vary in different times and geographical places. Such diversity provides opportunity for diminishing total cost via migration of jobs to places with lower energy prices. Most of the previous studies only focus on computing cost of data centers and disregard other significant parameters such as cooling cost of data centers. These approaches prefer data centers which are located in states with cheaper computing cost. Nonetheless, inappropriate workload migration may lead to a remarkable increase in the total cost because of ignoring the cooling cost of data centers. To address this challenge, we show that minimization of the total cost must cover both the computing and cooling cost while considering delay requirements of jobs. Moreover, we propose an analytical approach which captures the interaction between migration decisions and cooling cost in cloud data centers. Features that make our approach distinct from other similar approaches are the following: first, we consider that cooling cost increases in a nonlinear way with respect to the data center utilization; second, we model cooling cost without any assumption about how the data center cooling system works. In order to achieve energy saving, we determine how much workload should be migrated to other data centers and also the number of servers allocated to each data center for executing the workload. We accomplish migration of workload between data centers by utilizing variety in electricity prices in different locations and times and achieve lower total cost compared with previous schemes. Eventually, using MapReduce traces, we validate our method and indicate that remarkable cost saving, around 37 % can be obtained by cooling-aware job migration.
- 出版日期2015-3
