Proceedings of the 6th international workshop on Hardware/software codesign
On the complexity of list scheduling algorithms for distributed-memory systems
ICS '99 Proceedings of the 13th international conference on Supercomputing
Benchmarking the Task Graph Scheduling Algorithms
IPPS '98 Proceedings of the 12th. International Parallel Processing Symposium on International Parallel Processing Symposium
Making scheduling "cool": temperature-aware workload placement in data centers
ATEC '05 Proceedings of the annual conference on USENIX Annual Technical Conference
IEEE Transactions on Computers
ATC'07 2007 USENIX Annual Technical Conference on Proceedings of the USENIX Annual Technical Conference
IEEE Transactions on Parallel and Distributed Systems
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High performance computing data centers are playing increasingly important roles in our daily life. However, as data centers increase in size and number, the power consumption at the data centers has also increased dramatically. We are facing the challenge of reducing energy consumption, lowering down the peak inlet temperature and at the same time meeting short make span requirements. In this paper, we present two dependent task scheduling algorithms to balance the trade-offs among data center's power consumption, peak inlet temperature, and application's make span. We compare them with two existing algorithms, i.e., the List algorithm and the Coolest Inlets algorithms. Our extensive simulations show clear advantages of the proposed approaches over the List and the Coolest Inlets algorithms for both homogeneous and heterogeneous data centers.