Ninad Hogade
M.S. FinalMay 04, 2018, 1:30 pm - 3:30 pm
Physics Conference Room AR124 Engineering
Minimizing Energy Costs for Geographically Distributed Heterogeneous Data Centers
Abstract: The recent proliferation and associated high electricity costs of distributed data centers have motivated researchers to study energy-cost minimization at the geo-distributed level. The
development of time-of-use (TOU) electricity pricing models and renewable energy source models has provided the means for researchers to reduce these high energy costs through
intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from task co-location in servers, net-
metering, and peak demand pricing of electricity has led to sub-optimal results in prior work because these factors have a significant impact on energy costs and performance. In this
thesis, we propose a set of workload management techniques that take a holistic approach to the energy minimization problem for geodistributed data centers. Our approach considers
detailed data center cooling power, co-location interference, TOU electricity pricing, renewable energy, net metering, and peak demand pricing distribution models. We demonstrate the
value of utilizing such information by comparing against geo-distributed workload management techniques that possess varying amounts of system information. Our simulation results
indicate that our best proposed technique is able to achieve a 61% (on average) cost reduction compared to state-of-the-art prior work.
development of time-of-use (TOU) electricity pricing models and renewable energy source models has provided the means for researchers to reduce these high energy costs through
intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from task co-location in servers, net-
metering, and peak demand pricing of electricity has led to sub-optimal results in prior work because these factors have a significant impact on energy costs and performance. In this
thesis, we propose a set of workload management techniques that take a holistic approach to the energy minimization problem for geodistributed data centers. Our approach considers
detailed data center cooling power, co-location interference, TOU electricity pricing, renewable energy, net metering, and peak demand pricing distribution models. We demonstrate the
value of utilizing such information by comparing against geo-distributed workload management techniques that possess varying amounts of system information. Our simulation results
indicate that our best proposed technique is able to achieve a 61% (on average) cost reduction compared to state-of-the-art prior work.
Adviser: Sudeep Pasricha
Co-Adviser: Howard Jay Siegel
Non-ECE Member: Patrick J. Burns
Member 3: N/A
Addional Members: N/A
Co-Adviser: Howard Jay Siegel
Non-ECE Member: Patrick J. Burns
Member 3: N/A
Addional Members: N/A
Publications:
Ninad Hogade, Sudeep Pasricha, Howard Jay Siegel, Anthony A. Maciejewski, Mark A. Oxley, and Eric Jonardi, “Minimizing Energy Costs for Geographically Distributed Heterogeneous Data Centers,†IEEE Transactions on Sustainable Computing, 14 pp., accepted 2018, to appear.
Ninad Hogade, Sudeep Pasricha, Howard Jay Siegel, Anthony A. Maciejewski, Mark A. Oxley, and Eric Jonardi, “Minimizing Energy Costs for Geographically Distributed Heterogeneous Data Centers,†IEEE Transactions on Sustainable Computing, 14 pp., accepted 2018, to appear.
Program of Study:
CS 420
CS 545
ECE 554
GRAD 510
GRAD 511
CS 575
ECE 699
N/A
CS 420
CS 545
ECE 554
GRAD 510
GRAD 511
CS 575
ECE 699
N/A