Abstract: Cloud service providers are distributing data centers globally to reduce operating costs while also improving quality of service by using intelligent cloud management strategies. The development of time-of-use electricity pricing and renewable energy source models has provided the means to reduce high cloud operating costs through intelligent geographical workload distribution. However, neglecting important considerations such as data center cooling power, interference effects from workload co-location in servers, net-metering, peak demand pricing of electricity, data transfer costs, and data center queueing delay has led to sub-optimal results in prior work because these factors have a significant impact on cloud operating costs and performance. In this dissertation, we propose a suite of cloud management techniques that take a holistic approach to the cloud operating cost and energy optimization problem for geo-distributed data centers. Our algorithmic techniques perform intelligent workload management across geo-distributed data centers while considering heterogeneity in data center compute capability, cooling power, interference effects from workload co-location in servers, time-of-use electricity pricing, renewable energy, net metering, peak demand pricing distribution, and network costs. We demonstrate the value of utilizing such information by comparing against state-of-the-art geo-distributed workload management techniques that consider varying amounts of system information. Our experimental analysis indicates that the proposed techniques can minimize the cloud energy expenditures more effectively than existing approaches.

Adviser: Sudeep Pasricha
Co-Adviser: N/A
Non-ECE Member: Chuck Anderson, CS
Member 3: H. J. Siegel, ECE
Addional Members: Anthony Maciejewski, ECE

Publications:
[1] Hogade, N., Pasricha, S., Siegel, H.J., Maciejewski, A.A., Oxley, M.A. and Jonardi, E. Minimizing energy costs for geographically distributed heterogeneous data centers. IEEE Transactions on Sustainable Computing, 3(4), pp.318-331, 2018.
[2] Pasricha, S., Hogade, N., Siegel, H.J., Maciejewski, A.A. "Green Computing with Geo-Distributed Heterogeneous Data Centers." In 2019 Tenth International Green and Sustainable Computing Conference (IGSC), pp. 1-6, 2019.
[3] Hogade, N., Pasricha, S., Siegel, H.J. "Energy and Network Aware Workload Management for Geographically Distributed Data Centers." IEEE Transactions on Sustainable Computing, (Early Access), 2021.

Program of Study:
ECE-561 Hardware/Software Design of Embedded Systems
ECE-554 Computer Architecture
CS-575 Parallel Processing
CS-545 Machine Learning
CS-420 Introduction to Analysis of Algorithms
ENGR-510 Engineering Optimization: Method/Application
GRAD-511 High Performance Computing and Visualization
ECE-656 Machine Learning and Adaptive Systems