Hogade, Ninad, authorPasricha, Sudeep, advisorSiegel, Howard Jay, advisorBurns, Patrick J., committee member2018-09-102018-09-102018https://hdl.handle.net/10217/191286The 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 geo-distributed 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.born digitalmasters thesesengCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.memory interferencepeak shavinggeo-distributed data centersworkload managementnet meteringText