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Browsing by Author "Hogade, Ninad, author"

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    Energy-aware workload management for geographically distributed data centers
    (Colorado State University. Libraries, 2023) Hogade, Ninad, author; Pasricha, Sudeep, advisor; Siegel, Howard Jay, committee member; Maciejewski, Anthony, committee member; Anderson, Chuck, committee member
    Cloud service providers are distributing data centers globally to reduce operating costs while also improving the 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 essential 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, performance, and carbon emissions. This dissertation presents a series of critical research studies addressing the vital issues of energy efficiency, carbon emissions reductions, and operating cost optimization in geographically distributed data centers. It scrutinizes different approaches to workload management, considering the diverse, dynamic, and complex nature of these environments. Starting from an exploration of energy cost minimization through sophisticated workload management techniques, the research extends to integrate network awareness into the problem, acknowledging data transfer costs and queuing delays. These works employ mathematical and game theoretic optimization to find effective solutions. Subsequently, a comprehensive survey of state-of-the-art Machine Learning (ML) techniques utilized in cloud management is discussed. Then, the dissertation traverses into the realm of Deep Reinforcement Learning (DRL) based optimization for efficient management of cloud resources and workloads. Finally, the study culminates in a novel game-theoretic DRL method, incorporating non-cooperative game theory principles to optimize the distribution of AI workloads, considering energy costs, data transfer costs, and carbon footprints. The dissertation holds significant implications for sustainable and cost-effective cloud data center workload management.
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    Minimizing energy costs for geographically distributed heterogeneous data centers
    (Colorado State University. Libraries, 2018) Hogade, Ninad, author; Pasricha, Sudeep, advisor; Siegel, Howard Jay, advisor; Burns, Patrick J., committee member
    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 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.