Minimizing Energy Costs for Geographically Distributed Heterogeneous Data Centers

Abstract

MINIMIZING ENERGY COSTS FOR GEOGRAPHICALLY DISTRIBUTED HETEROGENEOUS DATA CENTERSThe 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.ii ACKNOWLEDGMENTS I would like to express my special appreciation to my advisors, Prof. Sudeep Pasricha, Prof.

Energy and Network Aware Workload Management for Geographically Distributed Data Centers

“…Moreover, these efforts do not consider heterogeneity in workload (applications/task types executing in the data center) and heterogeneity within the data center (types of servers, server rack arrangements, etc.). Our previous work [36] considers many of the above-mentioned aspects. We extend [36] by developing a new inter-data center network cost model, a data center queueing delay model, and considering these factors in our resource management problem.…”

“…Our previous work [36] considers many of the above-mentioned aspects. We extend [36] by developing a new inter-data center network cost model, a data center queueing delay model, and considering these factors in our resource management problem. Moreover, we propose a new game theorybased workload management framework that takes a holistic approach to the cloud operating cost minimization problem, which is shown to be more effective than the multiple resource management strategies presented in [36] (Section 7 presents a detailed analysis to quantify the improvement).…”

“…We use the detailed data center power model from our prior work [36]. Let , ( ) be the power consumed by a CRAC unit in data center .…”

Minimizing Energy Cost for Green Data Center by Exploring Heterogeneous Energy Resource

“…Reference [23] considered the co-optimization of server operation and power procurement, then proposed a new holistic approach to implement an efficient demand response. Reference [24] presented a group of energy management techniques to achieve the energy minimization for geographically distributed data centers. Detailed cooling power, co-location interference, and time-of-use (TOU) electricity pricing of data centers were considered in modelling of this problem.…”

Cost-Efficient and Quality of Experience-Aware Provisioning of Virtual Machines for Multiplayer Cloud Gaming in Geographically Distributed Data Centers

“…Locations of the data centers in the two configurations are chosen from major cities in the continental United States. Table 1 shows the price of electricity at different locations [21] and the PUE values of different data centers [22]. Each data center is composed of 10 homogeneous PMs with two dimensional resource capacities: CPU and GPU, and for each PM, the total resource capacities of these resources are set as 6200 MIPS and 32 vGPU with 750MHz, respectively.…”