Visible to the public Joint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse?

TitleJoint Bidding and Geographical Load Balancing for Datacenters: Is Uncertainty a Blessing or a Curse?
Publication TypeJournal Article
Year of Publication2018
AuthorsZhang, Y., Deng, L., Chen, M., Wang, P.
JournalIEEE/ACM Transactions on Networking
Date PublishedApril 2018
Keywordsbandwidth cost, bidding curve, cloud computing, cloud service provider, Collaboration, composability, computer centres, convex programming, cost reduction, cost-saving performance, CSP, data centers, datacenters, deregulated electricity market, Electricity supply industry, Geographic load balancing, geographical load balancing, GLB, Human Behavior, human factors, IEEE transactions, Internet-scale Computing Security, Internet-scale service, joint bidding, joint electricity procurement, Linear programming, Load management, market prices, Metrics, multiple geo, Policy Based Governance, price uncertainty, Pricing, Procurement, pubcrawl, Real-time Systems, realistic setting, resilience, Resiliency, resource allocation, Scalability, Standards, strategic bidding, total electricity, Uncertainty, wholesale markets, workloads

We consider the scenario where a cloud service provider (CSP) operates multiple geo-distributed datacenters to provide Internet-scale service. Our objective is to minimize the total electricity and bandwidth cost by jointly optimizing electricity procurement from wholesale markets and geographical load balancing (GLB), i.e., dynamically routing workloads to locations with cheaper electricity. Under the ideal setting where exact values of market prices and workloads are given, this problem reduces to a simple linear programming and is easy to solve. However, under the realistic setting where only distributions of these variables are available, the problem unfolds into a non-convex infinite-dimensional one and is challenging to solve. One of our main contributions is to develop an algorithm that is proven to solve the challenging problem optimally, by exploring the full design space of strategic bidding. Trace-driven evaluations corroborate our theoretical results, demonstrate fast convergence of our algorithm, and show that it can reduce the cost for the CSP by up to 20% as compared with baseline alternatives. This paper highlights the intriguing role of uncertainty in workloads and market prices, measured by their variances. While uncertainty in workloads deteriorates the cost-saving performance of joint electricity procurement and GLB, counter-intuitively, uncertainty in market prices can be exploited to achieve a cost reduction even larger than the setting without price uncertainty.

Citation Keyzhang_joint_2018