Simple and Effective Dynamic Provisioning for Power-Proportional Data Centers
Tan Lu, Minghua Chen (Department of Information Engineering, The, Chinese University of Hong Kong)

TL;DR
This paper investigates how future workload information impacts dynamic server provisioning in data centers, developing online algorithms with proven competitive ratios and demonstrating their effectiveness through simulations with real-world data.
Contribution
It introduces a novel divide-and-conquer approach to the offline problem, designs simple online algorithms with competitive guarantees, and shows future workload beyond a critical window does not improve performance.
Findings
Optimal offline provisioning structure characterized
Online algorithms with competitive ratios up to approximately 1.58 achieved
Future workload information beyond a critical window does not enhance performance
Abstract
Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much performance gain can knowing future workload information brings to dynamic provisioning. In particular, we study the dynamic provisioning problem under the cost model that a running server consumes a fixed amount energy per unit time, and develop online solutions with and without future workload information available. We first reveal an elegant structure of the off-line dynamic provisioning problem, which allows us to characterize and achieve the optimal solution in a {}"divide-and-conquer" manner. We then exploit this insight to design three online algorithms with…
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Taxonomy
TopicsOptimization and Search Problems · Cloud Computing and Resource Management · IoT and Edge/Fog Computing
