Geographic Trough Filling for Internet Datacenters
Dan Xu, Xin Liu

TL;DR
This paper introduces energy-efficient trough filling strategies for datacenters that leverage delay-tolerant jobs to optimize energy use and delay performance, using dynamic speed scaling and traffic shifting.
Contribution
It presents two novel joint speed scaling and traffic shifting schemes that operate with minimal statistical information, improving energy efficiency and delay management in datacenters.
Findings
Significant energy savings demonstrated with proposed schemes
Effective delay performance achieved through load shifting
Schemes validated on real and artificial datacenter traces
Abstract
To reduce datacenter energy consumption and cost, current practice has considered demand-proportional resource provisioning schemes, where servers are turned on/off according to the load of requests. Most existing work considers instantaneous (Internet) requests only, which are explicitly or implicitly assumed to be delay-sensitive. On the other hand, in datacenters, there exist a vast amount of delay-tolerant jobs, such as background/maintainance jobs. In this paper, we explicitly differentiate delay-sensitive jobs and delay tolerant jobs. We focus on the problem of using delay-tolerant jobs to fill the extra capacity of datacenters, referred to as trough/valley filling. Giving a higher priority to delay-sensitive jobs, our schemes complement to most existing demand-proportional resource provisioning schemes. Our goal is to design intelligent trough filling mechanisms that are energy…
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Taxonomy
TopicsCloud Computing and Resource Management · Caching and Content Delivery · Software-Defined Networks and 5G
