Managing energy and server resources in hosting centers

TLDR

Internet hosting centers serve multiple service sites from a common hardware base. This paper proposes an architecture for hosting‑center operating systems that automatically provisions server resources, adapts to load, and improves energy efficiency by dynamically resizing active server sets while responding to power or thermal events per SLAs. The system uses an economic model where services bid for resources, continuously monitors load to estimate performance impact, applies a greedy pricing algorithm to balance supply and demand, and employs a reconfigurable server‑switching infrastructure to route traffic to the allocated servers. Prototype experiments show the architecture adapts to load and availability, reducing server energy consumption by 29 % or more on typical web workloads.

Abstract

Internet hosting centers serve multiple service sites from a common hardware base. This paper presents the design and implementation of an architecture for resource management in a hosting center operating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provision server resources for co-hosted services in a way that automatically adapts to offered load, improve the energy efficiency of server clusters by dynamically resizing the active server set, and respond to power supply disruptions or thermal events by degrading service in accordance with negotiated Service Level Agreements (SLAs).Our system is based on an economic approach to managing shared server resources, in which services "bid" for resources as a function of delivered performance. The system continuously monitors load and plans resource allotments by estimating the value of their effects on service performance. A greedy resource allocation algorithm adjusts resource prices to balance supply and demand, allocating resources to their most efficient use. A reconfigurable server switching infrastructure directs request traffic to the servers assigned to each service. Experimental results from a prototype confirm that the system adapts to offered load and resource availability, and can reduce server energy usage by 29% or more for a typical Web workload.