Interact 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 op-erating system, with an emphasis on energy as a driving resource management issue for large server clusters. The goals are to provi-sion server resources for co-hosted services in a way that automati-cally adapts to offered load, improve the energy efficiency of server dusters 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 func-tion of delivered performance. The system continuously moni-tors 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 avail-ability, and can reduce server energy usage by 29 % or more for a typical Web workload. 1.

physical environment in real-time, and the need to reason about emerging aggregate properties as opposed to individual component behavior. In this research we propose to develop theory, methods and tools for massively distributed, environment-aware computing (more succinctly referred to as swarm computing). The state of swarm computing today is similar to that of sequential computing in the early 1950s. Developers painstakingly produce swarm programs by designing and programming the actions of individual devices, and converge on an acceptable program through extensive simulation and experimentation. In the pre-compiler era, skeptical programmers believed that a mechanical process could not possibly produce code of comparable quality to that produced by highly skilled machine coders and that the cost of machine time is high enough to outweigh any possible savings in programmer effort. The state of swarm programming today is similar: devices are still expensive enough an