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An autonomic manager for enterprise server hardware management, called AMP, is described. AMP is designed to handle multiple aspects of hardware management and to work in conjunction with other management components, in particular application managers, in a way that reduces energy waste, protects server health, and preserves a high degree of autonomy both for itself and for the managers with which...
Systems management techniques that allocate resources to running entities, such as processes and virtual machines (VMs), often require estimates of the resources required by each of these resource consumers. For example, many proposed virtual machine placement algorithms attempt to allocate VMs to physical hosts in such a way as to minimize the number of physical hosts that are occupied, while ensuring...
We study the problem of dynamic resource allocation to clustered Web applications. We extend application server middleware with the ability to automatically decide the size of application clusters and their placement on physical machines. Unlike existing solutions, which focus on maximizing resource utilization and may unfairly treat some applications, the approach introduced in this paper considers...
With the continued growth of computing power and reduction in physical size of enterprise servers, the need for actively managing electrical power usage in large datacenters is becoming ever more pressing. By far the greatest savings in electrical power can be effected by dynamically consolidating workload onto the minimum number of servers needed at a given time and powering off the remainder. However,...
Server virtualization opens up a range of new possibilities for autonomic datacenter management, through the availability of new automation mechanisms that can be exploited to control and monitor tasks running within virtual machines. This offers not only new and more flexible control to the operator using a management console, but also more powerful and flexible autonomic control, through management...
This paper presents an autonomic system in which two managers with different responsibilities collaborate to achieve an overall objective. The first, a node group manager, uses modeling and optimization algorithms to allocate server processes and individual requests among a set of server machines grouped into node groups, and also estimates its ability to fulfill its service-level objectives as a...
Autonomic computing has gained widespread attention over the last few years for its vision of developing applications with autonomic or self-managing behaviors (Kephart and Chess, 2003). New approaches to the design and implementation of autonomic systems have emerged, including the use of goal policies (Kephart and Walsh,2004), utility functions, intelligent monitoring, data mining, reinforcement...
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