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This paper tackles the issue of Data Centers (DCs) energy efficiency by proposing a Self-Adaptive Task Scheduler that proactively allocates incoming tasks to physical servers avoiding the need of consolidation and implicitly avoiding task migration from one server to another. The Self-Adaptive Task Scheduler is based on a MAPE architecture. The monitoring phase aggregates data about resource utilization,...
This paper addresses the problem of Data Centers (DC) energy efficiency by proposing a proactive optimization technique to schedule the day-ahead DC operation to minimize the operational cost. The proactive optimization technique is formalized as a Mixed Integer Optimal Control Problem, known to be NP-hard. Because the time needed for solving this problem by some of the gradient-based solvers depends...
This paper proposes the development of a management controller, which balances the service center servers’ workload and hardware resources usage to locally optimize energy consumption. The controller exploits energy saving opportunities due to short-term fluctuations in the performance request levels of the server running tasks. The paper proposes Dynamic Power Management strategies for processor...
This paper presents a bio-inspired system based on membrane computing for solving packing problems in complex dynamic systems where the characteristics of the packed items change continuously. For representing such systems, a bio-inspired model is defined, having as core entities cells and molecules, the packing problem translating into the problem of matching molecules to cells. A symbiotic relationship...
This paper proposes a genetic inspired algorithm for negotiating the tradeoffs between the workload Quality of Service requests and the service center computing resources energy consumption with the goal of allocating the service center computing resources in an energy efficient manner. The bilateral negotiation algorithm has two main parties: the workload task's Quality of Service request, as a client,...
In this paper we propose an energy aware dynamic consolidation algorithm for virtualized service centers based on reinforcement learning. The energy awareness is enacted by using the Energy Aware Context Model (EACM) to programmatically represent the current service center context situation by means of ontologies. We have defined the EACM model entropy metric for evaluating the service center greenness...
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