Energy consumption of IT increased continously during the last decades. Numerous works have been accomplished for improving energy efficiency of hardware whereas software energy efficiency has been ignoried for a long time. This contribution presents a novel approach for estimating energy consumption of applications in different execution environments. The system is the basis for automatic optimization of software execution in an energy-efficient way by finding the best-suiting host computer. Thus, it opens novel ways to further improve energy-efficiency of IT systems. Exemplary, the approach is tested in a virtualized data center environment, where virtual machines are the applications. The presented approach is a vehicle for automatically finding the most energy-efficient host machine for any virtual machine system.The paper presents a new algorithm for estimating virtual machine power consumption and shows the accuracy of the presented approach by means of measurements.

Energy consumption of IT increased continuously during the last decades. Numerous works have been accomplished for improving energy efficiency of hardware whereas software energy efficiency has been ignored for a long time. This contribution presents a novel approach for estimating energy consumption of computer systems in dependency of software-caused workloads in different execution environments. The system is the basis for automatic optimization of software execution in an energy-efficient way by finding the best-suiting host computer (and best-suiting peripheral devices). Thus, it opens novel ways to further improve energy-efficiency of IT systems by migrating software-caused load to an energy-efficient target. Exemplary, the approach is tested in a virtualized data center environment, where virtual machines are the applications. The presented approach is a vehicle for automatically computing an energy-efficient virtual machine placement. The paper presents a new algorithm for estimating virtual machine power consumption, which consists of CPU power consumption estimation as well as power usage estimation of peripheral components like hard disk drive and network interface controller. The accuracy of the presented approach is proved by means of measurements.

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As processors migrate to multi-and many-core architectures, the role of the communication network becomes more important. Efficient communication architecture can drastically improve overall system performance. Taking into account the application behavior can facilitate system-level solutions that manage the communication cost. To address this issue, we propose a Clustered Globally Asynchronous Locally Synchronous Network-on-Chip (C-GALS NoC) communication architecture. C-GALS NoC is composed of local, synchronous clusters and a global asynchronous network. Additionally, we propose a cluster based communication-aware mapping algorithm (CAM) for mapping the application tasks to the C-GALS NoC, while minimizing the communication cost. The synergy of the C-GLAS NoC and the CAM algorithm results in a system-level mechanism that, according to our results, provides up to 2x and 3x, in performance and power improvement, respectively, in comparison with a regular GALS NoC. Finally, we demonstrate that C-GALS NoC is standard-cell compatible by synthesizing it using Design Compiler.

Energy consumption of data centers has been increasing continuously during the last years due to rising demands of computational power especially in current Grid-and CloudComputing systems. One promising approach of reducing this energy consumption is the consolidation of servers by virtualization. Many low loaded computer systems are virtualized and run on few physical servers for reducing the number of energy-consuming computers. At present this consolidation is usually done statically, thus, the administrator of a data center manually migrates many virtual machines with low load onto one physical server which may lead to overloading when the workload is rising unexpectedly. Dynamic server migration that adapts the number of running physical machines to the current workload overcomes these problems. Physical machines can be highly loaded and in case of further rising load virtual machines are migrated to other physical server systems that have been switched on. Such dynamic load aggregation approaches are rarely used and typically only consider few criteria for migration. This paper presents a classification of migration criteria for live migration of virtual machines in load aggregation environments and proposes an algorithm for combining many different kinds of migration criteria to a clustering-based metric. Thus, the novel load aggregation algorithm optimizes energy consumption as well as other migration criteria like runtime performance of applications.