Service placement deals with the problem of selecting which node in a network is most suitable for hosting a service that responds to queries from other nodes. Optimally placing services reduces network traffic and improves connectivity between clients and servers. Service placement algorithms may thus be regarded as an interesting building block for research into service-oriented middleware. Recently, new approaches to address the service placement problem in the field of ad-hoc networking have been proposed. This paper surveys, classifies and evaluates ten representative approaches, thereby providing a summary of the state of the art in service placement. 1

Power has become the first class design constraint in modern processor design. To reduce the power density caused by aggressive, speculative execution seen in previous processor generations, computer architects have turned to a multicore design strategy with each core substantially simplified. Additionally, different power-saving features have been proposed and integrated into each core to adapt dynamic execution scenarios. Due in part to the independent nature of these cores, the power management has also become more flexible to further reduce the overall power consumption. With careful adaptation schemes, the system can save power by entering different idle states dynamically with minimal performance impact. Given the simultaneous emergence of virtualization technologies, the question, then, is how to effectively leverage these idle states in the context of multiple virtual machines (VMs) executing on multicore parts. Towards this end, we develop the IdlePower approach to managing idle states in virtualized systems. Our approach combines a novel batching algorithm that creates improved opportunities to enter deep idle states by removing unnecessary system wakeups depending upon monitored behavior of workloads. IdlePower also provides application awareness in another fashion by not only entering deep idle states based upon transition latencies, but also factoring in the performance degradation that can occur due to secondary effects such as data loss in cache structures. We extend the use of Bloom filters with IdlePower to detect application characteristics for dynamically predicting whether deep idle states are worthwhile based upon possible performance implications. Overall, IdlePower is shown to improve residencies in the deepest C3 idle state by up to 10%, and to avoid performance degradations in workloads of up to 26%. 1.