Existing Placement Techniques Research Articles

Exploiting Spatio-Temporal Tradeoffs for Energy-Aware MapReduce in the Cloud

MapReduce is a distributed computing paradigm widely used for building large-scale data processing applications. When used in cloud environments, MapReduce clusters are dynamically created using virtual machines (VMs) and managed by the cloud provider. In this paper, we study the energy efficiency problem for such MapReduce clouds. We describe a unique spatio-temporal tradeoff that includes efficient spatial fitting of VMs on servers to achieve high utilization of machine resources, as well as balanced temporal fitting of servers with VMs having similar runtimes to ensure a server runs at a high utilization throughout its uptime. We propose VM placement algorithms that explicitly incorporate these tradeoffs. Further, we propose techniques that dynamically scale MapReduce clusters to further improve energy consumption while ensuring that jobs meet or improve their expected runtimes. Our algorithms achieve energy savings over existing placement techniques, and an additional optimization technique further achieves savings while simultaneously improving job performance.

Alternative placement technique for antifer blocks used on breakwaters

In this study, a placement technique named as “alternative placement technique” was developed for antifer blocks and the results of its application to a breakwater model were presented. This placement technique was compared with the existing techniques such as the “regular placement technique”, the “irregular placement technique” and the “sloped wall placement technique” by experiments. The comparison was carried out considering armor layer stability, prototype placement, clarity of the placement technique’s definition, armor layer cost, and wave runup. As a result of this investigation the “alternative placement technique” was found to be superior to the other existing placement techniques.