Using a novel message-exchanging optimization (MEO) model to reduce energy consumption in distributed systems

The concept of optimizing energy efficiency in distributed systems has gained particular interest. Most of these efforts are focused on the core management concepts like resource discovery, scheduling and allocation without focusing on the actual communication method among system entities. Specifically, these do not consider the number of exchanged messages and the energy that they consume. In this work, we propose a model to optimize the energy efficiency of message-exchanging in distributed systems by minimizing the total number of messages when entities communicate. So we propose an efficient messaging-exchanging optimization (MEO) model that aims to minimize the sum of requests and responses as a whole rather than only the number of requests. The view is to optimize firstly the energy for communication (e.g. latency times) and secondly the overall system performance (e.g. makespan). To demonstrate the effectiveness of MEO model, the experimental analysis using the SimIC is based on a large-scale inter-cloud setting where the implemented algorithms offer optimization of various criteria including turnaround times and energy consumption rates. Results obtained are very supportive.