MOOA-CSF: A Multi-Objective Optimization Approach for Cloud Services Finding

Cloud computing performance optimization is the process of increasing the performance of cloud services at minimum cost, based on various features. In this paper, we present a new approach called MOOA-CSF (Multi-Objective Optimization Approach for Cloud Services Finding), which uses supervised learning and multi-criteria decision techniques to optimize price and performance in cloud computing. Our system uses an artificial neural network (ANN) to classify a set of cloud services. The inputs of the ANN are service features, and the classification results are three classes of cloud services: one that is favorable to the client, one that is favorable to the system, and one that is common between the client and system classes. The ELECTRE (ÉLimination Et Choix Traduisant la REalité) method is used to order the services of the three classes. We modified the genetic algorithm (GA) to make it adaptive to our system. Thus, the result of the GA is a hybrid cloud service that theoretically exists, but practically does not. To this end, we use similarity tests to calculate the level of similarity between the hybrid service and the other benefits in both classes. MOOA-CSF performance is evaluated using different scenarios. Simulation results prove the efficiency of our approach.

Study of the Mass Flow Rates on the Efficiency of Hybrid Thermal / Photvoltaique Sensor

Abstract Hybrid thermal/photovoltaic systems associating a solar concentrator with a heat exchanger are an effective way to improve solar energy conversion yield. We present here an analysis of the effect of the mass flow rates in such a collector. A numerical simulation of the performance of the thermal/photovoltaic sensor with a heat exchanger including fins attached to the absorber and using air as a coolant is presented. A thorough analysis of the influence of the mass flow rate on the efficiency and the working of a thermal/photovoltaic collector is presented. The analysis is made using the equations of the components of heat transfer cascade into a matrix of four unknown's which are the glass , cells, fluid and insulation plate temperature. This matrix is solved by the fixed point method and Gauss-Seidel, at the permanent regime. Results show that the overall conversion efficiency of the system is increasing from 27% to 65%, and the cell temperatures decreasing from 345°K to 335°K when mass flow rates varies from 0.02 kg/s to 0.1 kg/s

Fault Tolerance for Composite Cloud Services: A Novel Approach Based MAS

Several Cloud services may be composed in order to respond quickly to the needs of users. Unfortunately, when running such a service some faults may occur. The outcome of fault control is a big challenge. In this paper, the authors propose a new approach based back recovery and multi-agent planning methods. The proposed architecture based MAS (Multi-Agent System) is composed of two main types of Agents : a Composition Manager Agent (CMA) and a Supervisor Agent (SA). The role of the CMA is to create a set of plans as an oriented graph where the nodes are the Cloud services and the valued arcs represent the composition order of these services. This agent saves checkpoints (nodes) in a stable memory so that there are at least one possible path. However, the SA ensures that the running plan is working properly; otherwise, it informs the CMA to select another sub-plan. Experimental results show the performance and effectiveness of the proposed approach