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.

Maximum size of the pareto cost sets for multi-constrained optimal routing

Routing under multiple independent constrains in point-to-point networks has been studied for over 10 years. Its NP-hardness keeps pushing researchers to study approximate algorithms and heuristics, and many results have been published in these years. To the best of our knowledge, the nature of its average case has been explored only for the self adaptive multiple constraints routing algorithm (SAMCRA), which is an algorithm about multiple constraints routing. In this paper, we simplify SAMCRA into a format that is convenient for our average case analysis. This variant algorithm gives optimal solutions also for very large dimensional networks such as with more than 1000 nodes. Although it runs in exponential time in the worst case, we prove that its average case time complexity is bounded by a polynomial function of the number of nodes in the network. Lastly, we give empirical results that align with our theoretical work

An Interference-Aware Channel Assignment Scheme for Wireless Mesh Networks

Mesh network will become an additional access technology instead of being a renewed technology in the next generation networks which are called Fourth Generation. The most important advantage of wireless mesh networks is their capability of working without an infrastructure. In recent years, there have been many studies about wireless mesh networks especially for multi-channel multi-radio structures for their routing and channel assignment methods. Basically, multi-channel structures present higher data capacity. In this paper, multi-channel multi-radio wireless mesh networks and several channel assignment schemes are explained. A recently studied architecture Directional Mesh (DMesh), that supports directional antenna-based multi-channel structure, is particularly analyzed based on the channel assignment procedure. This architecture is improved with proposing a new channel assignment scheme. Several results of the experimental analysis that compares both architectures and proves the outperforming of the proposed method are also presented

A Cluster Based Scheme for Interference Mitigation in Mobile Macrocell-Femtocell Networks

In this paper, we consider a power control scheme for co-channel deployment of femtocells in the umbrella of macrocell LTE. The severe interference in the downlink from the femtocells should be controlled to guarantee the Quality of Service (QoS) required by the macrocell users. We used clustering method for grouping femtocells according to their geographical localizations. For this scenario, we proposed a hybrid scheme that combines both centralized and distributed schemes to minimize the total power consumption at femtocells, while guaranteeing the QoS of their users. Simulation results showed that our proposed power scheme can allocate power efficiently on the resource blocks in femtocells while obtaining a small performance reduction in femtocell compared to the fully distributed scheme. As well, our method minimized the signaling overhead compared to the fully centralized power allocation scheme

QoS Performance Analysis of Some Routing Protocols in MANET

Mobile Ad Hoc Network (MANET) is introduced for vital situations where the infrastructure or wireless networks fails to provide a connectiOD. during natural disasters and wars. This is because MANET is an independent network that relies on its own battery power for routing packets. In this paper, we eumined the Quality of Service (Q) parameters for two types of routing protocols; namely reactive and proactive protocols. In this study, we bandied the Ad Hoc On-Demand Distance Vector (AODV) and Dynamic Source Routing (DSR) protocols as the reactive protocols. On the other hand, we used the Destination­ Sequenced Distance Vector (DSDV) and Optimized Link State Routing (OLSR) protocols as the proactive protocols. We considered the throughput, end-te>-end delay and packet delivery ratio as the Q parameters. We carried out dift'erent scenarios where the network area size, network density, speed, and pause time were changed. We run the NS2 simulator to execute the performance of these scenarios through some numerical results