Enhanced FAST TCP by Solving Rerouting Problem

Delay-based congestion control algorithms inability to recognize increased RTT related to rerouting from increased RTT related to congestion is their most serious problem which has serious effect on their throughput. FAST TCP is one of delay-based TCP variants that although outperforms other TCP variants in high bandwidth-delay product networks, but suffers from several problems that inhere in its procedure to estimate trip delay. The most serious of these problems is rerouting. When rerouting occurs and round-trip time (RTT) of the new path is longer than RTT of the old path, the throughput of FAST TCP decreases sharply. Because FAST misinterprets the increased RTT as result of the network congestion and consequently decreases its own window size. This paper solves this problem by considering the relationship between sending rate and observed RTT. The simulation results show the effectiveness of proposed solution to solve rerouting problem while simultaneously preserves FAST TCP prominent primitive features

Load Balancing in Heterogeneous Cloud Environments by Using PROMETHEE Method

Abstract: Efficient Scheduling of tasks in a cloud environment improves resources utilization thereby meeting users' requirements. One of the most important objectives of a scheduling algorithm in cloud environment is a balanced load distribution over various resources for enhancing the overall performance of the cloud. Such a scheduling is complex in nature due to the dynamicity of resources and incoming application specifications. In this paper, we employ PROMETHEE decision making model to design a scheduling algorithm, called PROMETHEE Load Balancing (PLB).This paper formulates the load balancing issue as a multi-criteria decision making problem and aims to achieve well-balanced load across virtual machines for maximizing the overall throughput of the cloud. Extensive simulation results in CloudSim environment show that the proposed algorithm outperforms existing algorithms in terms of load balancing index (LBI), VM load variation, makespan, average execution time and waiting time

A two-level Product Recommender for E-commerce Sites by Using Sequential Pattern Analysis

With the development of communication networks and rapid growth of their applications, huge amount of information have been produced. Major part of these information are in electronic stores, and hence it's really hard to find desired products inside huggermugger. Product Recommendation System (PRS) tries to solve this problem by giving appropriate and fast recommendations to the customers. This paper proposes a two-level product recommender for E-commerce sites. At first, the available products are clustered by using C-Means algorithm to create groups of products with similar characteristics. Then, the second level considers the customers’ behavior and their purchase history for drawing the relationships between products by using Sequential Pattern Analysis (SPA) method. These relationships, eventually, will lead to appropriate recommendation for customers and also increases the likelihood of selling related products in electronic transactions. Extensive numerical simulations over UCI transactions 10k dataset indicates that 87% of records in mined sequential patterns are predicted correctly and the accuracy of recommendations is more than other RPSs

Energy-Aware Clustering in the Internet of Things by Using the Genetic Algorithm

Internet of things (IoT) uses a lot of key technologies to collect different types of data around the world to make an intelligent and integrated whole. This concept can be as simple as a connection between a smartphone and a smart TV, or can be complex communications between the urban infrastructure and traffic monitoring systems. One of the most challenging issues in the IoT environment is how to make it scalable and energy-efficient with regard to its growing dimensions. Object clustering is a mechanism that increases scalability and provides energy efficiency by minimizing communication energy consumption. Since IoT is a large scale dynamic environment, clustering of its objects is a NP-Complete problem. This paper formulates energy-aware clustering of things as an optimization problem targeting an optimum point in which, the total consumed energy and communication cost are minimal. Then. it employs the Genetic Algorithm (GA) to solve this optimization problem by extracting the optimal number of clusters as well as the members of each cluster. In this paper, a multi objective GA for clustering that has not premature convergence problem is used. In addition, for fast GA execution multiple implementation, considerations has been measured. Moreover, the consumed energy for received and sent data, node to node and node to BS distance have been considered as effective parameters in energy consumption formulation. Numerical simulation results show the efficiency of this method in terms of the consumed energy, network lifetime, the number of dead nodes and load balancing