JSSTR: A Joint Server Selection and Traffic Routing Algorithm for the Software-Defined Data Center

Server load balancing technology makes services highly functional by distributing the incoming user requests to different servers. Thus, it plays a key role in data centers. However, most of the current server load balancing schemes are designed without considering the impact on the network. More specifically, when using these schemes, the server selection and routing path calculation are usually executed sequentially, which may result in inefficient use of network resources or even cause some issues in the network. As an emerging architecture, Software-Defined Networking (SDN) provides new solutions to overcome these shortcomings. Therefore, taking advantages of SDN, this paper proposes a Joint Server Selection and Traffic Routing algorithm (JSSTR) based on improving the Shuffle Frog Leaping Algorithm (SFLA) to achieve high network utilization, network load balancing and server load balancing. Evaluation results validate that the proposed algorithm can significantly improve network efficiency and balance the network load and server load.

Using Particle Swarm Optimization, Genetic Algorithm, Honey Bee mating Optimization and Shuffle Frog Leaping Algorithm for solving OPF Problem with their Comparison

Today using evolutionary programing for solving complex, nonlinear mathematical problems like optimum power flow is commonly in use. These types of problems are naturally nonlinear and the conventional mathematical methods aren’t powerful enough for achieving the desirable results. In this study an Optimum Power Flow problem solved by means of minimization of fuel costs for IEEE 30 buses test system by Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Honey Bee Mating Optimization (HBMO) and Shuffle Frog Leaping Algorithm (SFLA), these algorithms has been used in MATLAB medium with help of MATHPOWER to achieving more precise results and comparing these results with the other proposed results in other published papers.

An Intrusion Detection Method Based on Shuffle Frog Leaping Algorithm

This paper presents an intrusion detection model based on shuffled frog leaping algorithm, the model search speed, high accuracy based on shuffled frog leaping algorithm, using the shuffled frog leaping algorithm generates a set of classification rules from the KDD99 data network audit data collection, quality and the use of objective function shuffled frog leaping algorithm to control the production rule, and then application of the rule of dynamically generated to rule based intrusion detection system, achieve the purpose of detection. The experimental results show that, this method of detection rules generation based on shuffled frog leaping algorithm, can significantly improve quality of generation rules, reduce the rules of intrusion detection system based on the false negative rate.