Enhancement of Ant Colony Optimization for Grid Job Scheduling and Load Balancing

Managing resources in grid computing system is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs are required or are assigned to the same resources which lead to the resources having high workload or the time taken to process a job is high. This research proposes an Enhanced Ant Colony Optimization (EACO) algorithm that caters dynamic scheduling and load balancing in the grid computing system. The proposed algorithm can overcome stagnation problem, minimize processing time, match jobs with suitable resources, and balance entire resources in grid environment. This research follows the experimental research methodology that consists of problem analysis, developing the proposed framework, constructing the simulation environment, conducting a set of experiments and evaluating the results. There are three new mechanisms in this proposed framework that are used to organize the work of an ant colony i.e. initial pheromone value mechanism, resource selection mechanism and pheromone update mechanism. The resource allocation problem is modeled as a graph that can be used by the ant to deliver its pheromone. This graph consists of four types of vertices which are job, requirement, resource and capacity that are used in constructing the grid job scheduling. The proposed EACO algorithm takes into consideration the capacity of resources and the characteristics of jobs in determining the best resource to process a job. EACO selects the resources based on the pheromone value on each resource which is recorded in a matrix form. The initial pheromone value of each resource for each job is calculated based on the estimated transmission time and execution time of a given job. Resources with high pheromone value are selected to process the submitted jobs. Global pheromone update is performed after the completion of processing the jobs in order to reduce the pheromone value of resources. A simulation environment was developed using Java programming to test the performance of the proposed EACO algorithm against existing grid resource management algorithms such as Antz algorithm, Particle Swarm Optimization algorithm, Space Shared algorithm and Time Shared algorithm, in terms of processing time and resource utilization. Experimental results show that EACO produced better grid resource management solution compared to other algorithms

Hybridization of enhanced ant colony system and Tabu search algorithm for packet routing in wireless sensor network

In Wireless Sensor Network (WSN), high transmission time occurs when search agent focuses on the same sensor nodes, while local optima problem happens when agent gets trapped in a blind alley during searching. Swarm intelligence algorithms have been applied in solving these problems including the Ant Colony System (ACS) which is one of the ant colony optimization variants. However, ACS suffers from local optima and stagnation problems in medium and large sized environments due to an ineffective exploration mechanism. This research proposes a hybridization of Enhanced ACS and Tabu Search (EACS(TS)) algorithm for packet routing in WSN. The EACS(TS) selects sensor nodes with high pheromone values which are calculated based on the residual energy and current pheromone value of each sensor node. Local optima is prevented by marking the node that has no potential neighbour node as a Tabu node and storing it in the Tabu list. Local pheromone update is performed to encourage exploration to other potential sensor nodes while global pheromone update is applied to encourage the exploitation of optimal sensor nodes. Experiments were performed in a simulated WSN environment supported by a Routing Modelling Application Simulation Environment (RMASE) framework to evaluate the performance of EACS(TS). A total of 6 datasets were deployed to evaluate the effectiveness of the proposed algorithm. Results showed that EACS(TS) outperformed in terms of success rate, packet loss, latency, and energy efficiency when compared with single swarm intelligence routing algorithms which are Energy-Efficient Ant-Based Routing (EEABR), BeeSensor and Termite-hill. Better performances were also achieved for success rate, throughput, and latency when compared to other hybrid routing algorithms such as Fish Swarm Ant Colony Optimization (FSACO), Cuckoo Search-based Clustering Algorithm (ICSCA), and BeeSensor-C. The outcome of this research contributes an optimized routing algorithm for WSN. This will lead to a better quality of service and minimum energy utilization

Hybrid ant colony optimization for grid computing

A hybrid ant colony optimization technique to solve the stagnation problem in grid computing is proposed in this paper.The proposed algorithm combines the techniques from Ant Colony System and Max – Min Ant System and focused on local pheromone trail update and trail limit.The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table.This facilitates the hybrid ant colony optimization technique in solving the stagnation problem in two ways within one cycle, thus minimize the total computational time of the jobs

Enhanced Ant-Based Routing for Improving Performance of Wireless Sensor Network

Routing packets from the source node to the destination node in wireless sensor networks WSN is complicated due to the distributed and heterogeneous nature of sensor nodes. An ant colony system algorithm for packet routing in WSN that focuses on a pheromone update technique is proposed in this paper. The proposed algorithm will determine the best path to be used in the submission of packets while considering the capacity of each sensor node such as the remaining energy and distance to the destination node. Global pheromone update and local pheromone update are used in the proposed algorithm with the aim to distribute the packets fairly and to prevent the energy depletion of the sensor nodes. Performance of the proposed algorithm has outperformed three (3) other common algorithms in static WSN environment in terms of throughput, energy consumption and energy efficiency which will result to reduction of packet loss rate during packet routing and increase of network lifetime

Grid load balancing using enhance ant colony optimization

This study presents a new algorithm based on ant colony optimization for load balancing management in grid computing. The concentration is on improving the way ants search the best resources in terms of minimizing the processing time of each job and at the same time balancing the workload on available resources. An enhanced technique is proposed for the pheromone update activities. Single colony of ants is used for searching the best resources to process jobs. The credibility of the proposed algorithm was tested with other load balancing algorithim and results showed that the proposed algorithm was able to balance the load on the resources

Grid load balancing using ant colony optimization

An enhanced ant colony optimization technique for jobs and resources scheduling in grid computing is proposed in this paper. The proposed technique combines the techniques from Ant Colony System and Max – Min Ant System and focused on local pheromone trail update and trail limit. The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table. This facilitates in scheduling jobs to available resources efficiently which will enable jobs to be processed in minimum time and also balance all the resource in grid system

Enhanced ant colony optimization for grid resource scheduling

Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources. Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources which will lead to the resources having high workload. Stagnation also may occur if the computational time of the processed job is high. An effective job scheduling algorithm is needed to avoid or reduce the stagnation problem. An Enhanced Ant Colony Optimization (EACO) technique for jobs and resources scheduling in grid computing is proposed in this paper. The proposed algorithm combines the techniques from Ant Colony System and Max - Min Ant System and focused on local pheromone trail update and trail limit. The agent concept is also integrated in this proposed technique for the purpose of updating the grid resource table. This facilitates in scheduling jobs to available resources efficiently which will enable jobs to be processed in minimum time and also balance all the resource in grid system

Ant colony algorithm for job scheduling in grid computing

Scheduling jobs to resources in grid computing is complicated due to the distributed and heterogeneous nature of the resources.Stagnation in grid computing system may occur when all jobs require or are assigned to the same resources.This will lead to resourccs having high workload and stagnation may occur if computational times of the processed jobs are high.This paper proposed an enhanced ant colony optimization algorithm for jobs and resources scheduling in grid computing.The proposed ant colony algorithm for job scheduling in the grid environment combines the techniques from Ant Colony System and Max - Min Ant System.The algorithm focuses on local pheromone trail update and the trail limit values. A matrix is used to record the status of the available resources.The agent concept is also integrated in this algorithm for the purpose of updating the grid resource table.Experimental results obtained showed that this is a promising ant colony algorithm for job scheduling in grid environment

Ant colony optimization approaches in wireless sensor network: Performance evaluation

Wireless Sensor Network (WSN) has been widely implemented in large sectors such as military, habitat, business, industrial, health and environment. WSN is part of a distributed system where elements such as routing, load balancing, energy efficiency, node localization, time synchronization, data aggregation and security need to be addressed to improve its efficiency, robustness, extendibility, applicability and reliability. Despite multiple approaches proposed to improve all these aspects, there is still room for improvement in order to enhance the capability of WSN in terms of routing and energy efficiency. Ant Colony Optimization (ACO) is one of the approaches used to extend WSN capabilities because its heuristic nature is very suitable with distributed and dynamic environments. This study covers the common WSN aspects and performance evaluation criteria in addition to the list of previous studies that have used ACO approaches in WSN

Parameter adaptation for ant colony system in wireless sensor network

The Ant Colony System (ACS) algorithm has been applied in solving packet routing problems in Wireless Sensor Networks (WSNs). Solving these problems is complicated as packets need to be submitted through sensor nodes which are spatially distributed and heterogeneous by nature. Without an effective packet routing algorithm, energy consumption will be increased while network lifetime will be reduced. Most researches are focused on optimizing the routing process by using predefined parameters within a certain range. However, this approach will not guarantee optimal performance. This paper presents the parameter adaptation values for ACS experimental set-up in validating its performance.Possible values of each parameter within a defined range were employed. Experiments were conducted to obtain the best value of each parameter to be used for throughput, energy consumption, and latency.Results of this study can be adopted to achieve optimal performance for the packet routing process