Absalom E. Ezugwu

An improved ant colony optimization algorithm with fault tolerance for job scheduling in grid computing systems

The Grid scheduler, schedules user jobs on the best available resource in terms of resource characteristics by optimizing job execution time. Resource failure in Grid is no longer an exception but a regular occurring event as resources are increasingly being used by the scientific community to solve computationally intensive problems which typically run for days or even months. It is therefore absolutely essential that these long-running applications are able to tolerate failures and avoid re-computations from scratch after resource failure has occurred, to satisfy the user’s Quality of Service (QoS) requirement. Job Scheduling with Fault Tolerance in Grid Computing using Ant Colony Optimization is proposed to ensure that jobs are executed successfully even when resource failure has occurred. The technique employed in this paper, is the use of resource failure rate, as well as checkpoint-based roll back recovery strategy. Check-pointing aims at reducing the amount of work that is lost upon failure of the system by immediately saving the state of the system. A comparison of the proposed approach with an existing Ant Colony Optimization (ACO) algorithm is discussed. The experimental results of the implemented Fault Tolerance scheduling algorithm show that there is an improvement in the user’s QoS requirement over the existing ACO algorithm, which has no fault tolerance integrated in it. The performance evaluation of the two algorithms was measured in terms of the three main scheduling performance metrics: makespan, throughput and average turnaround time.

Grid Resource Allocation with Genetic Algorithm Using Population Based on Multisets

Abstract The operational efficacy of the grid computing system depends mainly on the proper management of grid resources to carry out the various jobs that users send to the grid. The paper explores an alternative way of efficiently searching, matching, and allocating distributed grid resources to jobs in such a way that the resource demand of each grid user job is met. A proposal of resource selection method that is based on the concept of genetic algorithm (GA) using populations based on multisets is made. Furthermore, the paper presents a hybrid GA-based scheduling framework that efficiently searches for the best available resources for user jobs in a typical grid computing environment. For the proposed resource allocation method, additional mechanisms (populations based on multiset and adaptive matching) are introduced into the GA components to enhance their search capability in a large problem space. Empirical study is presented in order to demonstrate the importance of operator improvement on traditional GA. The preliminary performance results show that the proposed introduction of an additional operator fine-tuning is efficient in both speed and accuracy and can keep up with high job arrival rates.

Characterization of grid computing resources using measurement-based evaluation

An important factor that needs to be considered by every Grid application end-user and systems (such as schedulers or mediators), during Grid resource selection and mapping to applications, is the performance capacity of hardware resources attached to the Grid, and made available through its Virtual Organizations. In this paper, we represent the performance of a computational Grid as a regression model that can be used to fine-tune the selection of suitable Grid resources. A study on the performance of distributed systems with respect to particular variations in parameters is presented. Our objective is to use a measurement-based evaluation technique to characterize the specific performance contribution of the individual Grid resource configurations. In the process, we identify the key primary parameters (or factors) that should be considered when selecting and allocating a computational node for user application execution.

Resource management system for scientific virtual laboratory applications

The paper presents a conceptual framework for a resource management system designed for remote virtual laboratory experimentation in both the natural and physical sciences domains. One of the key problems addressed in this paper is the use of a mathematical model to solve resource allocation or task scheduling problems in a dynamic Grid environment that consists of a heterogeneous distributed cyberinfrastructure. The main focus of this paper however includes: architectural design model for scientific virtual laboratory tasks scheduling framework, resource allocation matchmaking algorithm design, mathematical modelling of resource allocation optimisation and computational analysis of the proposed system. The research work is in line with the on-going IT infrastructure networking project at the Ahmadu Bello University, Zaria, Nigeria.

Performance characterization of heterogeneous distributed commodity cluster resources

While distributed computing systems which generally involve the aggregation of geographically distributed heterogeneous resources can in principle be used as computing platform, in practice, the discovery and selection of quality resources that satisfy users application requirements remain an issue that is difficult to address. In this paper, we represent the performance of a computational cluster as a regression model that can be used to fine-tune the selection of suitable cluster resources. A study on the performance of distributed systems with respect to particular variations in parameters is presented. Our objective is to use a measurement-based evaluation technique to characterize the specific performance contribution of the individual cluster resource configurations. In the process we identify the key primary parameters (or factors) that should be considered when selecting and allocating a computational node for user application execution.

Neural network‐based multi‐agent approach for scheduling in distributed systems

A distributed system consists of a collection of autonomous heterogeneous resources that provide resource sharing and a common platform for running parallel compute‐intensive applications. The different application characteristics combined with the heterogeneity and performance variations of the distributed system make it difficult to find the optimal set of needed resources. When deployed, user applications are usually handled by application domain experts or system administrators who depending on the infrastructure provide a scheduling strategy for selecting the best candidate resource over a set of available resources. However, the provided strategy is usually generic, aimed at handling a wide array of applications and does not take into consideration specific application resource requirements. As such, an intelligent method for selecting the best resources based on expert knowledge is needed. In this paper, we propose a neural network‐based multi‐agent resource selection technique capable of mimicking the services of an expert user. In addition, to cope with the geographical distribution of the underlying system, we employ a multi‐agent coordination mechanism. The proposed neural network‐based scheduling framework combined with the multi‐agent intelligence is a unique approach to efficiently deal with the resource selection problem. Results run on a simulated environment show the efficiency of our proposed method. Several scheduling simulations were conducted to compare the performance of some conventional resource selection methods against the proposed agent‐based neural network technique. The results obtained indicate that the agent‐based approach outperformed the classical algorithms by reducing the amount of time required to search for suitable resources irrespective of the resource size. Copyright

Scheduling multi-component applications with mobile agents in heterogeneous distributed systems

In grid computing environment, several classes of multi‐component applications exist. These types of applications may often require additional resources of different types that go beyond what is available in any of the sites making up the grid resource composition. The heterogeneity nature of both the user application and the computing environment makes this a challenging problem. However, the current off‐the‐shelf scheduling software can hardly cope with these diversities in distributed computing application frameworks. Therefore, there is the need for an adequate scheduling system that would grant simultaneous or coordinated access to application of multi‐component nature that requires resources of possibly multiple types, in multiple locations, managed by different resource providers. The main focus of this paper is to develop a mobile agent scheduling model that addresses the aforementioned challenge. A scheduling policy that pertains to job scheduling and resource allocation is proposed. The scheduling policy treats different multi‐component applications requiring diverse heterogeneous resources fairly. The policy is used by mobile agents to schedule user applications and to also find available and suitable distributed resource that are capable of executing user application at a very minimal time. Copyright

A Multiagent-Based Approach to Scheduling of Multi-component Applications in Distributed Systems

In this paper, we present a multiagent-based scheduling framework for several classes of multi-component applications. We consider this scheduling problem in today’s heterogeneous distributed systems. The heterogeneous nature of most parallel applications and distributed computing resource environments, makes this a challenging problem. However, the current off-the-shelf scheduling software can hardly cope with the demands for high performance and scalable computing power required by these applications. This paper proposes a scheduling mechanism that integrates routing indices with multi-agent system, to perform global scheduling in a collaborative and coordinated manner. Our intent is to apply agent-based distributed problem solving technique to address the problem of multi-component system scheduling.

Enhanced intelligent water drops algorithm for multi-depot vehicle routing problem

The intelligent water drop algorithm is a swarm-based metaheuristic algorithm, inspired by the characteristics of water drops in the river and the environmental changes resulting from the action of the flowing river. Since its appearance as an alternative stochastic optimization method, the algorithm has found applications in solving a wide range of combinatorial and functional optimization problems. This paper presents an improved intelligent water drop algorithm for solving multi-depot vehicle routing problems. A simulated annealing algorithm was introduced into the proposed algorithm as a local search metaheuristic to prevent the intelligent water drop algorithm from getting trapped into local minima and also improve its solution quality. In addition, some of the potential problematic issues associated with using simulated annealing that include high computational runtime and exponential calculation of the probability of acceptance criteria, are investigated. The exponential calculation of the probability of acceptance criteria for the simulated annealing based techniques is computationally expensive. Therefore, in order to maximize the performance of the intelligent water drop algorithm using simulated annealing, a better way of calculating the probability of acceptance criteria is considered. The performance of the proposed hybrid algorithm is evaluated by using 33 standard test problems, with the results obtained compared with the solutions offered by four well-known techniques from the subject literature. Experimental results and statistical tests show that the new method possesses outstanding performance in terms of solution quality and runtime consumed. In addition, the proposed algorithm is suitable for solving large-scale problems.

Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network.