Parallel memetic algorithms for independent job scheduling in computational grids

In this chapter we present parallel implementations of Memetic Algorithms (MAs) for the problem of scheduling independent jobs in computational grids. The problem of scheduling in computational grids is known for its high demanding computational time. In this work we exploit the intrinsic parallel nature of MAs as well as the fact that computational grids offer large amount of resources, a part of which could be used to compute the efficient allocation of jobs to grid resources. The parallel models exploited in this work for MAs include both fine-grained and coarse-grained parallelization and their hybridization. The resulting schedulers have been tested through different grid scenarios generated by a grid simulator to match different possible configurations of computational grids in terms of size (number of jobs and resources) and computational characteristics of resources. All in all, the result of this work showed that Parallel MAs are very good alternatives in order to match different performance requirement on fast scheduling of jobs to grid resources.Peer ReviewedPostprint (author's final draft

Scheduling of Dependent Tasks Application using Random Search Technique

Since beginning of Grid computing, scheduling of dependent tasks application has attracted attention of researchers due to NP-Complete nature of the problem. In Grid environment, scheduling is deciding about assignment of tasks to available resources. Scheduling in Grid is challenging when the tasks have dependencies and resources are heterogeneous. The main objective in scheduling of dependent tasks is minimizing make-span. Due to NP-complete nature of scheduling problem, exact solutions cannot generate schedule efficiently. Therefore, researchers apply heuristic or random search techniques to get optimal or near to optimal solution of such problems. In this paper, we show how Genetic Algorithm can be used to solve dependent task scheduling problem. We describe how initial population can be generated using random assignment and height based approaches. We also present design of crossover and mutation operators to enable scheduling of dependent tasks application without violating dependency constraints. For implementation of GA based scheduling, we explore and analyze SimGrid and GridSim simulation toolkits. From results, we found that SimGrid is suitable, as it has support of SimDag API for DAG applications. We found that GA based approach can generate schedule for dependent tasks application in reasonable time while trying to minimize make-span

Genetic algorithm based schedulers for grid computing systems

In this paper we present Genetic Algorithms (GAs) based schedulers for efficiently allocating jobs to resources in a Grid system. Scheduling is a key problem in emergent computational systems, such as Grid and P2P, in order to benefit from the large computing capacity of such systems. We present an extensive study on the usefulness of GAs for designing efficient Grid schedulers when makespan and flowtime are minimized. Two encoding schemes have been considered and most of GA operators for each of them are implemented and empirically studied. The extensive experimental study showed that our GA-based schedulers outperform existing GA implementations in the literature for the problem and also revealed their efficiency when makespan and flowtime are minimized either in a hierarchical or a simultaneous optimization mode; previous approaches considered only the minimization of the makespan. Moreover, we were able to identify which GAs versions work best under certain Grid characteristics, which is very useful for real Grids. Our GA-based schedulers are very fast and hence they can be used to dynamically schedule jobs arriving in the Grid system by running in batch mode for a short time.Peer ReviewedPostprint (author's final draft

The application of Ants' society algorithm for Management of resources in continuous bilateral auction

Background: The main purpose of this paper is to improve the efficiency of grid computing by means of Ants' society algorithm. Application of this algorithm in various problem led to an improvement in efficiency and reduction in processing time. This enables us to use this algorithm in grid computing. Economic solutions in the field of management of heterogeneous resources for grid computing showed significant performance. The main idea was economic solutions for product exchange in market. This paper aims to introduce a new method for bilateral auction scenario by means of genetic algorithm (GA). In this method, by making resources intelligent, we move the packages of call for proposal so that it can reduce response time as well as being able to supply resources with lower prices. For simplicity in controlling packages, we used the network structure in implementation. Applied structure includes routers and communication of users and auctioners and auctioners and resources owners. The method was implemented using GridSim simulator. This is an open source software written in Java programming language. Results reveal that the method of bilateral auction using GA reduces sale stages and consequently leads to faster responding to requests and also resources are supplied with a lower cost

Use of genetic algorithms for scheduling jobs in large scale grid applications

In this paper we present the implementation of Genetic Algorithms (GA) for job scheduling on computational grids that optimizes the makespan and the total flowtime. Job scheduling on computational grids is a key problem in large scale grid‐based applications for solving complex problems. The aim is to obtain an efficient scheduler able to allocate a large number of jobs originated from large scale applications to grid resources. Several variations for GA operators are examined in order to identify which works best for the problem. To this end we have developed a grid simulator package to generate large and very large size instances of the problem and have used them to study the performance of GA implementation. Through extensive experimenting and fine tuning of parameters we have identified the configuration of operators and parameters that outperforms the existing implementations in the literature for static instances of the problem. The experimental results show the robustness of the implementation, improved performance of static instances compared to reported results in the literature and, finally, a fast reduction of the makespan making thus the scheduler of practical interest for grid environments. Genetinių algoritmų naudojimas kompiuterių tinkluose ir kalendorinis darbų planavimas Santrauka Aprašoma, kaip genetinis algoritmas taikomas darbų trukmėms optimizuoti kalendoriniam darbų planavimui, naudojant kompiuterių, sujungtų į tinklą, išteklius. Kalendorinis darbų planavimas, naudojant kompiuterių tinklą, yra aktuali problema, sprendžiant kompleksines, didelio masto problemas. Autorių tikslas ñ sukurti tokį algoritmą, kuris efektyviausiai paskirstytų teikiamų skaičiuoti darbų srautą į kompiuterių tinklą. Ištirti keli algoritmai, išrinktas geriausias. Sukurtas kompiuterių tinklo darbą imituojantis programinis paketas, jis patikrintas, sprendžiant konkrečius uždavinius. Eksperimentuojant rastas geriausias operatorių ir parametrų derinys, o eksperimento rezultatai atskleidė, jog darbų planavimo laikas sutrumpėjo. First Published Online: 21 Oct 2010 Reikšminiai žodžiai: genetinis algoritmas, kalendorinis darbų planavimas, kompiuterių tinklas, pavyzdžiai, darbų trukmė, laikas

A genetic approach for long term virtual organization distribution

Electronic versíon of an article published as International Journal on Artificial Intelligent Tools, Volume 20, issue 2, 2011. 10.1142/S0218213011000152. © World Scientific Publishing Company[EN] An agent-based Virtual Organization is a complex entity where dynamic collections of agents agree to share resources in order to accomplish a global goal or offer a complex service. An important problem for the performance of the Virtual Organization is the distribution of the agents across the computational resources. The final distribution should provide a good load balancing for the organization. In this article, a genetic algorithm is applied to calculate a proper distribution across hosts in an agent-based Virtual Organization. Additionally, an abstract multi-agent system architecture which provides infrastructure for Virtual Organization distribution is introduced. The developed genetic solution employs an elitist crossover operator where one of the children inherits the most promising genetic material from the parents with higher probability. In order to validate the genetic proposal, the designed genetic algorithm has been successfully compared to several heuristics in different scenarios. © 2011 World Scientific Publishing Company.This work is supported by TIN2008-04446, TIN2009-13839-C03-01, CSD2007-00022 and FPU grant AP2008-00600 of the Spanish government, and PROMETEO 2008/051 of the Generalitat Valenciana.Sánchez Anguix, V.; Valero Cubas, S.; García Fornes, AM. (2011). A genetic approach for long term virtual organization distribution. International Journal on Artificial Intelligence Tools. 20(2):271-295. https://doi.org/10.1142/S0218213011000152S27129520

Resource Characteristic Based Optimization for Grid Scheduling

Scheduling is an active research area in the Computational Grid environment. The objective of grid scheduling is to deliver both the Quality of Service (QoS) requirement of the grid users, as well as high utilization of the resources. To obtain optimal scheduling in the generalized grid environment is an NP-complete problem. A large number of researchers have presented heuristic algorithms to find a near-global optimum for the static scheduling model of the grid. Relatively a smaller number of researchers have worked on the scheduling problem for the dynamic scheduling model. This thesis proposes a new resource characteristic based optimization method, which may be combined with Earlier Gap, Earliest Deadline First (EG-EDF) policy to schedule jobs in a dynamic environment. The proposed algorithm generates near-optimal solutions, which are better than those reported in the literature for a specific range of datasets. Extensive experimentation has proved the efficacy of our method

An efficient, practical, portable mapping technique on computational grids

Grid computing provides a powerful, virtual parallel system known as a computational Grid on which users can run parallel applications to solve problems quickly. However, users must be careful to allocate tasks to nodes properly because improper allocation of only one task could result in lengthy executions of applications, or even worse, applications could crash. This allocation problem is called the mapping problem, and an entity that tackles this problem is called a mapper. In this thesis, we aim to develop an efficient, practical, portable mapper. To study the mapping problem, researchers often make unrealistic assumptions such as that nodes of Grids are always reliable, that execution times of tasks assigned to nodes are known a priori, or that detailed information of parallel applications is always known. As a result, the practicality and portability of mappers developed in such conditions are uncertain. Our review of related work suggested that a more efficient tool is required to study this problem; therefore, we developed GMap, a simulator researchers/developers can use to develop practical, portable mappers. The fact that nodes are not always reliable leads to the development of an algorithm for predicting the reliability of nodes and a predictor for identifying reliable nodes of Grids. Experimental results showed that the predictor reduced the chance of failures in executions of applications by half. The facts that execution times of tasks assigned to nodes are not known a priori and that detailed information of parallel applications is not alw ays known, lead to the evaluation of five nearest-neighbour (nn) execution time estimators: k-nn smoothing, k-nn, adaptive k-nn, one-nn, and adaptive one-nn. Experimental results showed that adaptive k-nn was the most efficient one. We also implemented the predictor and the estimator in GMap. Using GMap, we could reliably compare the efficiency of six mapping algorithms: Min-min, Max-min, Genetic Algorithms, Simulated Annealing, Tabu Search, and Quick-quality Map, with none of the preceding unrealistic assumptions. Experimental results showed that Quick-quality Map was the most efficient one. As a result of these findings, we achieved our goal in developing an efficient, practical, portable mapper