Modelling of user requirements and behaviors in computational grids

In traditional distributed computing systems a few user types are found having ratherPeer ReviewedPostprint (published version

A game-theoretic and hybrid genetic meta-heuristics model for security-assured scheduling of independent jobs in computational grids

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.Peer ReviewedPostprint (published version

Game-theoretic, market and meta-heuristics approaches for modelling scheduling and resource allocation in grid systems

Task scheduling and resource allocation are the crucial issues in any large scale distributed system, such as Computational Grids (CGs). However, traditional computational models and resolution methods cannot effectively tackle the complex nature of Grid, where the resources and users belong to many administrative domains with their own access policies, users' privileges, etc. Recently, researchers are investigating the use of game theoretic approaches for modelling task and resource allocation problems in CGs. In this paper, we present a compact survey of the most relevant research proposals in the literature to use game-based models for the resource allocation problems and their resolution using metaheuristic methods. We emphasize the need of the translation of the traditional economical models into the game scenarios and the use of metaheuristic schedulers for solving such games in order to address the new complex scheduling and allocation criterions. We study the case of asymmetric Stackelberg game used for modelling the Grid users' behavior, where the security and reliability criterions are aggregated and defined as the users' costs functions. The obtained results show the efficiency of the hybridization of heuristic-based approaches with game models, which enables to include additional requirements and features into the computational models and tackle more effectively the resolution of the applied schedulers.Peer ReviewedPostprint (published version

A Stackelberg game for modelling asymmetric users' behavior in grid scheduling

In traditional distributed computing the users and owners of the computational resources usually belong to the same administrative domain. Therefore all users are equally entitled to use the resources. The situation is completely different in large-scale emergent distributed computing systems, such as Grid systems, where the roles of the users are asymmetric as regards their access rights and usage of resources. Further, unlike traditional distributed computing case, Grid systems introduce hierarchical levels, which are to be taken into account for optimizing the overall system's performance. In this paper we present a Stackelberg game for modelling asymmetric users' behavior in Grid scheduling scenario. We define a two-level game with a Leader at the first level and the rest of users, called Followers, at the second one. The Leader is responsible for computing a planning of his tasks, which is usually a large fraction of the total pool of tasks in the batch. The Followers try to select the best strategy for the assignments of their tasks subject to Leader's strategy. The Stackelberg game is then translated into a hierarchical optimization problem, which is solved by Genetic Algorithm (GA) on the Leader's level and by ad hoc heuristic combined with GA on the Followers' level. We have experimentally evaluated the approach through a benchmark of static instances and report computational results for resource utilization, makespan and flowtimePeer ReviewedPostprint (published version

A web interface for meta-heuristics based grid schedulers

The use of meta-heuristics for designing efficient Grid schedulers is currently a common approach. One issue related to Grid based schedulers is their evaluation under different Grid configurations, such as dynamics of tasks and machines, task arrival, scheduling policies, etc. In this paper we present a web application that interfaces the final user with several meta-heuristics based Grid schedulers. The application interface facilities for each user the remote evaluation of the different heuristics, the configuration of the schedulers as well as the configuration of the Grid simulator under which the schedulers are run. The simulation results and traces are graphically represented and stored at the server and can retrieved in different formats such as spreadsheet form or pdf files. Historical executions are as well kept enabling a full study of use cases for different types of Grid schedulers. Thus, through this application the user can extract useful knowledge about the behavior of different schedulers by simulating realistic conditions of Grid system without needing to install and configure any specific software.Peer ReviewedPostprint (published version

A game-theoretic and hybrid genetic meta-heuristics model for security-assured scheduling of independent jobs in computational grids

Scheduling independent tasks in Computational Grids commonly arises in many Grid-enabled large scale applications. Much of current research in this domain is focused on the improvement of the efficiency of the Grid schedulers, both at global and local levels, which is the basis for Grid systems to leverage large computing capacities. However, unlike traditional scheduling, in Grid systems security requirements are very important to scheduling tasks/applications to Grid resources. The objective is thus to achieve efficient and secure allocation of tasks to machines. In this paper we propose a new model for secure scheduling at the Grid sites by combining game-theoretic and genetic-based meta-heuristic approaches. The game-theoretic model takes into account the realistic feature that Grid users usually perform independently of each other. The scheduling problem is then formalized as a noncooperative non-zero sum game with Nash equilibria as the solutions. The game cost function is minimized, at global and user levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model through a static benchmark of instances, for which we have measured two basic metrics, namely the makespan and flowtime. The obtained results suggest that it is more resilient for the Grid users (and local schedulers) to tolerate some job delays defined as additional scheduling cost due to security requirements instead of taking a risk of allocating at unreliable resources.Peer Reviewe

Modelling of user requirements and behaviors in computational grids

In traditional distributed computing systems a few user types are found having ratherPeer Reviewe

A web interface for meta-heuristics based grid schedulers

The use of meta-heuristics for designing efficient Grid schedulers is currently a common approach. One issue related to Grid based schedulers is their evaluation under different Grid configurations, such as dynamics of tasks and machines, task arrival, scheduling policies, etc. In this paper we present a web application that interfaces the final user with several meta-heuristics based Grid schedulers. The application interface facilities for each user the remote evaluation of the different heuristics, the configuration of the schedulers as well as the configuration of the Grid simulator under which the schedulers are run. The simulation results and traces are graphically represented and stored at the server and can retrieved in different formats such as spreadsheet form or pdf files. Historical executions are as well kept enabling a full study of use cases for different types of Grid schedulers. Thus, through this application the user can extract useful knowledge about the behavior of different schedulers by simulating realistic conditions of Grid system without needing to install and configure any specific software.Peer Reviewe

A Stackelberg game for modelling asymmetric users' behavior in grid scheduling

In traditional distributed computing the users and owners of the computational resources usually belong to the same administrative domain. Therefore all users are equally entitled to use the resources. The situation is completely different in large-scale emergent distributed computing systems, such as Grid systems, where the roles of the users are asymmetric as regards their access rights and usage of resources. Further, unlike traditional distributed computing case, Grid systems introduce hierarchical levels, which are to be taken into account for optimizing the overall system's performance. In this paper we present a Stackelberg game for modelling asymmetric users' behavior in Grid scheduling scenario. We define a two-level game with a Leader at the first level and the rest of users, called Followers, at the second one. The Leader is responsible for computing a planning of his tasks, which is usually a large fraction of the total pool of tasks in the batch. The Followers try to select the best strategy for the assignments of their tasks subject to Leader's strategy. The Stackelberg game is then translated into a hierarchical optimization problem, which is solved by Genetic Algorithm (GA) on the Leader's level and by ad hoc heuristic combined with GA on the Followers' level. We have experimentally evaluated the approach through a benchmark of static instances and report computational results for resource utilization, makespan and flowtimePeer Reviewe