Metascheduling and Heuristic Co-Allocation Strategies in Distributed Computing

In this paper, we address problems of efficient computing in distributed systems with non-dedicated resources including utility grid. There are global job flows from external users along with resource owner's local tasks upon the resource non-dedication condition. Competition for resource reservation between independent users, local and global job flows substantially complicates scheduling and the requirement to provide the necessary quality of service. A metascheduling concept, justified in this work, assumes a complex combination of job flow dispatching and application-level scheduling methods for parallel jobs, as well as resource sharing and consumption policies established in virtual organizations and based on economic principles. We introduce heuristic slot selection and co-allocation strategies for parallel jobs. They are formalized by given criteria and implemented by algorithms of linear complexity on an available slots number

Advances in Grid Computing

This book approaches the grid computing with a perspective on the latest achievements in the field, providing an insight into the current research trends and advances, and presenting a large range of innovative research papers. The topics covered in this book include resource and data management, grid architectures and development, and grid-enabled applications. New ideas employing heuristic methods from swarm intelligence or genetic algorithm and quantum encryption are considered in order to explain two main aspects of grid computing: resource management and data management. The book addresses also some aspects of grid computing that regard architecture and development, and includes a diverse range of applications for grid computing, including possible human grid computing system, simulation of the fusion reaction, ubiquitous healthcare service provisioning and complex water systems

Strategic negotiation models for grid scheduling

One of the key requirements for Grid infrastructures is the ability to share resources with nontrivial qualities of service. However, resource management in a decentralized infrastructure is a complex task as it has to cope with di erent policies and objectives of the di erent resource providers and the resource users. This problem is further complicated due to the diversity of the resource types and the heterogeneity of their local resource management systems. Agreement-based resource management can be used to address these issues because in the negotiation process of creating such bilateral service level agreements (SLAs) between Grid parties, the di erent polices of the resource providers and the users will be abstracted and observed. Such negotiation processes should be automated with no or minimal human interaction, considering the potential scale of Grid systems and the amount of necessary transactions. Therefore, strategic negotiation models play important roles. In this thesis, we have made several novel research contributions which are as follows: - An agreement based resource management approach is analyzed. Requirements for the automatic negotiation problems in Grid computing are introduced. Furthermore, related work in the areas of economics and agent communities are investigated. - Several negotiation models and negotiation strategies are proposed and examined. Simulation results demonstrate that these proposed negotiation models are suitable and e ective for Grid environments. - Firstly, a strategic negotiation model using time-based negotiation strategies is proposed and evaluated using discrete event based simulation techniques. - Secondly, time-based negotiation strategies are quite limited in the dynamically changing Grid environment because they are quite simple and static; so learning based negotiation strategies are investigated and evaluated, which are quite exible and e ective in the dynamically changing Grid environment. Also we adopted negotiation strategies considering opportunistic functions for Grid scheduling. - Thirdly, it is usually necessary that resources from di erent resource providers are co-allocated to satisfy the complex requirements of the users, so a strategic negotiation model supporting co-allocation and the tradeo between "first" and "best" agreements in the Grid computing is also proposed and evaluated. - Finally, the contributions of the current research work to the WSNegotiation protocol are analyzed

Cost-Efficient Scheduling for Deadline Constrained Grid Workflows

Cost optimization for workflow scheduling while meeting deadline is one of the fundamental problems in utility computing. In this paper, a two-phase cost-efficient scheduling algorithm called critical chain is presented. The proposed algorithm uses the concept of slack time in both phases. The first phase is deadline distribution over all tasks existing in the workflow which is done considering critical path properties of workflow graphs. Critical chain uses slack time to iteratively select most critical sequence of tasks and then assigns sub-deadlines to those tasks. In the second phase named mapping step, it tries to allocate a server to each task considering task's sub-deadline. In the mapping step, slack time priority in selecting ready task is used to reduce deadline violation. Furthermore, the algorithm tries to locally optimize the computation and communication costs of sequential tasks exploiting dynamic programming. After proposing the scheduling algorithm, three measures for the superiority of a scheduling algorithm are introduced, and the proposed algorithm is compared with other existing algorithms considering the measures. Results obtained from simulating various systems show that the proposed algorithm outperforms four well-known existing workflow scheduling algorithms

A Taxonomy of Workflow Management Systems for Grid Computing

With the advent of Grid and application technologies, scientists and engineers are building more and more complex applications to manage and process large data sets, and execute scientific experiments on distributed resources. Such application scenarios require means for composing and executing complex workflows. Therefore, many efforts have been made towards the development of workflow management systems for Grid computing. In this paper, we propose a taxonomy that characterizes and classifies various approaches for building and executing workflows on Grids. We also survey several representative Grid workflow systems developed by various projects world-wide to demonstrate the comprehensiveness of the taxonomy. The taxonomy not only highlights the design and engineering similarities and differences of state-of-the-art in Grid workflow systems, but also identifies the areas that need further research.Comment: 29 pages, 15 figure

Knowledge discovery for scheduling in computational grids

International audienceScheduling in computational grids addresses the allocation of computing jobs to globally distributed compute resources. In a frequently changing resource environment, scheduling decisions have to be made rapidly. Depending on both the job properties and the current state of the resources, those decisions are different. Thus, the performance of grid scheduling systems highly depends on their adaptivity and flexibility in changing environments. Under these conditions, methods from knowledge discovery yielded significant success to augment and substitute conventional grid scheduling techniques. This paper presents a survey on approaches to extract, represent, and utilize knowledge to improve the grid scheduling performance. It aims to give researchers insight into techniques used for knowledge-supported scheduling in large-scale distributed computing environments

Effective Computation Resilience in High Performance and Distributed Environments

The work described in this paper aims at effective computation resilience for complex simulations in high performance and distributed environments. Computation resilience is a complicated and delicate area; it deals with many types of simulation cores, many types of data on various input levels and also with many types of end-users, which have different requirements and expectations. Predictions about system and computation behaviors must be done based on deep knowledge about underlying infrastructures, and simulations' mathematical and realization backgrounds. Our conceptual framework is intended to allow independent collaborations between domain experts as end-users and providers of the computational power by taking on all of the deployment troubles arising within a given computing environment. The goal of our work is to provide a generalized approach for effective scalable usage of the computing power and to help domain-experts, so that they could concentrate more intensive on their domain solutions without the need of investing efforts in learning and adapting to the new IT backbone technologies

Resource Management in Grids: Overview and a discussion of a possible approach for an Agent-Based Middleware

14 pagesInternational audienceResource management and job scheduling are important research issues in computational grids. When software agents are used as resource managers and brokers in the Grid a number of additional issues and possible approaches materialize. The aim of this chapter is twofold. First, we discuss traditional job scheduling in grids, and when agents are utilized as grid middleware. Second, we use this as a context for discussion of how job scheduling can be done in the agent-based system under development