Job-flow Scheduling Research Articles

Coordinated global and private job-flow scheduling in grid virtual organizations

In this work, an approach for a preference-based job-flow scheduling in Grid virtual organizations is proposed and studied. Virtual organizations provide uniform rules of the resources sharing and consumption and should respect individual and common preferences of its stakeholders: users, resource providers and administrators. In most cases, a virtual organization’s stakeholders pursue contradictory market-based goals, which may be expressed in terms of time and cost. Still, mutually beneficial job-flow scheduling scenarios are possible in such multi-agent computing environments when the different optimization criteria are coordinated. For this purpose we consider different resources allocation heuristics and scenarios based on a simple linear combination of global (group) and private (user) job scheduling criteria. We study backfilling-based job queue scheduling procedure with a secondary preference-based optimization performed only on the resources allocation step. This approach allows us to respect backfilling-based jobs execution priorities together with more specific individual jobs criteria, including total execution cost or time minimization.

Job-flow Anticipation Scheduling in Grid

In this paper, a heuristic user job-flow scheduling approach for Grid virtual organizations with non-dedicated resources is discussed. Users’ and resource providers’ preferences, virtual organization’s internal policies, resources geographical distribution along with local private utilization impose specific requirements for efficient scheduling according to different, usually contradictive, criteria. With increasing resources utilization level the available resources set and corresponding decision space are reduced. This further complicates the task of efficient scheduling. In order to improve overall scheduling efficiency we propose a heuristic anticipation scheduling approach. Initially it generates a near optimal but infeasible scheduling solution which is then used as a reference for efficient resources allocation.

Multi-Level Job Flow Cyclic Scheduling in Grid Virtual Organizations

Distributed environments with the decoupling of users from resource providers are generally termed as utility Grids. The paper focuses on the problems of efficient job flow distribution and scheduling in virtual organizations (VOs) of utility Grids while ensuring the VO stakeholders preferences and providing dependable strategies for resources utilization. An approach based on the combination of the cyclic scheduling scheme, backfilling and several heuristic procedures is proposed and studied. Comparative simulation results are introduced for different algorithms and heuristics depending on the resource domain composition and heterogeneity. Considered scheduling approaches provide different benefits depending on the VO scheduling objectives. The results justify the use of the proposed approaches in a broad range of the considered resource environment parameters.

Методы и эвристики планирования в распределенных вычислениях с неотчуждаемыми ресурсами

The paper is devoted to the questions of effective computations scheduling in virtual organizations with non-dedicated resources, including utility Grid. A complex combination of job-flow and application-level scheduling methods and heuristics, providing virtual organization resources share and allocation policies based on the economicprinciples is studied.

Economic model of scheduling and fair resource sharing in distributed computations

A model for job flow scheduling and fair resource sharing in distributed computing environments is proposed. The model helps one to take into account requirements in the virtual organization imposed by the users on efficiency and job performance quality. With the proposed slot selection algorithms, one can find alternatives optimal with respect to a given criterion for each job in the batch.

Flexible reasoning using sensible agent-based systems: A case study in job flow scheduling

Planning and control systems for highly dynamic and uncertain manufacturing environments require adaptive flexibility and decision-making capabilities. Modern distributed manufacturing systems assess the utility of planning and executing solutions for both system goals (e.g. minimize manufacturing production time for all parts or minimize WIP) and local goals (e.g. expedite part A production schedule or maximize machine X utilization). Sensible Agents have the ability to alter their autonomy levels to choose among a set of decision models in order to handle the differences between local and system goals. In this paper, Sensible Agents are applied to a production planning and control problem in the context of job shop scheduling and decision model theory. Sensible Agents provide for trade-off reasoning mechanisms among system and local utilities that are flexible and responsive to an agent's abilities, situational context and position in the organizational structure of the system.

A Distributed Search and Simulation Method for Job Flow Scheduling

Simulation serves as an effective decision support tool in flexible manufacturing systems for understanding and analyzing the effect of changes to the system environment. Due to the variety of situations that have to be evaluated, a generative scheduler for such a system must be more flexible than its realistic equal while still producing detailed and accurate schedules. This research presents a possible solution to this problem and is intended for use in a distributed, reactive, virtual environment. The solution exploits the advantages gained by using multiple processes in a distributed environment to create, simulate, and evaluate possible schedules. The system is designed on the premise that a distributed parallel search and simulation of competing schedules with accompanying heuristics and subgoaling will greatly reduce the search space and control the state space explosion. Moreover, the need for cycle and deadlock detection is eliminated, thereby decreasing the overall complexity and computational requirements.

The SPT Priority Sequence Rule

Discusses the most widely accepted operations principle in the job shop working environment as the shortest processing time (SPT) job priority sequencing rule. It can be demonstrated that use of average job flow time as the measure of SPT efficiency in job flow scheduling introduces a mathematical artefact which can bias results in favour of SPT without adding any real efficiency. Average job flow time with SPT priority sequencing creates the illusion of efficiency. A simple simulation model is used to demonstrate that even though average job flow time is clearly and significantly shorter with SPT it can mask under‐utilization of capacity‐constrained work stations – bottlenecks – present in the flow. Giving priority to those jobs which fill capacity at a bottleneck significantly improves capacity utilization at the bottleneck and reduces the amount of work which awaits capacity at the conclusion of the work schedule.