Job Execution In Grid Research Articles

Grid workflow job execution service 'Pilot'

'Pilot' is a grid job execution service for workflow jobs. The main goal for the service is to automate computations with multiple stages since they can be expressed as simple workflows. Each job is a directed acyclic graph of tasks and each task is an execution of something on a grid resource (or 'computing element'). Tasks may be submitted to any WS-GRAM (Globus Toolkit 4) service. The target resources for the tasks execution are selected by the Pilot service from the set of available resources which match the specific requirements from the task and/or job definition. Some simple conditional execution logic is also provided.The 'Pilot' service is built on the REST concepts and provides a simple API through authenticated HTTPS. This service is deployed and used in production in a Russian national grid project GridNNN.

Meeting security and user behavior requirements in Grid scheduling

Most of current research in Grid computing is still focused on the improvement of the performance of Grid schedulers. However, unlike traditional scheduling, in Grid systems there are other important requirements to be taken into account. One such a requirement is the secure scheduling, namely achieving an efficient allocation of tasks to reasonable trustful resources. In this paper we formalize the Grid scheduling problem as a non-cooperative non-zero sum game of the Grid users in order to address the security requirements. The premise of this model is that in a large-scale Grid, the cooperation among all users in the system is unlikely to happen. The users’ cost of playing the game is interpreted as a total cost of the secure job execution in Grid. The game cost function is minimized, at global (Grid) and local (users) levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model under the heterogeneity, the large-scale and dynamics conditions using a Grid simulator. The relative performance of four hybrid schedulers is measured by the makespan and flowtime metrics. The obtained results suggested that it is more resilient for the Grid users to pay some additional scheduling cost, due to verification of the security conditions, instead of taking the risk of assigning their tasks to unreliable resources.

Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling

In scheduling a large number of user jobs for parallel execution on an open-resource grid system, the jobs are subject to system failures or delays caused by infected hardware, software vulnerability, and distrusted security policy. This paper models the risk and insecure conditions in grid job scheduling. Three risk-resilient strategies, preemptive, replication, and delay-tolerant, are developed to provide security assurance. We propose six risk-resilient scheduling algorithms to assure secure grid job execution under different risky conditions. We report the simulated grid performances of these new grid job scheduling algorithms under the NAS and PSA workloads. The relative performance is measured by the total job makespan, grid resource utilization, job failure rate, slowdown ratio, replication overhead, etc. In addition to extending from known scheduling heuristics, we developed a new space-time genetic algorithm (STGA) based on faster searching and protected chromosome formation. Our simulation results suggest that, in a wide-area grid environment, it is more resilient for the global job scheduler to tolerate some job delays instead of resorting to preemption or replication or taking a risk on unreliable resources allocated. We find that delay-tolerant min-min and STGA job scheduling have 13-23 percent higher performance than using risky or preemptive or replicated algorithms. The resource overheads for replicated job scheduling are kept at a low 15 percent. The delayed job execution is optimized with a delay factor, which is 20 percent of the total makespan. A Kiviat graph is proposed for demonstrating the quality of grid computing services. These risk-resilient job scheduling schemes can upgrade grid performance significantly at only a moderate increase in extra resources or scheduling delays in a risky grid computing environment.