Guided Search-Based Multi-Objective Evolutionary Algorithm for Grid Workflow Scheduling

The computational grid provides the global computing infrastructure for users to access the services over a network. However, grid service providers charge users for the services based on their usage and QoS level specified. Therefore, in order to optimize the grid workflow execution, a robust multi-objective scheduling algorithm is needed considering economic cost along with execution performance. Generally, in multi-objective problems, simulations rely on running large number of evaluations to obtain the accurate results. However, algorithms that consider the preferences of decision maker, convergence to optimal tradeoff solutions is faster. Thus, in this chapter, the author proposed the preference-based guided search mechanism into MOEAs. To obtain solutions near the pre-specified regions of interest, the author has considered two MOEAs, namely R-NSGA-II and R-ε-MOEA. Further, to improve the diversity of solutions, a modified form called M-R-NSGA-II is used. Finally, the experimental settings and performance metrics are presented for the evaluation of the algorithms.

ATLAS Grid Workflow Performance Optimization

The CERN ATLAS experiment grid workflow system manages routinely 250 to 500 thousand concurrently running production and analysis jobs to process simulation and detector data. In total more than 370 PB of data is distributed over more than 150 sites in the WLCG. At this scale small improvements in the software and computing performance and workflows can lead tosignificant resource usage gains. ATLAS is reviewing together with CERN IT experts several typical simulation and data processing workloads for potential performance improvements in terms of memory and CPU usage, disk and network I/O. All ATLASproduction and analysis grid jobs are instrumented to collect many performance metrics for detailed statistical studies using modern data analytics tools like ElasticSearch and Kibana. This presentation will review and explain the performance gains of several ATLAS simulation and data processing workflows and present analytics studies of the ATLAS grid workflows.

A scheduling algorithm for grid workflow using bottleneck detection and load balancing

Purpose – This paper aims to propose a scheduling technique for parameter sweep workflows, which are used in parametric study and optimization. When executed in multiple parallel instances in the grid environment, it is necessary to address bottleneck and load balancing to achieve an efficient execution. Design/methodology/approach – A bottleneck detection approach is based on commonly known performance metrics of grid resources. To address load balancing, a resource requirement similarity metric is introduced to determine the likelihood of the distribution of tasks across available grid resources, which is referred to as an execution context. The presence of a bottleneck and the execution context are used in the main algorithm, named ABeC, to schedule tasks selectively at run-time to achieve a better overall execution time or makespan. Findings – According to the results of the simulations against four existing algorithms using several scenarios, the proposed technique performs, at least, similarly to the existing four algorithms in most cases and achieves better performance when scheduling workflows have a parallel structure. Originality/value – The bottleneck detection and the load balancing proposed in this paper require only common resource and task information, rendering it applicable to most workflow systems. The proposed scheduling technique, through such selective behaviour, may help reduce the time required for the execution of multiple instances of a grid workflow that is to be executed in parallel.

Applied Technology in the Grid Workflow Quality of Service Calculation and Estimation

For the grid workflow system include a large of candidate services, the activity scheduling in workflow’s ex-ecution base on quality of service (QoS) constraints can pro-vide optimal service for users. This paper established the QoS’s parameter system with applied technology of grid workflow, and introduced the each parameter’s calculation model of activity’s QoS, the grid work-flow’s QoS estimation method based on the basic flow control structures defined by the abstract grid workflow language (AGWL). The experimental results show that the calculation method in this paper can estimate stably QoS parameter values of grid workflow combining with the multi candidate services, and, can provide a reasonable basis for process scheduling based on QoS constraints.

The Analysis and Application Research on the Performance of Describing Based on an Abstract Grid Workflow Language

Grid Workflow Language Yu wan jun1,2, Liu Yunxiang1,2, Fang Hua1,21 Shanghai institute of technology, School of computer science and information engineering, Shanghai, 201418, China 2Shanghai institute of technology, research center of machine olfaction, Shanghai, 201418, China Key words:Intelligant System; Grid Workflow; Activity; Parallel Structure; Abstract:Grids specification is one of the imporant contents in grid workflow fields, it can shield details of underling grid, so users can concentrate on the description of Grid applications. AGWL(Abstract Grid Workflow Language) is an abstract language based on XML, it was always used to describe grid workflow. The basic activities are connected by the control flow and data link. AGWL uses advanced control flow to support the parallel execution of conditions and loops. As an example, we finally introduced a sales process to show the capacity of the grid workflow language AGWL.