Fair Resource Sharing for Dynamic Scheduling of Workflows on Heterogeneous Systems

International audienceScheduling independent workflows on shared resources in a way that satisfy users Quality of Service is a significant challenge. In this study, we describe methodologies for off-line scheduling, where a schedule is generated for a set of knownworkflows, and on-line scheduling, where users can submit workflows at any moment in time. We consider the on-line scheduling problem in more detail and present performance comparisons of state-of-the-art algorithms for a realistic model of a heterogeneous system

Distributed data mining in grid computing environments

The official published version of this article can be found at the link below.The computing-intensive data mining for inherently Internet-wide distributed data, referred to as Distributed Data Mining (DDM), calls for the support of a powerful Grid with an effective scheduling framework. DDM often shares the computing paradigm of local processing and global synthesizing. It involves every phase of Data Mining (DM) processes, which makes the workflow of DDM very complex and can be modelled only by a Directed Acyclic Graph (DAG) with multiple data entries. Motivated by the need for a practical solution of the Grid scheduling problem for the DDM workflow, this paper proposes a novel two-phase scheduling framework, including External Scheduling and Internal Scheduling, on a two-level Grid architecture (InterGrid, IntraGrid). Currently a DM IntraGrid, named DMGCE (Data Mining Grid Computing Environment), has been developed with a dynamic scheduling framework for competitive DAGs in a heterogeneous computing environment. This system is implemented in an established Multi-Agent System (MAS) environment, in which the reuse of existing DM algorithms is achieved by encapsulating them into agents. Practical classification problems from oil well logging analysis are used to measure the system performance. The detailed experiment procedure and result analysis are also discussed in this paper

IMMEDIATE/BATCH MODE SCHEDULING ALGORITHMS FOR GRID COMPUTING: A REVIEW

Immediate/on-line and Batch mode heuristics are two methods used for scheduling in the computational grid environment. In the former, task is mapped onto a resource as soon as it arrives at the scheduler, while the later, tasks are not mapped onto resource as they arrive, instead they are collected into a set that is examined for mapping at prescheduled times called mapping events. This paper reviews the literature concerning Minimum Execution Time (MET) along with Minimum Completion Time (MCT) algorithms of online mode heuristics and more emphasis on Min-Min along with Max-Min algorithms of batch mode heuristics, while focusing on the details of their basic concepts, approaches, techniques, and open problems

A Scientific Workflow System For Genomic Data Analysis

Scientific workflows have become increasingly popular as a new computing paradigm for scientists to design and execute complex and distributed scientific processes to enable and accelerate many scientific discoveries. Although several scientific workflow management systems (SWFMSs) have been developed, there is a great need for an integrated scientific workflow system that enables the design and execution of higher-level scientific workflows, which integrate heterogeneous scientific workflows enacted by existing SWFMSs. On one hand, science is becoming increasingly collaborative today, requiring an integrated solution that combines the features and capabilities of different SWFMSs, which are typically developed and optimized towards one single discipline. One the other hand, such an integrated environment can immediately leverage existing and emerging techniques and strengths of various SWFMSs and their supported execution environments, such as Cluster, Grid, and Cloud. The main contributions of this dissertation are: 1) We propose a scientific workflow system, called GENOMEFLOW, to design, develop, and execute higher-level scientific workflows, whose workflow tasks are themselves scientific workflows enacted by existing SWFMSs; 2) We propose a workflow scheduling algorithm, called GSA, to enable the parallel execution of such heterogeneous scientific workflows in their native heterogeneous environments; and 3) We implemented GENOMEFLOW towards the life science community and developed several GENOMEFLOW scientific workflows to demonstrate the capabilities of our system for genome data analysis applications