An Adaptive Rescheduling Strategy for Grid Workflow Applications

Scheduling is the key to the performance of grid workflow applications. Various strategies are proposed, including static scheduling strategies which map jobs to resources before execution time, or dynamic alternatives which schedule individual job only when it is ready to execute. While sizable work supports the claim that the static scheduling performs better for workflow applications than the dynamic one, it is questioned how a static schedule works effectively in a grid environment which changes constantly. This paper proposes a novel adaptive rescheduling concept, which allows the workflow planner works collaboratively with the run time executor and reschedule in a proactive way had the grid environment changes significantly. An HEFT-based adaptive rescheduling algorithm is presented, evaluated and compared with traditional static and dynamic strategies respectively. The experiment results show that the proposed strategy not only outperforms the dynamic one but also improves over the traditional static one. Furthermore we observed that it performs more efficiently with data intensive application of higher degree of parallelism.

Cloud computing resource scheduling and a survey of its evolutionary approaches

A disruptive technology fundamentally transforming the way that computing services are delivered, cloud computing offers information and communication technology users a new dimension of convenience of resources, as services via the Internet. Because cloud provides a finite pool of virtualized on-demand resources, optimally scheduling them has become an essential and rewarding topic, where a trend of using Evolutionary Computation (EC) algorithms is emerging rapidly. Through analyzing the cloud computing architecture, this survey first presents taxonomy at two levels of scheduling cloud resources. It then paints a landscape of the scheduling problem and solutions. According to the taxonomy, a comprehensive survey of state-of-the-art approaches is presented systematically. Looking forward, challenges and potential future research directions are investigated and invited, including real-time scheduling, adaptive dynamic scheduling, large-scale scheduling, multiobjective scheduling, and distributed and parallel scheduling. At the dawn of Industry 4.0, cloud computing scheduling for cyber-physical integration with the presence of big data is also discussed. Research in this area is only in its infancy, but with the rapid fusion of information and data technology, more exciting and agenda-setting topics are likely to emerge on the horizon

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

Multi-objective scheduling of Scientific Workflows in multisite clouds

Clouds appear as appropriate infrastructures for executing Scientific Workflows (SWfs). A cloud is typically made of several sites (or data centers), each with its own resources and data. Thus, it becomes important to be able to execute some SWfs at more than one cloud site because of the geographical distribution of data or available resources among different cloud sites. Therefore, a major problem is how to execute a SWf in a multisite cloud, while reducing execution time and monetary costs. In this paper, we propose a general solution based on multi-objective scheduling in order to execute SWfs in a multisite cloud. The solution consists of a multi-objective cost model including execution time and monetary costs, a Single Site Virtual Machine (VM) Provisioning approach (SSVP) and ActGreedy, a multisite scheduling approach. We present an experimental evaluation, based on the execution of the SciEvol SWf in Microsoft Azure cloud. The results reveal that our scheduling approach significantly outperforms two adapted baseline algorithms (which we propose by adapting two existing algorithms) and the scheduling time is reasonable compared with genetic and brute-force algorithms. The results also show that our cost model is accurate and that SSVP can generate better VM provisioning plans compared with an existing approach.Work partially funded by EU H2020 Programme and MCTI/RNP-Brazil (HPC4E grant agreement number 689772), CNPq, FAPERJ, and INRIA (MUSIC project), Microsoft (ZcloudFlow project) and performed in the context of the Computational Biology Institute (www.ibc-montpellier.fr). We would like to thank Kary Ocaña for her help in modeling and executing the SciEvol SWf.Peer ReviewedPostprint (author's final draft

Autonomous resource-aware scheduling of large-scale media workflows

The media processing and distribution industry generally requires considerable resources to be able to execute the various tasks and workflows that constitute their business processes. The latter processes are often tied to critical constraints such as strict deadlines. A key issue herein is how to efficiently use the available computational, storage and network resources to be able to cope with the high work load. Optimizing resource usage is not only vital to scalability, but also to the level of QoS (e.g. responsiveness or prioritization) that can be provided. We designed an autonomous platform for scheduling and workflow-to-resource assignment, taking into account the different requirements and constraints. This paper presents the workflow scheduling algorithms, which consider the state and characteristics of the resources (computational, network and storage). The performance of these algorithms is presented in detail in the context of a European media processing and distribution use-case

Autonomic Cloud Computing: Open Challenges and Architectural Elements

As Clouds are complex, large-scale, and heterogeneous distributed systems, management of their resources is a challenging task. They need automated and integrated intelligent strategies for provisioning of resources to offer services that are secure, reliable, and cost-efficient. Hence, effective management of services becomes fundamental in software platforms that constitute the fabric of computing Clouds. In this direction, this paper identifies open issues in autonomic resource provisioning and presents innovative management techniques for supporting SaaS applications hosted on Clouds. We present a conceptual architecture and early results evidencing the benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape

Managing Uncertainty: A Case for Probabilistic Grid Scheduling

The Grid technology is evolving into a global, service-orientated architecture, a universal platform for delivering future high demand computational services. Strong adoption of the Grid and the utility computing concept is leading to an increasing number of Grid installations running a wide range of applications of different size and complexity. In this paper we address the problem of elivering deadline/economy based scheduling in a heterogeneous application environment using statistical properties of job historical executions and its associated meta-data. This approach is motivated by a study of six-month computational load generated by Grid applications in a multi-purpose Grid cluster serving a community of twenty e-Science projects. The observed job statistics, resource utilisation and user behaviour is discussed in the context of management approaches and models most suitable for supporting a probabilistic and autonomous scheduling architecture

Enabling Adaptive Grid Scheduling and Resource Management

Wider adoption of the Grid concept has led to an increasing amount of federated computational, storage and visualisation resources being available to scientists and researchers. Distributed and heterogeneous nature of these resources renders most of the legacy cluster monitoring and management approaches inappropriate, and poses new challenges in workflow scheduling on such systems. Effective resource utilisation monitoring and highly granular yet adaptive measurements are prerequisites for a more efficient Grid scheduler. We present a suite of measurement applications able to monitor per-process resource utilisation, and a customisable tool for emulating observed utilisation models. We also outline our future work on a predictive and probabilistic Grid scheduler. The research is undertaken as part of UK e-Science EPSRC sponsored project SO-GRM (Self-Organising Grid Resource Management) in cooperation with BT