Stochastic scheduling and workload allocation : QoS support and profitable brokering in computing grids

Abstract: The Grid can be seen as a collection of services each of which performs some functionality. Users of the Grid seek to use combinations of these services to perform the overall task they need to achieve. In general this can be seen as aset of services with a workflow document describing how these services should be combined. The user may also have certain constraints on the workflow operations, such as execution time or cost ----t~ th~ user, specified in the form of a Quality of Service (QoS) document. The users . submit their workflow to a brokering service along with the QoS document. The brokering service's task is to map any given workflow to a subset of the Grid services taking the QoS and state of the Grid into account -- service availability and performance. We propose an approach for generating constraint equations describing the workflow, the QoS requirements and the state of the Grid. This set of equations may be solved using Mixed-Integer Linear Programming (MILP), which is the traditional method. We further develop a novel 2-stage stochastic MILP which is capable of dealing with the volatile nature of the Grid and adapting the selection of the services during the lifetime of the workflow. We present experimental results comparing our approaches, showing that the . 2-stage stochastic programming approach performs consistently better than other traditional approaches. Next we addresses workload allocation techniques for Grid workflows in a multi-cluster Grid We model individual clusters as MIMIk. queues and obtain a numerical solutio~ for missed deadlines (failures) of tasks of Grid workflows. We also present an efficient algorithm for obtaining workload allocations of clusters. Next we model individual cluster resources as G/G/l queues and solve an optimisation problem that minimises QoS requirement violation, provides QoS guarantee and outperforms reservation based scheduling algorithms. Both approaches are evaluated through an experimental simulation and the results confirm that the proposed workload allocation strategies combined with traditional scheduling algorithms performs considerably better in terms of satisfying QoS requirements of Grid workflows than scheduling algorithms that don't employ such workload allocation techniques. Next we develop a novel method for Grid brokers that aims at maximising profit whilst satisfying end-user needs with a sufficient guarantee in a volatile utility Grid. We develop a develop a 2-stage stochastic MILP which is capable of dealing with the volatile nature . of the Grid and obtaining cost bounds that ensure that end-user cost is minimised or satisfied and broker's profit is maximised with sufficient guarantee. These bounds help brokers know beforehand whether the budget limits of end-users can be satisfied and. if not then???????? obtain appropriate future leases from service providers. Experimental results confirm the efficacy of our approach.Imperial Users onl

The OMII Software Distribution

This paper describes the work carried out at the Open Middleware Infrastructure Institute (OMII) and the key elements of the OMII software distribution that have been developed in collaboration with members of the Managed Programme Initiative. The main objective of the OMII is to preserve and consolidate the achievements of the UK e-Science Programme by collecting, maintaining and improving the software modules that form the key components of a generic Grid middleware. Recently, the activity at Southampton has been extended beyond 2009 through a new project, OMII-UK, that forms a partnership that now includes the OGSA-DAI activities at Edinburgh and the myGrid project at Manchester

Capacity planning and stochastic scheduling in grid computing environments

Imperial Users onl

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

Grid workflow scheduling in WOSE

Accepted versio

Service Quality and Profit Control in Utility Computing Service Life Cycles

Utility Computing is one of the most discussed business models in the context of Cloud Computing. Service providers are more and more pushed into the role of utilities by their customer's expectations. Subsequently, the demand for predictable service availability and pay-per-use pricing models increases. Furthermore, for providers, a new opportunity to optimise resource usage offers arises, resulting from new virtualisation techniques. In this context, the control of service quality and profit depends on a deep understanding of the representation of the relationship between business and technique. This research analyses the relationship between the business model of Utility Computing and Service-oriented Computing architectures hosted in Cloud environments. The relations are clarified in detail for the entire service life cycle and throughout all architectural layers. Based on the elaborated relations, an approach to a delivery framework is evolved, in order to enable the optimisation of the relation attributes, while the service implementation passes through business planning, development, and operations. Related work from academic literature does not cover the collected requirements on service offers in this context. This finding is revealed by a critical review of approaches in the fields of Cloud Computing, Grid Computing, and Application Clusters. The related work is analysed regarding appropriate provision architectures and quality assurance approaches. The main concepts of the delivery framework are evaluated based on a simulation model. To demonstrate the ability of the framework to model complex pay-per-use service cascades in Cloud environments, several experiments have been conducted. First outcomes proof that the contributions of this research undoubtedly enable the optimisation of service quality and profit in Cloud-based Service-oriented Computing architectures

Introducing risk management into the grid

Service Level Agreements (SLAs) are explicit statements about all expectations and obligations in the business partnership between customers and providers. They have been introduced in Grid computing to overcome the best effort approach, making the Grid more interesting for commercial applications. However, decisions on negotiation and system management still rely on static approaches, not reflecting the risk linked with decisions. The EC-funded project "AssessGrid" aims at introducing risk assessment and management as a novel decision paradigm into Grid computing. This paper gives a general motivation for risk management and presents the envisaged architecture of a "risk-aware" Grid middleware and Grid fabric, highlighting its functionality by means of three showcase scenarios

HPC Cloud for Scientific and Business Applications: Taxonomy, Vision, and Research Challenges

High Performance Computing (HPC) clouds are becoming an alternative to on-premise clusters for executing scientific applications and business analytics services. Most research efforts in HPC cloud aim to understand the cost-benefit of moving resource-intensive applications from on-premise environments to public cloud platforms. Industry trends show hybrid environments are the natural path to get the best of the on-premise and cloud resources---steady (and sensitive) workloads can run on on-premise resources and peak demand can leverage remote resources in a pay-as-you-go manner. Nevertheless, there are plenty of questions to be answered in HPC cloud, which range from how to extract the best performance of an unknown underlying platform to what services are essential to make its usage easier. Moreover, the discussion on the right pricing and contractual models to fit small and large users is relevant for the sustainability of HPC clouds. This paper brings a survey and taxonomy of efforts in HPC cloud and a vision on what we believe is ahead of us, including a set of research challenges that, once tackled, can help advance businesses and scientific discoveries. This becomes particularly relevant due to the fast increasing wave of new HPC applications coming from big data and artificial intelligence.Comment: 29 pages, 5 figures, Published in ACM Computing Surveys (CSUR