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

Considering Human Aspects on Strategies for Designing and Managing Distributed Human Computation

A human computation system can be viewed as a distributed system in which the processors are humans, called workers. Such systems harness the cognitive power of a group of workers connected to the Internet to execute relatively simple tasks, whose solutions, once grouped, solve a problem that systems equipped with only machines could not solve satisfactorily. Examples of such systems are Amazon Mechanical Turk and the Zooniverse platform. A human computation application comprises a group of tasks, each of them can be performed by one worker. Tasks might have dependencies among each other. In this study, we propose a theoretical framework to analyze such type of application from a distributed systems point of view. Our framework is established on three dimensions that represent different perspectives in which human computation applications can be approached: quality-of-service requirements, design and management strategies, and human aspects. By using this framework, we review human computation in the perspective of programmers seeking to improve the design of human computation applications and managers seeking to increase the effectiveness of human computation infrastructures in running such applications. In doing so, besides integrating and organizing what has been done in this direction, we also put into perspective the fact that the human aspects of the workers in such systems introduce new challenges in terms of, for example, task assignment, dependency management, and fault prevention and tolerance. We discuss how they are related to distributed systems and other areas of knowledge.Comment: 3 figures, 1 tabl

Enhancing the effective utilisation of grid clusters by exploiting on-line performability analysis

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Dynamic fault tolerant grid workflow in the water threat management project

Achieving fault tolerance is an inevitable problem in distributed systems, with it becoming more challenging in decentralized, heterogeneous, and dynamic-environment systems such as a Grid. When deploying applications requires time-criticality, how to allocate resources for jobs in a fault-tolerant manner is an important issue for the delivery of the services. The Water Threat Management project is a research to find solutions for the contamination incidents problems in urban water distribution systems, and it involves the development of the cyberinfrastructure in a Grid environment. To handle such urgent events properly, the deployment of the system demands real-time processing without the failure. Our approach of integrating a fault-tolerant framework into a Water Threat Management system provides fault tolerance at the queuing stage rather than the job-execution stage by scheduling jobs in fault-tolerant ways. This includes the development of the batch queuing system in the Cyberaide Shell project. In addition, we present a dynamic workflow in the Water Threat Management system that can reduce the queue wait time in the changing environment

An efficient grid scheduling algorithm with fault tolerance and user satisfaction

Problem Statement. The advances in human civilization lead to more complications in problem solving. Grid computing serves as an efficient technology in solving those complicated problems. In computational grids, the grid scheduler schedules the task and finds the appropriate resource for each task. The scheduler must consider several factors such as user demand, communication time, failure handling mechanisms, and reduced makespan. Most of the existing algorithms do not consider user satisfaction. Thus a scheduling algorithm that handles failure of resources and achieves user satisfaction gains more importance. Approach. A new bicriteria scheduling algorithm (BSA) that considers user satisfaction along with fault tolerance has been introduced. The main contribution of this paper includes achieving user satisfaction along with fault tolerance and minimizing the makespan of jobs. Results. The performance of this proposed algorithm is evaluated using GridSim based on makespan and number of jobs completed successfully within user deadline. Conclusions/Recommendations. The proposed BSA algorithm achieves reduced makespan and better hit rate with higher user satisfaction and fault tolerance

Generic business process modelling framework for quantitative evaluation

PhD ThesisBusiness processes are the backbone of organisations used to automate and increase the efficiency and effectiveness of their services and prod- ucts. The rapid growth of the Internet and other Web based technologies has sparked competition between organisations in attempting to provide a faster, cheaper and smarter environment for customers. In response to these requirements, organisations are examining how their business processes may be evaluated so as to improve business performance. This thesis proposes a generic framework to expand the applicability of various quantitative evaluation to a large class of business processes. The framework introduces a novel engineering methodology that defines a modelling formalism to represent business processes that can be solved for a set of performance and optimisation algorithms. The methodology allows various types of algorithms used in model-based business pro- cess improvement and optimisation to be plugged in a single modelling formalism. As a part of the framework, a generic modelling formalism (MWF-wR) is developed to represent business processes so as to allow quantitative evaluation and to select the parameters for the associated performance evaluation and optimisation. The generic framework is designed and implemented by developing soft- ware support tools using Java as object oriented programming language combining three main modules: (i) a business process specification mod- ule to define the components of the business process model, (ii) a stochas- tic Petri net module to map the business process model to a stochastic Petri net, and (iii) an algorithms module to solve the models for various performance optimisation objectives. Furthermore, a literature survey of different aspects of business processes including modelling and analy- sis techniques provides an overview of the current state of research and highlights gaps in business process modelling and performance analy- sis. Finally, experiments are introduced to investigate the validity of the presented approach

Performance optimization of big data computing workflows for batch and stream data processing in multi-clouds

Workflow techniques have been widely used as a major computing solution in many science domains. With the rapid deployment of cloud infrastructures around the globe and the economic benefits of cloud-based computing and storage services, an increasing number of scientific workflows have migrated or are in active transition to clouds. As the scale of scientific applications continues to grow, it is now common to deploy various data- and network-intensive computing workflows such as serial computing workflows, MapReduce/Spark-based workflows, and Storm-based stream data processing workflows in multi-cloud environments, where inter-cloud data transfer oftentimes plays a significant role in both workflow performance and financial cost. Rigorous mathematical models are constructed to analyze the intra- and inter-cloud execution process of scientific workflows and a class of budget-constrained workflow mapping problems are formulated to optimize the network performance of big data workflows in multi-cloud environments. Research shows that these problems are all NP-complete and a heuristic solution is designed for each that takes into consideration module execution, data transfer, and I/O operations. The performance superiority of the proposed solutions over existing methods are illustrated through extensive simulations and further verified by real-life workflow experiments deployed in public clouds

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