QoS and preemption aware scheduling in federated and virtualized Grid computing environments

Resource provisioning is one of the challenges in federated Grid environments. In these environments each Grid serves requests from external users along with local users. Recently, this resource provisioning is performed in the form of Virtual Machines (VMs). The problem arises when there are insufficient resources for local users to be served. The problem gets complicated further when external requests have different QoS requirements. Serving local users could be solved by preempting VMs from external users which impose overheads on the system. Therefore, the question is how the number of VM preemptions in a Grid can be minimized. Additionally, how we can decrease the likelihood of preemption for requests with more QoS requirements. We propose a scheduling policy in InterGrid, as a federated Grid, which reduces the number of VM preemptions and dispatches external requests in a way that fewer requests with QoS constraints get affected by preemption. Extensive simulation results indicate that the number of VM preemptions is decreased at least by 60%, particularly, for requests with more QoS requirements.     

A computational economy for grid computing and its implementation in the Nimrod-G resource broker

Computational grids that couple geographically distributed resources such as PCs, workstations, clusters, and scientific instruments, have emerged as a next generation computing platform for solving large-scale problems in science, engineering, and commerce. However, application development, resource management, and scheduling in these environments continue to be a complex undertaking. In this article, we discuss our efforts in developing a resource management system for scheduling computations on resources distributed across the world with varying quality of service (QoS). Our service-oriented grid computing system called Nimrod-G manages all operations associated with remote execution including resource discovery, trading, scheduling based on economic principles and a user-defined QoS requirement. The Nimrod-G resource broker is implemented by leveraging existing technologies such as Globus, and provides new services that are essential for constructing industrial-strength grids. We present the results of experiments using the Nimrod-G resource broker for scheduling parametric computations on the World Wide Grid (WWG) resources that span five continents.     

From volunteer to trustable computing: Providing QoS-aware scheduling mechanisms for multi-grid computing environments

The exploitation of service oriented technologies, such as Grid computing, is being boosted by the current service oriented economy trend, leading to a growing need of Quality of Service (QoS) mechanisms. However, Grid computing was created to provide vast amounts of computational power but in a best effort way. Providing QoS guarantees is therefore a very difficult and complex task due to the distributed and heterogeneous nature of their resources, specially the volunteer computing resources (e.g., desktop resources).The scope of this paper is to empower an integrated multi QoS support suitable for Grid Computing environments made of either dedicated and volunteer resources, even taking advantage of that fact. The QoS is provided through SLAs by exploiting different available scheduling mechanisms in a coordinated way, and applying appropriate resource usage optimization techniques. It is based on the differentiated use of reservations and scheduling in advance techniques, enhanced with the integration of rescheduling techniques that improve the allocation decisions already made, achieving a higher resource utilization and still ensuring the agreed QoS. As a result, our proposal enhances best-effort Grid environments by providing QoS aware scheduling capabilities.This proposal has been validated by means of a set of experiments performed in a real Grid testbed. Results show how the proposed framework effectively harnesses the specific capabilities of the underlying resources to provide every user with the desired QoS level, while, at the same time, optimizing the resources’ usage.     

Grid Economics: A Selective Discussion of Two Research Problems

The last 5 years have seen considerable discussion of various types of Grids—compute Grids, storage Grids, and data Grids. Using the checklist given in Foster (, 2002) to define a Grid, two important problems that arise in the context of resource sharing in Grid computing environments are discussed. First, the well documented problem in compute Grid environments that arises from the inability of consumers to accurately estimate their resource requirements is presented. This results in incorrect scheduling of requests for Grid resources and social welfare loss. To address this problem, two research proposals are briefly described. The first approach argues for the design of decision support tools to help users with resource estimation while the second approach studies the design of resource allocation mechanisms that can work with stochastic specifications of resource requirements. This is in contrast to the traditional point estimates of resource required by extant mechanisms. Next, resource provisioning and pricing problems that arise in data storage and retrieval Grids are described. These Grids differ fundamentally from compute Grids but share some economic characteristics with P2P file sharing networks. Drawing on this connection, pricing mechanisms and resource provisioning research is briefly discussed.     

Bi-level fuzzy based advanced reservation of Cloud workflow applications on distributed Grid resources

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application’s owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application’s workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG’s specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.     

网格环境下费用约束的科学工作流可靠调度算法

网格基础设施是目前科学工作流应用规划、部署和执行的主要支撑环境.然而由于网格资源的自治、动态及异构性,如何在保障用户QoS约束下有效调度科学工作流是一个研究热点.针对费用约束下的科学工作流调度问题,为了提高其执行的可靠性,本文使用随机服务模型描述资源节点的动态服务能力并考虑本地任务负载对资源执行性能的影响,给出一种资源可靠性的评估方法,在此基础上提出一种费用约束下的科学工作流可靠调度算法RSASW.仿真实验结果表明RSASW算法相对于GAIN3,GreedyTime-CD及PFAS算法,对工作流的执行具有很好的可靠性保障.     

Fault tolerant and prioritized scheduling in OGSA‐based mobile Grids

Grids and mobile Grids can form the basis and the enabling technology for pervasive and utility computing due to their ability to being open, highly heterogeneous and scalable. In this paper we present a scheme for advancing quality of service (QoS) attributes, such as fault tolerance and prioritized scheduling, in OGSA‐based mobile Grids. The fault tolerance is achieved by producing and managing sufficient replicas of tasks submitted for execution on the mobile Grid resources. We design a simple and efficient prioritization scheme, which allows the scheduling of the tasks submitted by the Grid users as distinguished priorities that can be managed and exploited as a QoS parameter by the Grid infrastructure operator. The results that are presented show the efficiency of the proposed scheme in being simple and additionally enriching with reliability and QoS features the applications that are built on the concept of mobile Grids. Copyright © 2008 John Wiley & Sons, Ltd.     

网格系统的服务质量保障与控制综述

针对类型多样的异构资源、动态协作的复杂网格环境,需要有效的服务质量（QoS）控制机制保障网格应用的需求,网格QoS需引入新的概念和机制,实现QoS需求的描述与映射、动态资源聚合与共享及任务间协作,灵活有效地进行网格应用流的聚合、区分和控制.在此基础上,介绍了网格QoS目前的研究情况,分析了网格QoS控制的特点、难点及其基本需求.结合实际研究,对网格QoS控制的关键技术——网格QoS管理、控制策略、资源预留、自适应控制、QoS协商进行了较深入的剖析,同时讨论了QoS评价的问题.最后总结了网格QoS的基本研究目标和未来的研究方向.     

基于缓冲和预选的网格资源调度优化

资源发现是影响调度效率的关键,针对网格资源发现中复杂的查询过程,通过资源代理,将用户任务发生过调度的资源在本地进行缓存,构成黑白资源名单。同时,在网格资源代理空闲时,按照最近调度的QoS参数为参考值,进行资源的预选择,存放在本地记录,由此构成了本地记录优先的资源发现。针对该方法,设计了支撑该方法的优化代理体系结构,讨论了记录管理问题,给出了优化资源调度过程的算法。实验表明,该方法缩短了任务的平均调度时间,从而缩短了任务平均完成时间,提高了吞吐量,达到了优化的目的。     

Anticipating resource saturation in Federated Grids

In a dynamic and geographically distributed Federated Grid where resources are shared between system participants, there is a lack of mechanisms capable of reallocating already scheduled tasks based on grid infrastructure owners’ current internal needs. In this paper we propose a set of policies for both, users and owners, that aid owners to satisfy internal peak demands and users to achieve the best makespan despite the circumstances. As in our previous work, the main purpose is to do so in the least intrusive way possible to maintain software stack independence of all participants, and to save time and communication bandwidth by anticipating grid resources saturation. These strategies suppose a novel approach for decentralized and non-cooperative workflow scheduling in a federation of heterogeneous grid infrastructures. We evaluate and prove the feasibility of our policies through a set of simulations that reflect the worst case where all resources are saturated. The results show that, in the worst scenario, our scheduling mechanism is beneficial to big infrastructure owners since they can achieve their own internal objectives, as well as to small users since they can reach the best possible completion time.     

Meeting security and user behavior requirements in Grid scheduling

Most of current research in Grid computing is still focused on the improvement of the performance of Grid schedulers. However, unlike traditional scheduling, in Grid systems there are other important requirements to be taken into account. One such a requirement is the secure scheduling, namely achieving an efficient allocation of tasks to reasonable trustful resources. In this paper we formalize the Grid scheduling problem as a non-cooperative non-zero sum game of the Grid users in order to address the security requirements. The premise of this model is that in a large-scale Grid, the cooperation among all users in the system is unlikely to happen. The users’ cost of playing the game is interpreted as a total cost of the secure job execution in Grid. The game cost function is minimized, at global (Grid) and local (users) levels, by using four genetic-based hybrid meta-heuristics. We have evaluated the proposed model under the heterogeneity, the large-scale and dynamics conditions using a Grid simulator. The relative performance of four hybrid schedulers is measured by the makespan and flowtime metrics. The obtained results suggested that it is more resilient for the Grid users to pay some additional scheduling cost, due to verification of the security conditions, instead of taking the risk of assigning their tasks to unreliable resources.     

时间约束下可靠性增强的网格工作流调度算法

如何在动态性极强的网格环境中有效调度工作流应用并满足用户的QoS需求是一个难题.传统的基于资源静态特征的启发式调度算法或预留策略缺乏对资源动态服务能力的有效评估而无法保证工作流应用的截止时间约束.本文采用随机服务模型建模网格资源的动态性能并考虑资源内处理单元失效的情况.利用生灭过程描述资源节点中处理单元数目的变化情况并给出了资源节点在任务截止时间内的可靠性评估方法.在此基础上,提出一种可靠性增强的网格工作流调度算法RSA_TC.实验结果表明RSA_TC算法相对于DSESAW和PFAS算法,能有效保证用户截止时间的要求,对动态网格环境有较好的自适应性.     

Enhancing Availability of Grid Computational Services to Ubiquitous Computing Applications

The Grid is an integrated infrastructure that can play the dual roles of a coordinated resource consumer as well as a donator in distributed computing environments. The enormous growth in the use of mobile and embedded devices in ubiquitous computing environment and their interaction with human beings produces a huge amount of data that need to be processed efficiently anytime anywhere. However, such devices often have limited resources in terms of CPU, storage, battery power, and communication bandwidth. Thus, there is a need to transfer ubiquitous computing application services to more powerful computational resources. In this paper, we investigate the use of the Grid as a candidate for provisioning computational services to applications in ubiquitous computing environments. In particular, we present a competitive model that describes the possible interaction between the competing resources in the Grid Infrastructure as service providers and ubiquitous applications as subscribers. The competition takes place in terms of quality of service (QoS) and cost offered by different Grid Service Providers (GSPs). We also investigate the job allocation of different GSPs by exploiting the noncooperativeness among the strategies. We present the equilibrium behavior of our model facing global competition under stochastic demand and estimate guaranteed QoS assurance level by efficiently satisfying the requirement of ubiquitous application. We have also performed extensive experiments over Distributed Parallel Computing Cluster (DPCC) and studied overall job execution performance of different GSPs under a wide range of QoS parameters using different strategies. Our model and performance evaluation results can serve as a valuable reference for designing appropriate strategies in a practical grid environment.     

Autonomic QoS control in enterprise Grid environments using online simulation

As Grid Computing increasingly enters the commercial domain, performance and quality of service (QoS) issues are becoming a major concern. The inherent complexity, heterogeneity and dynamics of Grid computing environments pose some challenges in managing their capacity to ensure that QoS requirements are continuously met. In this paper, a comprehensive framework for autonomic QoS control in enterprise Grid environments using online simulation is proposed. This paper presents a novel methodology for designing autonomic QoS-aware resource managers that have the capability to predict the performance of the Grid components they manage and allocate resources in such a way that service level agreements are honored. Support for advanced features such as autonomic workload characterization on-the-fly, dynamic deployment of Grid servers on demand, as well as dynamic system reconfiguration after a server failure is provided. The goal is to make the Grid middleware self-configurable and adaptable to changes in the system environment and workload. The approach is subjected to an extensive experimental evaluation in the context of a real-world Grid environment and its effectiveness, practicality and performance are demonstrated.     

Analysis and Provision of QoS for Distributed Grid Applications

Grid computing provides the infrastructure necessary to access and use distributed resources as part of virtual organizations. When used in this way, Grid computing makes it possible for users to participate in collaborative and distributed applications such as tele-immersion, visualization, and computational simulation. Some of these applications operate in a collaborative mode, requiring data to be stored and delivered in a timely manner. This class of applications must adhere to stringent real-time constraints and Quality-of-Service (QoS) requirements. A QoS management approach is therefore required to orchestrate and guarantee the timely interaction between such applications and services. We discuss the design and a prototype implementation of a QoS system, and demonstrate how we enable Grid applications to become QoS compliant. We validate this approach through a case study of an image processing task derived from a nanoscale structures application.     

网格环境下基于QoS需求的关联任务调度算法*

首先描述QoS调度问题,建立QoS需求模型;然后通过分析任务的依赖性,提出时间花费、资源价格和可靠性三种QoS参数的映射机制;最后针对网格环境的新特征,提出一种以优化用户效用为目标,基于QoS的关联任务调度算法(QBDTS_UO).仿真实验结果表明,该算法能以较小的时间花费为代价,有效满足用户的QoS需求,并能大大提高网格资源的使用率.     

Provide Virtual Distributed Environments for Grid computing on demand

Grid users always expect to meet some challenges to employ Grid resources, such as customized computing environment and QoS support. In this paper, we propose a new methodology for Grid computing – to use virtual machines as computing resources and provide Virtual Distributed Environments (VDE) for Grid users. It is declared that employing virtual environment for Grid computing can bring various advantages, for instance, computing environment customization, QoS guarantee and easy management. A light weight Grid middleware, Grid Virtualization Engine, is developed accordingly to provide functions of building virtual environment for Grids. We also present a typical use case, on-demand build a virtual e-Science infrastructure to justify the methodology.     

A Resource Selection System for Cycle Stealing in GPU Grids

This paper presents a resource selection system for exploiting graphics processing units (GPUs) as general-purpose computational resources in desktop Grid environments. Our system allows Grid users to share remote GPUs, which are traditionally dedicated to local users who directly see the display output. The key contribution of the paper is to develop this novel system for non-dedicated environments. We first show criteria for defining idle GPUs from the Grid users’ point of view. Based on these criteria, our system uses a screensaver approach with some sensors that detect idle resources at a low overhead. The idea for this lower overhead is to avoid GPU intervention during resource monitoring. Detected idle GPUs are then selected according to a matchmaking service, making the system adaptive to the rapid advance of GPU architecture. Though the system itself is not yet interoperable with current desktop Grid systems, our idea can be applied to screensaver-based systems such as BOINC. We evaluate the system using Windows PCs with three generations of nVIDIA GPUs. The experimental results show that our system achieves a low overhead of at most 267 ms, minimizing interference to local users while maximizing the performance delivered to Grid users. Some case studies are also performed in an office environment to demonstrate the effectiveness of the system in terms of the amount of detected idle time.     

Double auction-inspired meta-scheduling of parallel applications on global grids

Meta-schedulers map jobs to computational resources that are part of a Grid, such as clusters, that in turn have their own local job schedulers. Existing Grid meta-schedulers either target system-centric metrics, such as utilisation and throughput, or prioritise jobs based on utility metrics provided by the users. The system-centric approach gives less importance to users’ individual utility, while the user-centric approach may have adverse effects such as poor system performance and unfair treatment of users. Therefore, this paper proposes a novel meta-scheduler, based on the well-known double auction mechanism that aims to satisfy users’ service requirements as well as ensuring balanced utilisation of resources across a Grid. We have designed valuation metrics that commodify both the complex resource requirements of users and the capabilities of available computational resources. Through simulation using real traces, we compare our scheduling mechanism with other common mechanisms widely used by both existing market-based and traditional meta-schedulers. The results show that our meta-scheduling mechanism not only satisfies up to 15% more user requirements than others, but also improves system utilisation through load balancing.     

Real-time workflows oriented online scheduling in uncertain cloud environment

Workflow scheduling has become one of the hottest topics in cloud environments, and efficient scheduling approaches show promising ways to maximize the profit of cloud providers via minimizing their cost, while guaranteeing the QoS for users’ applications. However, existing scheduling approaches are inadequate for dynamic workflows with uncertain task execution times running in cloud environments, because those approaches assume that cloud computing environments are deterministic and pre-computed schedule decisions will be statically followed during schedule execution. To cover the above issue, we introduce an uncertainty-aware scheduling architecture to mitigate the impact of uncertain factors on the workflow scheduling quality. Based on this architecture, we present a scheduling algorithm, incorporating both event-driven and periodic rolling strategies (EDPRS), for scheduling dynamic workflows. Lastly, we conduct extensive experiments to compare EDPRS with two typical baseline algorithms using real-world workflow traces. The experimental results show that EDPRS performs better than those algorithms.