Utility-based scheduling for grid computing under constraints of energy budget and deadline

With recent advances in computing and communication technologies enabling mobile devices more powerful, the scope of Grid computing has been broadened to include mobile and pervasive devices. Energy has become a critical resource in such devices. So, battery energy limitation is the main challenge towards enabling persistent mobile grid computing. In this paper, we address the problem of energy constrained scheduling scheme for the grid environment. There is a limited energy budget for grid applications. The paper investigates both energy minimization for mobile devices and grid utility optimization problem. We formalize energy aware scheduling using nonlinear optimization theory under constraints of energy budget and deadline. The paper also proposes distributed pricing based algorithm that is used to tradeoff energy and deadline to achieve a system wide optimization based on the preference of the grid user. The simulations reveal that the proposed energy constrained scheduling algorithms can obtain better performance than the previous approach that considers both energy consumption and deadline.     

Utility-driven solution for optimal resource allocation in computational grid

Optimal resource allocation is a complex undertaking due to large-scale heterogeneity present in computational grid. Traditionally, the decision based on certain cost functions has been used in allocating grid resource as a standard method that does not take resource access cost into consideration. In this paper, the utility function is presented as a promising method for grid resource allocation. To tackle the issue of heterogeneous demand, the user's preference is represented by utility function, which is driven by a user-centric scheme rather than system-centric parameters adopted by cost functions. The goal of each grid user is to maximize its own utility under different constraints. In order to allocate a common resource to multiple bidding users, the optimal solution is achieved by searching the equilibrium point of resource price such that the total demand for a resource exactly equals the total amount available to generate a set of optimal user bids. The experiments run on a Java-based discrete-event grid simulation toolkit called GridSim are made to study characteristics of the utility-driven resource allocation strategy under different constraints. Results show that utility optimization under budget constraint outperforms deadline constraint in terms of time spent, whereas deadline constraint outperforms budget constraint in terms of cost spent. The conclusion indicates that the utility-driven method is a very potential candidate for the optimal resource allocation in computational grid.     

Replication based fault tolerant job scheduling strategy for economy driven grid

In this paper, the problem of fault tolerance in grid computing is addressed and a novel adaptive task replication based fault tolerant job scheduling strategy for economy driven grid is proposed. The proposed strategy maintains fault history of the resources termed as resource fault index. Fault index entry for the resource is updated based on successful completion or failure of an assigned task by the grid resource. Grid Resource Broker then replicates the task (submitting the same task to different backup resources) with different intensity, based on vulnerability of resource towards faults suggested by resource fault index. Consequently, in case of possible fault at a resource the results of replicated task(s) on other backup resource(s) can be used. Hence, user job(s) can be completed within specified deadline and assigned budget, even on the event of faults at the grid resource(s). Through extensive simulations, performance of the proposed strategy is evaluated and compared with the Time Optimization and Checkpointing based Strategy in an economy driven grid environment. The experimental results demonstrate that in the presence of faults, proposed fault tolerant strategy improves the number of tasks completed with varied deadline and fixed budget as well as number of tasks completed with varied budget and fixed deadline. Additionally, the proposed strategy used a smaller percentage of deadline time as compare to both Time Optimization and Checkpointing based Strategy. Although the proposed strategy has a percentage of budget spent greater than that of Time Optimization Strategy and Checkpointing based Strategy, it is accepted as a proposed strategy in time optimization where the main objective is to maximize tasks completed within a given deadline. It can be concluded from the experiments that the proposed strategy shows improvement in satisfying the user QoS requirements. It can effectively schedule tasks and tolerate faults gracefully even in the presence of failures, but the costs are slightly higher in terms of budget consumption. Hence, the proposed fault tolerant strategy helps in sustaining user??s faith in the grid, by enabling the grid to deliver reliable and consistent performance in the presence of faults.     

Scheduling parameter sweep applications on global Grids: a deadline and budget constrained cost–time optimization algorithm

Computational Grids and peer‐to‐peer (P2P) networks enable the sharing, selection, and aggregation of geographically distributed resources for solving large‐scale problems in science, engineering, and commerce. The management and composition of resources and services for scheduling applications, however, becomes a complex undertaking. We have proposed a computational economy framework for regulating the supply of and demand for resources and allocating them for applications based on the users' quality‐of‐service requirements. The framework requires economy‐driven deadline‐ and budget‐constrained (DBC) scheduling algorithms for allocating resources to application jobs in such a way that the users' requirements are met. In this paper, we propose a new scheduling algorithm, called the DBC cost–time optimization scheduling algorithm, that aims not only to optimize cost, but also time when possible. The performance of the cost–time optimization scheduling algorithm has been evaluated through extensive simulation and empirical studies for deploying parameter sweep applications on global Grids. Copyright © 2005 John Wiley & Sons, Ltd.     

Garbage collection-aware utility accrual scheduling

The convenience and robustness of automatic memory management have long been exploited by modern systems that use type-safe programming languages such as Java. The timeliness requirements of real-time systems, however, impose specific demands on the operational parameters of the garbage collector. The memory requirements of real-time tasks must be accommodated with a predictable impact on the time-line and under the purview of the scheduler. Utility Accrual is a method of dynamic overload scheduling that is designed to respond to overload conditions by producing a schedule that heuristically maximizes a pre-defined metric of utility. Traditionally, UA schedulers have focused primarily on CPU overload. We explore memory overload conditions in which the memory demands exceed the system’s available memory bandwidth. This paper presents a utility accrual algorithm for uniprocessor CPU and garbage collection scheduling that addresses such memory overload conditions. By tightly linking CPU and memory allocation, the scheduler can appropriately respond to overload along both dimensions. This scheduler is the first of its kind to enable the use of automatic memory management in a utility accrual system. Experimental results based on actual Java application profiles indicate the benefits of our model when compared to memory-unaware scheduling.     

Design and implementation of an adaptive job allocation strategy for heterogeneous multi‐cluster computing systems

Cluster computing is an attractive approach to provide high‐performance computing for solving large‐scale applications. Owing to the advances in processor and networking technology, expanding clusters have resulted in the system heterogeneity; thus, it is crucial to dispatch jobs to heterogeneous computing resources for better resource utilization. In this paper, we propose a new job allocation system for heterogeneous multi‐cluster environments named the Adaptive Job Allocation Strategy (AJAS), in which a self‐scheduling scheme is applied in the scheduler to dispatch jobs to the most appropriate computing resources. Our strategy focuses on increasing resource utility by dispatching jobs to computing nodes with similar performance capacities. By doing so, execution times among all nodes can be equalized. The experimental results show that AJAS can improve the system performance. Copyright © 2011 John Wiley & Sons, Ltd.     

Market-oriented Grids and Utility Computing: The State-of-the-art and Future Directions

Traditional resource management techniques (resource allocation, admission control and scheduling) have been found to be inadequate for many shared Grid and distributed systems, that consist of autonomous and dynamic distributed resources contributed by multiple organisations. They provide no incentive for users to request resources judiciously and appropriately, and do not accurately capture the true value, importance and deadline (the utility) of a user’s job. Furthermore, they provide no compensation for resource providers to contribute their computing resources to shared Grids, as traditional approaches have a user-centric focus on maximising throughput and minimising waiting time rather than maximising a providers own benefit. Consequently, researchers and practitioners have been examining the appropriateness of ‘market-inspired’ resource management techniques to address these limitations. Such techniques aim to smooth out access patterns and reduce the chance of transient overload, by providing a framework for users to be truthful about their resource requirements and job deadlines, and offering incentives for service providers to prioritise urgent, high utility jobs over low utility jobs. We examine the recent innovations in these systems (from 2000–2007), looking at the state-of-the-art in price setting and negotiation, Grid economy management and utility-driven scheduling and resource allocation, and identify the advantages and limitations of these systems. We then look to the future of these systems, examining the emerging ‘Catallaxy’ market paradigm. Finally we consider the future directions that need to be pursued to address the limitations of the current generation of market oriented Grids and Utility Computing systems.     

Context‐aware scheduling in MapReduce: a compact review

It is a fact that the attention of research community in computer science, business executives, and decision makers is drastically drawn by big data. As the volume of data becomes bigger, it needs performance‐oriented data‐intensive processing frameworks such as MapReduce, which can scale computation on large commodity clusters. Hadoop MapReduce processes data in Hadoop Distributed File System as jobs scheduled according to YARN fair scheduler and capacity scheduler. However, with advancement and dynamic changes in hardware and operating environments, the performance of clusters is greatly affected. Various efforts in literature have been made to address the issues of heterogeneity (i.e., clusters consisting of virtual machines and machines with different hardware), network communication, data locality, better resource utilization, and run‐time scheduling. In this paper, we present a survey to discuss various research efforts made so far to improve Hadoop MapReduce scheduling. We classify scheduling algorithms and techniques proposed in the literature so far based on their addressing areas and present a taxonomy. Furthermore, we also discuss various aspects of open issues and challenges in the scheduling of MapReduce to improve its performance. Copyright © 2015 John Wiley & Sons, Ltd.     

一种云环境中的动态细粒度资源调度方法

云计算平台中普遍采用固定资源量的粗粒度资源分配方式,由此会引起资源碎片、过度分配、低集群资源利用率等问题.针对此问题,提出一种细粒度资源调度方法,该方法根据相似任务运行时信息推测任务资源需求;将任务划分为若干执行阶段,分阶段匹配资源,从分配时间和分配资源量两方面细化资源分配粒度;资源匹配过程中,基于资源可压缩特性进一步提高资源利用率和性能;采用资源监控、策略调整、约束检查等机制保证资源使用效率和负载性能.在开源云资源管理平台中,基于细粒度资源调度方法实现了调度器.实验结果表明：细粒度资源调度方法可以在不丧失公平性且调度响应时间可接受的前提下,细化资源匹配的粒度,有效提高云计算平台资源利用率和性能.     

Kubernetes异构资源细粒度调度策略的设计与实现

在异构资源环境中高效利用计算资源是提升任务效率和集群利用率的关键。Kuberentes作为容器编排领域的首选方案,在异构资源调度场景下调度器缺少GPU细粒度信息无法满足用户自定义需求,并且CPU/GPU节点混合部署下调度器无法感知异构资源从而导致资源竞争。综合考虑异构资源在节点上的分布及其硬件状态,提出一种基于Kubernetes的CPU/GPU异构资源细粒度调度策略。利用设备插件机制收集每个节点上GPU的详细信息,并将GPU资源指标提交给调度算法。在原有CPU和内存过滤算法的基础上,增加自定义GPU信息的过滤,从而筛选出符合用户细粒度需求的节点。针对CPU/GPU节点混合部署的情况,改进调度器的打分算法,动态感知应用类型,对CPU和GPU应用分别采用负载均衡算法和最小最合适算法,保证异构资源调度策略对不同类型应用的正确调度,并且在CPU资源不足的情况下充分利用GPU节点的碎片资源。通过对GPU细粒度调度和CPU/GPU节点混合部署情况下的调度效果进行实验验证,结果表明该策略能够有效进行GPU调度并且避免资源竞争。     

混合计算环境中截止期约束下的科学工作流调度策略

整合云和网格基础设施,增强科研机构现有网格系统的计算能力并向应用提供截止时间保障的服务是科学研究领域的热点。在这种"网格-云"混合计算环境中,对何时租借云虚拟资源以及如何租借做出有效决策是一个难题。现有的一些调度策略主要在网格资源静态能力特征的基础上,以作业等待时间作为决策依据,缺乏对资源动态服务能力的有效评估,无法保证科学应用的截止时间需求。本文提出了一种混合环境下的科学工作流执行系统架构并对其核心组件进行了阐述。针对其中的工作流调度问题,利用随机服务模型建模已有网格系统中的资源的动态服务能力,以任务违约风险作为是否租借外部虚拟资源的判断指标,提出了一个科学工作流调度算法HCA_SASWD。实验结果表明,HCA_SASWD相对于其他算法,能有效保证用户的截止时间要求,为需要提供截止时间保障的系统架构提供了参考。     

Bandwidth‐aware divisible task scheduling for cloud computing

Task scheduling is a fundamental issue in achieving high efficiency in cloud computing. However, it is a big challenge for efficient scheduling algorithm design and implementation (as general scheduling problem is NP‐complete). Most existing task‐scheduling methods of cloud computing only consider task resource requirements for CPU and memory, without considering bandwidth requirements. In order to obtain better performance, in this paper, we propose a bandwidth‐aware algorithm for divisible task scheduling in cloud‐computing environments. A nonlinear programming model for the divisible task‐scheduling problem under the bounded multi‐port model is presented. By solving this model, the optimized allocation scheme that determines proper number of tasks assigned to each virtual resource node is obtained. On the basis of the optimized allocation scheme, a heuristic algorithm for divisible load scheduling, called bandwidth‐aware task‐scheduling (BATS) algorithm, is proposed. The performance of algorithm is evaluated using CloudSim toolkit. Experimental result shows that, compared with the fair‐based task‐scheduling algorithm, the bandwidth‐only task‐scheduling algorithm, and the computation‐only task‐scheduling algorithm, the proposed algorithm (BATS) has better performance. Copyright © 2012 John Wiley & Sons, Ltd.     

Dynamic task assignment and resource management in cloud services by using bargaining solution

Cloud computing provides infrastructure, platform and software as services to customers. For the purpose of providing reliable and truthful service, a fair and elastic resource allocation strategy is essential from the standpoint of service customers. In this paper, we propose a game theoretic mechanism for dynamic cloud service management, including task assignment and resource allocation to provide reliable and truthful cloud services. A user utility function is first devised considering the dynamic characteristics of cloud computing. The elementary stepwise system is then applied to efficiently assign tasks to cloud servers. A resource allocation mechanism based on bargaining game solution is also adopted for fair resource allocation in terms of quality of service of requested tasks. Through numerical experiments, it is shown that the proposed mechanism guarantees better system performance than several existing methods. The experimental results show that the mechanism completes the requested tasks earlier with relatively higher utility while providing a significant level of fairness compared with existing ones. The proposed mechanism is expected to support cloud service providers in elastically managing their limited resources in a cloud computing environment in terms of quality of service. Copyright © 2013 John Wiley & Sons, Ltd.     

SLA based resource allocation policies in autonomic environments

Nowadays, large service centers provide computational capacity to many customers by sharing a pool of IT resources. The service providers and their customers negotiate utility based Service Level Agreement (SLA) to determine the costs and penalties on the base of the achieved performance level. The system is often based on a multi-tier architecture to serve requests and autonomic techniques have been implemented to manage varying workload conditions. The service provider would like to maximize the SLA revenues, while minimizing its operating costs. The system we consider is based on a centralized network dispatcher which controls the allocation of applications to servers, the request volumes at various servers and the scheduling policy at each server. The dispatcher can also decide to turn ON or OFF servers depending on the system load. This paper designs a resource allocation scheduler for such multi-tier autonomic environments so as to maximize the profits associated with multiple class SLAs. The overall problem is NP-hard. We develop heuristic solutions by implementing a local-search algorithm. Experimental results are presented to demonstrate the benefits of our approach.     

基于层次化的网格资源三层调度模型

在分析现有的资源调度方案及模型的基础上,提出了基于层次化的网格资源三层调度模型．它由主调度器、次级调度器和计算节点组成。主调度器根据任务的性质和需求,并参考下层次级调度器的执行情况,将部分任务分发到各次级调度器上,实现了主调度器与次级调度器之间的并行工作。基于该模型提出轮循任务分发策略。通过分析和模拟．该资源调度模型及任务分发策略在调度性能上明显优于集中式调度方案。     

网格环境中节点CPU负载的分形预测

网格环境下，常常需要知道网格资源在未来某一时刻具有什么样的性能，比如，调度器需要该性能估测以便进行高效的资源调度、提供满足要求的QoS以及保证整个网格系统的负载平衡。正如在其他任何计算环境中一样，计算能力是所有网格资源中最为重要的资源，通常用CPU负载来刻画节点主机的忙碌程度、衡量节点所能提供的计算能力。已有的研究表明CPU负载具有自相似性和长相关性，这启发我们使用本文介绍的分形的方法进行CPU负载的预测。实验结果证明该方法具有较高的预测精度，因而具有较好的实用价值。     

基于网格计算市场模型的资源管理和调度的研究

网格计算市场模型是把经济学的概念应用到网格资源管理和调度的模型。基于计算市场模型的网格资源管理系统借鉴人类社会竞争的市场调节机制，根据用户的经济需求进行资源管理与任务调度，不仅使资源所有者和资源消费者都能实现各自的经济目标，而且使资源消费者使用轻负栽和廉价的资源，达到整个网格资源整体的全局最优及合理利用。     

Market‐based grid resource co‐allocation and reservation for applications with hard deadlines

Grid computing technology enables the creation of large‐scale IT infrastructures that are shared across organizational boundaries. In such shared infrastructures, conflicts between user requirements are common and originate from the selfish actions that users perform when formulating their service requests. The introduction of economic principles in grid resource management offers a promising way of dealing with these conflicts. We develop and analyze both a centralized and a decentralized algorithm for economic grid resource management in the context of compute bound applications with deadline‐based quality of service requirements and non‐migratable workloads. Through the use of reservations, we co‐allocate resources across multiple providers in order to ensure that applications finish within their deadline. An evaluation of both algorithms is presented and their performance in terms of realized user value is compared with an existing market‐based resource management algorithm. We establish that our algorithms, which operate under a more realistic workload model, can closely approximate the performance of this algorithm. We also quantify the effect of allowing local workload preemption and different scheduling heuristics on the realized user value. Copyright © 2009 John Wiley & Sons, Ltd.     

Cost minimization in utility computing systems

Utility computing is a form of computer service whereby the company providing the service charges the users for using the system resources. In this paper, we present system‐optimal and user‐optimal price‐based job allocation schemes for utility computing systems whose objective is to minimize the cost for the users. The system‐optimal scheme provides an allocation of jobs to the computing resources that minimizes the overall cost for executing all the jobs in the system. The user‐optimal scheme provides an allocation that minimizes the cost for individual users in the system for providing fairness. The system‐optimal scheme is formulated as a constraint minimization problem, and the user‐optimal scheme is formulated as a non‐cooperative game. The prices charged by the computing resource owners for executing the users jobs are obtained using a pricing model based on a non‐cooperative bargaining game theory framework. The performance of the studied job allocation schemes is evaluated using simulations with various system loads and configurations. Copyright © 2012 John Wiley & Sons, Ltd.     

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high‐performance applications, such as local clusters, high‐performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads; hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications.