A delay-based dynamic scheduling algorithm for bag-of-task workflows with stochastic task execution times in clouds

Bag-of-Tasks (BoT) workflows are widespread in many big data analysis fields. However, there are very few cloud resource provisioning and scheduling algorithms tailored for BoT workflows. Furthermore, existing algorithms fail to consider the stochastic task execution times of BoT workflows which leads to deadline violations and increased resource renting costs. In this paper, we propose a dynamic cloud resource provisioning and scheduling algorithm which aims to fulfill the workflow deadline by using the sum of task execution time expectation and standard deviation to estimate real task execution times. A bag-based delay scheduling strategy and a single-type based virtual machine interval renting method are presented to decrease the resource renting cost. The proposed algorithm is evaluated using a cloud simulator ElasticSim which is extended from CloudSim. The results show that the dynamic algorithm decreases the resource renting cost while guaranteeing the workflow deadline compared to the existing algorithms.     

采用随机规划模型的云资源分配算法

针对云资源提供问题,为了降低云消费者的资源使用成本,提出了一种采用随机规划模型的云资源分配算法.同时考虑按需实例和预留实例,采用两阶段随机整数规划对云资源提供问题进行建模,在资源预留阶段,根据长期的工作负载情况,确定预留实例的类型和数量,在按需分配阶段,根据当前的工作负载,确定动态分配的按需实例的类型和数量.采用抽样平均近似方法减少资源提供问题的场景数量,降低求解复杂度,并提出了一种基于阶段分解的混合进化算法求解资源提供问题.仿真实验结果表明,采用随机规划模型的云资源分配算法能够在较短时间内获得近似最优的云资源预留方案,有效降低了云消费者的资源使用成本.     

Proactive virtualized resource management for service workflows in the cloud

A Cloud platform offers on-demand provisioning of virtualized resources and pay-per-use charge model to its hosted services to satisfy their fluctuating resource needs. Resource scaling in cloud is often carried out by specifying static rules or thresholds. As business processes and scientific jobs become more intricate and involve more components, traditional reactive or rule-based resource management methods are not able to meet the new requirements. In this paper, we extend our previous work on dynamically managing virtualized resources for service workflows in a cloud environment. Extensive experimental results of an adaptive resource management algorithm are reported. The algorithm makes resource management decisions based on predictive results and high level user specified thresholds. It is also able to coordinate resources among the component services of a workflow so that unnecessary resource allocations and terminations can be avoided. Based on observations from previous experiments, the algorithm is extended with a new resource merge strategy in order to prevent average resource size from shrinking. Simulation results from synthetic workload data demonstrated the effectiveness of the extension.     

ElasticSim: A Toolkit for Simulating Workflows with Cloud Resource Runtime Auto-Scaling and Stochastic Task Execution Times

Resource provisioning and scheduling are crucial for cloud workflow applications. Simulation is one of the most promising evaluation methods for different resource provisioning and scheduling algorithms. However, existing simulators for Cloud workflow applications fail to provide support for resource runtime auto-scaling and stochastic task execution time modeling. In this paper, a workflow simulator ElasticSim is introduced, which is an extension of the popular used CloudSim simulator by adding support for resource runtime auto-scaling and stochastic task execution time modeling. Most of existing workflow scheduling algorithms are static and are based on deterministic task execution times. By the aid of ElasticSim, the practical performance of existing static algorithms, when they are put into practice with stochastic task execution times, is evaluated. Experimental results show that about 2.8 % to 20 % additional resource rental cost is incurred for different cases and workflow deadlines are violated for most cases because of stochastic task execution times. Therefore, ElasticSim is a promising platform for evaluating the practical performance of workflow resource provisioning and scheduling algorithms, which supports resource runtime auto-scaling and stochastic task execution time modeling.     

Scheduling of decentralized robot services in cloud manufacturing with deep reinforcement learning

Cloud manufacturing is a service-oriented manufacturing model that offers manufacturing resources as cloud services. Robots are an important type of manufacturing resources. In cloud manufacturng, large-scale distributed robots are encapsulated into cloud services and provided to consumers in an on-demand manner. How to effectively and efficiently manage and schedule decentralized robot services in cloud manufacturing to achieve on-demand provisioning is a challenging issue. During the past few years, Deep Reinforcement Learning (DRL) has become very popular and successfully been applied to many different areas such as games, robotics, and manufacturing. DRL also holds tremendous potential for solving scheduling issues in cloud manufacturing. To this end, this paper is devoted to exploring effective approaches for scheduling of decentralized robot manufacturing services in cloud manufacturing with DRL. Specifically, both Deep Q-Networks (DQN) and Dueling Deep Q-Networks (DDQN)-based scheduling algorithms are proposed. Performance of different algorithms, including DQN, DDQN, and other three benchmark algorithms, indicates that DDQN performs the best with respect to each indicator. Effects of different combinations of weight coefficients and influencing degrees of different indicators on the overall scheduling objective are analyzed. Results indicate that the DDQN-based scheduling algorithm is able to generate scheduling solutions efficiently.     

云计算环境下科学工作流两阶段任务调度策略

经过对云环境下科学工作流现有的任务调度策略进行分析研究,针对异态最早结束时间(HEFT)算法及其改进算法SHEFT在任务执行过程中出现的资源闲置现象,结合云计算的特点,在SHEFT算法的基础上提出了一种两阶段任务调度策略。该策略在完成时间最少的情况下能够对资源的闲置时间进行尽可能的利用。经过对该算法进行实验和性能分析,表明该策略在完成时间和资源利用方面都有很大改进。     

Fast GA-based project scheduling for computing resources allocation in a cloud manufacturing system

Cloud manufacturing is becoming an increasingly popular enterprise model in which computing resources are made available on-demand to the user as needed. Cloud manufacturing aims at providing low-cost, resource-sharing and effective coordination. In this study, we present a genetic algorithm (GA) based resource constraint project scheduling, incorporating a number of new ideas (enhancements and local search) for solving computing resources allocation problems in a cloud manufacturing system. A newly generated offspring may not be feasible due to task precedence and resource availability constraints. Conflict resolutions and enhancements are performed on newly generated offsprings after crossover or mutation. The local search can exploit the neighborhood of solutions to find better schedules. Due to its complex characteristics, computing resources allocation in a cloud manufacturing system is NP-hard. Computational results show that the proposed GA can rapidly provide a good quality schedule that can optimally allocate computing resources and satisfy users’ demands.     

云中截止时间动态分配的工作流调度成本优化算法

现如今,如何在满足截止时间约束的前提下降低工作流的执行成本,是云中工作流调度的主要问题之一。三步列表调度算法可以有效解决这一问题。但该算法在截止时间分配阶段只能形成静态的子截止时间。为方便用户部署工作流任务,云服务商为用户提供了的三种实例类型,其中竞价实例具有非常大的价格优势。为解决上述问题,提出了截止时间动态分配的工作流调度成本优化算法(S-DTDA)。该算法利用粒子群算法对截止时间进行动态分配,弥补了三步列表调度算法的缺陷。在虚拟机选择阶段,该算法在候选资源中增加了竞价实例,大大降低了执行成本。实验结果表明,相较于其他经典算法,该算法在实验成功率和执行成本上具有明显优势。综上所述,S-DTDA算法可以有效解决工作流调度中截止时间约束的成本优化问题。     

Cost-Time Efficient Scheduling Plan for Executing Workflows in the Cloud

The emergence of Cloud Computing as a model of service provisioning in distributed systems instigated researchers to explore its pros and cons on executing different large scale scientific applications, i.e., Workflows. One of the most challenging problems in clouds is to execute workflows while minimizing the execution time as well as cost incurred by using a set of heterogeneous resources over the cloud simultaneously. In this paper, we present, Budget and Deadline Constrained Heuristic based upon Heterogeneous Earliest Finish Time (HEFT) to schedule workflow tasks over the available cloud resources. The proposed heuristic presents a beneficial trade-off between execution time and execution cost under given constraints. The proposed heuristic is evaluated for different synthetic workflow applications by a simulation process and comparison is done with state-of-art algorithm i.e. BHEFT. The simulation results show that our proposed scheduling heuristic can significantly decrease the execution cost while producing makespan as good as the best known scheduling heuristic under the same deadline and budget constraints.     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

Application Layer Scheduling in Cloud: Fundamentals,Review and Research Directions

The cloud computing paradigm facilitates a finite pool of on-demand virtualized resources on a pay-per-use basis. For large-scale heterogeneous distributed systems like a cloud, scheduling is an essential component of resource management at the application layer as well as at the virtualization layer in order to deliver the optimal Quality of Services (QoS). The cloud scheduling, in general, is an NP-hard problem due to large solution space, thus, it is difficult to find an optimal solution within a reasonable time. In application layer scheduling, the tasks are mapped to logical resources (i.e., virtual machines), aiming to optimize one or more QoS parameters, and conforming to several constraints. Various algorithms have been proposed in the literature for application layer scheduling, where each of them is based on some fundamental design techniques like simple heuristics, meta-heuristics, and most recently hybrid heuristics. Although ample literature survey exists for cloud scheduling algorithms, none of them present their study exclusively for the application layer. In this survey paper, we present a study on task scheduling algorithms used only at the application layer of the cloud. We classify our study according to various fundamental techniques used in designing such scheduling algorithms. One of the main features of our study is that it covers numerous application type e.g., a set of independent tasks, simple workflow, scientific workflow, and MapReduce jobs. We also provide a comparative analysis of existing algorithms on various parameters like makespan, cost, resource utilization, etc. In the end, research directions for future work have been provided.     

Elastic resource provisioning in hybrid mobile cloud for computationally intensive mobile applications

The paper addresses the integration of hybrid cloud with mobile applications. The challenge about hybrid mobile cloud resource provisioning is the trade-offs between energy consumption, performance provided to users and how resources, such as processing power and network, are being utilized. The proposed elastic hybrid mobile cloud resource provisioning model is jointly optimized to improve mobile user experience within the constraints of available resources and user QoS requirement. The paper presents the system utility of hybrid cloud system involving local cloud and public cloud infrastructure. From the perspectives of both mobile applications and cloud providers, the proposed system utility is optimized to improve the performance of mobile applications and the utilization of cloud resources. The proposed elastic hybrid mobile cloud resource provisioning algorithm includes two sub-algorithms. To evaluate and validate performance of the proposed algorithm, a series of experiments are conducted. The comparison results and analyses are discussed. The experimental results show the improvement to previous works.     

DAG分割模型下的云工作流调度策略

为了优化云工作流调度的经济代价和执行效率,提出一种基于有向无循环图（DAG）分割的工作流调度算法PBWS。以工作流调度效率与代价同步优化为目标,算法将调度求解过程划分为三个阶段进行：工作流DAG结构分割、分割结构调整及资源分配。工作流DAG结构分割阶段在确保任务间执行顺序依赖的同时求解初始的任务分割图;分割结构调整阶段以降低执行跨度为目标,在不同分割间对任务进行重分配;资源分配阶段旨在选择代价最高效的任务与资源映射关系,确保资源的总空闲时间最小。利用五种科学工作流DAG模型对算法进行了仿真实验。结果表明。PBWS算法仅以较小的执行跨度为开销,极大降低了工作流执行代价,实现了调度效率与调度代价的同步优化,其综合性能是优于同类型算法的。     

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。     

基于Wi-Fi与Web的云计算资源调度算法研究

为缩短云计算执行时间,改善云计算性能,在一定程度上加强云计算资源节点完成任务成功率,需要对云计算资源进行调度;当前的云计算资源调度算法在进行调度时,通过选择合适的调度参数并利用CloudSim仿真工具,完成对云计算资源的调度;该算法在运行时无法有效地进行平衡负载,导致云计算资源调度的均衡性能较差,存在云计算资源调度结果误差大的问题;为此,提出一种基于Wi-Fi与Web的云计算资源调度算法;该算法首先利用自适应级联滤波算法对云计算资源数据流进行滤波降噪,然后以降噪结果为基础,采用本体论对云计算资源进行预处理操作,最后通过人工蜂群算法完成对云计算资源的调度;实验结果证明,所提算法可以良好地应用于云计算资源调度中,有效提高了云计算资源利用率,具有实用性以及可实践性,为该领域的后续研究发展提供了可靠支撑。     

A hybrid instance-intensive workflow scheduling method in private cloud environment

Aiming to solve the problem of instance-intensive workflow scheduling in private cloud environment, this paper first formulates a scheduling optimization model considering the communication time between tasks. The objective of this model is to minimize the execution time of all workflow instances. Then, a hybrid scheduling method based on the batch strategy and an improved genetic algorithm termed fragmentation based genetic algorithm is proposed according to the characters of instance-intensive cloud workflow, where task priority dispatching rules are also taken into account. Simulations are conducted to compare the proposed method with the canonical genetic algorithm and two heuristic algorithms. Our simulation results demonstrate that the proposed method can considerably enhance the search efficiency of the genetic algorithm and is able to considerably outperform the compared algorithms, in particular when the number of workflow instances is high and the computational resource available for optimization is limited.

     

An intelligent water drops-based workflow scheduling for IaaS cloud

Cloud computing is an emerging technology in a distributed environment with a collection of large-scale heterogeneous systems. One of the challenging issues in the cloud data center is to select the minimum number of virtual machine (VM) instances to execute the tasks of a workflow within a time limit. The objectives of such a strategy are to minimize the total execution time of a workflow and improve resource utilization. However, the existing algorithms do not guarantee to achieve high resource utilization although they have abilities to achieve high execution efficiency. The higher resource utilization depends on the reusability of VM instances. In this work, we propose a new intelligent water drops based workflow scheduling algorithm for Infrastructure-as-a-Service (IaaS) cloud. The objectives of the proposed algorithm are to achieve higher resource utilization and minimize the makespan within the given deadline and budget constraints. The first contribution of the algorithm is to find multiple partial critical paths (PCPs) of a workflow which helps in finding suitable VM instances. The second contribution is a scheduling strategy for PCP-VM assignment for assigning the VM instances. The proposed algorithm is evaluated through various simulation runs using synthetic datasets and various performance metrics. Through comparison, we show the superior performance of the proposed algorithm over the existing ones.     

一种云工作流任务调度能效优化算法

工作流任务执行时带来的高能耗不仅会增加云资源提供方的经济成本,而且会降低云系统的可靠性。为了满足截止时间的同时,降低工作流执行能耗,提出一种工作流能效调度算法CWEES。算法将能效优化调度划分为三个阶段：初始任务映射、处理器资源合并和任务松驰。初始任务映射旨在通过任务自底向上分级排序得到任务调度初始序列,处理器资源合并旨在通过重用松驰时间合并相对低效率的处理器,降低资源使用数量,任务松驰旨在为每个任务重新选择带有合适电压/频率等级的最优目标资源,在不违背任务顺序和截止时间约束前提下降低工作流执行总能耗。通过随机工作任务模型对算法的性能进行了仿真实验分析。结果表明,CWEES算法不仅资源利用率更高,而且可以在满足截止时间约束下降低工作流执行能耗,实现执行效率与能耗的均衡。     

云环境中基于分解的多目标工作流调度算法

云服务提供商在给用户提供海量虚拟资源的同时,也面临着一个现实的问题,即怎样调度这些资源,以最小的代价(完工时间、执行费用、资源利用率等)完成工作流的执行。针对IaaS环境下的工作流调度问题,以完工时间和执行费用作为目标,提出了一种基于分解的多目标工作流调度算法。该算法结合了基于列表的启发式算法和多目标进化算法的选择过程,采用一种分解方法,将多目标优化问题分解为一组单目标优化子问题,然后同时求解这些单目标子问题,使得调度过程更为简单有效。算法利用天马项目发布的现实世界中的工作流进行实验,结果表明,和MOHEFT算法以及NSGA-II*算法相比较,所提出的算法能得到更优的Pareto解集,同时具有更低的时间复杂度。     

A hybrid heuristic workflow scheduling algorithm for cloud computing environments

Cloud computing is a relatively new concept in the distributed systems and is widely accepted as a new solution for high performance and distributed computing. Its dynamisms in providing virtual resources for organisations and laboratories and its pay-per-use policy make it very popular. A workflow models a process consisting of a series of steps that shape an application. Workflow scheduling is the method for assigning each workflow task to a processing resource in a way that specific workflow rules are satisfied. Some scheduling algorithms for workflows may assume some quality of service parameter such as cost and deadline. Some efforts have been done on workflow scheduling on cloud computing environments with different service level agreements. But most of them suffer from low speed. Here, we introduce a new hybrid heuristic algorithm based on particle swarm optimisation (PSO) and gravitation search algorithms. The proposed algorithm, in addition to processing cost and transfer cost, takes deadline limitations into account. The proposed workflow scheduling approach can be used by both end-users and utility providers. The CloudSim toolkit is used as a cloud environment simulator and the Amazon EC2 pricing is the reference pricing used. Our experimental result shows about 70% cost reduction, in comparison to non-heuristic implementations, 30% cost reduction in comparison to PSO, 30% cost reduction in comparison to gravitational search algorithm and 50% cost reduction in comparison to hybrid genetic-gravitational algorithm.