Joint optimization method for task scheduling time and energy consumption in mobile cloud computing environment

An unheard of growth in mobile data traffic has drawn attention from academia and industry. Mobile cloud computing is an emerging computing paradigm combining cloud computing and mobile networks to alleviate resource-constrained limitations of mobile devices, which can greatly improve network quality of service and efficiency to make good use of available network resource. Mobile cloud computing not only inherits the advantages of strong computing capacity and massive storage of cloud computing, but also overcomes the time and geographical restrictions, bringing benefits for mobile users to offload complex computation to powerful cloud servers for execution anytime and anywhere. To this end, an optimal task workflow scheduling scheme is proposed for the mobile devices, based on the dynamic voltage and frequency scaling technique and the whale optimization algorithm. Through considering three factors: task execution position, task execution sequence, and operating voltage and frequency of mobile devices, this study makes a tradeoff between performance and energy consumption by solving the joint optimization for task completion time and energy consumption simultaneously. Finally, a series of extensive simulation results has demonstrated and verified the scheme has distinguished performance in terms of efficiency and operational cost, providing feasible solutions to similar optimization problems of mobile cloud computing.     

PSO-DS: a scheduling engine for scientific workflow managers

Cloud computing, an important source of computing power for the scientific community, requires enhanced tools for an efficient use of resources. Current solutions for workflows execution lack frameworks to deeply analyze applications and consider realistic execution times as well as computation costs. In this study, we propose cloud user–provider affiliation (CUPA) to guide workflow’s owners in identifying the required tools to have his/her application running. Additionally, we develop PSO-DS, a specialized scheduling algorithm based on particle swarm optimization. CUPA encompasses the interaction of cloud resources, workflow manager system and scheduling algorithm. Its featured scheduler PSO-DS is capable of converging strategic tasks distribution among resources to efficiently optimize makespan and monetary cost. We compared PSO-DS performance against four well-known scientific workflow schedulers. In a test bed based on VMware vSphere, schedulers mapped five up-to-date benchmarks representing different scientific areas. PSO-DS proved its efficiency by reducing makespan and monetary cost of tested workflows by 75 and 78%, respectively, when compared with other algorithms. CUPA, with the featured PSO-DS, opens the path to develop a full system in which scientific cloud users can run their computationally expensive experiments.     

异构云环境下AHP定权的多目标强化学习作业调度方法

随着新型基础设施建设(新基建)的加速,云计算将获得新的发展契机.数据中心作为云计算的基础设施,其内部服务器不断升级换代,这造成计算资源的异构化.如何在异构云环境下,对作业进行高效调度是当前的研究热点之一.针对异构云环境多目标优化调度问题,设计一种AHP定权的多目标强化学习作业调度方法.首先定义执行时间、平台运行能耗、成...     

Fuzzy Based Ant Colony Optimization Scheduling in Cloud Computing

Cloud computing is an Information Technology deployment model established on virtualization. Task scheduling states the set of rules for task allocations to an exact virtual machine in the cloud computing environment. However, task scheduling challenges such as optimal task scheduling performance solutions, are addressed in cloud computing. First, the cloud computing performance due to task scheduling is improved by proposing a Dynamic Weighted Round-Robin algorithm. This recommended DWRR algorithm improves the task scheduling performance by considering resource competencies, task priorities, and length. Second, a heuristic algorithm called Hybrid Particle Swarm Parallel Ant Colony Optimization is proposed to solve the task execution delay problem in DWRR based task scheduling. In the end, a fuzzy logic system is designed for HPSPACO that expands task scheduling in the cloud environment. A fuzzy method is proposed for the inertia weight update of the PSO and pheromone trails update of the PACO. Thus, the proposed Fuzzy Hybrid Particle Swarm Parallel Ant Colony Optimization on cloud computing achieves improved task scheduling by minimizing the execution and waiting time, system throughput, and maximizing resource utilization.     

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.     

Optimization of cloud computing task execution time and user QoS utility by improved particle swarm optimization

In order to optimize the quality of service (QoS) and execution time of task, a new resource scheduling based on improved particle swarm optimization (IPSO) is proposed to improve the efficiency and superiority. In cloud computing, the first principle of resource scheduling is to meet the needs of users, and the goal is to optimize the resource scheduling scheme and maximize the overall efficiency. This requires that the scheduling of cloud computing resources should be flexible, real-time and efficient. In this way, the mass resources of cloud computing can effectively meet the needs of the cloud users. Field Programmable Gate Arrays (FPGA), high performance and energy efficiency in one field. Most of them would have been the particle algorithm. The current technological development is still in-depth at super-resolution image research at an unprecedentedly fast pace. In particular, systemic origin applications get a lot of attention because they have a wide range of abnormal results. The scientific resource scheduling algorithm is the key to improve the efficiency of cloud computing resources distribution and the level of cloud services. In addition, the physical model of cloud computing resource scheduling is established. The performance of the IPSO algorithm applied to cloud computing resource scheduling is analysed in the design experiment. The comparison result shows that the new algorithm improves the PSO by taking full account of the user's Qu's requirements and the load balance of the cloud environment. In conclusion, the research on cloud computing resource scheduling based on IPSO can solve the problem of resource scheduling to a certain extent.     

A fault‐tolerant dynamic scheduling method on hierarchical mobile edge cloud computing

Mobile edge cloud computing has been a promising computing paradigm, where mobile users could offload their application workloads to low‐latency local edge cloud resources. However, compared with remote public cloud resources, conventional local edge cloud resources are limited in computation capacity, especially when serve large number of mobile applications. To deal with this problem, we present a hierarchical edge cloud architecture to integrate the local edge clouds and public clouds so as to improve the performance and scalability of scheduling problem for mobile applications. Besides, to achieve a trade‐off between the cost and system delay, a fault‐tolerant dynamic resource scheduling method is proposed to address the scheduling problem in mobile edge cloud computing. The optimization problem could be formulated to minimize the application cost with the user‐defined deadline satisfied. Specifically, firstly, a game‐theoretic scheduling mechanism is adopted for resource provisioning and scheduling for multiprovider mobile applications. Then, a mobility‐aware dynamic scheduling strategy is presented to update the scheduling with the consideration of mobility of mobile users. Moreover, a failure recovery mechanism is proposed to deal with the uncertainties during the execution of mobile applications. Finally, experiments are designed and conducted to validate the effectiveness of our proposal. The experimental results show that our method could achieve a trade‐off between the cost and system delay.     

An efficient job management of computing service using integrated idle VM resources for high-performance computing based on OpenStack

In recent years, various studies on OpenStack-based high-performance computing have been conducted. OpenStack combines off-the-shelf physical computing devices and creates a resource pool of logical computing. The configuration of the logical computing resource pool provides computing infrastructure according to the user’s request and can be applied to the infrastructure as a service (laaS), which is a cloud computing service model. The OpenStack-based cloud computing can provide various computing services for users using a virtual machine (VM). However, intensive computing service requests from a large number of users during large-scale computing jobs may delay the job execution. Moreover, idle VM resources may occur and computing resources are wasted if users do not employ the cloud computing resources. To resolve the computing job delay and waste of computing resources, a variety of studies are required including computing task allocation, job scheduling, utilization of idle VM resource, and improvements in overall job’s execution speed according to the increase in computing service requests. Thus, this paper proposes an efficient job management of computing service (EJM-CS) by which idle VM resources are utilized in OpenStack and user’s computing services are processed in a distributed manner. EJM-CS logically integrates idle VM resources, which have different performances, for computing services. EJM-CS improves resource wastes by utilizing idle VM resources. EJM-CS takes multiple computing services rather than single computing service into consideration. EJM-CS determines the job execution order considering workloads and waiting time according to job priority of computing service requester and computing service type, thereby providing improved performance of overall job execution when computing service requests increase.

     

混合云环境中数据敏感工作流调度

混合云环境下调度包含敏感数据的工作流主要考虑在满足数据安全性以及工作流截止时间的前提下,对工作流任务在混合云上进行分配,实现计算资源与任务的映射,并优化调度费用。采用了整数规划来建模求解包含数据敏感性、截止时间和调度费用3种约束条件的混合云工作流调度问题,同时为优化模型求解速度,基于“帕雷托最优”原理对工作流任务在混合云上的分配方案进行筛选以减小模型求解规模。实验表明,优先排除不合理的任务分配方案可有效减小整数规划模型的求解规模,缩短模型计算时间,在产生较小误差的情况下获得较优的调度结果。     

异构云环境下基于分簇的云资源感知任务调度方案

针对提高异构云平台中资源调度的效率,提出了一种基于任务和资源分簇的异构云计算平台任务调度方案。利用K-means算法,根据任务的CPU和I/O处理时间对任务分簇,根据资源的计算能力对资源分簇;然后,将任务簇对应到合适的资源簇,并利用最早截止时间优先（EDF）算法对任务簇中的独立任务进行调度,利用提出的改进型最小关键路径（MCP）算法对依赖性任务进行调度。实验结果表明,在资源异构的云计算环境中,该方案执行任务时间短、能耗低。     

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。     

Static scheduling of multiple workflows with soft deadlines in non-dedicated heterogeneous environments

Typical patterns of using scientific workflows include their periodical executions using a fixed set of computational resources. Using the statistics from multiple runs, one can accurately estimate task execution and communication times to apply static scheduling algorithms. Several workflows with known estimates could be combined into a set to improve the resulting schedule. In this paper, we consider the mapping of multiple workflows to partially available heterogeneous resources. The problem is how to fill free time windows with tasks from different workflows, taking into account users’ requirements of the urgency of the results of calculations. To estimate quality of schedules for several workflows with various soft deadlines, we introduce the unified metric incorporating levels of meeting constraints and fairness of resource distribution.The main goal of the work was to develop a set of algorithms implementing different scheduling strategies for multiple workflows with soft deadlines in a non-dedicated environment, and to perform a comparative analysis of these strategies. We study how time restrictions (given by resource providers and users) influence the quality of schedules, and which scheme of grouping and ordering the tasks is the most effective for the batched scheduling of non-urgent workflows. Experiments with several types of synthetic and domain-specific sets of multiple workflows show that: (i) the use of information about time windows and deadlines leads to the significant increase of the quality of static schedules, (ii) the clustering-based scheduling scheme outperforms task-based and workflow-based schemes. This was confirmed by an evaluation of studied algorithms on a basis of the CLAVIRE workflow management platform.     

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。     

Online optimization for scheduling preemptable tasks on IaaS cloud systems

In Infrastructure-as-a-Service (IaaS) cloud computing, computational resources are provided to remote users in the form of leases. For a cloud user, he/she can request multiple cloud services simultaneously. In this case, parallel processing in the cloud system can improve the performance. When applying parallel processing in cloud computing, it is necessary to implement a mechanism to allocate resource and schedule the execution order of tasks. Furthermore, a resource optimization mechanism with preemptable task execution can increase the utilization of clouds. In this paper, we propose two online dynamic resource allocation algorithms for the IaaS cloud system with preemptable tasks. Our algorithms adjust the resource allocation dynamically based on the updated information of the actual task executions. And the experimental results show that our algorithms can significantly improve the performance in the situation where resource contention is fierce.     

满足工作流执行时限的可抢占虚拟机实例配置和调度方法研究

随着云计算的迅速发展,将工作流部署到云计算平台已经成为了常见的选择。相比于传统的本地工作流,云工作流不仅要考虑计算时长等要求,还要考虑其产生的经济开销。而云计算服务商为了提高资源利用率,提供了可抢占虚拟机实例这种非常廉价但是不稳定的资源。针对工作流在云计算中的调度和执行问题,提出一种满足工作流执行时限的可抢占虚拟机实例配置和调度方法。该方法使用马尔科夫模型和动态规划方法,对可抢占虚拟机实例的价格进行预测,并得到成本最低的出价策略。同时,结合工作流的执行时限要求,在估计的出价策略下对工作流中使用的实例进行配置。实验结果显示,相比于全部使用按需付费虚拟机实例,该方法在满足工作流执行时限的前提下最高可以节省89.9%的计算成本。     

A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs

As cloud computing evolves, it is becoming more and more apparent that the future of this industry lies in interconnected cloud systems where resources will be provided by multiple “Cloud” providers instead of just one. In this way, the hosts of services that are cloud-based will have access to even larger resource pools while at the same time increasing their scalability and availability by diversifying both their computing resources and the geographical locations where those resources operate from. Furthermore the increased competition between the cloud providers in conjunction with the commoditization of hardware has already led to large decreases in the cost of cloud computing and this trend is bound to continue in the future. Scientific focus in cloud computing is also headed this way with more studies on the efficient allocation of resources and effective distribution of computing tasks between those resources. This study evaluates the use of meta-heuristic optimization algorithms in the scheduling of bag-of-tasks applications in a heterogeneous cloud of clouds. The study of both local and globally arriving jobs has been considered along with the introduction of sporadically arriving critical jobs. Simulation results show that the use of these meta-heuristics can provide significant benefits in costs and performance.     

Adaptive scheduling for parallel tasks with QoS satisfaction for hybrid cloud environments

A hybrid cloud integrates private clouds and public clouds into one unified environment. For the economy and the efficiency reasons, the hybrid cloud environment should be able to automatically maximize the utilization rate of the private cloud and minimize the cost of the public cloud when users submit their computing jobs to the environment. In this paper, we propose the Adaptive-Scheduling-with-QoS-Satisfaction algorithm, namely AsQ, for the hybrid cloud environment to raise the resource utilization rate of the private cloud and to diminish task response time as much as possible. We exploit runtime estimation and several fast scheduling strategies for near-optimal resource allocation, which results in high resource utilization rate and low execution time in the private cloud. Moreover, the near-optimal allocation in the private cloud can reduce the amount of tasks that need to be executed on the public cloud to satisfy their deadline. For the tasks that have to be dispatched to the public cloud, we choose the minimal cost strategy to reduce the cost of using public clouds based on the characteristics of tasks such as workload size and data size. Therefore, the AsQ can achieve a total optimization regarding cost and deadline constraints. Many experiments have been conducted to evaluate the performance of the proposed AsQ. The results show that the performance of the proposed AsQ is superior to recent similar algorithms in terms of task waiting time, task execution time and task finish time. The results also show that the proposed algorithm achieves a better QoS satisfaction rate than other similar studies.     

基于IFWA-ABC的云计算资源任务调度算法的研究

针对云计算资源任务调度效率低,资源分配不均的情况,将改进的烟花算法和人工蜂群算法算法进行融合为IFWA-ABC。首先,对云计算资源任务调度进行描述;其次,在FWA初始化中采用混沌反向学习和柯西分布进行优化,对核心烟花和非核心烟花的半径分别进行优化,将FWA中最优个体通过改进的ABC算法进行获得;最后,将IFWA-ABC算法用于云计算任务调度。仿真实验中,通过与FWA、ABC在虚拟机、执行时间、消耗成本、能量消耗指标对比中,IFWA-ABC具有明显的优势能够有效地提高云计算资源分配效率。     

云计算环境任务调度方法研究综述

随着应用程序计算需求的快速增长,异构计算资源不断地增多,任务调度成为云计算领域中重要的研究问题。任务调度负责将用户任务匹配给合适的虚拟计算资源,算法的优劣将直接影响响应时间、最大完工时间、能耗、成本、资源利用率等一系列与用户和云服务供应商经济利益密切相关的性能指标大小。针对独立任务和科学工作流这两类云环境主流任务,结合不同云环境特征对任务调度算法研究进展进行综述和讨论。回顾梳理已有的任务调度类型、调度机制及其优缺点;归纳单云环境和混合云、多云及联盟云等跨云环境下任务调度特征,并对部分相关典型文献的使用方法、优化目标、优缺点等方面进行阐述,在此基础上讨论各个环境下任务调度研究现状;进一步对各类环境下文献使用的调度优化方法进行梳理,明确其使用范围;总结并指出需要对计算数据密集型应用在跨云环境下的任务调度研究进行重点关注。     

A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds

Security is increasingly critical for various scientific workflows that are big data applications and typically take quite amount of time being executed on large-scale distributed infrastructures. Cloud computing platform is such an infrastructure that can enable dynamic resource scaling on demand. Nevertheless, based on pay-per-use and hourly-based pricing model, users should pay attention to the cost incurred by renting virtual machines (VMs) from cloud data centers. Meanwhile, workflow tasks are generally heterogeneous and require different instance series (i.e., computing optimized, memory optimized, storage optimized, etc.). In this paper, we propose a security and cost aware scheduling (SCAS) algorithm for heterogeneous tasks of scientific workflow in clouds. Our proposed algorithm is based on the meta-heuristic optimization technique, particle swarm optimization (PSO), the coding strategy of which is devised to minimize the total workflow execution cost while meeting the deadline and risk rate constraints. Extensive experiments using three real-world scientific workflow applications, as well as CloudSim simulation framework, demonstrate the effectiveness and practicality of our algorithm.