Queue Waiting Time Aware Dynamic Workflow Scheduling in Multicluster Environments

余志峰,; 施巍松,

doi:10.1007/s11390-010-9371-8

Cited by 7 publications

(4 citation statements)

References 19 publications

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“…These models can be categorized into a single workflow and multi-workflow allocation models. Further, Single workflow models are classified into static workflow allocation models [25,26,27,28,29,30,31,32,33,34,39,40,41,42,43,44,45,46,47,48] and dynamic workflow allocation models [35,36,37,38]. Static workflow allocation is done at compile time.…”

Section: Related Workmentioning

confidence: 99%

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

et al. 2022

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Cloud Service Providers are speedily becoming the target platform for scientific workflow computations due to the massive possible and flexible pay-as-you-go pricing model. Workflow allocation problem in cloud systems is considered NP-hard. A heterogeneous IaaS cloud could be fully effective if the allocation method provides an efficient mapping between virtual machines (VMs) and workflow applications demanding execution. First, we model multiple workflow allocation problem in the cloud environment. Then, we propose a levelized multiple workflow allocation strategy with task merging (LMWS-TM) to optimize turnaround time for multiple workflow applications in the Infrastructure as a Service (IaaS) cloud environment to achieve better performance. The task merging scheme is incorporated into workflows after partitioning and prior to allocation to reduce inter-task communication share and the total number of depth levels for improving the overall completion time. Moreover, it considers the inter-task communication and inter-machine distance for estimating communication cost share among tasks on the schedule generated. Furthermore, the scheme is capable enough to use simple and flexible level attributes to tackle precedence constraints. Afterward, we conducted an experimental study to evaluate LMWS-TM by comparative performance analysis with its peers, namely SLBBS, DLS, and HEFT, on quality of service (QoS) parameters, namely, turnaround time, system utilization, flow time, and response time. The study reveals the superior performance of LMWS-TM among its considered peers in almost all the cases for almost all considered parameters under investigation. Finally, we performed statistical testing to test the significance level using SPSS 20, confirming the hypothesis drawn in the experimental study.

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Section: Related Workmentioning

confidence: 99%

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

et al. 2022

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“…The Multi-Cluster computing paradigm [44] employs multiple distributed clusters to build large computing infrastructure for HPC applications. There have been a considerable portion of research devoted to the multi-cluster scheduling, for example, scheduling of workflow applications [45], [46], [47] and scheduling of independent tasks [48], [49], [50] across the multi-cluster infrastructure. Compared with the aforementioned work, our research in this paper is devoted to scheduling of MapReduce jobs to multiple clusters, where we use a different task model and the data-centric scheduling heuristic.…”

Section: Multi-cluster Computingmentioning

confidence: 99%

Stochastic Workload Scheduling for Uncoordinated Datacenter Clouds with Multiple QoS Constraints

Chen

Wang

Chen

et al. 2020

IEEE Trans. Cloud Comput.

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Cloud computing becomes a well-adopted computing paradigm. With the unprecedented scalability and flexibility, the computational cloud is able to carry out large scale computing tasks in the parallel fashion. The datacenter cloud is a new cloud computing model that use multi-datacenter architectures for large scale massive data processing or computing. In datacenter cloud computing, the overall efficiency of the cloud depends largely on the workload scheduler, which allocates clients' tasks to different Cloud datacenters. Developing high performance workload scheduling techniques in Cloud computing imposes a great challenge which has been extensively studied. Most previous works aim only at minimizing the completion time of all tasks. However, timeliness is not the only concern, while reliability and security are also very important. In this work, a comprehensive Quality of Service (QoS) model is proposed to measure the overall performance of datacenter clouds. An advanced Cross-Entropy based stochastic scheduling (CESS) algorithm is developed to optimize the accumulative QoS and sojourn time of all tasks. Experimental results show that our algorithm improves accumulative QoS and sojourn time by up to 56.1% and 25.4% compared to the baseline algorithm, respectively. The runtime of our algorithm grows only linearly with the number of Cloud datacenters and tasks. Given the same arrival rate and service rate ratio, our algorithm steadily generates scheduling solutions with satisfactory QoS without sacrificing sojourn time. Index Terms-Cloud Computing, DataCenter Clouds, Quality of Service, Workload Scheduling ! 1 INTRODUCTION C LOUD computing [1], which delivers computing as a service, has emerged as a well-adopted computing paradigm which offers vast computing power and flexibility, and an increasing number of commercial cloud computing services are deployed into the market such as Amazon EC2 [2], Google Compute Engine [3], and Rackspace Cloud [4]. The new computing paradigms of "Cloud of Clouds" [5] and "datacenter clouds" [6], [7] are a creation of federated Cloud computing environment that coordinates distributed datacenter computing and achieves high QoS for Cloud applications. Large-scale data-intensive applications across distributed modern datacenter infrastructures is a good implementation and use case of the "Cloud of Clouds" paradigm. A good example for data-intensive analysis is the field of High Energy Physics (HEP). The four main detectors including ALICE, ATLAS, CMS and LHCb at the Large Hadron Collider (LHC) produced about 13 petabyes of data in 2010 [8]. This huge amount of data are stored on the Worldwide LHC Computing Grid that consists of more

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“…In [117], BMBP is implemented in an on-line service system QBETS. These characteristics make BMBP an attractive technique to select resource for workflow scheduling in [118,184].…”

Section: Dynamic Workflow Schedulingmentioning

confidence: 99%

Workflow scheduling in cloud: a survey

2015

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To program in distributed computing environments such as grids and clouds, workflow is adopted as an attractive paradigm for its powerful ability in expressing a wide range of applications, including scientific computing, multi-tier Web, and big data processing applications. With the development of cloud technology and extensive deployment of cloud platform, the problem of workflow scheduling in cloud becomes an important research topic. The challenges of the problem lie in: NP-hard nature of task-resource mapping; diverse QoS requirements; on-demand resource provisioning; performance fluctuation and failure handling; hybrid resource scheduling; data storage and transmission optimization. Consequently, a number of studies, focusing on different aspects, emerged in the literature. In this paper, we firstly conduct taxonomy and comparative review on workflow scheduling algorithms. Then, we make a comprehensive survey of workflow scheduling in cloud environment in a problem-solution manner. Based on the analysis, we also highlight some research directions for future investigation.

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Queue Waiting Time Aware Dynamic Workflow Scheduling in Multicluster Environments

Cited by 7 publications

References 19 publications

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

Levelized Multiple Workflow Allocation Strategy Under Precedence Constraints With Task Merging in IaaS Cloud Environment

Stochastic Workload Scheduling for Uncoordinated Datacenter Clouds with Multiple QoS Constraints

Workflow scheduling in cloud: a survey

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