Bi-Criteria Scheduling of Scientific Grid Workflows

Prodan, Radu; Wieczorek, Marek

doi:10.1109/tase.2009.2014643

Cited by 69 publications

(43 citation statements)

References 17 publications

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“…More complex scenarios, such as workflows [34], [42], service composition [35], and economical provisioning [47] will be considered. In these scenarios, VMs can be used for each stage in a process and optimized to achieve a balanced resource allocation and thus a better overall performance.…”

Section: Discussionmentioning

confidence: 99%

Dynamic Control of Data Streaming and Processing in a Virtualized Environment

Cao

Wen

Tan

2012

IEEE Trans. Automat. Sci. Eng.

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Abstract-Performance of data streaming applications is co-determined by both networking and computing resources, and therefore they should be co-scheduled and co-allocated in an integrated and coordinated way. Dynamic control of resource scheduling and allocation is required, because unilateral redundancy in either networking or computing resources may result in the overprovision of it and the other may become a bottleneck. To avoid resource shortage as well as overprovision, in this paper, a virtualized platform is utilized to implement data streaming and processing. In this platform, fuzzy logic controllers are designed to allocate CPU resources; iterative bandwidth allocation is applied and is processing-and storage-aware to guarantee on-demand data provisioning. Experimental results show that our approach leads to higher application performance as well as higher resource utilization, compared with other resource scheduling and allocation methods.Note to Practitioners-Data streaming has become an important paradigm in many business and scientific applications, such as fraud detection in banking industry and gravitational-wave observation in astronomy. In this paper, we apply virtualization to provide better support for these applications, leveraging its advantage in resource on-demand allocation. A novel dynamic control method is proposed so that CPU and bandwidth can be co-scheduled and co-allocated since for data streaming applications these resources are tightly coupled from the performance perspective. In this control method, fuzzy logic control is applied for CPU allocation and an iterative algorithm is adopted for processing-, congestion-, and storage-aware bandwidth allocation. We use fuzzy control because it does not rely on the mathematical modeling of an object and can implement human experts' heuristic knowledge via IF-THEN rules. Therefore, it is desirable in our data streaming scenario, due to the variable coupling and heavily nonlinear nature of the system.

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Section: Discussionmentioning

confidence: 99%

Dynamic Control of Data Streaming and Processing in a Virtualized Environment

Cao

Wen

Tan

2012

IEEE Trans. Automat. Sci. Eng.

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“…Wieczorek and Prodan et al [129,164] used -approach to solve a bi-criteria scheduling problem. They described the problem as an extension of the multiplechoice knapsack problem and proposed an algorithm called DCA based on dynamic programming method.…”

Section: -Approachmentioning

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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“…Existing work on task scheduling and resource provisioning addresses various optimization goals in different computing systems such as grids [1,9,20,22] and clouds [3,11,17]. Execution time and cost optimization is the focus in many studies [14,25,27].…”

Section: Related Workmentioning

confidence: 99%

“…If the algorithm does not succeed, it reverts to computing a schedule at maximum frequency using HEFT (lines [19][20].…”

Section: Algorithm 2 Cost-based Stepwise Frequency Selection From Minmentioning

confidence: 99%

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Pietri

Sakellariou

2016

Economics of Grids, Clouds, Systems, and Services

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Abstract. Cloud providers now offer resources as combinations of CPU frequencies and prices, with faster resources (which operate at higher frequencies) charged at a higher monetary cost. With the emergence of this new pricing scheme, the problem of choosing cost-efficient configurations is becoming even more challenging for users. The frequencies required to achieve cost-efficient configurations may vary in different scenarios, depending on both the provider's pricing model and the application characteristics. In this paper, two cost-aware algorithms that select low-cost CPU frequencies for each resource to complete a scientific workflow application within a deadline and at a minimum cost are presented. The proposed approaches are evaluated and compared through simulation using different pricing models that charge resource provisioning also based on the CPU frequency.

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Bi-Criteria Scheduling of Scientific Grid Workflows

Cited by 69 publications

References 17 publications

Dynamic Control of Data Streaming and Processing in a Virtualized Environment

Dynamic Control of Data Streaming and Processing in a Virtualized Environment

Workflow scheduling in cloud: a survey

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

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