A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments

Fard, Hamid Mohammadi; Prodan, Radu; Barrionuevo, Juan Jose Durillo; Fahringer, Thomas

doi:10.1109/ccgrid.2012.114

Cited by 135 publications

(81 citation statements)

References 18 publications

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“…Transfer costs are calculated based on average bandwidth between the nodes, without regards to I/O contention, multiples accesses to the same resource, containers and VMs co-located in the same node sharing network and I/O resources, among other factors. This is also observed in other works such as [15,33,64,95]. Other models consider transfers as part of computation time, such as [70].…”

Section: Data-intensive Loadssupporting

confidence: 59%

“…Fard et al [33] defines a reliability factor by assuming a statistically independent constant failure rate, but this rate only reflects the probability of successful completion of a task -there is no clear connection between this concept and a factual and measurable metric from hardware and software point of view. Hakem and Butelle [43] also proposes a reliability-based resource allocation solution by defining a reliability model divided in processor, link, and system.…”

Section: Reliabilitymentioning

confidence: 99%

“…Fard et al [33] propose a multi-objective scheduling solution and present a case study comprising makespan, cost, energy, and reliability. The workflow is modeled as a very simple DAG with fixed size data dependencies among tasks.…”

Section: Further Analysismentioning

confidence: 99%

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Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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Cloud computing evolved from the concept of utility computing, which is defined as the provision of computational and storage resources as a metered service. Another key characteristic of cloud computing is multitenancy, which enables resource and cost sharing among a large pool of users. Characteristics such as multitenancy and elasticity perfectly fit the requirements of modern data-intensive research and scientific endeavors. In parallel, as science relies on the analysis of very large data sets, data management and processing must be performed in a scalable and automated way. Workflows have emerged as a way to formalize and structure data analysis, thus becoming an increasingly popular paradigm for scientists to handle complex scientific processes. One of the key enablers of this conjunction of cloud computing and scientific workflows is resource management. However, several issues related to data-intensive loads, complex infrastructures such as hybrid and multicloud environments to support large-scale execution of workflows, performance fluctuations, and reliability, pose as challenges to truly position clouds as viable high-performance infrastructures for scientific computing. This paper presents a survey on cloud resource management that provides an extensive study of the field. A taxonomy is proposed to analyze the selected works and the analysis ultimately leads to the definition of gaps and future challenges to be addressed by research and development.

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Section: Data-intensive Loadssupporting

confidence: 59%

Section: Reliabilitymentioning

confidence: 99%

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Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

2017

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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%

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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“…Moreover, requirements for job execution tends to become diverse due to multiple demands from the job submitter. To handle with such diversity, multi-objective approaches for task scheduling and resource allocation model have been proposed and being under development [3], [4], [5]. Those approaches assume that requirements (e.g., QoS, deadline for the job execution, total costs, and so on) are decided by a job submitter, i.e., an user.…”

Section: Introductionmentioning

confidence: 99%

Effective use of computational resources in multicore distributed systems

Kanemitsu

Hanada²,

Hoshiai

et al. 2014

16th International Conference on Advanced Communication Technology

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Abstract-In the last decades, many kinds of task execution models such as grid and cloud computing have been developed. In such distributed systems, each task is processed by respective processor in multicored computers e.g., household PCs which we can easily harness in recent years. If there is one policy to automatically decide the "best" combination and the number of processors (and computers), we effectively utilize those computational resources, thereby large number of jobs can be executed in parallel. In this paper, we propose a method for mapping of execution units for such environments. The method adopts a remapping technology after processor-execution unit mapping[11] is finished. Experimental comparisons by a simulation show the advantages of the proposed method.

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A Multi-objective Approach for Workflow Scheduling in Heterogeneous Environments

Cited by 135 publications

References 18 publications

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cloud resource management: towards efficient execution of large-scale scientific applications and workflows on complex infrastructures

Cost-Efficient CPU Provisioning for Scientific Workflows on Clouds

Effective use of computational resources in multicore distributed systems

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