Heuristics for Provisioning Services to Workflows in XaaS Clouds

Cai, Zhicheng; Li, Xiaoping; Gupta, Jatinder N. D.

doi:10.1109/tsc.2014.2361320

Cited by 65 publications

(41 citation statements)

References 25 publications

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“…Differently parameterised service instances are provided in cloud platform [9], [10], [11]. Let us use the symbol s u k as the type u of k-th service instance; m denotes the number of obtainable service types, u: {1,2,…,m} refers service type index.…”

Section: A Service Instance Modellingmentioning

confidence: 99%

“…With contrast to the traditional scheduling schemes [9], [12], [13], on the arrival of new workflows (in Figure 3), the ranking of tasks is the prime step in this algorithm. Then, immediately, all the ready tasks are allocated to the machines as executing tasks or the waiting tasks and all the waiting tasks are placed in the task pool.…”

Section: Algorithm Descriptionmentioning

confidence: 99%

See 1 more Smart Citation

i- Rosa, Improved Uncertainty Aware Workflow Scheduling Algorithm

Bulchandani¹,

Agrawal²,

Chourasia³

et al. 2019

IJITEE

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Many applications running in cloud computing environment are workflow applications which contain large number of precedence specific tasks and so require proper schedule in order to complete successfully. Efficient scheduling of workflow applications is a challenging task. In workflows, the uncertainties like ‘uncertain data transfer time’ among dependent tasks and the uncertain task execution time, if ignored may lead to deadline violation. The proposed workflow scheduling algorithms so far unconcerned these uncertainties. This paper presents an improved unceRtainty aware workflow scheduling algorithm abbreviated as i-ROSA, that considers the uncertainties of scheduling workflows such as the uncertain running time of tasks in distributed environment when focused upon gave the superior outcome in way of cost and resource utilization, for DAG when compared with the original algorithm. The compared task scheduling algorithms are implemented in Workflowsim.

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Section: A Service Instance Modellingmentioning

confidence: 99%

Section: Algorithm Descriptionmentioning

confidence: 99%

i- Rosa, Improved Uncertainty Aware Workflow Scheduling Algorithm

Bulchandani¹,

Agrawal²,

Chourasia³

et al. 2019

IJITEE

View full text Add to dashboard Cite

show abstract

“…Several studies are focused on the adaptation [Andrikopoulos et al, 2013] and the predictive cloud selections [Brogi et al, 2014, Qu et al, 2015, Vu and Asal, 2012 for the efficient and robust deployment of legacy systems on cloud environments [Cai et al, 2016, Papazoglou et al, 2007.…”

Section: Related Workmentioning

confidence: 99%

Component-wise Application Migration in Bidimensional Cross-cloud Environments

Carrasco

Durán

Pimentel

2017

Proceedings of the 7th International Conference on Cloud Computing and Services Science

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Abstract:We propose an algorithm for the migration of cloud applications' components between different providers, possibly changing their service level between IaaS and PaaS. Our solution relies on three of the key ingredients of the trans-cloud approach: a unified API, agnostic topology descriptions, and mechanisms for the independent specification of providers. We show how our approach allows us to overcome some of the current interoperability and portability issues of cloud environments to propose a solution for migration, present an implementation of our proposed solution, and illustrate it with a case study and experimental results.

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“…Features like resource control, resource customization, virtual platforms and elasticity in cloud environments enable easy migration of data intensive applications to the cloud. Frameworks and architectures required to enable efficient use of cloud resources at minimal costs for data intensive applications is an issue that exists [7] [8]. The MapReduce model developed by Google [9] is the most popular model developed for distributed and complex computation executions on the cloud.…”

Section: Introductionmentioning

confidence: 99%

A Novel Parallel Computation Model with Efficient Local Memory Management for Data-Intensive Applications

Al-Absi

Kang

2015

2015 IEEE 8th International Conference on Cloud Computing

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The provisioning of high-performance computing infrastructure through cloud environments enables data intensive processing to be a viable solution. In this paper, we introduce a novel parallel computation model similar to MapReduce framework. The proposed parallelized model incorporates a parallel execution strategy in worker nodes to decrease execution response times in cloud environments. The parallelized model adopts efficient local memory management techniques in the worker nodes to reduce memory transfer overheads. For evaluation, we compared the proposed framework with the state of art Hadoop MapReduce framework. From experiments on benchmark datasets, it turns out that the parallelized model reduces the execution times by about 45.86%. Those experimental results indicate the efficiency and the scalability of proposed framework on cloud environments.

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Heuristics for Provisioning Services to Workflows in XaaS Clouds

Cited by 65 publications

References 25 publications

i- Rosa, Improved Uncertainty Aware Workflow Scheduling Algorithm

i- Rosa, Improved Uncertainty Aware Workflow Scheduling Algorithm

Component-wise Application Migration in Bidimensional Cross-cloud Environments

A Novel Parallel Computation Model with Efficient Local Memory Management for Data-Intensive Applications

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