Efficient task scheduling algorithms for heterogeneous multi-cloud environment

Panda, Sanjaya Kumar; Jana, Prasanta K.

doi:10.1007/s11227-014-1376-6

Cited by 169 publications

(36 citation statements)

References 40 publications

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“…Panda and Jana have proposed a task scheduling framework including three heuristic algorithms in a multicloud environment. Simulation results showed improvement in makespan and average cloud utilization.…”

Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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Summary Today, cloud computing has developed as one of the important emergent technologies in communication and Internet. It offers on demand, pay per use access to infrastructure, platforms, and applications. Due to the increase in its popularity, the huge number of requests need to be handled in an efficient manner. Task scheduling as one of the challenges in the cloud computing supports the requests for assigning a particular resource so as to perform effectively. In the resource management, task scheduling is performed where there is the dependency between tasks. Many approaches and case studies have been developed for the scheduling of these tasks. Up to now, a systematic literature review (SLR) has not been presented to discover and evaluate the task scheduling approaches in the cloud computing environment. To overcome, this paper presents an SLR‐based analysis on the task scheduling approaches that classify into (a) single cloud environments that evaluate cost‐aware, energy‐aware, multi‐objective, and QoS‐aware approaches in task scheduling; (b) multicloud environment that evaluates cost‐aware, multi‐objective, and QoS‐aware task scheduling; and (c) mobile cloud environment that is energy‐aware and QoS‐aware task scheduling. The analytical discussions are provided to show the advantages and limitations of the existing approaches.

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Section: Analysis Of Task Scheduling Approachesmentioning

confidence: 99%

Task scheduling mechanisms in cloud computing: A systematic review

Motlagh

Movaghar

Rahmani

2019

Int J Communication

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“…The technique used provides linear transformation on original range of data, and is called Min-Mix normalization [17], [18]. The technique keeps the relationship among original data.…”

Section: B Data Normalizationmentioning

confidence: 99%

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

Sugiartawan¹,

Pulungan²,

Sari³

2017

ijacsa

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Abstract-Data originating from some specific fields, for instance tourist arrivals, may exhibit a high degree of fluctuations as well as non-linear characteristics due to time varying behaviors. This paper proposes a new hybrid method to perform prediction for such data. The proposed hybrid model of wavelet transform and long-short-term memory (LSTM) recurrent neural network (RNN) is able to capture non-linear attributes in tourist arrival time series. Firstly, data is decomposed into constitutive series through wavelet transform. The decomposition is expressed as a function of a combination of wavelet coefficients, which have different levels of resolution. Then, LSTM neural network is used to train and simulate the value at each level to find the bias vectors and weighting coefficients for the prediction value. A sliding windows model is employed to capture the time series nature of the data. An evaluation is conducted to compare the proposed model with other RNN algorithms, i.e., Elman RNN and Jordan RNN, as well as the combination of wavelet transform with each of them. The result shows that the proposed model has better performance in terms of training time than the original LSTM RNN, while the accuracy is better than the hybrid of wavelet-Elman and the hybrid of wavelet-Jordan.

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“…The experimental results show that the proposed framework performs better in terms of resource utilization and energy consumption along with other Quality of Service (QoS) parameters. Panda & Jana [14] presented the three task scheduling algorithms, called Minimum Completion Cloud (MCC), MEdian MAX (ME-MAX) and Cloud Min-Max Normalization (CMMN) for heterogeneous multi-cloud environment, which aim to be minimized the makespan and maximized the average cloud utilization. The proposed MCC algorithm was a single-phase scheduling whereas rests are two-phase scheduling.…”

Section: Literature Surveymentioning

confidence: 99%

A fuzzy energy and security aware scheduling in cloud

Swamy¹,

Mandapati²

2017

IJET

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The cloud computing is the one that deals with the trading of the resources efficiently in accordance to the user's need. A Job scheduling is the choice of an ideal resource for any job to be executed with regard to waiting time, cost or turnaround time. A cloud job scheduling will be an NP-hard problem that contains n jobs and m machines and every job is processed with each of these m machines to minimize the make span. The security here is one of the top most concerns in the cloud. In order to calculate the value of fitness the fuzzy inference system makes use of the membership function for determining the degree up to which the input parameters that belong to every fuzzy set is relevant. Here the fuzzy is used for the purpose of scheduling energy as well as security in the cloud computing.

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Efficient task scheduling algorithms for heterogeneous multi-cloud environment

Cited by 169 publications

References 40 publications

Task scheduling mechanisms in cloud computing: A systematic review

Task scheduling mechanisms in cloud computing: A systematic review

Prediction by a Hybrid of Wavelet Transform and Long-Short-Term-Memory Neural Network

A fuzzy energy and security aware scheduling in cloud

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