A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment

Panda, Sanjaya Kumar; Nag, Subhrajit; Jana, Prasanta K.

doi:10.1109/pdgc.2014.7030716

Cited by 23 publications

(4 citation statements)

References 21 publications

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“…Even in the case a linear output function, is used, it is also stated that normalizing the inputs as well as the outputs has many advantages. Some of them avoid computational problems, so all data input must be normalized from 0 to 1 by using Max-Min normalization equation [19][20][21][22][23].…”

Section: B1 the Preprocessing Of Datamentioning

confidence: 99%

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Mahedy¹,

Abdelsalam²,

Mohamed³

et al. 2020

IJITCS

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The neural networks, AI applications, are effective prediction methods. Therefore, in the current research a prediction system was proposed using these neural networks. It studied the technical share indices, viewing price not only as a function of time, but also as a function depending on several indices among which were the opening and closing, top and bottom trading session prices or trading volume. The above technical indices of a number of Egyptian stock market shares during the period from 2007 to 2017, which can be used for training the proposed system, were collected and used as follows: The data were divided into two sets. The first one contained 67% of the total data and was used for training neural networks and the second contained 33% and was used for testing the proposed system. The training set was segmented into subsets used for training a number of neural networks. The output of such networks was used for training another network hierarchically. The system was, then, tested using the rest of the data.

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Section: B1 the Preprocessing Of Datamentioning

confidence: 99%

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Mahedy¹,

Abdelsalam²,

Mohamed³

et al. 2020

IJITCS

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“…The study has also presented an optimization www.ijacsa.thesai.org principle to reduce the storage cost as well as the memory of VM. Panda et al [16] have discussed an algorithm that targets multiple environments of cloud based on the smoothening concept. The evaluation of the study was carried out using a bigger dataset of heterogeneous types.…”

Section: A Backgroundmentioning

confidence: 99%

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Ngenzi¹,

Selvarani²,

Suchithra³

2016

ijacsa

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Abstract-Live Migration is one of the essential operations that require more attention to addressing its high variability problems with virtual machines. We review the existing techniques of resource management to find that there are less modeling to solve this problem. The present paper introduces a novel framework that mainly targets to achieve a computational effective resource management technique. The technique uses the stochastic approach in modelling to design a new traffic management scheme that considers multiple traffic possibilities over VMs along with its switching states. Supported by an analytical modelling approach, the proposed technique offers an efficient placement of virtual machine to the physical server, performs the computation of blocks, and explores reduced resource usage. The study outcome was found to possess potential reduction in live migration, more extent of VM mapping with physical servers, and increased level of capacity.

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“…In the similar fashion, eight clouds hold 32 VMs in 1024 × 32 dataset. These datasets are used in task scheduling [15][16][17][18][19][20][21][22][23]. The synthetic dataset consists of six instances.…”

Section: Datasetsmentioning

confidence: 99%

Allocation-aware Task Scheduling for Heterogeneous Multi-cloud Systems

Panda

Gupta

Jana

2015

Procedia Computer Science

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Cloud computing is one of the growing technology usage for the day-to-day business operations in today's IT industry. The diverse features of cloud such as on-demand self-service, quality of service, pay-per-usage pricing, virtualization and elasticity make the cloud more popular in industries as well as research communities. However, the mapping of the cloud resources in forms of virtual machines (VMs) to fulfill the customer requests is very challenging and a well-known NP-Complete problem. In this paper, we propose an allocation-aware task scheduling (ATS) algorithm for heterogeneous multi-cloud systems. The algorithm has three phases, namely matching, allocating and scheduling that aim to map the customer requests (or tasks) to the VMs of the clouds such that the overall completion time i.e., makespan is minimized. Moreover, the algorithm introduces a new phase called allocating to reschedule the tasks to meet the requirement of scheduling strategy. We perform rigorous experiments on benchmark as well as synthetic datasets and compare the experimental results by extending two existing multi-cloud scheduling algorithms as per the proposed model. The results clearly indicate that the proposed algorithm outperforms both the algorithms in terms of makespan and average cloud utilization.

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A smoothing based task scheduling algorithm for heterogeneous multi-cloud environment

Cited by 23 publications

References 21 publications

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Utilizing Neural Networks for Stocks Prices Prediction in Stocks Markets

Framework of Resource Management using Server Consolidation to Minimize Live Migration and Load Balancing

Allocation-aware Task Scheduling for Heterogeneous Multi-cloud Systems

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