Job Migration Policies for Grid Environment

Rathore, Neeraj Kumar; Chana, Inderveer

doi:10.1007/s11277-016-3264-2

Cited by 26 publications

(8 citation statements)

References 29 publications

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“…The percentage of wrong detection of flower is still high and needs further improvement. The proposed deep learningbased convolution neural network [32][33][34][35][36][37][38][39][40][41][42][43][44][45][46][47][48] provides best flower classification results on all datasets. Fully convolution neural networks is a rich class of models in which modern classification convolution neural network is a special case.…”

Section: Results Analysis Of Experimentsmentioning

confidence: 99%

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

Rathore

Jaiswal

Sharma

et al. 2021

Preprint

Self Cite

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Deep-Convolution Neural Network (CNN) is the branch of computer science. Deep Learning CNN is a methodology that teaches computer systems to do what comes naturally to humans. It is a method that learns by example and experience. This is a heuristic-based method to solve computationally exhaustive problems that are not resolved in a polynomial computation time like NP-Hard problems. The purpose of this research is to develop a hybrid methodology for the detection and segmentation of flower images that utilize the extension of the deep CNN. The plant, leaf, and flower image detection are the most challenging issues due to a wide variety of classes, based on their amount of texture, color distinctiveness, shape distinctiveness, and different size. The proposed methodology is implemented in Matlab with deep learning Tool Box and the dataset of flower image is taken from Kaggle with five different classes like daisy, dandelion, rose, tulip, and sunflower. This methodology takes an input of different flower images from data sets, then converts it from RGB (Red, Green, Blue) color model to the L*a*b color model. L*a*b has reduced the effort of image segmentation. The flower image segmentation has been performed by the canny edge detection algorithm which provided better results. The implemented extended deep learning convolution neural network can accurately recognize varieties of flower images. The learning accuracy of the proposed hybrid method is up to 98% that is maximizing up to + 1.89% from state of the art.

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Section: Results Analysis Of Experimentsmentioning

confidence: 99%

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

Rathore

Jaiswal

Sharma

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…(6) For every one of the evidence (7) Ensure that {Block_Hash && IP_Source} are right and correct. (8) If this is the case (Verification D True) (9) Verify the signature//Validation of evidence (10) If this is the case (Signature is valid) (11) Collect reliable evidence (12) Else (13) Prepare illegitimate evidence (14) End if (15) End if (16) End for (17) Prepare and share the copy of evidence with the jurisdictionary court. (18) End ALGORITHM 5: For forensic investigation.…”

Section: Use Case Diagram Of Our Designed Architecture Of Cloudmentioning

confidence: 99%

“…Digital evidence or stored on cloud using the data flow switches during the forensic examination. A (PDMS) data management system of provenance aware has been invented and built on the existing provenance monitoring framework [8].…”

Section: Introductionmentioning

confidence: 99%

“…Victor R Kebane built a cloud forensic preparation model as a test of the application software [7]. Grobler et al suggested a six-dimensional virtual forensic approach to include the theory-based modern forensics solution [8]. Valjarevic and Ventor created a model consisting of three preparation phase assessments in the deployment and planning model.…”

Section: Introductionmentioning

confidence: 99%

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An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Khan

Verma

2021

Scientific Programming

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Cloud forensics is an extension of contemporary forensic science that guards against cybercriminals. However, consolidated data assortment and storage compromise the legitimacy of digital indication. This essay proposes an evolving modern algorithm automated forensic platform based on the blockchain idea. This proposes forensic structure design, evidence gathering, and storage on a blockchain that are peer to peer. Secure Block Verification Mechanism (SBVM) will protect unauthorised users. Secret keys are optimally produced using the cuckoo search optimization method. All data are saved and encrypted at the cloud authentication server for secrecy. Confidentiality-Based Algebraically Homomorphism, a new encryption method, is given to cryptosystem learning. Every data is assigned a block in the SDN controller, and the history is kept as metadata about data. Each block has a Secure Hash Algorithm version 3 of 512-bit hash-based tree. Our approach uses graph theory-based graph neural networks in Smart Contracts to track users’ data (GNNSC). Finally, a blockchain-based evidence graph allows for evidence analysis. The experiments were run in a cloud environment with Python and network simulator-3.30 (for software-defined network). We achieved good results in terms of evidence response time, cloud evidence insertion time, cloud evidence verification time, computational overhead, hash calculation time, key generation times, and entire overall change rate of indication using our newly deliberated forensic construction using blockchain (FAuB).

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“…The Grid paradigm has acquired popularity as a result of its capability to give facile access to geographically distributed resources functioning through various administrative domains. The Grid environment looks as a combination of heterogeneous, dynamic, and shared resources in order to give reliable and faster access to Grid resources (Rathore & Chana, 2016). The main aim of load balancing is to enhance the application response time by which load would be distributed according to resources.…”

Section: Introductionmentioning

confidence: 99%

Load balancing with Job Migration Algorithm for improving performance on grid computing: Experimental Results

Wided

Kazar

Bouakkaz³

2020

ADCAIJ

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Grid is the collection of geographically distributed computing resources. For efficient management of these resources, the manager must maximize its utilization, which can be achieved by efficient load balancing with Job Migration techniques. Job Migration from overloaded resources to underloaded is an attempt to load balancing across all processors, thus reduce average response time. The decision of migration is based on the information exchange between resources.In this paper, the authors propose a novel Job Migration Algorithm for Dynamic Load Balancing (JMADLB), in which parameters such as CPU load and queue length have been considered and have been used for the selection of overloaded resources (or underloaded ones) in Grid. Here, the overloaded resources do not accept any new job; but, the new jobs are migrated to underloaded resources, even though this mechanism migrate extra jobs to obtain load balancing. The performances of the proposed algorithms were tested in Alea 2 simulator by using different parameters like response time, resources utilization and waiting time in the global queue. In addition, they were compared with other scheduling algorithms such as First Come First Served (FCFS) and Earliest Deadline First (EDF).

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Job Migration Policies for Grid Environment

Cited by 26 publications

References 29 publications

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Load balancing with Job Migration Algorithm for improving performance on grid computing: Experimental Results

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Job Migration Policies for Grid Environment

Cited by 26 publications

References 29 publications

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation &amp; Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation &amp; Recognition with extended Deep-Convolution Neural Network (CNN)

An Intelligent Blockchain and Software-Defined Networking-Based Evidence Collection Architecture for Cloud Environment

Load balancing with Job Migration Algorithm for improving performance on grid computing: Experimental Results

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WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)

WITHDRAWN: A Hybrid Methodology for Flower Images Segmentation & Recognition with extended Deep-Convolution Neural Network (CNN)