ARPS: An Autonomic Resource Provisioning and Scheduling Framework for Cloud Platforms

Kumar, Mohit; Kishor, Avadh; Abawajy, Jemal; Agarwal, Prabal; Singh, Amritpal; Zomaya, Albert Y.

doi:10.1109/tsusc.2021.3110245

Cited by 48 publications

(18 citation statements)

References 40 publications

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“…But still, there is room for others to work in improving our introduced algorithm as it can be hybridized with most recent swarm algorithms, such as backtracking search optimization algorithm [44,45], the variants of evolutionary clustering algorithm star [46][47][48][49], chaotic sine cosine firefly algorithm [50], and hybrid artificial intelligence algorithms [51]. Furthermore, DCNN-G-HHO can be applied to more complex and real-world applications to explore more deeply the advantages and drawbacks of the algorithm or improve its efficiencies, such as engineering application problems [50], laboratory management [52], e-organization and egovernment services [53], online analytical processing [54], web science [55], the Semantic Web ontology learning [56], cloud computing paradigms [57][58][59] [60], and evolutionary machine learning techniques [49,61,62].…”

Section: Discussionmentioning

confidence: 99%

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

Qader

Hassan

Rashid

2022

Multimed Tools Appl

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Automated brain tumor detection is becoming a highly considerable medical diagnosis research.In recent medical diagnoses, detection and classification are highly considered to employ machine learning and deep learning techniques. Nevertheless, the accuracy and performance of current models need to be improved for suitable treatments. In this paper, an improvement in deep convolutional learning is ensured by adopting enhanced optimization algorithms, Thus, Deep Convolutional Neural Network (DCNN) based on improved Harris Hawks Optimization (HHO), called G-HHO has been considered. This hybridization features Grey Wolf Optimization (GWO) and HHO to give better results, limiting the convergence rate and enhancing performance.Moreover, Otsu thresholding is adopted to segment the tumor portion that emphasizes brain tumor detection. Experimental studies are conducted to validate the performance of the suggested method on a total number of 2073 augmented MRI images. The technique's performance was ensured by comparing it with the nine existing algorithms on huge augmented MRI images in terms of accuracy, precision, recall, f-measure, execution time, and memory usage. The performance comparison shows that the DCNN-G-HHO is much more successful than existing methods, especially on a scoring accuracy of 97%. Additionally, the statistical performance analysis indicates that the suggested approach is faster and utilizes less memory at identifying and categorizing brain tumor cancers on the MR images. The implementation of this validation is conducted on the Python platform. The relevant codes for the proposed approach are available at: https://github.com/bryarahassan/DCNN-G-HHO.

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Section: Discussionmentioning

confidence: 99%

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

Qader

Hassan

Rashid

2022

Multimed Tools Appl

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“…The improved technique uses less power, better load balancing, and improved trust and resource management on the network. Kumar et al [52] developed a novel approach using the autonomic resource provisioning and scheduling (ARPS) framework combined with the spider monkey optimization (SMO) algorithm. The effectiveness of the proposed approach was assessed by using the CloudSim framework.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Research author Framework Techniques utilized Gai et al [20] Cloud (generally) D2ES Potey et al [21] Amazon Web Homomorphic Vijayakumar et al [22] Healthcare area Diffie-Hellman, AES Mathur et al [58] Cloud (generally) SHA-1, AES Lee et al [25] Heroku AES Biswas et al [26] Online examination system Not used More et al [27] Banking systems ABE and BRE algorithm Attar and Shahin [28] Cloud (generally) AES Abdulhamid [29] Microsoft Azure Blowfish Kumar et al [30] IT organizations Binary tree Sajay [31] Organizations Homographic, Blowfish Subashanthini and Pounambal [32] Electronic commerce IWT, chaotic maps, and DNA Xu et al [33] Electronic healthcare systems Modified Paillier cryptosystem, truth discovery technology, and the Dirichlet distribution Naidu et al [34] Bluemix SHA 512 Philip and Shah [35] Microsoft Azure Biometrics for authentication Malviya and Dave [36] Open cloud Homomorphic, AES Dong et al [37] SecureMR Homomorphic Wu et al [38] mIoT Public key encipherment with keyword search Namasudra [39] IOT ABE, distributed hash network, and identity-based timed-release encryption Sarode and Bhalla [40] MCC AES, RSA, and QR code Wang et al [41] EHR Public key encipherment with conjunctive keyword search Hiemenz and Krämer [42] Geospatial AES Chauhan et al [43] SRM University Teacher's fingerprint, AES Qiu et al [45] EHR Selective encipherment algorithm combined with fragmentation and dispersion Goyal and Kant [46] IT industry AES, SHA-1, and ECC Kumar and Roberts [47] CC Digital signatures Kumar and Shafi [48] CC modified RSA Teng et al [49] Hadoop improved AES Abroshan [50] CC Elliptic curve technique and enhanced Blowfish Awan et al [51] CC Enhanced AES (128) Kumar et al [52] ARPS and CloudSim SMO algorithm Mata et al [53]...…”

Section: Table 1 Framework and Techniques Utilizedmentioning

confidence: 99%

Cloud data security and various cryptographic algorithms

Alemami

Al-Ghonmein

Al-Moghrabi

et al. 2023

IJECE

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<p>Cloud computing has spread widely among different organizations due to its advantages, such as cost reduction, resource pooling, broad network access, and ease of administration. It increases the abilities of physical resources by optimizing shared use. Clients’ valuable items (data and applications) are moved outside of regulatory supervision in a shared environment where many clients are grouped together. However, this process poses security concerns, such as sensitive information theft and personally identifiable data leakage. Many researchers have contributed to reducing the problem of data security in cloud computing by developing a variety of technologies to secure cloud data, including encryption. In this study, a set of encryption algorithms (advance encryption standard (AES), data encryption standard (DES), Blowfish, Rivest-Shamir-Adleman (RSA) encryption, and international data encryption algorithm (IDEA) was compared in terms of security, data encipherment capacity, memory usage, and encipherment time to determine the optimal algorithm for securing cloud information from hackers. Results show that RSA and IDEA are less secure than AES, Blowfish, and DES). The AES algorithm encrypts a huge amount of data, takes the least encipherment time, and is faster than other algorithms, and the Blowfish algorithm requires the least amount of memory space.</p>

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“…1. The Autonomic Resource Provisioning and Scheduling (ARPS) framework was designed and developed by the authors [37]. The ARPS framework provides the ability to schedule jobs at the optimal resources within the deadline, maximizing both execution time and cost.…”

Section: Related Workmentioning

confidence: 99%

A New Reliable System For Managing Virtual Cloud Network

Alshathri¹,

Talaat²,

Nasr³

2022

Computers, Materials &Amp; Continua

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Virtual cloud network (VCN) usage is popular today among large and small organizations due to its safety and money-saving. Moreover, it makes all resources in the company work as one unit. VCN also facilitates sharing of files and applications without effort. However, cloud providers face many issues in managing the VCN on cloud computing including these issues: Power consumption, network failures, and data availability. These issues often occur due to overloaded and unbalanced load tasks. In this paper, we propose a new automatic system to manage VCN for executing the workflow. The new system called Multi-User Hybrid Scheduling (MUSH) can solve running issues and save power during workflow execution. It consists of three phases: Initialization, virtual machine allocation, and task scheduling algorithms. The MUSH system focuses on the execution of the workflow with deadline constraints. Moreover, it considers the utilization of virtual machines. The new system can save makespan and increase the throughput of the execution operation.

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ARPS: An Autonomic Resource Provisioning and Scheduling Framework for Cloud Platforms

Cited by 48 publications

References 40 publications

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

An improved deep convolutional neural network by using hybrid optimization algorithms to detect and classify brain tumor using augmented MRI images

Cloud data security and various cryptographic algorithms

A New Reliable System For Managing Virtual Cloud Network

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