CWOA: Hybrid Approach for Task Scheduling in Cloud Environment

Pradeep, K.; Ali, L. Javid; Gobalakrishnan, N; Raman, Chandrasekharan; Manikandan, N.

doi:10.1093/comjnl/bxab028

Cited by 14 publications

(6 citation statements)

References 26 publications

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“…100 Vms and 200 VMs were considered for execution purpose. The proposed GWOC had been compared with GSA [27], WOA [15] and GWO [28] algorithms.…”

Section: Resultsmentioning

confidence: 99%

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Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

2023

IJIES

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An enriched computational platform has been unfolded with the introduction of cloud technology offering ensemble services to users that includes storage, database and processing power. More recently, the cloud technology has been upgraded to a federated environment that offers even more features where various service providers could interconnect for providing an integrated service in a transparent way to cloud users. Applications that demand enormous computing resources like for instance bioinformatics workflow applications could very well make use of the abundant cloud resources for effective execution. Fine tuning the task scheduling activity in cloud could further boost the overall cloud performance. In this paper, we had designed an optimal and ideal task scheduling algorithm that primarily focuses on reducing the cost and makespan QoS parameters, eventually leading to enhanced cloud performance. The proposed algorithm, which is a meta-heuristic enhanced hybrid version named, grey wolf optimizer cuckoo (GWOC) is formally designed from the existing grey wolf optimizer and cuckoo search algorithms. results obtained clearly justify the goal accomplishment of the proposed GWOC algorithm and its swiftness in achieving convergence, thereby clearly outperforming existing contemporaries like gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm. The proposed GWOC technique had produced an improvement of 2.11%, 3.5% and 5.17% for makespan and had reduced the cost to the tune of 7.71%, 11.3%, and 15.4% when compared with gravitational search algorithm (GSA), whale optimization algorithm (WOA) algorithm and grey wolf optimizer (GWO) algorithm respectively when used with 100 VMs. Detailed results have been presented in section 5.

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“…100 Vms and 200 VMs were considered for execution purpose. The proposed GWOC had been compared with GSA [27], WOA [15] and GWO [28] algorithms.…”

Section: Resultsmentioning

confidence: 99%

“…Service quality and energy consumption are vital for realizing an enhanced cloud performance. The authors [15] had designed an improvised task scheduling algorithm by integrating the Cuckoo search algorithm (CSA) and whale optimization algorithm (WOA), named the CWOA to enhance the cloud performance.…”

Section: Related Workmentioning

confidence: 99%

Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

2023

IJIES

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“…Artificial neural network, support vector machines, and deep learning are the widely used techniques for intrusion detection. [15][16][17] An IDS 18,19 mainly secures the system and alerts you in case of any malicious entry. There are two types of IDS: host-based IDS (HIDS) and network-based IDS (NIDS).…”

Section: Introductionmentioning

confidence: 99%

“…Anomaly detection mainly identifies malicious behavior by comparing the anomalous behavior with the normal data. Artificial neural network, support vector machines, and deep learning are the widely used techniques for intrusion detection 15–17 …”

Section: Introductionmentioning

confidence: 99%

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Alajlan

Almasri

2022

Trans Emerging Tel Tech

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Cloud computing is one example of a technological revolution that has drastically altered the way traditional commercial operations are conducted. Because cloud platforms are fundamentally revolutionizing the area of computing, they must provide safe transmission and storage of user data by assuring the confidentiality, integrity, and availability of the data. As a result, establishing a secure cloud computing framework is crucial, and security cannot only rely on the user's credentials being verified. To overcome this problem, this article presents a multi‐population genetic algorithm optimized dynamic kernel convolutional neural network to analyze the user's behavior to identify their malicious intent. This article describes a two‐stage malicious detection and prediction scheme. The proposed system's efficiency is measured by its ability to discriminate between nine different types of attacks in the UNSW‐NB15 dataset (abnormal behavior exhibited by users). The average true positive rate, false positive rate, precision, and F‐measure for the nine attack classes were 99.22%, 99.11%, 99.11%, and 99.22%, respectively, when evaluated using the UNSW‐NB15 dataset. The experimental results demonstrate that this technique is effective in detecting malicious behaviors in the cloud environment with higher accuracy.

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“…For future research, the author planned to use evolutionary algorithms such as PSO and GA, to solve the task scheduling problem. (Pradeep, et al, 2021) have combined two techniques, the Cuckoo search algorithm (CSA) and the Whale Optimization Algorithm (WOA) to develop an efficient hybrid algorithm called (CWOA). The proposed strategy improved two important factors that affect the efficiency of cloud computing environment namely: quality of service and energy consumption.…”

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confidence: 99%

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

2022

IKM

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Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms.

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CWOA: Hybrid Approach for Task Scheduling in Cloud Environment

Cited by 14 publications

References 26 publications

Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

Task Scheduling Based on Cost and Execution Time Using Ameliorate Grey Wolf Optimizer Algorithm in Cloud Computing

Malicious behavior detection in cloud using self‐optimized dynamic kernel convolutional neural network

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

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