QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

Mohapatra, Soumya Snigdha; Kumar, Rakesh Ranjan; Alenezi, Mamdouh; Zamani, Abu Taha; Parveen, Nikhat

doi:10.3390/electronics11213469

Cited by 3 publications

(2 citation statements)

References 39 publications

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“…Soumya Snigdha Mohapatra et al [37] used Genetic Algorithms and the Eagle Strategy algorithm to balance exploration and exploitation in order to suggest cloud services with the best Quality of Service (QoS). It covers the issue of choosing cloud services that provide the appropriate QoS.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

Kumer,

Prabakaran,

Mohan

et al. 2023

IEEE Access

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Dynamic and flexible computing resources are offered by cloud computing (CC), which has gained popularity as a computing technology. Efficient task scheduling (TS) plays a critical role in CC by optimizing the distribution of tasks across available resources to achieve maximum performance. The allocation of computational tasks in a cloud environment is a complicated process that is affected by multiple factors, such as available network bandwidth, make span, and cost considerations. Therefore, it is crucial to optimize available bandwidth for efficient TS in CC. In the present research, a novel pelican-based approach is introduced to optimize TS in the CC environment. The newly developed method also utilizes a security approach called Polymorphic Advanced Encryption Standard (P-AES) to encode cloud information during scheduling. The study evaluates the proposed algorithm's performance in terms of the make span, resource utilization, cost, response time, throughput, latency, execution time, speed, and bandwidth utilization. The simulation is carried out using the Python tool, and it effectively handles a wide range of tasks from 1000 to 5000. The proposed algorithm offers a new perspective on utilizing pelican algorithms to optimize task scheduling in CC. The hybrid optimization enables the proposed algorithm to provide efficient task scheduling by exploiting the strengths of entire algorithms. The proposed approach offers an innovative solution to the challenges of scheduling tasks in cloud environments and provides a more effective and secure way of optimizing cloud services. Overall, this study provides valuable insights into task scheduling optimization in CC and offers an effective approach for enhancing the performance of CC services.

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Section: Related Workmentioning

confidence: 99%

Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

Kumer,

Prabakaran,

Mohan

et al. 2023

IEEE Access

View full text Add to dashboard Cite

show abstract

“…The trend today is rapidly changing towards cloud computing, and computing, storage and network resources are in the Cloud [22][23][24]. This leads to many MNC companies such as AWS, Azure, Google and Oracle to have their own cloud services to provide the IaaS, PaaS, SaaS services to their customers [25][26][27][28][29]. Especially during the COVID-19 period, there was a drastic growth in cloud provider service usage [30,31].…”

Section: Literature Surveymentioning

confidence: 99%

Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm

Balajee

Kannan

2023

Electronics

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The network security and cloud environment have been playing vital roles in today’s era due to increased network data transmission, the cloud’s elasticity, pay as you go and global distributed resources. A recent survey for the cloud environment involving 300 organizations in North America with 500 or more employees who had spent a minimum of USD 1 million on cloud infrastructure, as per March 2022 statistics, stated that 79% of organizations experienced at least one cloud data breach. In the year 2022, the AWS cloud provider leads the market share with 34% and a USD 200 billion cloud market, proving important and producing the motivation to improve the detection of intrusion with respect to network security on the basis of the AWS cloud dataset. The chosen CSE-CIC-IDS-2018 dataset had network attack details based on the real time attack carried out on the AWS cloud infrastructure. The proposed method here is the hybrid deep learning based approach, which uses the raw data first to do the pre-processing and then for normalization. The normalized data have been feature extracted from seventy-six fields to seven bottlenecks using Principal Component Analysis (PCA); those seven extracted features of every packet have been categorized as two-way soft-clustered (attack and non-attack) using the Smart Monkey Optimized Fuzzy C-Means algorithm (SMO-FCM). The attack cluster data have been further provided as inputs for the deep learning based AutoEncoder algorithm, which provides the outputs as attack classifications. Finally, the accuracy of the results in intrusion detection using the proposed technique (PCA + SMO-FCM + AE) is achieved as 95% over the CSE-CIC-IDS-2018 dataset, which is the highest known for state-of-the-art protocols compared with 11 existing techniques.

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Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection

Sus,

Nawrocki

2024

J Grid Computing

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One of the challenges in managing cloud computing clusters is assigning resources based on the customers’ needs. For this mechanism to work efficiently, it is imperative that there are sufficient resources reserved to maintain continuous operation, but not too much to avoid overhead costs. Additionally, to avoid the overhead of acquisition time, it is important to reserve resources sufficiently in advance. This paper presents a novel reliable general-purpose mechanism for prediction-based resource usage reservation. The proposed solution should be capable of operating for long periods of time without drift-related problems, and dynamically adapt to changes in system usage. To achieve this, a novel signature-based ensemble prediction method is presented, which utilizes multiple distinct prediction algorithms suited for various use-cases, as well as an anomaly detection mechanism used to improve prediction accuracy. This ensures that the mechanism can operate efficiently in different real-life scenarios. Thanks to a novel signature-based selection algorithm, it is possible to use the best available prediction algorithm for each use-case, even over long periods of time, which would typically lead to drifts. The proposed approach has been evaluated using real-life historical data from various production servers, which include traces from more than 1,500 machines collected over more than a year. Experimental results have demonstrated an increase in prediction accuracy of up to 21.4 percent over the neural network approach. The evaluation of the proposed approach highlights the importance of choosing the appropriate prediction method, especially in diverse scenarios where the load changes frequently.

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QoS-Aware Cloud Service Recommendation Using Metaheuristic Approach

Cited by 3 publications

References 39 publications

Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

Enhancing Cloud Task Scheduling With a Robust Security Approach and Optimized Hybrid POA

Intrusion Detection on AWS Cloud through Hybrid Deep Learning Algorithm

Signature-based Adaptive Cloud Resource Usage Prediction Using Machine Learning and Anomaly Detection

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