SLA-Based Agile VM Management in Cloud &amp; Datacenter

Sharma, Nikita; Maurya, Sudarshan

doi:10.1109/comitcon.2019.8862260

Cited by 10 publications

(1 citation statement)

References 15 publications

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“…Execution time and various empirical analyses were not considered by the algorithm. SLA agile-based VMs is suggested by Sharma et al to minimize reaction time [71]. This study used ghost VM to cut the amount of VM output by roughly 12 percent, resulting in two algorithms improving performance.…”

Section: Approaches Used In Load Balancingmentioning

confidence: 99%

Enhancing Cloud Performance Using File Format Classifications

Junaid¹,

Sohail²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

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Metaheuristic approaches in cloud computing have shown significant results due to their multi-objective advantages. These approaches are now considering hybrid metaheuristics combining the relative optimized benefits of two or more algorithms resulting in the least tradeoffs among several factors. The critical factors such as execution time, throughput time, response time, energy consumption, SLA violations, communication overhead, makespan, and migration time need careful attention while designing such dynamic algorithms. To improve such factors, an optimized multi-objective hybrid algorithm is being proposed that combines the relative advantages of Cat Swarm Optimization (CSO) with machine learning classifiers such as Support Vector Machine (SVM). The adopted approach is based on SVM one to many classification models of machine learning that performs the classifications of various data format types in the cloud with best accuracy. In CSO, grouping phase is used to divide the data files as audio, video, image, and text which is further extended by polynomial Kernel function based on various input features and used for optimized load balancing. Overall, proposed approach works well and achieved performance efficiency in evaluated QoS metrics such as average energy consumption by 12%, migration time by 9%, and optimization time by 10%, in the presence of competitor baselines.

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Section: Approaches Used In Load Balancingmentioning

confidence: 99%

Enhancing Cloud Performance Using File Format Classifications

Junaid¹,

Sohail²,

Alkinani³

et al. 2022

Computers, Materials &Amp; Continua

View full text Add to dashboard Cite

show abstract

DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment

Tong¹,

Ye²,

Liu³

et al. 2021

Neurocomputing

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A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

et al. 2020

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Maintaining accuracy in load balancing using metaheuristics is a difficult task even with the help of recent hybrid approaches. In the existing literature, various optimized metaheuristic approaches are being used to achieve their combined benefits for proper load balancing in the cloud. These approaches often adopt multi-objective QoS metrics, such as reduced SLA violations, reduced makespan, high throughput, low overload, low energy consumption, high optimization, minimum migrations, and higher response time. The cloud applications are generally computation-intensive and can grow exponentially in memory with the increase in size if no proper effective and efficient load balancing technique is adopted resulting in poor quality solutions. To provide a better load balancing solution in cloud computing, with extensive data, a new hybrid model is being proposed that performs classification on the number of files present in the cloud using file type formatting. The classification is performed using Support Vector Machine (SVM) considering various file formats such as audio, video, text maps, and images in the cloud. The resultant data class provides high classification accuracy which is further fed into a metaheuristic algorithm namely Ant Colony Optimization (ACO) using File Type Formatting FTF for better load balancing in the cloud. Frequently used QoS metrics, such as SLA violations, migration time, throughput time, overhead time, and optimization time are evaluated in the cloud environment and comparative analysis is performed with recent metaheuristics, such as Ant Colony Optimization-Particle Swarm Optimization (ACOPS), Chaotic Particle Swarm Optimization (CPSO), Q-learning Modified Particle Swarm Optimization (QMPSO), Cat Swarm Optimization (CSO) and D-ACOELB. The proposed algorithm outperforms them and provides good performance with scalability and robustness.

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SLA-Based Agile VM Management in Cloud & Datacenter

Cited by 10 publications

References 15 publications

Enhancing Cloud Performance Using File Format Classifications

Enhancing Cloud Performance Using File Format Classifications

DDQN-TS: A novel bi-objective intelligent scheduling algorithm in the cloud environment

A Hybrid Model for Load Balancing in Cloud Using File Type Formatting

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