Moving average fuzzy resource scheduling for virtualized cloud data services

Priya, .; Babu, C. Nelson Kennedy

doi:10.1016/j.csi.2016.10.011

Cited by 16 publications

(5 citation statements)

References 15 publications

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“…To achieve this, they incorporated a DVFS governor known as the Utility-based DVFS Governor (UDG) and a multi-objective resource allocation policy called Power and SLA Fuzzy Weighted TOPSIS (PSFWT). These components together aimed to strike a balance between [120] proposed a Moving Average-based fuzzy resource scheduling framework (MV-FRS) designed to optimize resource allocation and scheduling in virtualized environments. MV-FRS leveraged cloud computing resources by predicting resource requirements, measuring resource availability relationships, and applying fuzzy control theory to enhance system accessibility for cloud users.…”

Section: A State-of-the-art Contributionsmentioning

confidence: 99%

“…This systematic approach adeptly transforms uncertain input into a well-defined decision-making process, leveraging fuzzy sets and membership functions to navigate the intricacies of load balancing. The representation of uncertainty, demonstrated in studies like Arianyan et al's energy-efficient resource management [119] and Priya et al's Moving Averagebased fuzzy resource scheduling [120], highlights the adaptability and effectiveness of fuzzy logic in optimizing virtual machine allocation, reducing energy consumption, and enhancing overall system accessibility. In the realm of fuzzy-based load balancing, the lessons learned extend beyond theoretical frameworks to practical applications.…”

Section: B Takeaways and Lessons Learnedmentioning

confidence: 99%

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A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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Section: A State-of-the-art Contributionsmentioning

confidence: 99%

Section: B Takeaways and Lessons Learnedmentioning

confidence: 99%

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

Saxena,

Singh

2024

Preprint

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“…A fuzzy clustering method is used in [18] as a pre-processing operation to classify cloud resources; then directed graphs are used to schedule jobs to run on distinct clusters of hardware resources. In paper [19] was used fuzzy control theory for accomplish system accessibility between user requirements and users resources availability. In [20], the particle swarm optimization (PSO) has been used as an optimal answer searching method for the optimization problem and paper [21] was proposed hybrid task scheduling method by PSO and GA. Also paper [22] is presented a survey of scheduling algorithms based on PSO in cloud computing.…”

Section: -Related Workmentioning

confidence: 99%

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

Esfandi,

Akbari Torkestani,

Karimi

et al. 2024

jist

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Due to the completely random and dynamic nature of the cloud environment, as well as the high volume of jobs, one of the significant challenges in this environment is proper online job scheduling. Most of the algorithms are presented based on heuristic and meta-heuristic approaches, which result in their inability to adapt to the dynamic nature of resources and cloud conditions. In this paper, we present a distributed online algorithm with the use of two different learning automata for each scheduler to schedule the jobs optimally. In this algorithm, the placed workload on every virtual machine is proportional to its computational capacity and changes with time based on the cloud and submitted job conditions. In proposed algorithm, two separate phases and two different LA are used to schedule jobs and allocate each job to the appropriate VM, so that a two phase adaptive algorithm based on LA is presented called TPALA. To demonstrate the effectiveness of our method, several scenarios have been simulated by CloudSim, in which several main metrics such as makespan, success rate, average waiting time, and degree of imbalance will be checked plus their comparison with other existing algorithms. The results show that TPALA performs at least 4.5% better than the closest measured algorithm.

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“…The effective resource allocation [19][20][21][22][23][24] is the critical parameter for any cloud based services and the resource allocation has been performed based on the priority of the incoming users requesting for the types of cloud services. The priority analyzer determined the priority of the incoming user request using the equation 15.…”

Section: Enhanced Resource Scheduling Using Vibrant Neighborhood Part...mentioning

confidence: 99%

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

Tamilarasu

Singaravel²

2022

Preprint

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As the digital era has progressed, so has the volume of digital data, making secure storage of this vast amount of data an increasingly important concern for data administrators. Cloud computing and the ease of storing and accessing data attracted a large number of IT professionals and administrators to migrate from physical infrastructure to cloud infrastructure. Cloud computing provides a wide range of services, but its primary functionality is the efficient distribution of resources among the many people who need to use the cloud. Effective resource allocation, decreased response time, and increased resource availability are all benefits of optimising cloud computing Quality of Service (QoS), which also helps to keep user data safe in the cloud infrastructure. Even though many studies have sought a high quality of service (QoS), no success has been found in combining efficient resource sharing with privacy protection. This paper uses Block chain technology and the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm to overcome the aforementioned challenge of achieving better QoS with high levels of data security. While the Block chain has a reduced parameter, it is highly efficient in protecting cloud network storage data, while the Vibrant Neighborhood Particle Swarm Optimization (VNPSO) algorithm is an improvised version of Particle Swarm Optimization (PSO) and is efficient in resource allocation without any restrictions or boundaries in the neighbourhood available resources. Experimentation shows that the proposed meta-heuristic hybrid algorithm outperforms conventional algorithms in terms of Quality of Service.

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Moving average fuzzy resource scheduling for virtualized cloud data services

Cited by 16 publications

References 15 publications

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

A Comprehensive Survey on Sustainable Resource Management in Cloud Computing Environments

TPALA: Two Phase Adaptive Algorithm based on Learning Automata for job scheduling in cloud Environment

An Efficient Hybrid Block Chain -vibrant Neighborhood Partile Swarm Optimization (B- Vnpso) Algorithm for Enhancing Qos in Cloud Computing Services

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