Maximize Resource Utilization Using ACO in Cloud Computing Environment for Load Balancing

Kushwah, Virendra Singh; Goyal, Sandip Kumar; Sharma, Avinash

doi:10.1007/978-981-15-0751-9_54

Cited by 3 publications

(2 citation statements)

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“…GA is famous for reducing the makespan to a considerable degree. For example, ACO is well known for its effectiveness in path-finding and scheduling tasks, essential to cloud computing load balancing [41][42][43]. GWO is also gaining attention because of its improved convergence [44,45].…”

Section: Targeted Algorithms For Load Balancing In Cloud Computing Ba...mentioning

confidence: 99%

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Syed,

Shaikh,

Alshahrani

et al. 2024

IEEE Access

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In the ever-evolving technological landscape, ensuring high system availability has become a paramount concern. This research paper focuses on cloud computing, a domain witnessing exponential growth and emerging as a critical use case for high-availability systems. To fulfil the criteria, many services in cloud infrastructures should be combined, relying on the user's demands. Central to this study is load balancing, an integral element in harnessing the full potential of heterogeneous computing systems. In cloud environments, dynamic management of load balancing is crucial. This study explores how virtual machines can effectively remap resources in response to fluctuating loads dynamically, optimizing overall network performance. The core of this research involves an in-depth analysis of several metaheuristic algorithms applied to load balancing in cloud computing. These include Genetic Algorithm, Particle Swarm Optimization, Ant Colony Optimization, Artificial Bee Colony, and Grey Wolf Optimization. Utilizing CloudAnalyst, the study conducts a comparative analysis of these techniques, focusing on key performance metrics such as Total Response Time (TRT) and Data Center Processing Time (DCPT). The findings of this research offer insights into the varying behaviors of these algorithms under different cloud configurations and user retention levels. The ultimate aim is to pave the way for developing innovative load-balancing strategies in cloud computing. By providing a comprehensive evaluation of existing metaheuristic methods, this paper contributes to advancing high-availability systems, underscoring the importance of tailored solutions in the dynamic realm of cloud technology.

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Section: Targeted Algorithms For Load Balancing In Cloud Computing Ba...mentioning

confidence: 99%

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Syed,

Shaikh,

Alshahrani

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Subbaraj and Savarimuthu 29 proposed an EigenTrust noncooperative game model based on QoS constraints, which helps to select a backup path when the effective route fails. Kushwah et al 30 proposed an improved ACO, which improves the resource utilization of cloud space in a short period of time.…”

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confidence: 99%

Application of hybrid swarming algorithm on flexible job shop scheduling problems

Zhang

Sun

2021

Concurrency and Computation

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In this article, we present an improved hybrid algorithm based on ant colony optimization and the polycephalum algorithm. First, we use improved the probability selection mechanism in the ant colony algorithm in order to improve the efficiency of next point searching. Second, in each iteration we update the pheromone concentration of the optimal route by using the polycephalum algorithm. We regard the starting point of the optimal route as the water injection point and the end point as the water outlet point.The hybrid algorithm is compared on multiple TSPLIB problems and flexible job shop scheduling problems. And experiments show that the improved algorithm has good application results and resultful in accuracy and optimal solutions. K E Y W O R D Sant colony algorithm, FJSP, Physarum polycephalum algorithm INTRODUCTIONIn 1990, Bruker and Schlie 1 created a polynomial graph algorithm for solving flexible job shop scheduling problem (FJSP), but this algorithm cannot effectively solve multijob, multimachine, and multiconstraint problems. Later, some researchers used heuristic algorithms to handle this problem.For example, Brandimarte 2 suggested a tabu search algorithm based on a hierarchical strategy, assigned each operation of each job to an equivalent computer, and solved FJSP. Some scholars used the neighborhood structure to enhance the development capabilities of tabu search algorithms, and then perform machine reallocation within a predetermined time to solve FJSP. 3,4 Alzaqebah et al. 5 proposed a BSO algorithm that mimics the brainstorming process of humans, and added clustering ideas to polish up the local intensification of the algorithm, and disposed of FJSP. Ding and Gu 6 proposed an improved particle swarm optimization algorithm to dispose of the FJSP by improving the chain-based coding scheme, information exchange mechanism between particles, and the selection regulations of the next generation. Zhu and Zhou 7 proposed an efficient evolutionary multiobjective gray wolf optimizer. By improving the social hierarchy and leadership strategy, the goal of reducing machine workload in FJSP is minimized. Li et al. 8 created a hybrid artificial bee colony algorithm to solve FJSP, introduced a replanning strategy and clustering grouping roulette method, which improved the efficiency of the solution. Phanden and Ferreira 9 presented proposed a population-based biogeographic algorithm inspired by nature to solve FJSP. By citing neighborhood search methods, local search capabilities are enhanced. Xing et al. 10 initiated an ant colony optimization algorithm for knowledge heuristic search to solve FJSP. It can be seen that heuristic algorithms are very effective and popular in solving FJSP problems, and their performance is far superior to traditional algorithms. Cai et al. 11 proposed an intelligent algorithm of sine function to implement multicloud and scheduling problems in the Internet of Things (IoT). Cui et al. 12 proposed a multiobjective RBM model combined with the NSGA-II algorithm to improve the data cla...

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Improved Ant Colony Optimization for Optimal Resource Utilization in Cloud Computing

Bhardwaj

Gupta

Sharma

2022

Lecture Notes in Networks and Systems

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Maximize Resource Utilization Using ACO in Cloud Computing Environment for Load Balancing

Cited by 3 publications

References 9 publications

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

A Comparative Analysis of Metaheuristic Techniques for High Availability Systems

Application of hybrid swarming algorithm on flexible job shop scheduling problems

Improved Ant Colony Optimization for Optimal Resource Utilization in Cloud Computing

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