Particle swarm optimisation with grey wolf optimisation for optimal container resource allocation in cloud

Abstract

In the cloud sector, as the applications used by users are exploited via micro-service pattern, the container allocation seems to be the most vital process. This has further been concentrated with more care for its beneficiary acts like easier employment, limited overheads and higher portability. For the past few decades, various contributions have been made under the container management and allocation as well. Under these circumstances, this study intends to design an optimal resource allocation and management model by incorporating the concept of optimisation, which guarantees optimal container allocation. To make this possible, this study establishes a novel hybrid algorithm, namely velocity-updated grey wolf optimisation (VU-GWO), which is the hybridisation of two renowned algorithms particle swarm optimisation and grey wolf optimization, respectively. More importantly, the solution of optimised resource allocation is influenced by the designing of a novel objective function, which concerns the constraints like balanced cluster use, threshold distance, system failure, and total network distance as well. At last, the performance of the presented scheme is evaluated over other traditional schemes, and the betterment of the proposed model is validated.

Metaheuristic based auto-scaling for microservices in cloud environment: a new container-aware application scheduling

“…Different experiments have been carried out, particularly, Experiment 1 was done using 250 machines with a capacity 100, Experiment 2 was done using 300 machines with a capacity 200, Experiment 3 was done using 350 machines with a capacity 400 and Experiment 4 was done using 400 machines with capacity 800”. In addition, the presented model was compared over other existing models such as whale random update assisted lion algorithm (WR-LA) (Vhatkar and Bhole, 2019), velocity updated grey wolf (VU-GWO) (Vhatkar and Bhole, 2020a), SSA (Jain et al , 2019) and DA (Mohammad and Mohammad Hossein, 2017) and the outcomes were examined regarding statistical analysis. The proposed DLU-SA outperforms the other compared algorithms because the optimal solutions are selected using a new hybrid optimization algorithm DLU-SA and it makes the system more effective.…”

Investigating The Influence of E-Loyalty And E-Satisfaction on Repurchase Behavioral Intentions in the Online Shopping Realm

“…Repurchase behavioral intentions are largely influenced by e-satisfaction. Customers are far more likely to return for additional transactions when they have a favorable e-satisfaction outcome, which is the result of a flawless online buying experience, effective customer care, and meeting consumer expectations [19]. According to the expectancy-disconfirmation hypothesis, the emotional and cognitive components of e-satisfaction have a direct influence on consumers' perceptions and evaluations of their previous interactions, which in turn shapes their intention to repurchase [20,22].…”

Construction of an intelligent optimization model for water resource allocation under drought conditions

“…With the increasing global shortage of freshwater resources, the phenomenon of river water resources has become increasingly severe and has led to a large number of cases of water conflict [1][2]. The unclear ownership and unfair distribution of water resources are the leading root causes of water conflicts.…”