Multipath-routing based load-balancing in SDN networks

Chahlaoui, Farah; Dahmouni, Hamza; Alami, Hassan El

doi:10.1109/ciot53061.2022.9766801

Cited by 10 publications

(2 citation statements)

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“…The most often used strategies in cloud data centres to lower energy consumption and SLA violations are optimization procedures [14]. For resource allocation, energy consumption, VM migration, SLA, and QoS, the cloud uses the firefly method, WAO, heuristic approach, and cat swarm optimization [15][16][17]. As a result, multi-objective optimization techniques [18] are used to concurrently enhance QoS, lower energy usage, and lower SLA violation.…”

Section: The Contribution Of This Research Are As Followsmentioning

confidence: 99%

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Multipath Routing in Cloud Computing using Fuzzy based Multi-Objective Optimization System in Autonomous Networks

Pathak

Angurala

2022

ijfrcsce

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Intelligent houses and buildings, autonomous automobiles, drones, robots, and other items that are successfully incorporated into daily life are examples of autonomous systems and the Internet of Things (IoT) that have advanced as research areas. Secured data transfer in untrusted cloud applications has been one of the most significant requirements in the cloud in recent times. In order to safeguard user data from unauthorised users, encrypted data is stored on cloud servers. Existing techniques offer either security or efficiency for data transformation. They fail to retain complete security while undergoing significant changes. This research proposes novel technique in multipath routing based energy optimization of autonomous networks. The main goal of this research is to enhance the secure data transmission in cloud computing with network energy optimization. The secure data transmission is carried out using multi-authentication attribute based encryption with multipath routing protocol. Then the network energy has been optimized using multi-objective fuzzy based reinforcement learning. The experimental analysis has been carried out based on secure data transmission and energy optimization of the network. The parameters analysed in terms of scalability of 79%, QoS of 75%, encryption time of 42%, latency of 96%, energy efficiency of 98%, end-end delay of 45%.

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Section: The Contribution Of This Research Are As Followsmentioning

confidence: 99%

“…Bellman eqn (16) indicates that Q * is a fixed point of T using this notation, i.e., Q * = T(Q * ). Since T is a contraction with factor 1 in infinity norm, it is true that for each pair of functions Q and Q0, kT(Q) − T(Q0 )k∞ ≤ γ kQ − Q0k∞.…”

Section: Multi-objective Fuzzy Based Reinforcement Learning In Networ...mentioning

confidence: 99%