A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology

Ouhame, Soukaina; Hadi, Youssef; Arifullah, Arifullah

doi:10.3991/ijoe.v16i14.16623

Cited by 29 publications

(14 citation statements)

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“…This process merge new service models that contain or conversional all cloud computing layer. Simple we can say all layer are working collectively in this section [22]. Different heterogeneous network and framework and system with different patterns of communication like system to system, human to system and system to human.…”

Section: Integration Rule Of Cloud Computing and Internet Of Thingmentioning

confidence: 99%

Cloud and internet-of-things secure integration along with security concerns

Ullah

Lansari

Şahin

et al. 2023

IJ-ICT

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<span lang="EN-US">Cloud computing is a new technology which refers to an infrastructure where both software and hardware application are operate for the network with the help of internet. Cloud computing provide these services with the help of rule know as you pay as you go on. Internet of things (IoT) is a new technology which is growing rapidly in the field of telecommunications. The aim of IoT devices is to connect all things around us to the Internet and thus provide us with smarter cities, intelligent homes and generally more comfortable lives. The combation of cloud computing and IoT devices make rapid development of both technologies. In this paper, we present information about IoT and cloud computing with a focus on the security issues of both technologies. Concluding we present the contribution of cloud computing to the IoT technology. Thus, it shows how the cloud computing technology improves the function of the IoT. Finally present the security challenges of both technologies IoT and cloud computing.</span>

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Section: Integration Rule Of Cloud Computing and Internet Of Thingmentioning

confidence: 99%

Cloud and internet-of-things secure integration along with security concerns

Ullah

Lansari

Şahin

et al. 2023

IJ-ICT

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“…A Hybrid GWO and ABC Algorithm: A hybrid solution is proposed by Soukaina Ouhame, Youssef Hadi, and Arifullah [83]. The resource allocation method in VM often fails because of a lack of proper load balancing.…”

Section: Gwo and Its Variationsmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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“…Normally two main algorithms are adopted are distinctive and classification technique. Energy proficient routing protocols are used to increase the network lifetime or node lifetime and geographic adaptive fidelity (GAF) is one of them [13]. In WSNs, routing protocols are divided according to the network structure like flat-based, clustering-based, hierarchical based and geographical routing.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

Aznaoui

Raghay²,

Ullah

et al. 2021

Int. J. Onl. Eng.

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<p class="0affiliation">Due to the rapid growth in technologies has led to the development of sensor nodes. As we know that wireless sensor network is collection of small and large number of sensor node. These sensor nodes are used for different domain like environmental research, health care, monitor, military, and record the physical activity. These nodes communicate with each other and forward the message to base station. For communication of these node different algorithms used Geographical adaptive fidelity (GAF) is one of them. Dropping energy utilization in wireless sensor network directly affects the network lifetime. HGAF is one of the important multiple location based on routing system algorithm. The main function of HGAF technique is to turn-off the unnecessary nodes in the network without interrupting the other connected node. In this paper we proposed a technique known as modified HGAF and it design as a power saving method. In the proposed technique the size of cell structure in grid changed and communication method improve due to those changes. Based on the result the proposed technique increase 25% in dead node ratio and also increase the network.</p>

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A Hybrid Grey Wolf Optimizer and Artificial Bee Colony Algorithm Used for Improvement in Resource Allocation System for Cloud Technology

Cited by 29 publications

References 0 publications

Cloud and internet-of-things secure integration along with security concerns

Cloud and internet-of-things secure integration along with security concerns

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

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