A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

The Internet of Things (IoT) is a new technology that employs a variety of sensors and wireless communication protocols. People are leveraging IoT to make their lives easier by using innovative and intelligent equipment. This paper presents a new approach, developed in order to increase the reliability in the IoT using the Harris Hawks Optimization (HHO) algorithm in a big data environment. Reliability and availability in big data represents critical criteria for selecting the most suitable protocol. Also, in most scenarios, the sensors' battery cannot be replaced, so energy consumption is another important issue in such IoT system. This research provides an enhanced artificial intelligence approach, based on the HHO technique, to handle load balancing between the Cluster Heads (CH) in a big data environment. A proper load balancing between CHs is vital in sensor network communication since it reduces the energy consumption of the devices. The HHO algorithm selects the appropriate CH by using a hunting technique, that is divided into 2 categorizes, soft besiege and hard besiege. The hunting depends on the remaining energy of the rabbit; at first, rabbit energy is high, but, after a while, the energy of rabbit decreases, so the Harris Hawks attack to this animal in soft besiege. Using this technique, we have tried to balance the energy and reliability of the Internet of Things between CHs. This proposed method was simulated on Network Simulator 3 (NS3).

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“…The availability shows the ability of IoT service to be functioning when requested, and it may be computed using formula [12]. , 1,...,…”

Section: Proposed Methodsmentioning

confidence: 99%

Enhancement of Quality of Service (QoS) in Internet of Things based on big data environment using the Harris Hawks algorithm

Sefati

Halunga²

2023

Advanced Topics in Optoelectronics, Microelectronics, and Nanotechnologies XI

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“…K ‐means clustering is applied to put services with the same categories in the same groups. A hybrid of genetic algorithm and artificial bee colony algorithm is presented for service composition 28 . In this work, first, the genetic algorithm chooses services, and then these services are processed by the artificial bee colony algorithm to satisfy the user's needs.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A hybrid of genetic algorithm and artificial bee colony algorithm is presented for service composition. 28 In this work, first, the genetic algorithm chooses services, and then these services are processed by the artificial bee colony algorithm to satisfy the user's needs. A spider monkey optimization algorithm is used for service composition for minimizing the response time.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Higher QoS can raise the reputation and profits of service providers 27 . Since there are different services with the same functionality and different QoS, a service composition algorithm is required to select appropriate services between services to fulfill the user's preferences 28 . Since there are many candidate services with the same functionality and different QoS for each task, the exact algorithms cannot find a solution for web service composition when the number of task and microservices increase.…”

Section: Introductionmentioning

confidence: 99%

“…27 Since there are different services with the same functionality and different QoS, a service composition algorithm is required to select appropriate services between services to fulfill the user's preferences. 28 Since there are many candidate services with the same functionality and different QoS for each task, the exact algorithms cannot find a solution for web service composition when the number of task and microservices increase. Thus, the service composition problem is considered as an NP-hard optimization problem, and different optimization algorithms have been presented to solve web service composition problems including harmony search algorithm and its variants, 29 a linear programming approach, 30 a biobjective particle swarm optimization algorithm, 31 a flying ant colony optimization algorithm with modified neighboring selection, 32 an improved multiverse optimization (IMVO) algorithm, 26 and many other algorithms that will be discussed in the next section.…”

Section: Introductionmentioning

confidence: 99%

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A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

Maroosi

Muniyandi

2023

Int J Communication

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Summary Cloud computing is an emerging technology in computing that provides different services over the Internet. It needs composite services to perform a complex task. Optimal selection of services that provides both functionality and nonfunctionality requirements is an NP‐hard problem. This study uses nondeterministic parallel and distributed structures of membrane systems for the recently improved multiverse optimization algorithm to improve the quality of solutions. In the previous membrane‐inspired algorithm, the population was divided into subpopulations that evolve different dynamic membranes. This study not only uses a conventional membrane‐inspired approach to introduce a conventional membrane‐inspired multiverse optimizer (CMIMVO) for the first time but also proposes an algorithm that divides the variables (dimension) into subgroups for different membranes called proposed membrane‐inspired multiverse optimizer (PMIMVO). Thus, in PMIMVO, each membrane works on a subgroup to gain global information, which considers the best values obtained by other membranes for other variables. The PMIMVO shows promising results on benchmark function problems. Furthermore, simulation results show that the PMIMVO approach could achieve up to 38% improvement in integrated quality of service (QoS) with attributes including response time, price, availability, and reliability in comparison with the previous approaches, including genetics algorithm (GA), particle swarm optimization (PSO), gravitational search algorithm (GSA), moth–flame optimization (MFO) improved multiverse optimizer (MVO), and CMIMVO.

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Ultra‐reliability and low‐latency communications on the internet of things based on5Gnetwork: Literature review, classification, and future research view

Sefati

Halunga

2023

Trans Emerging Tel Tech

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A new technology known as the Internet of Things (IoT) uses several sensor devices and communication protocols. By implementing cutting‐edge and modern equipment, people use IoT to make their lives easier. Home automation is one of them, and it works with actuators and sensors. However, increasing the number of devices in the IoT network could degrade the Quality of Service (QoS). Therefore, an appropriate framework in software and hardware can improve the Quality of Experience (QoE) and QoS for all users. One of the critical QoS measures in IoT is called Ultra Reliability and Low Latency Communication (URLLC). URLLC is essential in the IoT network released from the third Generation Partnership Project (3GPP) cellular. However, a systematic and comprehensive investigation of the practical procedures for URLLC in IoT needs to be done. This paper comprehensively investigates the existing methodologies in this subject. All the chosen techniques are separated into four categories to obtain a complete picture of the topic: structure‐based, diversity‐based, metaheuristic algorithm‐based, and channel state information. In this paper, we also investigate more benefits and drawbacks of other QoS when URLLC is applied in the IoT network. This paper highlights the challenges of URLLC in IoT networks and describes future open issues in detail to provide an efficient way for researchers in this field.

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A Hybrid Service Selection and Composition for Cloud Computing Using the Adaptive Penalty Function in Genetic and Artificial Bee Colony Algorithm

Cited by 20 publications

References 49 publications

Enhancement of Quality of Service (QoS) in Internet of Things based on big data environment using the Harris Hawks algorithm

Enhancement of Quality of Service (QoS) in Internet of Things based on big data environment using the Harris Hawks algorithm

A novel membrane‐inspired multiverse optimizer algorithm for quality of service‐aware cloud web service composition with service level agreements

Ultra‐reliability and low‐latency communications on the internet of things based on5Gnetwork: Literature review, classification, and future research view

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