Resource Allocation based on Genetic Algorithm for Cloud Computing

Chen, Yiliang; Huang, Shih-Yun; Chang, Yao-Chung; Chao, Han‐Chieh

doi:10.1109/wocc53213.2021.9603125

Cited by 3 publications

(3 citation statements)

References 5 publications

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“…Challenges include potential issues in the resource allocation technique for virtual machines, impacting processing times. To address the resource allocation problem in cloud computing, [29] proposed a genetic algorithm. However, unequal resource allocation for virtual machines could lead to extended processing times.…”

Section: Literature Reviewmentioning

confidence: 99%

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Mushtaq,

Chughtai,

Zhang

et al. 2024

IEEE Access

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With the increasing prevalence of multi-user, multi-service, and heterogeneous multi-device environments, there is a need to address the imperative for efficient resource allocation in contemporary wireless networks, such as those involving unmanned aerial vehicles (UAVs) or drones. In this regard, this work addresses the challenges within a five-dimensional heterogeneous wireless network model, focusing on diverse services such as Big Data Analytics, Video Rendering, and Computer-Aided Design, and the allocation of resources among heterogeneous devices, including UAVs, tethered balloons, and multi-rotors. The resource allocation is facilitated through multiple interfaces like LTE, Wifi, LoRa, and Sigfox, catering to the diverse needs of users operating in aerial Networks. Additionally, this work introduces a novel Intelligent Relaxation using the Penalty Function (IRPF) approach for resource allocation, treating it as an integer programming problem to balance user needs while ensuring affordability. A comparative analysis is conducted between the proposed approach and the traditional branch-and-bound algorithm. In scenarios requiring resource allocation for numerous services based on user demand and device capabilities, the proposed work presents a penalty-based integrality gap solution adept at managing fractional values. The resulting optimization framework is meticulously designed to minimize activation and operating costs while optimizing utility. Additionally, the computing efficiency of the proposed approach is demonstrated by extensive simulations that prove its superiority over the traditional algorithm. Consequently, this research emphasizes the essential role of the proposed model in navigating the intricate challenges of resource allocation in modern drone-centric wireless networks.

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Section: Literature Reviewmentioning

confidence: 99%

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Mushtaq,

Chughtai,

Zhang

et al. 2024

IEEE Access

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“…A trial-and-catch resource allocation system based on genetic algorithms is suggested. Virtual machines are used to anticipate workload, and resource allocation is carried out as a result [19]. Although automating cloud resource management processes has always been a desirable objective, it has also proven to be difficult.…”

Section: Year 2021mentioning

confidence: 99%

A Survey on Resource Allocation Techniques in Cloud Computing

Priyadarshini¹,

Nithya²,

Reka³

2022

IJRITCC

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Cloud is an important and emerging technology utilized by various fields for storing, processing and retrieving of data anywhere and anytime without any interruption. Cloud is now acting as a platform for many companies for storing and other computational purposes to reduce infrastructure and maintenance cost similarly they can utilize their application widely based on pay per use. To make available of data to all cloud users Resource Allocation (RA) is mandatory process. In cloud hardware, software and platform are the resources utilized to satisfy user needs hence sharing these resources according to users need is a difficult task. Cloud service provider and cloud service consumer plays the major role in RA. The parameters under resource allocation, its issues and challenges are needed to be analyzed deeply before implementing any optimizing approach in RA. Hence in this work various resource allocation methods have been studied and issues in it is analyzed and presented as a survey. This work is useful for both cloud users and researchers in overcoming the challenges faced in RA.

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“…In cloud computing environments, virtual machines are an important resource, making resource allocation for them a significant problem. Chen et al [19] designed a genetic algorithm to solve the allocation problem. Nagpure et al [20] proposed a dynamic resource allocation system to solve the problem of allocating virtual machine resources.…”

Section: Related Workmentioning

confidence: 99%

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

Zhang

Zong

2022

Processes

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The concept of the metaverse has gained increasing attention in recent years, and the development of various new technologies, including digital twin technology, has made it possible to see the metaverse coming to pass. Many academics have begun to investigate various problems after realizing the importance of digital twin technology in building the metaverse. However, when utilizing digital twin technology to construct a metaverse, there remains limited research on how to allocate multibase station resources. This research translates a multibase station wireless resource allocation problem into an integer linear programming constraint model when virtual service providers construct a metaverse. In addition, the optimal VCG reverse auction (OPT-VCGRA) mechanism is designed to maximize social welfare and solve the problem of IoT devices competing for base station wireless resources. Specifically, the problem of the optimal allocation of wireless channel resources and payment rule based on the Vickrey–Clarke–Groves mechanism is solved to achieve optimal allocation and calculation of payment prices. Since the optimal allocation problem is NP-hard, this paper also designs a metaverse digital twin resource allocation and pricing (MDTRAP) mechanism based on monotonic allocation and key value theory. The mechanism sends the resource allocation results of multiple base stations to IoT devices and calculates the price payment when building a metaverse in the real world. This paper shows that both auction mechanisms have incentive compatibility and individual rationality properties. Through experiments, this paper compares the two mechanisms in terms of social welfare, the number of winners, and the overall payment. The MDTRAP mechanism performs similarly to the OPT-VCGRA mechanism in terms of social welfare, the number of winners, and channel utilization but is far superior to the OPT-VCGRA mechanism in terms of execution time and total payment. The trustful experiment also verified the truthfulness of the MDTRAP mechanism. The experimental results show that the MDTRAP mechanism can be used to solve the resource allocation problem of multiple base stations to IoT devices when building a metaverse in the real world and can effectively maximize social welfare.

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Resource Allocation based on Genetic Algorithm for Cloud Computing

Cited by 3 publications

References 5 publications

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

Framework for Optimized Resource Allocation in Multi-User, Multi-Service, Multi-Device Aerial Networks

A Survey on Resource Allocation Techniques in Cloud Computing

A Truthful Mechanism for Multibase Station Resource Allocation in Metaverse Digital Twin Framework

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