Efficient resource allocation in cloud data centers through genetic algorithm

Arianyan, Ehsan; Maleki, Davood; Yari, Alireza; Arianyan, Iman

doi:10.1109/istel.2012.6483053

Cited by 13 publications

(7 citation statements)

References 5 publications

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“…This is not the first attempt to deploy GA for optimization of resource allocation. Mature solutions have been proposed for allocation of generic resource [23], [24], radio resource [25] and cloud resource [26], [27]. All these approaches consider the problem of global resource optimization, where the system allocates resource blocks from a certain pool to a known set of targets (activities, links, users, etc.).…”

Section: Proposed Methods a Slicing Strategies As Binary Sequence Codesmentioning

confidence: 99%

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

2018

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In the context of Fifth Generation (5G) mobile networks, the concept of "Slice as a Service" (SlaaS) promotes mobile network operators to flexibly share infrastructures with mobile service providers and stakeholders. However, it also challenges with an emerging demand for efficient online algorithms to optimize the request-and-decision-based inter-slice resource management strategy. Based on genetic algorithms, this paper presents a novel online optimizer that efficiently approaches towards the ideal slicing strategy with maximized long-term network utility. The proposed method encodes slicing strategies into binary sequences to cope with the request-and-decision mechanism. It requires no a priori knowledge about the traffic/utility models, and therefore supports heterogeneous slices, while providing solid effectiveness, good robustness against non-stationary service scenarios, and high scalability.INDEX TERMS 5G mobile communication, business model, communication system operations and management, genetic algorithms, network slicing, optimal scheduling, optimization, resource management

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Section: Proposed Methods a Slicing Strategies As Binary Sequence Codesmentioning

confidence: 99%

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

2018

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“…There is no competition among the users for obtaining a set of required resources. Another approach uses genetic algorithms to find the optimal resource allocation and assigns resources to the clients or users based on the outcome of the genetic algorithm [14].…”

Section: Resource Allocation In a Cloud Computing Environmentmentioning

confidence: 99%

“…This module is shown in Figure 2. The expected arrival rateω i (k) of web requests at the service i can be estimated by equation 14.…”

Section: Forecasting the Environmental Inputs At Each Service Providermentioning

confidence: 99%

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Mehrotra

Srivastava

Banicescu

et al. 2016

Future Generation Computer Systems

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Efficient resource allocation of computational resources to services is one of the predominant challenges in a cloud computing environment. Furthermore, the advent of cloud brokerage and federated cloud computing systems increases the complexity of cloud resource management. Cloud brokers are considered third party organizations that work as intermediaries between the service providers and the cloud providers. Cloud brokers rent different types of cloud resources from a number of cloud providers and sublet these resources to the requesting service providers. In this paper, an autonomic performance management approach is introduced that provides dynamic resource allocation capabilities for deploying a set of services over a federated cloud computing infrastructure by considering the availability as well as the demand of the cloud computing resources. A distributed control based approach is used for providing autonomic computing features to the proposed framework via a feedback-based control loop. This distributed control based approach is developed using one of the decomposition-coordination methodology, named interaction balance, for interactive bidding of cloud computing resources. The primary goals of the proposed approach are to maintain the service level agreements, maximize the profit, and minimize the operating cost for the service providers and the cloud broker. The application of interaction balance methodology and prioritization of profit maximization for the cloud broker and the service providers during resource allocation are novel contributions of the proposed approach.

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“…Yet, proficient management of resources and cost containment in cloud ecosystems remains a significant challenge [2]. Contemporary optimization techniques, including Particle Swarm Optimization (PSO) [3], Ant Colony Optimization (ACO) [4], and Genetic Algorithms (GA) [5], are tailored to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Studies such as those by Jun et al have revealed the potential of nature-inspired algorithms like ACO in optimizing cloud resources [11]. Meanwhile, Arianyan et al's focus on efficient resource allocation through genetic algorithms provides another perspective on the topic [5]. The versatility and adaptability of such algorithms in various application contexts are further echoed in works by Liu and Li [12] and Yichen et al [13].…”

Section: Introductionmentioning

confidence: 99%

Cloud Computing With Resource Allocation Based on Ant Colony Optimization

Kniazhyk,

Muliarevych

2023

ACPS

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In this study, we explore the intricacies of cloud computing technologies, with an emphasis on the challenges and concerns pertinent to resource allocation. Three opti- mization techniques—Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) — have been meticulously analyzed concerning their applications, objectives, and operational methodologies. The study underscores these algorithms' pivotal role in enhancing cloud resource optimization, while also elucidat- ing their respective merits and limitations. As the complexity of cloud computing escalates, devising efficacious strategies for resource management and alloca- tion becomes imperative. Such strategies are paramount in aiding organizations in cost containment and performance amplification. The ensuing comparative analysis has been crafted to offer a holistic insight into the three algorithms, thus empowering cloud providers to judiciously select an optimization technique that aligns with the unique de- mands and challenges of their cloud computing infrastructure.

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Efficient resource allocation in cloud data centers through genetic algorithm

Cited by 13 publications

References 5 publications

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks

Towards an autonomic performance management approach for a cloud broker environment using a decomposition–coordination based methodology

Cloud Computing With Resource Allocation Based on Ant Colony Optimization

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