A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Chen, Jing; Wang, Yinglong; Liu, Tao

doi:10.1186/s13638-021-01912-8

Cited by 18 publications

(13 citation statements)

References 47 publications

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“…When the search starts, the slave node issues a query message. It plays the role of ant in the improved Ant colony algorithm [15]. All ants follow the pheromone node with high probability, and the pheromone with less node probability chooses the next hop node and leaves the pheromone on the passing path node.…”

Section: Use Improved Ant Colony Algorithm To Find the Most Suitable ...mentioning

confidence: 99%

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

Chen

2023

Teh. vjesn.

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The new energy industry development is affected by many factors. Among them, the resources utilization ratio is a major reason for the low productivity of enterprises. As the core problem of cloud computing, the resource allocation problem has been widely concerned by the people, and the resource allocation problem of the new energy industry as the key to energy innovation and transformation should be more paid attention to. In multi-resource cloud computing scenarios, requests made by users often involve multiple types of resources. Traditional resource allocation algorithms have a single optimization object, typically time efficiency. In order to achieve cluster load balancing, utilization of system resources and improvement of system work efficiency, this paper proposes a new cloud computing allocation algorithm based on improved ant colony algorithm. According to the limit conditions of cloud computing environment and computing resources, this paper finds the shortest response time of all resource nodes and gets a set of best available nodes. This method can meet the quality requirements of cloud computing, and the task completion time of the improved algorithm is shorter, the number of algorithm iterations is less, and the load balancing effect is better. Through MATLAB simulation experiments, the effectiveness of the proposed method is verified.

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Section: Use Improved Ant Colony Algorithm To Find the Most Suitable ...mentioning

confidence: 99%

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

Chen

2023

Teh. vjesn.

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“…Jing Chen et al [16] offered a proactive resource allocation strategy in cloud computing based on adaptive resource request prediction. It creates a paradigm for multiobjective resource allocation optimization that reduces resource allocation delay and balances the consumption of different types of physical machine resources.…”

Section: Related Workmentioning

confidence: 99%

CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

Seno

Mohammad

Dhannoon

2022

Int. J. Interact. Mob. Technol.

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The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study. This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability).

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“…To compare the proposed FSAOS resource allocation algorithm based on makespan time with that of the state-ofthe-art approaches (e.g., in [32][33][34][35]), the population size, minimum and maximum iteration parameters used were similar for the benchmarked schemes. Twenty (20) independent simulation runs were conducted on the tasks instances: 200, 400, 600, 800 and 1000, and an average of the results obtained from the clod platform is reported in Table 10.…”

Section: Comparison With State-of-the-art Approachesmentioning

confidence: 99%

“…The effectiveness of their proposed approach was verified via simulation, and their proposed GA-RF model was able to improve resource utilization, energy consumption and execution time compared to the benchmarked models. In a similar development, [33] stated that the development of big data and artificial intelligence provide more concern to the cloud resource requests, thereby presenting more complex features like being sudden, arriving in batches and being diverse. According to the researchers, these could potentially cause resource allocation to lag far behind the resource requests and an unbalanced resource utilization that wastes resources.…”

Section: Introductionmentioning

confidence: 99%

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

Gabi

Dankolo

Muslim

et al. 2022

Neural Comput & Applic

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Achieving sustainable profit advantage, cost reduction and resource utilization are always a bottleneck for resource providers, especially when trying to meet the computing needs of resource hungry applications in mobile edge-cloud (MEC) continuum. Recent research uses metaheuristic techniques to allocate resources to large-scale applications in MECs. However, some challenges attributed to the metaheuristic techniques include entrapment at the local optima caused by premature convergence and imbalance between the local and global searches. These may affect resource allocation in MECs if continually implemented. To address these concerns and ensure efficient resource allocation in MECs, we propose a fruit fly-based simulated annealing optimization scheme (FSAOS) to serve as a potential solution. In the proposed scheme, the simulated annealing is incorporated to balance between the global and local search and to overcome its premature convergence. We also introduce a trade-off factor to allow application owners to select the best service quality that will minimize their execution cost. Implementation of the FSAOS is carried out on EdgeCloudSim Simulator tool. Simulation results show that the FSAOS can schedule resources effectively based on tasks requirement by returning minimum makespan and execution costs, and achieve better resource utilization compared to the conventional fruit fly optimization algorithm and particle swarm optimization. To further unveil how efficient the FSAOSs, a statistical analysis based on 95% confidential interval is carried out. Numerical results show that FSAOS outperforms the benchmark schemes by achieving higher confidence level. This is an indication that the proposed FSAOS can provide efficient resource allocation in MECs while meeting customers’ aspirations as well as that of the resource providers.

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A proactive resource allocation method based on adaptive prediction of resource requests in cloud computing

Cited by 18 publications

References 47 publications

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

An Improved Ant Colony Algorithm for New energy Industry Resource Allocation in Cloud Environment

CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

Dynamic scheduling of heterogeneous resources across mobile edge-cloud continuum using fruit fly-based simulated annealing optimization scheme

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