Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing

Wei, Xianyong

doi:10.1007/s12652-020-02614-7

Cited by 67 publications

(45 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the previous section, the ant colony needs to conduct a global search according to the transition probability shown in Equation (24). When the transition probability is greater than the set transition probability, it needs to move to the best point.…”

Section: Improved Ant Colony Algorithmmentioning

confidence: 99%

“…This section mainly uses the improved ant colony algorithm to optimize the equivalent factor of charging and discharging [24]. There are two objects to be optimized; the position of the ant is initialized as:…”

Section: Solve the Optimal Equivalent Factormentioning

confidence: 99%

“…The results showed that the proposed energy management strategy took into account both diesel generator efficiency and ship economy, which could reduce ship fuel consumption and improve the service life of the diesel generator. Wei [24] proposed a task scheduling optimization strategy using an improved ACO algorithm in cloud computing in order to solve the problems of load imbalance, slow convergence, and low resource utilization of a virtual machine in the existing task scheduling optimization strategy. Firstly, a scheduling model using an improved ant colony algorithm was proposed to avoid the dilemma of local optimization.…”

Section: Introductionmentioning

confidence: 99%

“…Other ants that do not find the optimal solution will conduct a global search, and the path of global search will move according to the pheromone of the ant with the optimal solution [27]. The rule of transition probability is shown in Equation (24).…”

mentioning

confidence: 99%

See 3 more Smart Citations

An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Xiang

Yang

2021

Energies

View full text Add to dashboard Cite

In order to reduce fuel consumption and reduce the deviation between the final battery state-of-charge (SOC) value and the target value at the same time, a novel double-layer multi-objective optimization method is proposed, which adopts an improved ant colony optimization (ACO) algorithm and the equivalent fuel consumption minimization strategy (ECMS) considering mode switching. The proposed strategy adopts a two-layer structure. In the inner layer, the ECMS considering mode switching was adopted to optimize the working mode and working point, so as to achieve the goal of reducing fuel consumption. In the outer layer, aiming at the shortcomings of traditional ACO, the heuristic factor and adaptive volatilization factor were introduced. An improved ACO method was proposed to optimize the equivalent factor, so as to achieve the goal of reducing the deviation between the final value of SOC and the target value. In order to verify the effectiveness of the proposed algorithm, it is compared with the traditional ECMS strategy and the rule-based (RB) ECMS strategy. The simulation results show that the proposed energy management strategy combining an improved ACO algorithm with ECMS considering mode switching can reduce the energy consumption of the whole ship and control the battery power.

show abstract

Section: Improved Ant Colony Algorithmmentioning

confidence: 99%

Section: Solve the Optimal Equivalent Factormentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

See 2 more Smart Citations

An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Xiang

Yang

2021

Energies

View full text Add to dashboard Cite

show abstract

“…In the cloud computing environment, many forms of scheduling methods have been adopted, including priority-based scheduling [ 12 – 14 ], cluster-based scheduling [ 15 , 16 ], QoS-based scheduling [ 17 , 18 ], multiprocessor-based scheduling [ 19 ], and heuristic algorithm-based scheduling [ 20 ]. We can use exhaustive scheduling algorithms to meet scheduling requirements when the task request is small and the scale of cloud computing is also small.…”

Section: Introductionmentioning

confidence: 99%

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Sun

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

An important challenge facing cloud computing is how to correctly and effectively handle and serve millions of users’ requests. Efficient task scheduling in cloud computing can intuitively affect the resource configuration and operating cost of the entire system. However, task and resource scheduling in a cloud computing environment is an NP-hard problem. In this paper, we propose a three-layer scheduling model based on whale-Gaussian cloud. In the second layer of the model, a whale optimization strategy based on the Gaussian cloud model (GCWOAS2) is used for multiobjective task scheduling in a cloud computing which is to minimize the completion time of the task via effectively utilizing the virtual machine resources and to keep the load balancing of each virtual machine, reducing the operating cost of the system. In the GCWOAS2 strategy, an opposition-based learning mechanism is first used to initialize the scheduling strategy to generate the optimal scheduling scheme. Then, an adaptive mobility factor is proposed to dynamically expand the search range. The whale optimization algorithm based on the Gaussian cloud model is proposed to enhance the randomness of search. Finally, a multiobjective task scheduling algorithm based on Gaussian whale-cloud optimization (GCWOA) is presented, so that the entire scheduling strategy can not only expand the search range but also jump out of the local maximum and obtain the global optimal scheduling strategy. Experimental results show that compared with other existing metaheuristic algorithms, our strategy can not only shorten the task completion time but also balance the load of virtual machine resources, and at the same time, it also has a better performance in resource utilization.

show abstract

Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

Nayak,

Singh,

Kushwaha

et al. 2023

Concurrency and Computation

View full text Add to dashboard Cite

SummaryA difficult problem in the service‐oriented computing paradigm is improving task scheduler policy or resource provisioning.In order to increase the performance of cloud applications, this article primarily focuses on tasks for resource mapping policy optimization. With the aim of reducing makespan and execution overhead and increasing the average resource utilization, we suggested an efficient independent task scheduler employing supervised neural networks in this paper. The suggested ANN‐based scheduler uses the status of the cloud environment and incoming tasks as inputs to determine the optimal computing resource for a given assignment as a result that assembles our goal. We proposed a novel algorithm in this paper that uses a hybrid methodology based on a swarm intelligence algorithm (PSO) in combination with a machine learning technique (ANN). PSO is used to prepare the train and test dataset for the neural network. Results clearly state that suggested work achieves significant improvement to considered algorithms in makespan (45%–55%), average VM utilization (15%–20%), and execution overhead(20%–30%).

show abstract

Task scheduling optimization strategy using improved ant colony optimization algorithm in cloud computing

Cited by 67 publications

References 22 publications

An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

GCWOAS2: Multiobjective Task Scheduling Strategy Based on Gaussian Cloud‐Whale Optimization in Cloud Computing

Efficient task scheduling on the cloud using artificial neural network and particle swarm optimization

Contact Info

Product

Resources

About