Template-Based Genetic Algorithm for QoS-Aware Task Scheduling in Cloud Computing

Sheng, Xiaodong; Li, Qiang

doi:10.1109/cbd.2016.015

Cited by 16 publications

(5 citation statements)

References 7 publications

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“…GA makes a populace of arrangements and applies control operators like mutation and crossover to find the best among them. At each step, there is a random selection of individuals from the current population [97] [86].…”

Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Singh

Tyagi

Kumar

et al. 2021

Simulation Modelling Practice and Theory

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Section: Genetic Algorithm (Ga)mentioning

confidence: 99%

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Singh

Tyagi

Kumar

et al. 2021

Simulation Modelling Practice and Theory

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“…However, because the migration time of VMs is also a key factor affecting the host performance, the SimCMA model incorporates the improved ant colony algorithm to ensure a global optimal VM migration path and the minimum VM migration time. The ant colony algorithm uses the walking paths of ants to represent the feasible solution of a problem to be optimized, and all the paths of the entire ant colony constitute the solution space of the problem to be optimized [30]. Ants with shorter paths release more pheromones.…”

Section: ) Establishment Of the Resource Allocation Model For Conflimentioning

confidence: 99%

“…The value of the initial ̂ is determined according to the minimum value of the remaining resources of the host in time t, as shown in formula (27). Formulas (28), (29), and (30) illustrate changes in ̂ over time under the conditions that the remaining resources of the destination host are greater than the boundary threshold, equal to the boundary threshold, and less than the boundary threshold, respectively. It can be seen that the greater the remaining resources of the destination host are, the stronger the effect of the inhibitor.…”

Section: ) Establishment Of the Resource Allocation Model For Conflimentioning

confidence: 99%

Balanced Cloud Edge Resource Allocation Based on Conflict Conditions

et al. 2020

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Under a multiscenario environment with frequent bursts of data in the edge cloud, the resource allocation in the edge cloud will affect the stability of its nodes. To address this problem, a balanced virtual resource allocation model based on conflict conditions is proposed. Based on a thorough study of the similarity between task attributes and resources used by the host, the concept of a task backlog is implemented to achieve a preliminary balanced allocation of tasks; thus, a conflict condition based on the remaining resources of the physical and virtual machines is proposed. Further, a matrix of phased conflict coefficients is built to establish a balanced virtual machine allocation model. The results of experiments comparing the performance of the proposed model with that of other existing models indicate that the proposed model can reduce the virtual machine scheduling time by up to 8.33%, save up to 6.25% of host energy consumption, and improve the algorithm efficiency by 20.47% compared with the other algorithms. To avoid the local optimal problem caused by dynamic virtual machine migration, an improved ant colony algorithm is combined with the above model, and concepts of a pheromone volatility factor and suppression factor are implemented to optimize the pheromone measurement function and ensure that the virtual machine migration path is globally optimal. Overall, the model reduces the conflict rate of resources on the physical machine and can maintain stable operation under CPU usage fluctuation, thus realizing a balanced allocation of node resources.

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“…(2) Genetic algorithm Genetic algorithm is a heuristic algorithm inspired by Darwin's theory of evolution, which mimics the evolutionary process of biological chromosomes in nature [19]. The algorithm follows the natural evolution principle of survival of the fittest, and is randomly optimized through the combination of self-selection and environmental choice between individuals.…”

Section: Related Workmentioning

confidence: 99%

MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

et al. 2019

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Cloud computing is popular in nowadays for its convenient and cheap. Grid provides services of available everywhere, and shares everyone. Therefore, smart grid cloud is a good way to manage data for sharing with all power supply stations. Grid cloud task scheduling is one of the key technologies that affect resource allocation efficiency in cloud computing environment. The advantages and disadvantages of scheduling algorithms will directly affect the scheduling performance of both cloud computing and the stability of the entire system platform. Cloud task scheduling problem has been proved to be a NP-hard problem, The traditional task scheduling algorithm can no longer meet the actual needs of cloud task scheduling, but the Heuristic algorithm is an effective method to solve this problem. This paper studies and analyzes the application of heuristic algorithms in cloud task scheduling problems, and proposes a cloud task scheduling strategy to minimize the task completion time and execution cost (MCTE) for the smart grid cloud. Then, carry out mathematical modeling on the grid cloud task scheduling problem. The experimental results show MCTE is well for the smart grid cloud. INDEX TERMS Smart grid cloud, task scheduling, particle swarm optimization-genetic algorithm.

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Template-Based Genetic Algorithm for QoS-Aware Task Scheduling in Cloud Computing

Cited by 16 publications

References 7 publications

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Metaheuristics for scheduling of heterogeneous tasks in cloud computing environments: Analysis, performance evaluation, and future directions

Balanced Cloud Edge Resource Allocation Based on Conflict Conditions

MCTE: Minimizes Task Completion Time and Execution Cost to Optimize Scheduling Performance for Smart Grid Cloud

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