In cloud-edge computing paradigms, the integration of edge servers and task offloading mechanisms has posed new challenges to developing task scheduling strategies. This paper proposes an efficient convergent firefly algorithm (ECFA) for scheduling security-critical tasks onto edge servers and the cloud datacenter. The proposed ECFA uses a probability-based mapping operator to convert an individual firefly into a scheduling solution, in order to associate the firefly space with the solution space. Distinct from the standard FA, ECFA employs a low-complexity position update strategy to enhance computational efficiency in solution exploration. In addition, we provide a rigorous theoretical analysis to justify that ECFA owns the capability of converging to the global best individual in the firefly space. Furthermore, we introduce the concept of boundary traps for analyzing firefly movement trajectories, and investigate whether ECFA would fall into boundary traps during the evolutionary procedure under different parameter settings. We create various testing instances to evaluate the performance of ECFA in solving the cloud-edge scheduling problem, demonstrating its superiority over FA-based and other competing metaheuristics. Evaluation results also validate that the parameter range derived from the theoretical analysis can prevent our algorithm from falling into boundary traps.

ECFA: An Efficient Convergent Firefly Algorithm for Solving Task Scheduling Problems in Cloud-Edge Computing

Resource scheduling in cloud computing environment is the current research focus, aiming at the shortage of traditional particle swarm optimization algorithm, it puts forward a cloud computing resource scheduling algorithm based on Particle Swarm Optimization algorithm and Membrane Computing theory(PSO-MC). The cloud resource scheduling problem is analyzed to get the cloud resource scheduling objective function, and the membrane system is used as framework, the particles in main membrane are used to the local optimization while the particles in auxiliary membrane mainly used for global search,search results are transferred among different membrane areas to find the cloud resource scheduling optimization solution, the simulation experiment is carried out in CloudSim. The results show that the proposed algorithm can reduce the average completion time of tasks to improve the efficiency of task processing and can get more reasonable cloud computing resource scheduling result.

Cloud resource scheduling based on improved particle swarm optimization algorithm by membrane computing

Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm

In order to improve the utilization ratio of virtual machine resource, and then realize reasonable scheduling of it. In this paper, we propose a novel cloud computing resource scheduling optimization approach based on firefly algorithm. Firstly, mathematical model is built according to virtual machine resource scheduling problem. Then, considering the optimal time span and load function, we propose improved firefly algorithm, named selective elimination and decision domain strategy of firefly algorithm (SDFA) and use the SDFA to search the optimal scheme. Finally, we use the CloudSim platform to evaluate the effectiveness of our proposed approach. Experimental results show that our proposed approach can obtain good scheduling scheme to ensure load balance of virtual machine resource, which can meet the user's preferences.

Resource Scheduling in Cloud Computing Based on Firefly Algorithm

The invention discloses a data modeling processing structure of a big data genetic algorithm, which comprises an information encoder, a data modeling processor and an information compressor; an abutting data pin is arranged on the information encoder, the information encoder is connected with a temporary storage stacker; the inner part of the temporary storage stacker is provided with a managementport, and a storage grain is arranged on the temporary storage stacker; a storage plug slot is further arranged on the temporary storage stacker, the data modeling processor is arranged beneath the temporary storage stacker. A radiating fan is arranged on the data modeling processor, the side of the radiating fan is provided with an assembling groove. The data modeling processing structure has the beneficial effects that the data modeling processing structure has active radiating system and can guarantee good performance in working process; meanwhile, the data modeling processing structure has data stacking and temporary storage functions, and can store files under the emergency power failure; moreover, the data modeling processing structure has the functions of modeling, feedback and re-modeling, thus the modeling quality is improved.

Data modeling processing structure of big data genetic algorithm

For the problem of slow scheduling speed and poor load balancing of agricultural information resource scheduling in the current cloud computing, an agricultural information resource scheduling algorithm based on firefly algorithm is proposed in this paper. In cloud computing, according to the idea of earliest completing resource scheduling and using the least cost, the agricultural information resource scheduling model with time constraint is constructed. The minimum completion time and scheduling cost of resource scheduling are determined and the objective function of resource scheduling is built. The chaos algorithm is used to optimize the firefly algorithm to solve the objective function and improve the convergence speed of resource scheduling. Agricultural information resource scheduling is achieved according to the convergence results and by using Lagrangian relaxation function. Experimental results show that the proposed algorithm is faster in scheduling agricultural information resources and higher in load balancing.

Agricultural information resource scheduling algorithm based on firefly algorithm in cloud computing

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Resource Scheduling in Cloud Computing Based on Firefly Algorithm

Data modeling processing structure of big data genetic algorithm

Agricultural information resource scheduling algorithm based on firefly algorithm in cloud computing