Since the existing pruning strategies of the Convolutional Neural Network (CNN) model are different and have general effects, an Activation-Entropy based Layer-wise Iterative Pruning (AE-LIP) strategy was proposed to reduce the parameter amount of the model while ensuring the accuracy of the model within a controllable range. Firstly, combined with the neuronal activation value and information entropy, a weight evaluation criteria based on activation-entropy was constructed, and the weight importance score was calculated. Secondly, the pruning was performed layer by layer, the weights were sorted according to the importance score, and the pruning number in each layer was combined to filter out the weights to be pruned and set them to zero. Finally, the model was fine-tuned, and the above process was repeated until the iteration ended. The experimental results show that the activation-entropy based layer-wise iterative pruning strategy makes the AlexNet model compressed 87.5%, and the corresponding accuracy is reduced by 2.12 percentage points, which is 1.54 percentage points higher than that of the magnitude-based weight pruning strategy and 0.91 percentage points higher than that of the correlation-based weight pruning strategy; the strategy makes VGG-16 model compressed 84.1%, and the corresponding accuracy is reduced by 2.62 percentage points, which is 0.62 and 0.27 percentage points higher than those of the two above strategies. It can be seen that the proposed strategy reduces the size of the CNN model effectively while ensuring the accuracy of the model, and is helpful for the deployment of CNN model on mobile devices with limited storage.
To realize reasonable allocation and scheduling of mobile user task requests under cloud and fog collaboration, a task assignment algorithm based on cloud-fog collaboration model, named IGA (Improved Genetic Algorithm), was proposed. Firstly, individuals were coded in the way of mixed coding, and initial population was generated randomly. Secondly, the objective function was set as the cost of service providers. Then select, cross, and mutate were used to produce new qualified individuals. Finally, the request type in a chromosome was assigned to the corresponding resource node and iteration counter was updated until the iteration was completed. The simulation results show that compared with traditional cloud model, cloud-frog collaboration model reduces the time delay by nearly 30 seconds, reduces Service Level Objective (SLO) violation rate by nearly 10%, and reduces the cost of service providers.
Aiming at the resource scheduling problem across data centers, a Priority Combinatorial Double Auction (PCDA) resource scheduling scheme was proposed. Firstly, cloud resource auction was divided into three parts:cloud user agent bidding, cloud resource provider bid, auction agent organization auction. Secondly, on the basis of defining user priority and task urgency, the violation of Service Level Agreement (SLA) of each job during auction was estimated and the revenue of cloud provider was calculated. At the same time, a number of transactions were allowed in each round of bidders. Finally, reasonable allocation of cloud resource scheduling according to user level could be achieved. The simulation results show that the algorithm guarantees the success rate of auction. Compared with traditional auction, PCDA reduces energy consumption by 35.00% and the profit of auction cloud provider is about 38.84%.
Concerning the delay of related task scheduling in cloud computing, a Related Task Scheduling algorithm based on Task Hierarchy and Time Constraint (RTS-THTC) was proposed. The related tasks and task execution order were represented by Directed Acyclic Graph (DAG), and the task execution concurrency was improved by using the proposed hierarchical task model. Through the calculation of the total time constraint in each task layer, the tasks were dispatched to the resource with the minimum execution time. The experimental results demonstrate that the proposed RTS-THTC algorithm can achieve better performance than Heterogeneous Earliest-Finish-Time (HEFT) algorithm in the terms of the total execution time and task delay.