Analysis of CNN Model with Traditional Approach and Cloud AI based Approach

Kushwaha, Utkarsh; Gupta, Puja; Airen, Sonu; Kuliha, Megha

doi:10.1109/icacrs55517.2022.10029179

Cited by 3 publications

(1 citation statement)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many studies focus on cloud computing scheduling, including algorithms based on deep reinforcement learning [2], system security [3], ordinal optimization [4], and multi-objective trustawareness [5]. The time cost of AI tasks can be reduced by applying cloud computing in AI [6]. The conventional approach to combining cloud computing with AI is to upload all AI tasks and data to be computed to the cloud to leverage its high computing capacity, which, however, potentially results in significant transmission delay, hinders task completion, and incurs high energy cost in the case of a long distance between the cloud and the user and a large amount of calculation data.…”

Section: Introductionmentioning

confidence: 99%

A cloud-edge collaborative task scheduling method based on model segmentation

Zhang,

Chen,

et al. 2024

J Cloud Comp

View full text Add to dashboard Cite

With the continuous development and combined application of cloud computing and artificial intelligence, some new methods have emerged to reduce task execution time for training neural network models in a cloud-edge collaborative environment. The most attractive method is neural network model segmentation. However, many factors affect the segmentation point, such as resource allocation, system energy consumption, load balancing, and network Bandwidth allocation. Some segmentation methods consider the shortest task execution time, which ignores the utilization of resources at the edge and can result in resource waste. Additionally, these factors are difficult to measure, which presents a challenge in calculating the best segmentation point to achieve the goal of maximum resource utilization and minimum task execution time. To solve this problem, this paper proposes a cloud-edge collaborative task scheduling method based on model segmentation (CECMS). This method first analyzes the factors affecting the segmentation point of the model and then obtains accurate factors that affect the segmentation point calculation through the pre-execution method. Furthermore, a multi-objective solution algorithm is improved to calculate the optimal model segmentation point. And tasks are separately offloaded to the edge and cloud based on the optimal model segmentation point. Finally, the experiments are conducted to verify the effectiveness of this method. Finally, the effectiveness of the CECMS method was verified through simulation experiments. Compared with the Dynamic Adaptive DNN Surgery (DADS) method and an adaptive DNN inference acceleration framework algorithm with end–edge–cloud collaborative computing algorithm (ADC), CECMS achieves the same effectiveness as DADS and ADC in optimizing task execution time by comprehensively considering the utilization of edge resources and minimizing task execution time, while also effectively ensuring resource utilization.

show abstract