E. Liu, X. Deng, Z. Cao, et al. Design and evaluation of a prediction-based dynamic edge computing system[C]//2018 IEEE Global Communications Conference (GLOBECOM). IEEE, 2018: 1-6.
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Release time:2024-03-13
Journal:2018 IEEE Global Communications Conference (GLOBECOM)
Abstract:Abstract—We investigate a mobile edge computing environment where edge computing nodes provide their computation
capacities to process the computation intensive tasks submitted
by end users. We introduce a Cloudlet Assisted Cooperative Task
Assignment (CACTA) system that organizes edge nodes that are
geographically close to a user into a cluster to collaboratively
work on the user’s tasks. The system enables a user to minimize
his/her total cost which is a weighted combination of latency
(i.e., the task’s completion time), and the costs incurred in
working on the task. The total cost captures the tradeoff that
the user would like to make between latency and computing
related costs. It is challenging for the system to find an optimal
strategy that assigns workload to edge nodes to meet the user’s
optimization goal, due to the time-varying available capacities
and the mobility of edge nodes. To address the challenge, we
model the system as a discrete time system in which each edge
node’s capacity and cost vary over different time slots, and
the system assigns parts of the task to the edge nodes in the
cluster over time. We introduce a prediction-based dynamic
task assignment algorithm, referred to PA-OPT, that assigns
workload to edge nodes in each time slot based on the prediction
of their capacities/costs and an empirical optimal allocation
strategy which is learned from an offline optimal solution from
historical data. Then we apply our system design to a video
data analysis application, and conduct extensive simulations
driven by a Google cloud data trace. We have demonstrated
that our proposed algorithm/system achieves significantly higher
performance than several other algorithms, and especially its
performance is very close to that of an offline optimal solution.
Note:http://faculty.csu.edu.cn/dengxiaoheng/zh_CN/lwcg/10445/content/49194.htm
Translation or Not:no
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Attachments:
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54-Design_and_Evaluation_of_a_Prediction-Based_Dynamic_Edge_Computing_System.pdf
Pre One:L. C, X. Deng, H. Shen, et al. Dycusboost: Adaboost-based imbalanced learning using dynamic clustering and undersampling[C]//2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th Intl Conf on Pervasive Intelligence and Computing, 4th Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech). IEEE, 2018: 208-215.
Next One:Jie Luo, Xiaoheng Deng, Honggang Zhang, Huamei Qi. Ultra-Low Latency Service Provision in Edge Computing. ICC 2018:1-6
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