scholarly journals OPPOCO: From Ad Hoc Cloudlet-assisted Edge Computation to Opportunistic Computation Offloading

Author(s):  
Jun Yang ◽  
Yiming Miao ◽  
Chao Han ◽  
Yuanwen Tian ◽  
Xinghui You ◽  
...  
Author(s):  
Bo Li ◽  
Ziyi Peng ◽  
Peng Hou ◽  
Min He ◽  
Marco Anisetti ◽  
...  

AbstractIn the Internet of Vehicles (IoV), with the increasing demand for intelligent technologies such as driverless driving, more and more in-vehicle applications have been put into autonomous driving. For the computationally intensive task, the vehicle self-organizing network uses other high-performance nodes in the vehicle driving environment to hand over tasks to these nodes for execution. In this way, the computational load of the cloud alleviated. However, due to the unreliability of the communication link and the dynamic changes of the vehicle environment, lengthy task completion time may lead to the increase of task failure rate. Although the flooding algorithm can improve the success rate of task completion, the offloading expend will be large. Aiming at this problem, we design the partial flooding algorithm, which is a comprehensive evaluation method based on system reliability in the vehicle computing environment without infrastructure. Using V2V link to select some nodes with better performance for partial flooding offloading to reduce the task complete time, improve system reliability and cut down the impact of vehicle mobility on offloading. The results show that the proposed offloading strategy can not only improve the utilization of computing resources, but also promote the offloading performance of the system.


Author(s):  
Muralidhar K. ◽  
Madhavi K.

Despite the rapid growth in popularity and hardware capacity in mobile devices, they suffer from resource poverty, which limits their ability to meet increasing mobile users' demands. Computation offloading may give a prominent solution. But it relies on the connection to the remote cloud and may fail in situations where there is poor or no connectivity. Cloudlet was introduced to cover this problem, but mobile users miss free mobility when using cloudlets. Offloading to the cloud or cloudlet is not always the preferred solution. An alternative is to utilize the nearby mobile devices as local resource suppliers and pull their capabilities as a mobile device cloud. In this paper, the authors present such an approach known as ad hoc computing as a service (AhCaaS) model for computation offloading in an ad hoc manner by connecting to nearby mobile devices. They define a multi-attribute selection strategy to determine the optimal computation offloadee. They evaluated the proposed model, and the result shows that AhCaaS reduces execution time, battery consumption, and avoids task reassignment.


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