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task offloading

Task offloading refers to the process of transferring computational tasks from a device, such as an IoT sensor or mobile device, to edge servers for execution. This is designed to improve the performance and efficiency of the local device by leveraging the more powerful computational resources available at the edge of the network[1][3][9].

There are two primary models of computation offloading in MEC: binary and partial offloading. Binary offloading requires each task to be either computed locally or offloaded to the MEC server as a whole. On the other hand, partial offloading allows a task to be partitioned and executed both locally and at the MEC server[9].

The goal of task offloading is often to minimize the overall delay or energy consumption for user devices[6]. However, offloading tasks can result in increased delay and energy consumption, while edge servers may have limited resources, which can lead to increased computing latency[8]. Therefore, it’s crucial to consider these trade-offs before making an offloading decision.

In more complex scenarios, such as in the Internet of Vehicles (IoV), task offloading can involve transferring computational tasks to both mobile edge nodes and fixed edge nodes. This approach can achieve low latency and low operational cost under the tasks' delay constraints[15].

In the context of your interest in AI and marketing, understanding task offloading in MEC can be beneficial. For instance, it can help in designing efficient systems for processing large volumes of data generated in marketing campaigns, thereby improving the speed and efficiency of data analysis and decision-making processes.

Citations:

[1] https://www.sciencedirect.com/science/article/abs/pii/S1383762121001570

[2] https://www.sciencedirect.com/science/article/pii/S2352864822000505

[3] https://www.mdpi.com/2079-9292/12/11/2452

[4] https://www.mdpi.com/2079-9292/12/2/366

[5] https://www.sciencedirect.com/science/article/pii/S1877050921006724

[6] https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9452000/

[7] https://ieeexplore.ieee.org/document/8795335

[8] https://journalofcloudcomputing.springeropen.com/articles/10.1186/s13677-023-00461-3

[9] https://arxiv.org/pdf/1810.11199.pdf

[10] https://ieeexplore.ieee.org/document/8647845

[11] https://ieeexplore.ieee.org/document/10007120