# Exploiting Duplications for Efficient Task Offloading in Multi-User Edge Computing
## 2 Related Work  
Recently, multi-access edge computing was recognized by the European 5G PPP (5G infrastructure public, private partnership) research body as one of the key emerging technologies for 5G networks. There have been numerous works on the computation offloading problem in the literature, and various offloading policies have been proposed, which can be classified into two stages.  
For the first stage, the researchers do not distinguish subtasks inside the application and upload the entire task as a whole to the edge servers. In [18], they study the multi-user offloading problem for edge computing in a multi-channel wireless interference environment and prove that this is NP-hard to obtain the optimal solution, and then they adopted a heuristic approach for achieving efficient computation offloading in a distributed manner.  
Simulation results show that the proposed algorithm achieves superior computation offloading performance and scales well as the user size increases. Furthermore, the authors [19] studied a distributed offloading scheme for a multi-user and multi-server based on orthogonal frequency-division multiple access in small-cell networks. They formulated a distributed overhead minimization problem and then adopted the potential game theory to prove that their proposed decision is a potential game problem.  
Finally, compared with other existing computation algorithms, the simulation results show that this new algorithm can guarantee saving overheads. The other works in the first stage, such as [20; 21; 22], also consider a multi-user and multi-server scenario and propose a heuristic offloading decision algorithm to assign different tasks to the corresponding channel and server to minimize the average completion time.  
With the increasing complexity of applications, the offloading related work comes into the second stage. This task has been considered as DAGs in order to identify a fine-grained offloading opportunity. In [23], the authors considered that the application consists of a series of sequential modules and then decide which module to be offloaded and how these modules should be executed (cloud computing or edge computing). Furthermore, the authors in [8] investigated the problem of fine-grained task offloading in edge computing for low-power IoT systems.  
They aim to map the subtasks of different applications onto the heterogeneous server and order their executions so that task-precedence requirements are satisfied and a minimum overall completion time is obtained. Moreover, in order to improve the efficiency of the offloading decisions, they further designed a distributed computation offloading algorithm (DEFO) based on game theory. However, this work introduces extra communication delay between subtasks.  
Along a different line, in our work, we utilize the processor idle time to duplicate the predecessor task to further reduce the completion time of services. We duplicate the task selectively only if it helps in improving the performance to avoid superfluous energy consumption. There has been extensive work on a duplication-base clustering algorithm for the datacenter [24; 25; 26].  
Our work has the following main differences: (1) The existing studies mainly target an unbounded number of computing machines in the datacenter. (2) Communications in the edge network are via wireless channels, thus it suffers more severe interference than communications in the datacenter. (3) The duplication-based algorithms in the datacenter will replicate all possible ancestors of a given task. However, it is unreasonable and impractical to consider the resource-constraint character of IoT devices.  
This paper is the first of its kind that work proposes a fine granularity duplication-based offloading strategy in the edge network in order to reduce the end-to-end delay of application while considering the energy consumption of the devices.