# Optimal Task Offloading and Resource Allocation in Mobile-Edge Computing With Inter-User Task Dependency

IEEE Transactions on Wireless Communications, pp. 235-250, 2020.

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Abstract:

Mobile-edge computing (MEC) has recently emerged as a cost-effective paradigm to enhance the computing capability of hardware-constrained wireless devices (WDs). In this paper, we first consider a two-user MEC network, where each WD has a sequence of tasks to execute. In particular, we consider task dependency between the two WDs, where t...More

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Introduction

- The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices.
- The key idea of MEC is to offload intensive computation tasks to the edges of radio access network, where much more powerful servers will compute on behalf of the resource-limited WDs. Compared with the traditional mobile cloud computing, MEC can overcome the drawbacks of high overhead and long backhaul latency.
- Binary offloading requires each task to be either computed locally or offloaded to the MEC server as a whole.
- On the other hand, allows a task to be partitioned and executed both locally and at the MEC server.
- The authors consider binary computation offloading, which is commonly used in IoT systems for processing non-partitionable simple tasks [4], [5]

Highlights

- The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices
- We assume that the transmit power at the access point is fixed as 1 W and the peak transmit power of each wireless devices is 100 mW
- This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user Mobile edge computing network
- We proposed efficient algorithms to optimize the resource allocation and task offloading decisions, with the goal of minimizing the weighted sum of the wireless devices’ energy consumption and task execution time
- We assumed in this paper that each wireless devices is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
- We can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions

Results

- The authors conduct numerical simulations to evaluate the performance of the optimal strategies.
- Consider an example call graph in Fig. 6.
- As for the computing workload, the authors assume that {Li,1} = [65.5 40.3 96.6] (Mcycles) Task in WD1 Task in WD2.
- The edge server speed fc and the peak computational frequency of each WD fpeak are equal to 1010 and 108 cycles/s, respectively.
- The authors consider a commercial mobile device in practice with the computing efficiency parameter κ = 10−26, which is consistent with the measurements in [29]

Conclusion

- This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user MEC network.
- The authors assumed in this paper that each WD is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
- The consideration of both bandwidth and computing resources competitions is needed when the authors extend the work to a large-size network.
- The authors can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions

Summary

## Introduction:

The explosive growth of Internet of Things (IoT) in recent years enables cost-effective interconnections between tens of billions of wireless devices (WDs), such as sensors and wearable devices.- The key idea of MEC is to offload intensive computation tasks to the edges of radio access network, where much more powerful servers will compute on behalf of the resource-limited WDs. Compared with the traditional mobile cloud computing, MEC can overcome the drawbacks of high overhead and long backhaul latency.
- Binary offloading requires each task to be either computed locally or offloaded to the MEC server as a whole.
- On the other hand, allows a task to be partitioned and executed both locally and at the MEC server.
- The authors consider binary computation offloading, which is commonly used in IoT systems for processing non-partitionable simple tasks [4], [5]
## Results:

The authors conduct numerical simulations to evaluate the performance of the optimal strategies.- Consider an example call graph in Fig. 6.
- As for the computing workload, the authors assume that {Li,1} = [65.5 40.3 96.6] (Mcycles) Task in WD1 Task in WD2.
- The edge server speed fc and the peak computational frequency of each WD fpeak are equal to 1010 and 108 cycles/s, respectively.
- The authors consider a commercial mobile device in practice with the computing efficiency parameter κ = 10−26, which is consistent with the measurements in [29]
## Conclusion:

This paper has studied the impact of inter-user task dependency on the task offloading decisions and resource allocation in a two-user MEC network.- The authors assumed in this paper that each WD is allocated with an orthogonal channel and the CPU frequency of the edge server is fixed
- The consideration of both bandwidth and computing resources competitions is needed when the authors extend the work to a large-size network.
- The authors can apply the deep reinforcement learning technique to quickly find a mapping between the timevarying channel gains and optimal offloading decisions

- Table1: The number of searches performed by the one-climb based scheme and the brute-force method under different M and N

Reference

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