# Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing

^{*}

## Abstract

**:**

## 1. Introduction

- We investigate an innovative framework of task offloading in a scalable vehicle-assisted MEC (SVMEC), where the MEC capacity is extended by renting resources from a remote cloud and vehicular cloud. We stand on the perspective of a MEC provider, whose objective is to minimize the total computation overhead in terms of the weighted-sum of task completion time and monetary cost for using computing resources.
- We formulate the problem of joint node selection and resource allocation as a Mixed Integer Nonlinear Programming (MINLP) problem that jointly optimizes the task offloading decisions and computing resource allocation to the offloaded tasks, so as to minimize the total computation overhead of the MEC provider.
- We solve the problem by decomposing the original problem into two-subproblem (i) Resource allocation (RA) problem with fixed task offloading decision and (ii) Node selection (NS) problem that optimizes the optimal-value function based on the solution of RA problem.
- We also justify the efficiency of our proposed scheme by extensive simulations. We compare the performance in terms of total computation overhead between our proposed scheme and three other strategies. The comparison is conducted under different situations, such as different number of tasks and task’s profiles (i.e., compute-intensive and data-intensive tasks). In each situation, we also analyze the trend of task distribution on MEC, remote cloud, and MVNs in order to explain the achieved result of our proposal.
- Based on the experimental results, we can conclude that compared with other strategies, our proposed scheme provides the MEC provider a better solution to optimize the total computation overhead.

## 2. Related Work

## 3. System Model and Problem Formulation

#### 3.1. Scenario Description

#### 3.2. Computing Node and Task Model

#### 3.3. Local Computing on MEC

#### 3.4. Offloading to Remote Cloud

#### 3.5. Offloading to Mobile Volunteer Node

#### 3.6. Problem Formulation

## 4. Joint Node Selection and Resource Allocation Solution

- (i)
- Computing resource allocation problem: When the strategy of node selection is given, i.e., $X={X}^{0}$, the original problem in (10) is a convex problem with respect to F. Then we can obtain the optimal solution ${F}^{*}$ by using the Karush-Kuhn-Tucker (KKT) conditions.
- (ii)
- Node selection problem: Based on the solution ${F}^{*}$, the sub-problem $G(X,{F}^{*})$ is transferred to 0–1 integer programming problem with respect to X. By adopting branch-and-bound algorithm, the optimal solution ${X}^{*}$ can be obtained.

#### 4.1. Computing Resource Allocation Problem

**Theorem**

**1.**

**Proof**

**of Theorem 1.**

#### 4.2. Node Selection Problem

## 5. Performance Evaluation

#### 5.1. Simulation Settings

#### 5.2. Simulation Results

- MEC only scheme: The system includes only MEC server and all computation tasks are executed locally on MEC server. In this case, there is no monetary cost for using computing resources. The total computation overhead considers only the completion time of tasks. The resource allocation strategy in Section 4.1 is applied.
- MEC + Cloud scheme: The system combines MEC server and remote cloud server. The proposed joint node selection and resource allocation strategy is applied to allocate each computation task to the MEC server or the remote cloud server in order to achieve optimal total computation overhead.
- Random offloading in MEC + Cloud + MVNs scheme (RO_ECM): The system includes MEC server, remote cloud server, and MVNs. Each computation task is randomly assigned to only one computing node, i.e., either the MEC server, the remote cloud server or a MVN with equal probability such that the resource constraint and duration constraint of the selected computing node are satisfied. The resource allocation is given by the strategy in Section 4.1.

## 6. Conclusions

## Author Contributions

## Funding

## Conflicts of Interest

## Abbreviations

IoT | Internet of Things |

MCC | Mobile cloud computing |

MEC | Multi-access edge computing |

SVMEC | Scalable vehicle-assisted multi-access edge computing |

VC | Vehicular cloud |

VCC | vehicular cloud controller |

MVN | Mobile volunteer node |

QoS | Quality of service |

JNSRA | Joint node selection and resource allocation |

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**Figure 2.**Performance evaluation under different number of tasks. (

**a**) Comparison of our proposed scheme and three other schemes; (

**b**) Task distribution of the proposed scheme.

**Figure 3.**Performance evaluation under different tasks’ input data sizes. (

**a**) Comparison of our proposed scheme and three other schemes; (

**b**) Task distribution of the proposed scheme.

**Figure 4.**Performance evaluation under different tasks’ computation intensities. (

**a**) Comparison of our proposed scheme and three other schemes; (

**b**) Task distribution of the proposed scheme.

**Figure 6.**Effect of the weighted parameters on the completion time of tasks and monetary cost for using computing resources.

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**MDPI and ACS Style**

Pham, X.-Q.; Nguyen, T.-D.; Nguyen, V.; Huh, E.-N.
Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing. *Symmetry* **2019**, *11*, 58.
https://doi.org/10.3390/sym11010058

**AMA Style**

Pham X-Q, Nguyen T-D, Nguyen V, Huh E-N.
Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing. *Symmetry*. 2019; 11(1):58.
https://doi.org/10.3390/sym11010058

**Chicago/Turabian Style**

Pham, Xuan-Qui, Tien-Dung Nguyen, VanDung Nguyen, and Eui-Nam Huh.
2019. "Joint Node Selection and Resource Allocation for Task Offloading in Scalable Vehicle-Assisted Multi-Access Edge Computing" *Symmetry* 11, no. 1: 58.
https://doi.org/10.3390/sym11010058