# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
Footnote †: This paper is an extended version of our paper published in IEEE INFOCOM 2020 workshop on ICCN, 7–9 July. This version proposes a resource provisioning strategy to balance the resources of virtual nodes that reducing the power consumption and guarantees the user's delay as well. It also includes the problem formulation, strategy description, and discussion of the simulation. [PERSON]  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (J.F. ); [EMAIL_ADDRESS] (Y.C. )1  
[PERSON]  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (J.F. ); [EMAIL_ADDRESS] (Y.C. )1  
[PERSON]  
1 Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China; [EMAIL_ADDRESS] (J.F. ); [EMAIL_ADDRESS] (Y.C. )1  
Footnote 1: email: {[EMAIL_ADDRESS]}  
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near-user side and puts tasks that could only be processed on the cloud computing platform into the edge server [10, 11]. Since the devices on the edge layer are limited power and computing capacities, poor resource allocation for the tasks will result in high latency and power consumption. In this paper, we focus on the reduced power consumption problem in edge computing. This problem is non-trivial mainly due to the following challenges: (i) Since the Internet of Things (IoT) devices generate data constantly, and the analysis must be very rapid [12, 13]. One important problem is to find a scheduling strategy of edge devices that can realize the tasks within a lower latency. (ii) Partially battery-powered devices are introduced in the edge-cloud architecture, such as smartphones, laptops, and so on. Thus, the other important problem is to consider the resource allocation for the tasks to reduce the power consumption and realize the endurance of the batteries [14, 15]. A preliminary version of this work has been accepted by IEEE INFOCOM 2020 workshop on ICCN [16]. We have included substantial extension over the preliminary version and a summary of differences is included here. * We first extend our problem into multiple users and propose a two stages scheduling strategy which includes the scheduling and resource provisioning. The published version mainly introduces the resource provisioning stage. While this paper focuses on the scheduling problem between users and edge nodes with energy efficiency. In this paper, we re-formulate the problem and re-define the model. Base on that, we propose two energy-efficient scheduling strategies. * We propose an efficient scheme based on the branch and bound method for the scheduling stage and discuss the complexity. In this paper, we first propose a scheduling scheme based on the branch and bound method in Algorithm 1, which is a fast search tree algorithm based on a depth-first searching algorithm. Then we discuss the complexity of this algorithm which is \(O(3^{n})\). Furthermore, we propose a heuristic algorithm that minimizes the power consumption and guarantees the users' deadline as well as lower complexity. For Algorithm 1, the time complexity increases exponentially with the expansion of sub-tasks. Thus, we propose a heuristic algorithm in Algorithm 2. The main idea of the algorithm is to select the best node in every layer. * We conduct various simulations for our scheduling strategy by using the platform we designed. The results are shown from different perspectives to provide conclusions. In this paper, we focus on saving the power consumption of the system to provide an efficient scheduling strategy in edge computing. Our objective is to reduce the power consumption for multiple users while meeting the resources and delay constraints.