# Energy-Efficient Resource Provisioning Strategy for Reduced Power Consumption in Edge Computing +
## 2 Related Work  
Edge computing as a new paradigm that used to extend cloud computing to the edge of the network, thus enabling a new breed of applications and services [10]. Nowadays, there are many papers that focus on the power consumption problem in edge computing. [PERSON] et al. [17] present a basic module mapping algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. [PERSON] et al. [18] regard energy consumption as the major driver of today's network and cloud operational costs, and characterize the heterogeneous set of IoT-Cloud network resources according to their associated sensing, computing, transport capacity, and energy efficiency. They mathematically formulate the service distribution problem in IoT-Cloud networks, as a minimum cost mixed-cast flow problem. [PERSON] et al. [19] study the energy-efficient computation offloading mechanisms in 5G heterogeneous networks for mobile edge computing. They formulate an optimization problem to minimize the energy consumption of the offloading system, where the energy cost of both task computing and file transmission are taken into consideration. [PERSON] et al. [20] consider energy-efficient resource allocation for a multi-user mobile edge computing system which establishes on two computation-efficient models, and they obtain a suboptimal solution with low-complexity by connecting it to a three-stage flow-shop scheduling problem and wisely utilizing [PERSON]'s algorithm. [PERSON] et al. [21; 22] propose a service merging strategy for mapping and co-locating multiple services on devices. They use integer programming to solve this problem in a multi-hop network, use heuristic algorithms to deal with the same problem in a single hop network. These works address the energy-efficient problem using mathematical formulation and propose heuristic algorithms to minimize the energy consumption. [PERSON] et al. [23] study the potential of edge computing to address the energy management related applications for limited power-restricted IoT devices while providing low-latency processing of high-resolution visual data. To address the trade-off between processing throughput and energy efficiency, they proposed a smart-driven edge routing based on a sustainable strategy algorithm that uses machine learning. [PERSON] et al. [24] propose different methods for implementing edge computing paradigms by using Machine to Machine (M2M) communication in a dense network system with the aim of providing reliability for the delay-sensitive services. [PERSON] et al. [25] propose a dynamic network virtualization technique to integrate the network resources, and further design a cooperative computation offloading model to achieve high energy-efficiency and reliability in edge computing. [PERSON] et al. [26] aim to minimize both total tasks' execution latency and the MD's energy consumption by jointly optimizing the task allocation decision and the MD's central process unit (CPU) frequency. They propose an optimization framework of offloading from a single mobile device (MD) to multiple edge devices. [PERSON] et al. [27] demonstrates the modeling and resource management policies of the IoT environment by using the iFogSim simulator. Besides, they verify the scalability of RAM consumption and execution time simulation tools in different situations. These works address the energy-efficient problem by considering the joint optimization which includes power consumption, latency, and reliability. However, the above works still have some shortcomings. For example, the scheduling strategy based on machine learning and the addition of new communication links will increase the burden of the edge computing architecture. What's more, most of these works have only focused on reducing the power consumption, however, they ignored the maximum tolerant delay of the task.