# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 1 Introduction  
Presently, together with the population explosion of the Internet of Things (IoT) devices, many new services are constantly emerging and attracting a lot of attention, including virtual/augmented reality (VR/AR), industrial robotics, face recognition, natural language processing, etc. These services are usually compute-intensive and/or data-intensive while IoT devices have certain limitations in terms of processing and storage capacity, battery life, and network resources. The emergence of mobile cloud computing (MCC) [1] supported IoT devices by enabling offloading heavy computing service requests up to the centralized cloud and providing huge data storage capabilities to the services. For example, in the healthcare sector, clinical centers are paying attention to the IoT and MCC technologies to develop next-generation pervasive healthcare solutions [2; 3; 4]. Based on MCC infrastructure, physiological parameters and vital signs collected from wireless sensor body networks in the form of wearable accessories or devices attached to the human body could be transmitted to a centralized and powerful computing platform in the cloud for aggregation, analysis, and storage. Accordingly, MCC-based healthcare systems not only facilitate information sharing among patients, caregivers, and physicians in a structured and cost-effective manner but also enable advanced bigdata analytics and machine learning techniques to derive insights from sensor data, thereby providing better treatment and assistant services (e.g., telemonitoring, ambient assisted living, electronic health record, and multi-agent medical consultation, etc.) to patients and disabled people in their everyday life [5; 6; 7]. However, the far distance between the end-users and the centralized cloud is perceived to be a major barrier to meeting the diverse quality requirements of emerging services. Services with strict latency requirements cannot tolerate the high network latency incurred to reach the remote cloud. For example, in real-time medical services that require immediate feedback, such as real-time cardiac monitoring, fall detection, emergency medical services, just a little delay or lack of service availability due to any reason (e.g., loss of Internet connectivity or cloud failure) may put patients' health at risk. Moreover, transmitting a large amount of data from IoT devices to the remote cloud will pose a substantial burden on the backhaul links, degrade network performance, and increase energy consumption. To address the above issues, a new paradigm named multi-access edge computing (MEC) [8] or formerly mobile edge computing was proposed as the extension of cloud computing to the edge of networks. The main idea of MEC is to bring computing power and storage resources close to end-users by attaching small servers (a.k.a, MEC servers) to WiFi access points or mobile base stations (BS) at the network edge. By this way, MEC is expected to fill the gap between the centralized cloud and the IoT. The combination of MEC and the IoT recently opened many new frontiers for service providers and telecom operators to deploy versatile services on IoT applications (e.g., industrial internet, smart city, connected car, telehealth, etc.) [9; 10]. The reasons are as follows. First, MEC can help alleviate the network traffic congestion by aggregating and preprocessing data of massive IoT applications at the network edge, saving the time and cost of transmitting data to the centralized cloud. Second, due to the reduced communication distance, MEC can facilitate ultra-reliable and low-latency services, which is very critical for IoT applications. Third, MEC can help IoT devices (limited and unstable power supply, e.g., energy harvesting sensor nodes) mitigate computational and communication overhead by offloading their heavy workloads to MEC servers [11]. In doing so, the lifetime of IoT devices or even the entire IoT network could be enhanced.