# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 2 Related Work  
The problem of edge workload scheduling or computation offloading is an attractive and challenging topic in MEC, which decides whether/how to schedule/offload users' tasks (requests) from their devices to the appropriate computing nodes in the MEC or the cloud. Various aspects of this problem were investigated in the literature, considering different objectives (e.g., minimizing the makespan [16] or the energy [17] or the cost [18]), workload models (e.g., independent atomic tasks [19], composite applications/workflows [20]), and MEC architectures and offloading schemes (e.g., non-collaborative [18] versus collaborative [14], flat versus hierarchical [15]). However, in these works, MEC nodes are implicitly assumed to execute any types of computation tasks without considering whether the corresponding services are available on the MEC nodes. Content placement in cache networks is another line of related work, considering storage resources sharable among requests of the same type. Caching popular contents at the network edgecannot only reduce network latency and energy consumption but also prevent redundant transmissions of the same content, and thus save network traffic. Various content caching policies have been introduced. Least frequently used (LFU) and least recently used (LRU) are the most widely used caching policies in the literature [31]. User preference profile (UPP)-based caching policy was proposed in [32] based on the observation that a user typically has a strong preference toward specific content categories. Meanwhile, based on machine learning technology, the authors in [33] introduced learning-based content caching policy for wireless networks with a priori unspecified content dissemination. Also, the caching policies can decide the content to cache at each MEC without considering the cooperation among the MEC nodes [32] or in a cooperative manner [34]. The problem of joint request routing and content caching in heterogeneous cache networks was also investigated in [35]. Nevertheless, research on content placement problem only concerns storage resources while neglecting other types of resources (e.g., CPU). Meanwhile, the service provisioning problem in MEC has to take into account both computing and storage constraints as well as jointly optimize the service placement and request scheduling decisions to maximize the overall system performance. For examples, [PERSON] et al. [36] addressed the problems of VM placement and task distribution for supporting multiple applications in MEC, in which the objective is to minimize the average latency of a request. However, this work does not take into account the deadline requirement of the latency, especially for latency-sensitive applications. In [37], a dynamic service placement and workload scheduling algorithm based on Lyapunov optimization framework was proposed, but there is no hard constraint on computation resources. In [22], a genetic algorithm was developed for the load distribution and placement problem of IoT services to minimize the potential violation of their QoS requirements considering both computation and storage constraints of edge resources. However, the authors neglect the storage demand of data-intensive services (e.g., VR/AR, video analytics, distributed machine learning, etc. ), which requires a pre-stored library/dataset shareable among requests of the same type. Until recently, few studies [38; 39] have addressed this limitation to serve data-intensive services from the edge. They jointly optimized the service placement and request scheduling decisions while considering both shareable (i.e., storage) and non-shareable resources (i.e., CPU). In [23], FogPlan, a QoS-aware service provision framework, was proposed to deliver fog services to edge devices or the cloud to minimize the resource cost while meeting the latency and QoS constraints. However, we note that these works focus on the QoS metrics rather than the user experience or satisfaction in the service provisioning process.