# Utility-Centric Service Provisioning in Multi-Access Edge Computing
## 4 Proposed Nested-Genetic Algorithm
In the worst case, major migrations may cause a tremendous amount of data to move back and forth between computing nodes, thereby overloading the backhaul links. To avoid it, we can impose a budget constraint on the migration cost, allowing only incremental adjustments. Figure 8: (**a**) Total utility; (**b**) Cloud load; (**c**) Percentage of dissatisfied users versus computation capacity of MEC. 2. The energy consumption of both the centralized cloud and MEC nodes may be soaring while processing a large volume of service requests. Hence, an energy-efficient service provisioning policy is needed while guaranteeing QoE is still a challenge. 3. In some cases, the service provisioning may have to consider the monetary cost of using resources. For example, the centralized cloud and the MEC nodes are managed on different administration domains. A cloud service provider (CSP) does not have MEC infrastructure, and thus rents the MEC resources of network operators (e.g., computing, storage, network) to deploy services closer to the IoT devices or end-users. Due to the wide geographical distribution of IoT devices, the MEC resources may be offered from different parties with diverse prices. In this case, the objective of service provisioning policy is to maximize the total utility of all users under the budget constraint of the CSP in using MEC resources. 4. It is also necessary to design provision policy for composite services consisting of multiple interdependent components. In this case, it becomes the problem of mapping task graphs onto a processor graph. In particular, the task graph represents service components and communication among these components while the processor graph represents the computing nodes and communication links in the physical system. ## 6 Conclusions

In this paper, we investigated the utility-centric service provisioning considering service placement and request scheduling under both the storage and computation resource constraints in MEC systems. The major objective of the research is to maximize the total utility of all users, and thus provide a service provisioning policy that effectively guarantees the QoE of users. We formulate the problem as an INLP and then propose a metaheuristic algorithm, namely Nested Genetic Algorithm (Nested-GA), which consists of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, we verify the efficiency of the Nested-GA through experiments. The results indicate that our proposal achieves better total utility than other baseline methods and can achieve close-to-optimal solutions in reasonable running time. In future work, it is more robust to take into account the migration cost, energy consumption, resource cost, or composite services in the service provisioning policy. Methodology, X-Q.P. ; supervision, E.-N.H.; validation, X.-Q.P. ; writing--original draft, X.-Q.P. ; writing--review and editing, X.-Q.P., T.-D.N., and V.N. This research was supported by the MSIT(Ministry of Science and ICT), Korea, under the ICT Consilience Creative program (IITP-2019-2015-0-00742) and \"Service mobility support distributed cloud technology\" (No. 2017-0-00294) supervised by the IITP(Institute for Information & communications Technology Planning & Evaluation). The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} IoT & Internet of Things \\ VR/AR & Virtual/Augmented reality \\ MCC & Mobile cloud computing \\ MEC & Multi-access edge computing \\ BS & Base station \\ QoS & Quality of service \\ QoE & Quality of experience \\ MOS & Mean opinion score \\ SDN & Software Defined Networking \\ CSP & Cloud Service Provider \\ \end{tabular} 

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