Abstract
Recently, multi-access edge computing (MEC) is a promising paradigm to offer resource-intensive and latency-sensitive services for IoT devices by pushing computing functionalities away from the core cloud to the edge of networks. Most existing research has focused on effectively improving the use of computing resources for computation offloading while neglecting non-trivial amounts of data, which need to be pre-stored to enable service execution (e.g., virtual/augmented reality, video analytics, etc.). In this paper, we, therefore, investigate service provisioning in MEC consisting of two sub-problems: (i) service placement determining services to be placed in each MEC node under its storage capacity constraint, and (ii) request scheduling determining where to schedule each request considering network delay and computation limitation of each MEC node. The main objective is proposed to ensure the quality of experience (QoE) of users, which is also yet to be studied extensively. A utility function modeling user perception of service latency is used to evaluate QoE. We formulate the problem of service provisioning in MEC as an Integer Nonlinear Programming (INLP), aiming at maximizing the total utility of all users. We then propose a Nested-Genetic Algorithm (Nested-GA) consisting of two genetic algorithms, each of whom solves a sub-problem regarding service placement or request scheduling decisions. Finally, simulation results demonstrate that our proposal outperforms conventional methods in terms of the total utility and achieves close-to-optimal solutions.
Highlights
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
We investigated the utility-centric service provisioning considering service placement and request scheduling under both the storage and computation resource constraints in multi-access edge computing (MEC)
The major objective of the research is to maximize the total utility of all users, and provide a service provisioning policy that effectively guarantees the quality of experience (QoE) of users
Summary
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. 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.
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