Dynamic Service Placement in Multi-Access Edge Computing: A Systematic Literature Review

Abstract

The advent of new cloud-based applications such as mixed reality, online gaming, autonomous driving, and healthcare has introduced infrastructure management challenges to the underlying service network. Multi-access edge computing (MEC) extends the cloud computing paradigm and leverages servers near end-users at the network edge to provide a cloud-like environment. The optimum placement of services on edge servers plays a crucial role in the performance of such service-based applications. Dynamic service placement problem addresses the adaptive configuration of application services at edge servers to facilitate end-users and those devices that need to offload computation tasks. While reported approaches in the literature shed light on this problem from a particular perspective, a panoramic study of this problem reveals the research gaps in the big picture. This paper introduces the dynamic service placement problem and outline its relations with other problems such as task scheduling, resource management, and caching at the edge. We also present a systematic literature review of existing dynamic service placement methods for MEC environments from networking, middleware, applications, and evaluation perspectives. In the first step, we review different MEC architectures and their enabling technologies from a networking point of view. We also introduce different cache deployment solutions in network architectures and discuss their design considerations. The second step investigates dynamic service placement methods from a middleware viewpoint. We review different service packaging technologies and discuss their trade-offs. We also survey the methods and identify eight research directions that researchers follow. Our study categorises the research objectives into six main classes, proposing a taxonomy of design objectives for the dynamic service placement problem. We also investigate the reported methods and devise a solutions taxonomy comprising six criteria. In the third step, we concentrate on the application layer and introduce the applications that can take advantage of dynamic service placement. The fourth step investigates evaluation environments used to validate the solutions, including simulators and testbeds. We introduce real-world datasets such as edge server locations, mobility traces, and service requests used to evaluate the methods. We compile a list of open issues and challenges categorised by various viewpoints in the last step.

Resource Allocation in Fog Computing: A Systematic Review

“…1. The overall architecture consists of six distinct layers, a physical and virtualization layer, a monitoring layer, pre-processing layer, temporary storage layer, security layer and transport layer [1]. Starting from the bottom layer, at the physical layer, the edge nodes that contain any type of IoT devices, virtual nodes, sensors, and actuators are distributed geographically, and data is collected, normalized, and accumulated from them.…”

“…During the last few years, cloud computing has significantly improved access to networked computing resources and how they can be utilized, often based on payas-you-go pricing models [1,2]. The cloud environment reduces traditional data storage systems' computing and storage load.…”

Real-world Machine Learning Systems: A survey from a Data-Oriented Architecture Perspective

“…These approaches are nowadays implemented as edge architectures [8,103] which aim to improve systems performance (e.g. latency, resource usage) while preserving the control provided by a central server in the back [95,117]. There are no central entities behind nodes in decentralised approaches.…”

“…that constitute a fertile ground for ML [5,100]. Edge Computing is a research area that enables low latency by processing data at the edge through small data centers closer to end-users [26,110,117]. Such local processing implies the deployment of software components (e.g., ML models) in a distributed fashion.…”

“…Edge Computing and Federated Learning naturally complement each other. For example, service placement and offloading algorithms from the edge computing domain [117] can be used as schedulers to optimise the training process in terms of efficiency and resource usage. These service placement and offloading algorithms are already based on ML models that can be implemented using available Federated Learning technologies such as TensorFlow Federated 8 .…”