Orchestrating Service Migration for Low Power MEC-Enabled IoT Devices

Multi-Access Edge Computing (MEC) is a key enabling technology for Fifth Generation (5G) mobile networks. MEC facilitates distributed cloud computing capabilities and information technology service environment for applications and services at the edges of mobile networks. This architectural modification serves to reduce congestion, latency, and improve the performance of such edge colocated applications and devices. In this paper, we demonstrate how reactive service migration can be orchestrated for low-power MEC-enabled Internet of Things (IoT) devices. Here, we use open-source Kubernetes as container orchestration system. Our demo is based on traditional client-server system from user equipment (UE) over Long Term Evolution (LTE) to the MEC server. As the use case scenario, we post-process live video received over web real-time communication (WebRTC). Next, we integrate orchestration by Kubernetes with S1 handovers, demonstrating MEC-based software defined network (SDN). Now, edge applications may reactively follow the UE within the radio access network (RAN), expediting low-latency. The collected data is used to analyze the benefits of the low-power MEC-enabled IoT device scheme, in which end-to-end (E2E) latency and power requirements of the UE are improved. We further discuss the challenges of implementing such schemes and future research directions therein

Adaptive Process Distribution at the Edge of IoT using the Integration of BPMS and Containerization

Täna levivad pilvepõhised värkvõrgu (asjade interneti) süsteemid tuginevad protsesside halduseks kaugel asuvatel andmekeskustel, mis toob endaga kaasa latentsusprobleeme. Vastusena sellele probleemile on varem välja pakutud servaarvutuse lähenemine, kus arvutused viiakse läbi asjade interneti süsteemi võrgule füüsiliselt lähemal. Mitmete servaarvutuse metoodikate seas on uduarvutus lähenemine, kus rõhk on arvutuste liigutamisel värkvõrgu seadmetele endile. Ehkki uduarvutusel põhinev arhitektuur on paljutõotav, tõstatab see küsimuse – kuidas värkvõrgu protsessihaldussüsteemid (BPMS4IoT-süsteemid) äriprotsesse heterogeensetele värkvõrgu seadmetele jaotama peaksid? Levinud on lähenemine, kus protsesside töövooülesannete käituseks tuginetakse ühisele platvormile. Näiteks, kui haldusserver defineerib teatud töövoo ülesandena Pythoni skripti ja määrab selle seadmele, siis peab seadme töövookäitusmootor toetama vastavat mehhanismi skriptide jooksutamiseks. Selline nõue ei ole paindlik, arvestades värkvõrgu seadmete heterogeensust. Käesolevas magistritöös pakub autor välja raamistiku, mis eraldab töövoo ülesannete käitusmeetodi käitusmootorist kasutades selleks konteinertehnoloogiat. Töö käigus arendati välja raamistiku prototüüp ning viidi läbi katseid mikroarvutitel põhinevail seadmetel. Lisaks võrreldi väljapakutud uduarvutuse raamistiku jõudlust pilvearvutusel põhineva süsteemiga.Emerging cloud-centric Internet of Things (IoT) system relies on distant data centers to manage the entire processes, which raises the issue of latency. To address the issue, researchers have introduced the Edge computing methodologies that carry out computation closer to the edge network of IoT system. Among the numerous Edge computing approaches, Mist computing paradigm emphasises the mechanism that moves the computation further to the front-end IoT devices. Although the architecture of Mist computing is promising, it raises a new challenge in how the Business Process Management System for IoT (BPMS4IoT) distributes the business process workflow to the heterogeneous IoT devices? In general, executing business process workflows relies on the common platform for executing customized tasks. For example, if the management server defines a Python script task in a workflow, which has been allocated to an IoT device, the workflow engine of the IoT device must have the compatible execution method. Such a requirement is less flexible when one considers the heterogeneity of the IoT devices. Therefore, in this thesis, the author proposes a framework to decouple the workflow task execution method from the workflow engines using the containerization technology. A proof-of-concept prototype has been developed and has been tested on several single-board computers-based IoT devices. Further, a case study has been performed to demonstrate the performance of the proposed framework comparing to the cloud-centric system

Data-Centric Resource Management in Edge-Cloud Systems for the IoT

A major challenge in emergent scenarios such as the Cloud-assisted Internet of Things is efficiently managing the resources involved in the system while meeting requirements of applications. From the acquisition of physical data to its transformation into valuable services or information, several steps must be performed, involving the various players in such a complex ecosystem. Support for decentralized data processing on IoT devices and other devices near the edge of the network, in combination with the benefits of cloud technologies has been identified as a promising approach to reduce communication overhead, thus reducing delay for time sensitive IoT applications. The interplay of IoT, edge and cloud to achieve the final goal of producing useful information and value-added services to end user gives rise to a management problem that needs to be wisely tackled. The goal of this work is to propose a novel resource management framework for edge-cloud systems that supports heterogeneity of both devices and application requirements. The framework aims to promote the efficient usage of the system resources while leveraging the Edge Computing features, to meet the low latency requirements of emergent IoT applications. The proposed framework encompasses (i) a lightweight and data-centric virtualization model for edge devices, (ii) a set of components responsible for the resource management and the provisioning of services from the virtualized edge-cloud resources

A survey on mobility-induced service migration in the fog, edge, and related computing paradigms

The final publication is available at ACM via http://dx.doi.org/10.1145/3326540With the advent of fog and edge computing paradigms, computation capabilities have been moved toward the edge of the network to support the requirements of highly demanding services. To ensure that the quality of such services is still met in the event of users’ mobility, migrating services across different computing nodes becomes essential. Several studies have emerged recently to address service migration in different edge-centric research areas, including fog computing, multi-access edge computing (MEC), cloudlets, and vehicular clouds. Since existing surveys in this area focus on either VM migration in general or migration in a single research field (e.g., MEC), the objective of this survey is to bring together studies from different, yet related, edge-centric research fields while capturing the different facets they addressed. More specifically, we examine the diversity characterizing the landscape of migration scenarios at the edge, present an objective-driven taxonomy of the literature, and highlight contributions that rather focused on architectural design and implementation. Finally, we identify a list of gaps and research opportunities based on the observation of the current state of the literature. One such opportunity lies in joining efforts from both networking and computing research communities to facilitate future research in this area.Peer ReviewedPreprin