Enabling Mobile Service Continuity across Orchestrated Edge Networks

Edge networking has become an important technology for providing low-latency services to end users. However, deploying an edge network does not guarantee continuous service for mobile users. Mobility can cause frequent interruptions and network delays as users leave the initial serving edge. In this paper, we propose a solution to provide transparent service continuity for mobile users in large-scale WiFi networks. The contribution of this work has three parts. First, we propose ARNAB architecture to achieve mobile service continuity. The term ARNAB means rabbit in Arabic, which represents an Architecture for Transparent Service Continuity via Double-tier Migration. The first tier migrates user connectivity, while the second tier migrates user containerized applications. ARNAB provides mobile services just like rabbits hop through the WiFi infrastructure. Second, we identify the root-causes for prolonged container migration downtime. Finally, we enhance the container migration scheme by improving system response time. Our experimental results show that the downtime of ARNAB container migration solution is 50% shorter than that of the state-of-the-art migration.This work has been partially funded by the H2020 Europe/Taiwan joint action 5G-DIVE (Grant #859881) and also partially funded by the Ministry of Science and Technology, under the Grant Number MOST 108-2634-F-009-006 - through Pervasive Artificial Intelligence Research (PAIR) Labs, Taiwan

Energy-aware scheduling of streaming applications on edge-devices in IoT based healthcare

The reliance on Network-on-Chip (NoC) based Multiprocessor Systems-on-Chips (MPSoCs) is proliferating in modern embedded systems to satisfy the higher performance requirement of multimedia streaming applications. Task level coarse grained software pipeling also called re-timing when combined with Dynamic Voltage and Frequency Scaling (DVFS) has shown to be an effective approach in significantly reducing energy consumption of the multiprocessor systems at the expense of additional delay. In this paper we develop a novel energy-aware scheduler considering tasks with conditional constraints on Voltage Frequency Island (VFI) based heterogeneous NoC-MPSoCs deploying re-timing integrated with DVFS for real-time streaming applications. We propose a novel task level re-timing approach called R-CTG and integrate it with non linear programming based scheduling and voltage scaling approach referred to as ALI-EBAD. The R-CTG approach aims to minimize the latency caused by re-timing without compromising on energy-efficiency. Compared to R-DAG, the state-of-the-art approach designed for traditional Directed Acyclic Graph (DAG) based task graphs, R-CTG significantly reduces the re-timing latency because it only re-times tasks that free up the wasted slack. To validate our claims we performed experiments on using 12 real benchmarks, the results demonstrate that ALI-EBAD out performs CA-TMES-Search and CA-TMES-Quick task schedulers in terms of energy-efficiency.N/

Approximate Computing Survey, Part I: Terminology and Software & Hardware Approximation Techniques

The rapid growth of demanding applications in domains applying multimedia processing and machine learning has marked a new era for edge and cloud computing. These applications involve massive data and compute-intensive tasks, and thus, typical computing paradigms in embedded systems and data centers are stressed to meet the worldwide demand for high performance. Concurrently, the landscape of the semiconductor field in the last 15 years has constituted power as a first-class design concern. As a result, the community of computing systems is forced to find alternative design approaches to facilitate high-performance and/or power-efficient computing. Among the examined solutions, Approximate Computing has attracted an ever-increasing interest, with research works applying approximations across the entire traditional computing stack, i.e., at software, hardware, and architectural levels. Over the last decade, there is a plethora of approximation techniques in software (programs, frameworks, compilers, runtimes, languages), hardware (circuits, accelerators), and architectures (processors, memories). The current article is Part I of our comprehensive survey on Approximate Computing, and it reviews its motivation, terminology and principles, as well it classifies and presents the technical details of the state-of-the-art software and hardware approximation techniques.Comment: Under Review at ACM Computing Survey