Evaluating Container Deployment Implementations for Foglets

In recent years, the number of devices connected to local networks has rapidly expanded to create a new internet known as The Internet of Things. The applications run on these devices often require lower latency solutions than cloud computing can provide in order to perform time-sensitive interactions with other devices near the network’s edge. One solution to this problem is fog computing, a geo-distributed architecture that provides computational resources closer to the edge of the network. This proximity yields low-latency connections among such devices. In order to implement a powerful fog computing network, applications must be able to deploy and migrate quickly throughout the geo-distributed resources. In the Foglets project, containers are used to efficiently deploy applications. The Foglets project currently contains two platforms that handle container deployment: one that utilizes system calls, and another that uses the well-established Docker API. In this work, we evaluate the latency and throughput of the two deployment platforms, as well as the impact of container commands and size on these metrics. We found that while serving many simultaneous deployments through multithreading, the Docker API yields lower latency and higher throughput. We also found that the size of the container and commands run on the container had a negligible impact on the deployment’s latency and throughput.Undergraduat

Resource Aware Placement of IoT Application Modules in Fog-Cloud Computing Paradigm

With the evolving IoT scenario, computing has spread to the most minuscule everyday activities, leading to a momentous shift in the way applications are developed and deployed. With the volume of impact increasing exponentially, a coherent approach of deploying these applications is critical for an efficient utilization of the network infrastructure. A typical IoT application consists of various modules running together with active interdependencies; traditionally running on the Cloud hosted in global data centres. In this paper, we present a Module Mapping Algorithm for efficient utilization of resources in the network infrastructure by efficiently deploying Application Modules in Fog-Cloud Infrastructure for IoT based applications. With Fog computing into picture, computation is dynamically distributed across the Fog and Cloud layer, and the modules of an application can thus be deployed closer to the source on devices in the Fog layer. The result of this work can serve as a Micro-benchmark in studies/research related with IoT and Fog Computing, and can be used for Quality of Service (QoS) and Service Level Objective benchmarking for IoT applications. The approach is generic, and applies to a wide range of standardized IoT applications over varied network topologies irrespective of load

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