A Self-Configuration Controller To Detect, Identify, and Recover Misconfiguration At IoT Edge Devices and Containerized Cluster System

Source at https://icissp.scitevents.org/.Securing workloads and information flow against misconfiguration in container-based clusters and edge medical devices is an important part of overall system security. This paper presented a controller that analyzes the misconfiguration, maps the observation to its hidden misconfiguration type, and selects the optimal recovery policy to maximize the performance of defined metrics. In the future, we will integrate streaming from different edge devices, expand the recovery mechanism, and conduct more experiments

Mobile Service Continuity for Edge Train Networks

This paper has been presented at : IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (IEEE PIMRC 2019). 8-11 September 2019 Istanbul, TurkeyIn press / En prensaIn moving train networks, two-hop architecture is adopted to improve users experience by reducing the interaction between on-board users and base stations on the train route. In addition, edge networking have emerged as a solution for bringing services to the proximity of the users. However, deploying two-hop and edge networks do not guarantee a continuous service delivery for train users. When a large number of users transit from the train to the land, they experience service interruption due to control signalling storm and backhaul latency. In this paper, we propose a holistic edge service management system to provide mobile service continuity. The contribution of this paper is twofold. First, we develop an enhanced handover scheme that reduces control signals by handling user mobility at the edge. Second, we develop a pre-copy migration scheme that eliminates backhaul latency by relocating containerized applications to the user proximity across edge train networks. Our experimental results show that the two proposed solution can reduce the control signals and migration downtime by 50% and 36%, respectively.This work has been partially funded by the H2020 col-laborative Europe/Taiwan research project 5G-CORAL (grant no. 761586). This research is also partially supported 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

Innovative techniques for deployment of microservices in cloud-edge environment

PhD ThesisThe evolution of microservice architecture allows complex applications to be structured into independent modular components (microservices) making them easier to develop and manage. Complemented with containers, microservices can be deployed across any cloud and edge environment. Although containerized microservices are getting popular in industry, less research is available specially in the area of performance characterization and optimized deployment of microservices. Depending on the application type (e.g. web, streaming) and the provided functionalities (e.g. ltering, encryption/decryption, storage), microservices are heterogeneous with speci c functional and Quality of Service (QoS) requirements. Further, cloud and edge environments are also complex with a huge number of cloud providers and edge devices along with their host con gurations. Due to these complexities, nding a suitable deployment solution for microservices becomes challenging. To handle the deployment of microservices in cloud and edge environments, this thesis presents multilateral research towards microservice performance characterization, run-time evaluation and system orchestration. Considering a variety of applications, numerous algorithms and policies have been proposed, implemented and prototyped. The main contributions of this thesis are given below: Characterizes the performance of containerized microservices considering various types of interference in the cloud environment. Proposes and models an orchestrator, SDBO for benchmarking simple webapplication microservices in a multi-cloud environment. SDBO is validated using an e-commerce test web-application. Proposes and models an advanced orchestrator, GeoBench for the deployment of complex web-application microservices in a multi-cloud environment. GeoBench is validated using a geo-distributed test web-application. - i - Proposes and models a run-time deployment framework for distributed streaming application microservices in a hybrid cloud-edge environment. The model is validated using a real-world healthcare analytics use case for human activity recognition.

ARNAB: Transparent Service Continuity across Orchestrated Edge Networks

Paper presented at: IEEE GLOBECOM 2018 Workshops: Intelligent Network orchestration and interaction in 5G and beyond. Abu Dabhi. 9-13 December 2018In this paper, we present an architecture for transparent service continuity for cloud-enabled WiFi networks called ARNAB: ARchitecture for traNsparent service continuity viA douBle-tier migration. The term arnab means rabbit in Arabic. It is dubbed for the proposed service architecture because a mobileuser service with ARNAB behaves like a rabbit hopping through the WiFi infrastructure. To deliver continuous services, deploying edge clouds is not sufficient. Users may travel far from the initial serving edge and also perform multiple WiFi handoffs during mobility. To solve this, ARNAB employs a double-tier migration scheme. One migration tier is for user connectivity, and the other one is for edge applications. Our experimental results show that ARNAB can not only enable continuous service delivery but also outperform the existing work in the area of container live migration across edge clouds.This work has been partially supported by the H2020 collaborative Europe/Taiwan research project 5G-CORAL (grant num. 761586)

A Survey and Future Directions on Clustering: From WSNs to IoT and Modern Networking Paradigms

Many Internet of Things (IoT) networks are created as an overlay over traditional ad-hoc networks such as Zigbee. Moreover, IoT networks can resemble ad-hoc networks over networks that support device-to-device (D2D) communication, e.g., D2D-enabled cellular networks and WiFi-Direct. In these ad-hoc types of IoT networks, efficient topology management is a crucial requirement, and in particular in massive scale deployments. Traditionally, clustering has been recognized as a common approach for topology management in ad-hoc networks, e.g., in Wireless Sensor Networks (WSNs). Topology management in WSNs and ad-hoc IoT networks has many design commonalities as both need to transfer data to the destination hop by hop. Thus, WSN clustering techniques can presumably be applied for topology management in ad-hoc IoT networks. This requires a comprehensive study on WSN clustering techniques and investigating their applicability to ad-hoc IoT networks. In this article, we conduct a survey of this field based on the objectives for clustering, such as reducing energy consumption and load balancing, as well as the network properties relevant for efficient clustering in IoT, such as network heterogeneity and mobility. Beyond that, we investigate the advantages and challenges of clustering when IoT is integrated with modern computing and communication technologies such as Blockchain, Fog/Edge computing, and 5G. This survey provides useful insights into research on IoT clustering, allows broader understanding of its design challenges for IoT networks, and sheds light on its future applications in modern technologies integrated with IoT.acceptedVersio

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

Towards MLOps in Mobile Development with a Plug-in Architecture for Data Analytics

Smartphones are increasingly used as universal IoT gateways collecting data from connected sensors in a wide range of industrial applications. With the increasing computing capabilities, they are used not just for simple data aggregation and transferring, but have now become capable of performing advanced data analytics. As AI has become a key element in enterprise software systems, many software development teams rely on dedicated Machine Learning (ML) engineers who often follow agile development practices in their work. However, in the context of mobile app development, there is still limited tooling support for MLOps, mainly due to unsuitability of native programming languages such as Java and Kotlin to support ML-related programming tasks. This paper aims to address this gap and describes a plug-in architecture for developing, deploying and running ML modules for data analytics on the Android platform. The proposed approach advocates for modularity, extensibility, customisation, and separation of concerns, allowing ML engineers to develop their components independently from the main application in an agile and incremental manner.acceptedVersio

Enabling P4 Network Telemetry in Edge Micro Data Centers With Kubernetes Orchestration

Integrating computation resources with networking technologies is an hot research topic targeting the optimization of containers deployment on a set of host machines interconnected by a network infrastructure. Particularly, next generation edge nodes will offer significant advantages leveraging on integrated computation resources and networking awareness, enabling configurable, granular and monitorable quality of service to different micro-services, applications and tenants, especially in terms of bounded end-to-end latency. In this regard, SDN is a key technology enabling network telemetry and traffic switching with the granularity of the single traffic flow. However, currently available solutions are based on legacy SDN techniques, not enabling the matching of tunneled traffic, and thus require a tricky integration inside the hosts where containers are deployed. This work considers Kubernetes clusters deployed on next generation edge micro data center platforms and proposes an innovative SDN solution exploiting the P4 technology to gain visibility inside tunnelled traffic exchanged among pods. This way, the integration is achieved at the control plane level through the communication between Kubernetes and the SDN controller. The proposed solution is experimentally validated including a comprehensive framework enabling effective traffic switching and in-band telemetry at pod level. The major paper contributions consist in the design and the development of: (i) the networking applications at SDN control plane level; (ii) the P4 switch pipeline at the data plane level; (iii) the monitoring system used to collect, aggregate and elaborate the telemetry data