Energy and QoE aware Placement of Applications and Data at the Edge

Recent years are witnessing extensions of cyber-infrastructures towards distributed environments. The Edge of the network is gaining a central role in the agenda of both infrastructure and application providers. Following the actual distributed structure of such a computational environment, nowadays, many solutions face resource and application management needs in Cloud/Edge continua. One of the most challenging aspects is ensuring highly available computing and data infrastructures while optimizing the system's energy consumption. In this paper, we describe a decentralized solution that limits the energy consumption by the system without failing to match the users' expectations, defined as the services' Quality of Experience (QoE) when accessing data and leveraging applications at the Edge. Experimental evaluations through simulation conducted with PureEdgeSim demonstrate the effectiveness of the approach

Geo-Distance Based 2- Replica Maintaining Algorithm for Ensuring the Reliability forever Even During the Natural Disaster on Cloud Storage System

In today's digitalized and globalized scenario, everyone has moved to cloud computing for storing their information on cloud storage to access their data from anywhere at any time. The most significant feature of cloud storage is its high availability and reliability then it has the capability of reducing management factors as well as incurred lower storage cost compared with some other storing methods, it is most suitable for a high volume of data storage. In order to meet the requirements of high availability and reliability, the system adopts a replication system concept. In replicating systems, the objects are replicated many times, with each copy residing in a different geographical location. Though it is beneficial to the users, it leads to some issues like security, integrity, consistency and hidden storage and maintenance cost, etc. Therefore, it is exposed to a few threats to the Cloud Storage System (CSS) user and the provider as well. So, this research seeks to explore the mechanisms to rectify the above-mentioned issues. Thus, the predecessor of the research work has proposed an algorithm named as 2-Replica Placing (2RP) algorithm which is used to reduce the storage cost, maintenance cost; and maintenance overheads as well as increase the available storage spaces for the providers by placing the data files on two locations based on Geo-Distance. But it fails to address the recovery mechanism when a natural disaster happens because providing reliability with less than 2 replicas is a challenging task for the providers. Thus, the research proposed Geo-distance based 2-Replica Maintaining (2RM) algorithm which is used to consider that issue for ensuring reliability forever even during natural disaster

Resilient IoT-based Monitoring System for Crude Oil Pipelines

International audiencePipeline networks dominate the oil and gas mid-stream sector, and although the safest means of transportation for oil and gas products, they are susceptible to failures. These failures are due to manufacturing defects, environmental effects, material degradation, or third party interference through sabotage and vandalism. Internet of Things (IoT)-based solutions are promising to address these by monitoring and predicting failures. However, some challenges remain in the deployment of industrial IoT-based solutions, as the reliability, the robustness, the maintainability, the scalability, the energy consumption, etc. This paper is therefore aimed at highlighting potential solutions for detection and mitigation of pipeline failures while addressing the robustness, the cost and scalability issues of such approach efficiently across the network infrastructure, data and service layers

Quality of Service-aware matchmaking for adaptive microservice-based applications

Applications that make use of Internet of Things (IoT) can capture an enormous amount of raw data from sensors and actuators, which is frequently transmitted to cloud data centers for processing and analysis. However, due to varying and unpredictable data generation rates and network latency, this can lead to a performance bottleneck for data processing. With the emergence of fog and edge computing hosted microservices, data processing could be moved towards the network edge. We propose a new method for continuous deployment and adaptation of multi-tier applications along edge, fog, and cloud tiers by considering resource properties and non-functional requirements (e.g., operational cost, response time and latency etc.). The proposed approach supports matchmaking of application and Cloud-To-Things infrastructure based on a subgraph pattern matching (P-Match) technique. Results show that the proposed approach improves resource utilization and overall application Quality of Service. The approach can also be integrated into software engineering workbenches for the creation and deployment of cloud-native applications, enabling partitioning of an application across the multiple infrastructure tiers outlined above

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

Vue d'ensemble du problème de placement de service dans Fog and Edge Computing

To support the large and various applications generated by the Internet of Things(IoT), Fog Computing was introduced to complement the Cloud Computing and offer Cloud-like services at the edge of the network with low latency and real-time responses. Large-scale, geographical distribution and heterogeneity of edge computational nodes make service placement insuch infrastructure a challenging issue. Diversity of user expectations and IoT devices characteristics also complexify the deployment problem. This paper presents a survey of current research conducted on Service Placement Problem (SPP) in the Fog/Edge Computing. Based on a new clas-sification scheme, a categorization of current proposals is given and identified issues and challenges are discussed.Pour prendre en charge les applications volumineuses et variées générées par l'Internet des objets (IoT), le Fog Computing a été introduit pour compléter le Cloud et exploiter les ressources de calcul en périphérie du réseau afin de répondre aux besoins de calcul à faible latence et temps réel des applications. La répartition géographique à grande échelle et l'hétérogénéité des noeuds de calcul de périphérie rendent difficile le placement de services dans une telle infrastructure. La diversité des attentes des utilisateurs et des caractéristiques des périphériques IoT complexifie également le probllème de déploiement. Cet article présente une vue d'ensemble des recherches actuelles sur le problème de placement de service (SPP) dans l'informatique Fog et Edge. Sur la base d'un nouveau schéma de classification, les solutions présentées dans la littérature sont classées et les problèmes et défis identifiés sont discutés

On the use of intelligent models towards meeting the challenges of the edge mesh

Nowadays, we are witnessing the advent of the Internet of Things (IoT) with numerous devices performing interactions between them or with their environment. The huge number of devices leads to huge volumes of data that demand the appropriate processing. The “legacy” approach is to rely on Cloud where increased computational resources can realize any desired processing. However, the need for supporting real-time applications requires a reduced latency in the provision of outcomes. Edge Computing (EC) comes as the “solver” of the latency problem. Various processing activities can be performed at EC nodes having direct connection with IoT devices. A number of challenges should be met before we conclude a fully automated ecosystem where nodes can cooperate or understand their status to efficiently serve applications. In this article, we perform a survey of the relevant research activities towards the vision of Edge Mesh (EM), i.e., a “cover” of intelligence upon the EC. We present the necessary hardware and discuss research outcomes in every aspect of EC/EM nodes functioning. We present technologies and theories adopted for data, tasks, and resource management while discussing how machine learning and optimization can be adopted in the domain