A processor-sharing scheduling strategy for NFV nodes

The introduction of the two paradigms SDN and NFV to "softwarize" the current Internet is making management and resource allocation two key challenges in the evolution towards the Future Internet. In this context, this paper proposes Network-Aware Round Robin (NARR), a processor-sharing strategy, to reduce delays in traversing SDN/NFV nodes. The application of NARR alleviates the job of the Orchestrator by automatically working at the intranode level, dynamically assigning the processor slices to the virtual network functions (VNFs) according to the state of the queues associated with the output links of the network interface cards (NICs). An extensive simulation set is presented to show the improvements achieved with respect to two more processor-sharing strategies chosen as reference

Loghub: A Large Collection of System Log Datasets towards Automated Log Analytics

Logs have been widely adopted in software system development and maintenance because of the rich system runtime information they contain. In recent years, the increase of software size and complexity leads to the rapid growth of the volume of logs. To handle these large volumes of logs efficiently and effectively, a line of research focuses on intelligent log analytics powered by AI (artificial intelligence) techniques. However, only a small fraction of these techniques have reached successful deployment in industry because of the lack of public log datasets and necessary benchmarking upon them. To fill this significant gap between academia and industry and also facilitate more research on AI-powered log analytics, we have collected and organized loghub, a large collection of log datasets. In particular, loghub provides 17 real-world log datasets collected from a wide range of systems, including distributed systems, supercomputers, operating systems, mobile systems, server applications, and standalone software. In this paper, we summarize the statistics of these datasets, introduce some practical log usage scenarios, and present a case study on anomaly detection to demonstrate how loghub facilitates the research and practice in this field. Up to the time of this paper writing, loghub datasets have been downloaded over 15,000 times by more than 380 organizations from both industry and academia.Comment: Dateset available at https://zenodo.org/record/322717

QoE management of multimedia streaming services in future networks : a tutorial and survey

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A Processor-Sharing Scheduling Strategy for NFV Nodes

The introduction of the two paradigms SDN and NFV to “softwarize” the current Internet is making management and resource allocation two key challenges in the evolution towards the Future Internet. In this context, this paper proposes Network-Aware Round Robin (NARR), a processor-sharing strategy, to reduce delays in traversing SDN/NFV nodes. The application of NARR alleviates the job of the Orchestrator by automatically working at the intranode level, dynamically assigning the processor slices to the virtual network functions (VNFs) according to the state of the queues associated with the output links of the network interface cards (NICs). An extensive simulation set is presented to show the improvements achieved with respect to two more processor-sharing strategies chosen as reference

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

HoneyDOC: An Efficient Honeypot Architecture Enabling All-Round Design

Honeypots are designed to trap the attacker with the purpose of investigating its malicious behavior. Owing to the increasing variety and sophistication of cyber attacks, how to capture high-quality attack data has become a challenge in the context of honeypot area. All-round honeypots, which mean significant improvement in sensibility, countermeasure and stealth, are necessary to tackle the problem. In this paper, we propose a novel honeypot architecture termed HoneyDOC to support all-round honeypot design and implementation. Our HoneyDOC architecture clearly identifies three essential independent and collaborative modules, Decoy, Captor and Orchestrator. Based on the efficient architecture, a Software-Defined Networking (SDN) enabled honeypot system is designed, which supplies high programmability for technically sustaining the features for capturing high-quality data. A proof-of-concept system is implemented to validate its feasibility and effectiveness. The experimental results show the benefits by using the proposed architecture comparing to the previous honeypot solutions.Comment: Non

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

Engineering self-managed adaptive networks

In order to meet the requirements of emerging services, the future Internet will need to be flexible, reactive and adaptive with respect to arising network conditions. Network management functionality is essential in providing dynamic reactiveness and adaptability but current management approaches have limitations which prevent them from meeting these requirements. In search for a paradigm shift, recent research efforts have been focusing on autonomic/self-management principles, whereby network elements can adapt themselves to contextual changes without any external intervention through adaptive and flexible functionality. This thesis investigates how autonomic principles can be extended and applied to fixed networks for quality of service and performance management. It presents a novel resource management framework which enables intelligence to be introduced within the network in order to support self-management functionality in a coordinated and controllable manner. The proposed framework relies on a distributed infrastructure, called the management substrate, which is a logical structure formed by the ingress nodes of the network. The role of the substrate is illustrated on realistic resource management application scenarios for the emerging self-managed Internet. These cover solutions for dynamic traffic engineering (load balancing across multiple paths), energy efficiency and cache management in Internet Service Providers. The thesis addresses important research challenges associated with the proposed framework, such as the design of specific organisational, communication and coordination models required to support the different management control loops. Furthermore, it develops, for each application scenario, specific mechanisms to realise the relevant resource management functionality. It also considers issues related to the coexistence of multiple control loops and investigates an approach by which their interactions can be managed. In order to demonstrate the benefits of the proposed resource management solution, an extensive performance evaluation of the different mechanisms described in this thesis have been performed based on realistic traffic traces and network topologies