International Journal of Network Management最新文献

This paper proposes a prediction-based scaling and placement of service function chains (SFCs) to improve service level agreement (SLA) and reduce operation cost. We used a variant of recurrent neural network (RNN) called gated recurrent unit (GRU) for resource demand prediction. Then, considering these predictions, we built an intuitive scale in/out algorithm. We also developed an algorithm that applies Q-Learning on Edge computing environment (EdgeQL) to place these scaled-out VNFs in appropriate locations. The integrated algorithm that combines prediction, scaling, and placement are called RNN-EdgeQL. RNN-EdgeQL (v2) is further improved to achieve application agnostic group level elasticity in the chain, independent of applications installed on the VNFs. We tested our algorithm on two realistic temporal dynamic load models including Internet traffic (Abilene) and an application specific traffic (Wiki) on an OpenStack testbed. The contribution of this article is threefold. First, prediction model prepares the target SFC for the upcoming load. Second, an application agnostic characteristics of the algorithm achieves the group-level elasticity in SFC. Finally, the EdgeQL placement model minimizes the end-to-end path of an SFC in multi-access edge computing (MEC) environment. As a result, RNN-EdgeQL (v2) gives the lowest overall latency, lowest SLA violations, and lowest VNFs requirement, compared to RNN-EdgeQL (v1) and Threshold-Openstack default placement.

The last decade has witnessed important development of network softwarization that has revolutionized the practice of networks. Virtualized networks bring novel and specific requirements for the control and orchestration of containerized network functions that are scattered across the network. In this regard, the migration of virtualized network functions plays a pivotal role to best meet the requirements of optimal resource utilization, load balancing and fault tolerance. The purpose of this survey is to offer a detailed overview of the progress on container migration so as to provide a better understanding of the trade-off between the benefits associated with the migration and the practical challenges. The paper includes a classification of the placement algorithms that map the containerized network functions on the virtualized infrastructure. Following, a taxonomy of the migration techniques that perform the transfer of the containerized microservices is proposed.

In DASH (Dynamic Adaptive Streaming over HTTP), it is required to store multiple encoding for same video segments which increases the storage requirement of CDN (content delivery network) servers. Further, to keep network delay in check, it is not feasible to continuously increase the number of CDN servers in different geographical locations as per the ever-increasing demand. Peer-to-peer (P2P) CDN acts as an elegant alternative to supplement existing CDN servers to harness the benefits of both P2P and CDN technologies. We present the concept of “CDN at the edge,” where end hosts can collaborate for video streaming in pure P2P manner to provide better QoE (Quality of Experience). However, dynamically changing behavior of underlying network, uncertain leaving, joining pattern of clients, and frequent change in source node during video streaming session make the selection of the desired bitrate (at any given time) extremely difficult. In this paper, we have designed and implemented a software-defined network (SDN)-based P2P CDN system that supports browser-based adaptive video streaming. SDN provides global view of the topology and thus assists in providing timely information about leaving or joining peers in the proposed “CDN at the edge.” Further, this SDN-based P2P CDN is compared with non-SDN-based P2P CDN using both virtual switch-based emulations and physical switch-based actual implementation. The comparison is performed under different churn rates with respect to QoE parameters such as start-up delay, bitrate, stall count, and its duration. The experimental results prove the applicability of our approach.

Network Function Virtualization (NFV) offers flexibility in traffic engineering and network resource management, by taking advantage of Software Defined Networking (SDN). By using these network technologies, it is possible to enhance the performance of video streaming applications by placing network functions in suitable locations and rerouting flows. Our study addresses the “virtual cache placement” problem in dynamic networks, where traffic patterns and attachment points of the clients are changing rapidly. The cache placement is done by determining how many virtual caches are necessary to be able to provide acceptable service to the clients, as well as where to place those caches to meet demand. To this end, we provide a heuristic solution by taking advantage of NFV-SDN and having the assistance of Server and Network Assisted DASH (SAND). Experimental results show that the proposed algorithms can improve the video client rebuffering by 150%–270% and also can provide an 8%–12% increase in average bitrate received by the client, compared to a number of benchmark algorithms. The obtained results indicate that the co-operation between the client and the operator of an SDN-enabled network, by exchanging client and network information, allows network resources to be efficiently used, and as a consequence, the Quality of Experience (QoE) on the client's side is improved.

The mobile traffic grows every year. Resource requirements of mobile applications, as processing power and storage capacity, transformed the architecture of cellular networks into a centralized infrastructure, the C-RANs. On the other hand, providing resources close to base stations, as the multi-access edge computing suggests, allows an immediate processing for delay sensitive applications. In this work, we formulate as a mixed integer linear programming the retrieval of media contents through caches acting as virtual network functions. The optimization model minimizes both the media contents' retrieval cost and the number of instantiated virtual network functions. Results show that the characteristic that has most impact on caching is the high throughput between the node that stores the virtual network function images and the virtualized environment that hosts the image, where up to 70% of the requests are satisfied. When caches have low storage capacity and incurs high transferring delay cost for deployment, the most important characteristic is the throughput distribution between the virtualized environment and the base stations.

The Internet of Things (IoT) has made possible the development of increasingly driven services, like industrial Industrial Internet of Things (IIoT) services, that often deal with massive amounts of data. Meantime, as IIoT networks grow, the threats are even greater, and false data injection (FDI) attacks stand out as being one of the most aggressive. The majority of current solutions to handle this attack do not take into account the data validation, especially on the data clustering service. Aiming to advance on the issue, this work introduces CONsensus Based Data FIlteriNg for IIoT (CONFINIT), an intrusion detection system for mitigating FDI attacks on the data dissemination service performing in dense IIoT networks. CONFINIT combines watchdog surveillance and collaborative consensus strategies for assertively excluding various FDI attacks. The simulations showed that CONFINIT compared with Dynamic Data-aware Firefly-based Clustering (DDFC) increased by up to 35%–40% the number of clusters without attackers in a gas pressure IIoT environment. CONFINIT achieved attack detection rates (DRs) of 99%, accuracy of 90, and F1 score of 0.81 in multiple IIoT scenarios, with only up to 3.2% and 3.6% of false negatives and positives rates, respectively. Moreover, under two variants of FDI attacks, called Churn and Sensitive attacks, CONFINIT achieved DRs of 100%, accuracy of 99, and F1 of 0.93 with less than 2% of false positives and negatives rates.