Journal of Network and Systems Management

Hosted by the Distributed Management Task Force (DMTF) and collocated with the International Conference on Network and Service Management, SVM 2012 was the sixth International DMTF Academic Alliance Workshop on Systems and Virtualization Management. SVM 2012 was held in Las Vegas, Nevada on October 26, 2012. Featuring presentations surrounding cloud, OVF, open standards, and CIM, SVM 2012 also included a poster session, lightning talks, and a keynote speech given by the DMTF President, Jeff Hilland. This report provides a summary of the highlights that took place at the workshop.

Despite the fact that satellite-terrestrial systems have advantages such as high throughput, low latency, and low energy consumption, as well as low exposure to physical threats and natural disasters and cost-effective global coverage, their integration exposes both of them to particular security challenges that can arise due to the migration of security challenges from one to another. Intrusion Detection Systems (IDS) can also be used to provide a high level of protection for modern network environments such as satellite-terrestrial integrated networks (STINs). To optimize the detection performance of malicious activities in network traffic, four hybrid intrusion detection systems for satellite-terrestrial communication systems (SAT-IDSs) are proposed in this paper. All the proposed systems exploit the sequential forward feature selection (SFS) method based on random forest (RF) to select important features from the dataset that increase relevance and reduce complexity and then combine them with a machine learning (ML) or deep learning (DL) model; Random Forest (RF), Long Short-Term memory (LSTM), Artificial Neural Networks (ANN), and Gated Recurrent Unit (GRU). Two datasets—STIN, which simulates satellite networks, and UNSW-NB15, which simulates terrestrial networks—were used to evaluate the performance of the proposed SAT-IDSs. The experimental results indicate that selecting significant and crucial features produced by RF-SFS vastly improves detection accuracy and computational efficiency. In the first dataset (STIN), the proposed hybrid ML system SFS-RF achieved an accuracy of 90.5% after using 10 selected features, compared to 85.41% when using the whole dataset. Furthermore, the RF-SFS-GRU model achieved the highest performance of the three proposed hybrid DL-based SAT-IDS with an accuracy of 87% after using 10 selected features, compared to 79% when using the entire dataset. In the second dataset (UNSW-NB15), the proposed hybrid ML system SFS-RF achieved an accuracy of 78.52% after using 10 selected features, compared to 75.4% when using the whole dataset. The model with the highest accuracy of the three proposed hybrid DL-based SAT-IDS was the RF-SFS-GRU model. It achieved an accuracy of 79% after using 10 selected features, compared to 74% when using the whole dataset.

Minimizing transaction latency and network traffic is pivotal in large-scale Internet of Things (IoT) applications. This paper investigates the fundamentals of distributed caching, cache coordination, network function virtualization, fog computing, and software-defined networking to avoid service loss and enhance quality of experience (QoE) in IoT applications. We visualize caching as a virtual network function (VNF) and use fog nodes to persistently host a large number of micro-caches as VNFs in the vicinity of their interest locations. We formulate the cache placement and migration process as a multi integer linear programming (MILP) problem. Firstly, we propose a cache consensus function to decide whether a content needs caching or not. Secondly, we propose a fuzzy inference based algorithm to solve the MILP problem for dynamic placement and migration of micro-caches at appropriate locations in geographically co-located 5G radio access networks. Another significant contribution of the proposed scheme is the inter-RAN cooperation among micro-caches to augment service quality by mitigating network traffic. Simulation results show the superiority of the proposed scheme over existing approaches.

To predict the delay between a source and a destination as well as to identify anomalies in a network, it is useful to continuously monitor the network by sending probes between all sources and destinations. Some of the problems of such probing strategies are: (1) there is a very large amount of information to analyze in real time; and (2) the probes themselves could add to the congestion. Therefore it is of prime importance to reduce the number of probes drastically and yet be able to reasonably predict delays and identify anomalies. In this paper we formulate a graph-theoretic problem called the Constrained Coverage Problem to optimally select a subset to traceroute-type probes to monitor networks where the topology is known. To solve this problem, we develop a heuristic algorithm called the Constrained Coverage Heuristic (CCH) algorithm, which works in polynomial time, as an alternative to the standard exponential-time integer programming solution available in commercial software. The application of the Constrained Coverage Problem to randomly generated topologies yielded an 88.1% reduction in the number of monitored traceroute-type probes on average. In other words, networks can be successfully monitored using only 11.9% of all possible probes. For these examples, the polynomial time CCH algorithm performed remarkably well in comparison to the standard exponential time integer programming algorithm and obtained the optimal (in 24 of 30 examples) or near optimal (second best solution in the remaining examples) solutions in a comparatively negligible amount of time.

Smart meters enable a fine-granular monitoring of power consumption and distributed power production in costumers’ premises, which are used to predict the power requirements for the near future. The goals are to offer more security of supply as well as to minimize the power requirement estimation errors. However, to benefit from this information, the communication infrastructure that transmits the energy-related data needs to fulfill stringent requirements with respect to dependability, while remaining monetarily feasible. This paper discusses the usage of network function virtualization (NFV) technologies and constructs a virtual advanced metering infrastructure (AMI) network to transmit energy-related information in a dependable and cost-effective way. After the discussion of dependability requirements of AMI and the shortcomings of current approaches, the reliability and availability of a new architecture based on NFV is analyzed using analysis. Finally, a cost model is developed to compare the Virtual Network Function approach to current AMIs.

The evolving Interactive Multimedia (IMM)services require the development of flexible and easilyadaptable design tools that can interoperate with theunderlying broadband communication network. In this paper, we adopt a design philosophy based onthe Intelligent Network (IN) concept. The main advantageof this approach is the transparency of theservice-specific features to the underlying signalingsystem.Hence, a fast introduction of demanding services becomespossible even if the signaling of the network is notsophisticated enough to fully cover the requirements ofIMM services. The standard International TelecommunicationUnion (ITU) ServiceIndependent Block (SIB)-based methodology is used as abasis and further extended, where necessary, to covernew requirements, not originally predicted forNarrowband-Integrated Services Digital Network (N-ISDN), but arisingfrom IMM services. The paper further proceeds with thedevelopment of an Interactive Multimedia Object ClassLibrary (IMOCL) which adopts a high-levelobject-oriented approach that supports an easy evolution ofIN-based service design towards implementation. TheIMOCL is kept generic to the most possible extent aimingto provide a common framework for the design of a wide variety of IMM services using the INconcept. The IMOCL encompasses service managementfunctionality enabling the designer and provider of anIN service to manage the newly introduced service. Both ITU SIB-based and IMOCL methodologies, togetherwith the standard Specification and Description Language(SDL) technique are demonstrated in the design ofselected parts of a typical Video on Demand (VoD) service.

We describe a system concept for therevenue-producing disposition of surplus capacity atoff-peak times in real trunking networks. The idea is toapproximate a competitive market for distribution of the networks' time-varying excess capacity with apricing strategy controlled by the network. The schemeis intended to allow network operators to stimulatebackground traffic loads to gain new revenues from otherwise idle time on existing installedresources. The concept is suitable for low prioritydelay-tolerant or opportunistic applications such asremote backups, software distribution, dispatchingbatched faxes, disseminating newsgroup updates,updating web page caches or routing tables. Backgroundservice subscribers are notified of price reductions atoff-peak times to elicit additional traffic for the network. Traffic aggregators act on behalf ofsubscribing organizations or groups of users. Thebackground service is completely subordinate to theconventional tariff-priced on-demand calling services and the variable background pricing merges withthe foreground under suitable total load. This paperfocuses on the network problem of price setting tocontinually maximize the price-volume product in a time-varying price-sensitive trafficenvironment such as this concept implies. Aprice-stimulated offered traffic environment issimulated in which time of day, price, and hidden demandlatency and demand curve characteristics all affect the offeredtraffic. An analytically optimum strategy is availablefor the particular traffic model used and theperformance of a fuzzy logic price controller is tested against the revenue-optimal strategy. Dependingon econometric assumptions for latent traffic demand andprice-volume curves, increases in revenue from 4%-20%are obtained in simulation of a 30-trunk group having a typical daily load pattern.