Stenio Fernandes

Vertical Mobility Management Architectures in Wireless Networks: A Comprehensive Survey and Future Directions

Mobile users and applications are putting pressure on wireless network operators to improve the seamless handover of devices and services. Strong business competition for subscribers, along with the ever increasing availability of wireless networks will give nomadic and mobile users the opportunity, and systems the power, to make better handover decisions. In this paper, we present a comprehensive review of the literature on mobility management architectures for seamless handover of mobile users in heterogeneous networks. We describe the design rationale for selected architectures, with an in-depth analysis of their main goals, assumptions, and requirements. We also provide directions for further work in this field by highlighting the mandatory requirements and the features of future architectures. We then present a new architecture called Context-Aware Mobility Management System (CAMMS). CAMMS is a new cross-layer, context-aware and interactive approach to seamless handover of users and services. With that proposal, we identified the essential functional entities that must be part of future architectures.

Better network traffic identification through the independent combination of techniques

Traffic identification is currently an important challenge for network management and dimensioning. In recent years, some new algorithms and the different uses of known techniques have been proposed, yet the results are so far limited in scope and frequently disappointing. Furthermore, existing results cannot be directly compared, since networks and traffic profiles differ significantly among collected traces. When submitted to an analysis, considering different networks, data granularities and baselines, most algorithms perform well in one or two scenarios. However, no algorithm has proven better than the others in the majority of the scenarios. Summarizing four years of research in traffic identification, this work shows that the identification abilities of algorithms vary for different situations and proposes a new methodology for traffic identification through the combination of any set of algorithms for traffic identification. Four different combination mechanisms (and many variations) are validated against four different network scenarios that are commonly used in the literature. Combination shows promising results, mainly because it revealed to be robust against bias towards any scenario, which happens in previous identification algorithms.

Dependability assessment of virtualized networks

One of the key characteristics of virtual networks (VN) is their intrinsic dynamical aspects. VNs allow network operators to have on-demand negotiation of a variety of services. Risks are inherent to virtualized infrastructures since the underlying physical network components are failure-prone. Therefore evaluation and analysis of risks in VNs, from assessment of dependability attributes, are of paramount importance for accurate management and control tasks. This paper proposes and evaluates a method to assess dependability in virtualized network environments. Through the use of Reliability Block Diagram (RBD) and Stochastic Petri Nets (SPN), we provide a first look at risks in network virtualization by means of dependability modeling and analysis. Our methodology allows the networking research community to develop new heuristics when dealing with resource allocation in VNs, while taking into account dependability attributes.

A stratified traffic sampling methodology for seeing the big picture

This work explores the use of statistical techniques, namely stratified sampling and cluster analysis, as powerful tools for deriving traffic properties at the flow level. Our results show that the adequate selection of samples leads to significant improvements allowing further important statistical analysis. Although stratified sampling is a well-known technique, the way we classify the data prior to sampling is innovative and deserves special attention. We evaluate two partitioning clustering methods, namely clustering large applications (CLARA) and K-means, and validate their outcomes by using them as thresholds for stratified sampling. We show that using flow sizes to divide the population we can obtain accurate estimates for both size and flow durations. The presented sampling and clustering classification techniques achieve data reduction levels higher than that of existing methods, on the order of 0.1% while maintaining good accuracy for the estimates of the sum, mean and variance for both flow duration and sizes.

Performance analysis of modern TCP variants: A comparison of Cubic, Compound and New Reno

TCP is the main and most widely used transport protocol for reliable communication. Because of its widespread need, researchers have been studying and proposing new TCP variants trying to improve its behavior towards congestion to make it use the most available bandwidth while preserving a logical level of fairness towards other protocols. This paper aims at evaluating and comparing the performance of the most recent TCP implementations deployed in popular Operating Systems. We carefully choose scenarios to investigate the goodput, intra- and inter-protocol fairness of these TCP variants. Results show that running Cubic over wired links outperforms Compound and New Reno in the presence of reverse traffic. However, the protocols behave differently over wireless links where they achieve a low goodput with a very small variation. Also, all three variants are fair to other TCP traffic and achieve the same intra-protocol fairness over wireless links.

Approaches to resource reservation for migrating real-time sessions in future mobile wireless networks

This paper presents two novel frameworks for session admission control and resource reservation in the context of next generation mobile and cellular networks. We also devised a special scheme that avoids per-user reservation signaling overhead in order to meet scalability requirements needed for next generation multi-access networks. The first proposal, Distributed Call Admission Control with Aggregate Resource Reservation (VR), uses mobility prediction based on mobile positioning system location information and takes into account the expected bandwidth to be used by calls handing off to and from neighboring cells within a configurable estimation time window. In conjunction, a novel concept called virtual reservation has been devised to prevent per-user reservation. Our second proposal, Local Call Admission Control and Time Series-based Resource Reservation, takes into account the expected bandwidth to be used by calls handed off from neighboring cells based only on local information stored into the current cell a user is seeking admission to. To this end, we suggest the use of two time series-based models for predicting handoff load: the Trigg and Leach (TL), which is an adaptive exponential smoothing technique, and Autoregressive Integrated Moving Average (ARIMA) that uses the Box and Jenkins methodology. It is worth to emphasize that the use of bandwidth prediction based on ARIMA technique still exist for wireless networks. The novelty of our approach is to build an adaptive framework based on ARIMA technique that takes into account the measured handoff dropping probability in order to tuning the prediction time window size so increasing the prediction accuracy. The proposed schemes are compared through simulations with the fixed guard channel (GC) and other optimized dynamic reservation-based proposals present in the literature. The results show that our schemes outperform many others and that the simpler local proposal based on TL can grant nearly similar levels of handoff dropping probability as compared to those from more the complex distributed approach.

Characterizing signature sets for testing DPI systems

Deep Packet Inspection (DPI) is a crucial tool that enables more accurate network management and traffic profiling. Its high computational demands have been pushing networking researchers to study how to improve DPI technologies. However, in this quest for better performance, little effort has been devoted to making fair performance comparisons among DPIs. Even worse, most studies show performance gains by applying DPI techniques using specific, and sometimes small, signature sets. Consequently, one DPI engine may report a certain maximum throughput while another one may report a similar performance but may have used a significantly smaller signature set. This fact makes it clear that the research community needs a well-defined framework for testing new DPI systems. This paper proposes a new framework for characterizing signature sets for evaluating DPI systems and provides an in-depth analysis of signature sets from application, protocol, and intrusion detection systems.

A context-aware framework for health care governance decision-making systems: A model based on the Brazilian Digital TV

This paper proposes a governance decision-making framework for public health care systems. It encompasses and integrates data about family homes in a new intelligent health care information system. In order to support end-user interactions, the framework has been built on the GINGA middleware developed for the Brazilian Digital TV, whose full access will be country-wide in 2015. Based on five governance fields, namely knowledge, normative, clinical-epidemiological, administrative, and shared management, the framework relies on an Optical-WiMAX communication infrastructure (Brazilian Digital Belt), which will reach 82% of urban population in the Ceara State. In addition, we present a case study showing how the framework could be used for improving health care governance decisions.

Deterministic Finite Automaton for scalable traffic identification: The power of compressing by range

Deep Packet Inspection (DPI) systems have been becoming an important element in traffic measurement ever since port-based classification was deemed no longer appropriate, due to protocol tunneling and misuses of well-defined ports. Current DPI systems express application signatures using regular expressions and it is usual to perform pattern matching through the use of Finite Automaton (FA). Although DPI systems are essentially more accurate, they are also resource-intensive and do not scale well with link speeds. Looking to this area of interest, this paper proposes a novel Deterministic Finite Automaton, called Ranged Compressed Deterministic Finite Automaton (RCDFA), that compresses transitions without additional memory lookups. Experimental results show that RCDFA yields space savings of 97% over the original DFA and up to 93% better compression when compared to the DFAs state-of-the-art compression techniques.

A Software Engineering Perspective on SDN Programmability

Software-defined networking (SDN) has received a great deal of attention from both academia and industry in recent years. Studies on SDN have brought a number of interesting technical discussions on network architecture design, along with scientific contributions. Researchers, network operators, and vendors are trying to establish new standards and provide guidelines for proper implementation and deployment of such novel approach. It is clear that many of these research efforts have been made in the southbound of the SDN architecture, while the northbound interface still needs improvements. By focusing in the SDN northbound, this paper surveys the body of knowledge and discusses the challenges for developing SDN software. We investigate the existing solutions and identify trends and challenges on programming for SDN environments. We also discuss future developments on techniques, specifications, and methodologies for programmable networks, with the orthogonal view from the software engineering discipline.