Aggregated Multicast — A Comparative Study

Though IP multicast is resource ef£cient in delivering data to a group of members simultaneously, it suffers from scalability problem with the number of concurrently active multicast groups because it requires a router to keep forwarding state for every multicast tree passing through it. To solve this state scalability problem, we proposed a scheme, called aggregated multicast. The key idea is that multiple groups are forced to share a single delivery tree. In our earlier work, we introduced the basic concept of aggregated multicast and presented some initial results to show that multicast state can be reduced. In this paper, we develop a more quantitative assessment of the cost/bene£t trade-offs. We propose an algorithm to assign multicast groups to delivery trees with controllable cost and introduce metrics to measure multicast state and tree management overhead for multicast schemes. We then compare aggregated multicast with conventional multicast schemes, such as source speci£c tree scheme and shared tree scheme. Our extensive simulations show that aggregated multicast can achieve signi£cant routing state and tree management overhead reduction while containing the expense of extra resources (bandwidth waste and tunnelling overhead). We conclude that aggregated multicast is a very cost-effective and promising direction for scalable transit domain multicast provisioning.

V2V/V2I augmented maps : state-of-the-art and contribution to real-time crash risk assessment

A number of advanced driver assistance systems (ADAS) are currently being released on the market, providing safety functions to the drivers such as collision avoidance, adaptive cruise control or enhanced night-vision. These systems however are inherently limited by their sensory range: they cannot gather information from outside this range, also called their “perceptive horizon”. Cooperative systems are a developing research avenue that aims at providing extended safety and comfort functionalities by introducing vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) wireless communications to the road actors. This paper presents the problematic of cooperative systems, their advantages and contributions to road safety and exposes some limitations related to market penetration, sensors accuracy and communications scalability. It explains the issues of how to implement extended perception, a central contribution of cooperative systems. The initial steps of an evaluation of data fusion architectures for extended perception are exposed.

On the Scalability-Performance Tradeoffs in MPLS and IP Routing

MPLS (Multi-Protocol Label Switching) has recently emerged to facilitate the engineering of network traffic. This can be achieved by directing packet flows over paths that satisfy multiple requirements. MPLS has been regarded as an enhancement to traditional IP routing, which has the following problems: (1) all packets with the same IP destination address have to follow the same path through the network; and (2) paths have often been computed based on static and single link metrics. These problems may cause traffic concentration, and thus degradation in quality of service. In this paper, we investigate by simulations a range of routing solutions and examine the tradeoff between scalability and performance. At one extreme, IP packet routing using dynamic link metrics provides a stateless solution but may lead to routing oscillations. At the other extreme, we consider a recently proposed Profile-based Routing (PBR), which uses knowledge of potential ingress-egress pairs as well as the traffic profile among them. Minimum Interference Routing (MIRA) is another recently proposed MPLS-based scheme, which only exploits knowledge of potential ingress-egress pairs but not their traffic profile. MIRA and the more conventional widest-shortest path (WSP) routing represent alternative MPLS-based approaches on the spectrum of routing solutions. We compare these solutions in terms of utility, bandwidth acceptance ratio as well as their scalability (routing state and computational overhead) and load balancing capability. While the simplest of the per-flow algorithms we consider, the performance of WSP is close to dynamic per-packet routing, without the potential instabilities of dynamic routing.

A robust tri-state election algorithm for scalable applications

Existing election algorithms suffer limited scalability. This limit stems from the communication design which in turn stems from their fundamentally two-state behaviour. This paper presents a new election algorithm specifically designed to be highly scalable in broadcast networks whilst allowing any processing node to become coordinator with initially equal probability. To achieve this, careful attention has been paid to the communication design, and an additional state has been introduced. The design of the tri-state election algorithm has been motivated by the requirements analysis of a major research project to deliver robust scalable distributed applications, including load sharing, in hostile computing environments in which it is common for processing nodes to be rebooted frequently without notice. The new election algorithm is based in-part on a simple 'emergent' design. The science of emergence is of great relevance to developers of distributed applications because it describes how higher-level self-regulatory behaviour can arise from many participants following a small set of simple rules. The tri-state election algorithm is shown to have very low communication complexity in which the number of messages generated remains loosely-bounded regardless of scale for large systems; is highly scalable because nodes in the idle state do not transmit any messages; and because of its self-organising characteristics, is very stable.

A Scalability Study and New Algorithms for Large-Scale Many-Objective Optimization

Many real-world optimization problems contain multiple (often conflicting) goals to be optimized concurrently, commonly referred to as multi-objective problems (MOPs). Over the past few decades, a plethora of multi-objective algorithms have been proposed, often tested on MOPs possessing two or three objectives. Unfortunately, when tasked with solving MOPs with four or more objectives, referred to as many-objective problems (MaOPs), a large majority of optimizers experience significant performance degradation. The downfall of these optimizers is that simultaneously maintaining a well-spread set of solutions along with appropriate selection pressure to converge becomes difficult as the number of objectives increase. This difficulty is further compounded for large-scale MaOPs, i.e., MaOPs possessing large amounts of decision variables. In this thesis, we explore the challenges of many-objective optimization and propose three new promising algorithms designed to efficiently solve MaOPs. Experimental results demonstrate the proposed optimizers to perform very well, often outperforming state-of-the-art many-objective algorithms.

Maximising concurrency and scalability in a consistent, causal, distributed virtual reality system, whilst minimising the effect of network delays

The development of large scale virtual reality and simulation systems have been mostly driven by the DIS and HLA standards community. A number of issues are coming to light about the applicability of these standards, in their present state, to the support of general multi-user VR systems. This paper pinpoints four issues that must be readdressed before large scale virtual reality systems become accessible to a larger commercial and public domain: a reduction in the effects of network delays; scalable causal event delivery; update control; and scalable reliable communication. Each of these issues is tackled through a common theme of combining wall clock and causal time-related entity behaviour, knowledge of network delays and prediction of entity behaviour, that together overcome many of the effects of network delay.

Maximising concurrency and scalability in a consistent, causal, distributed virtual reality system, whilst minimising the effect of network delays

The development of large scale virtual reality and simulation systems have been mostly driven by the DIS and HLA standards community. A number of issues are coming to light about the applicability of these standards, in their present state, to the support of general multi-user VR systems. This paper pinpoints four issues that must be readdressed before large scale virtual reality systems become accessible to a larger commercial and public domain: a reduction in the effects of network delays; scalable causal event delivery; update control; and scalable reliable communication. Each of these issues is tackled through a common theme of combining wall clock and causal time-related entity behaviour, knowledge of network delays and prediction of entity behaviour, that together overcome many of the effects of network delays.

Cloud-assisted body area networks: state-of-the-art and future challenges

Body area networks (BANs) are emerging as enabling technology for many human-centered application domains such as health-care, sport, fitness, wellness, ergonomics, emergency, safety, security, and sociality. A BAN, which basically consists of wireless wearable sensor nodes usually coordinated by a static or mobile device, is mainly exploited to monitor single assisted livings. Data generated by a BAN can be processed in real-time by the BAN coordinator and/or transmitted to a server-side for online/offline processing and long-term storing. A network of BANs worn by a community of people produces large amount of contextual data that require a scalable and efficient approach for elaboration and storage. Cloud computing can provide a flexible storage and processing infrastructure to perform both online and offline analysis of body sensor data streams. In this paper, we motivate the introduction of Cloud-assisted BANs along with the main challenges that need to be addressed for their development and management. The current state-of-the-art is overviewed and framed according to the main requirements for effective Cloud-assisted BAN architectures. Finally, relevant open research issues in terms of efficiency, scalability, security, interoperability, prototyping, dynamic deployment and management, are discussed.

Time-Dependent Density Functional Molecular Orbital and Excited State Calculations on Bis(porphyrinyl)butadiynes in the Monocationic, Neutral, Monoanionic, and Dianionic Oxidation States

We report a theoretical study of the multiple oxidation states (1+, 0, 1−, and 2−) of a meso,meso-linked diporphyrin, namely bis[10,15,20-triphenylporphyrinatozinc(II)-5-yl]butadiyne (4), using Time-Dependent Density Functional Theory (TDDFT). The origin of electronic transitions of singlet excited states is discussed in comparison to experimental spectra for the corresponding oxidation states of the close analogue bis{10,15,20-tris[3‘,5‘-di-tert-butylphenyl]porphyrinatozinc(II)-5-yl}butadiyne (3). The latter were measured in previous work under in situ spectroelectrochemical conditions. Excitation energies and orbital compositions of the excited states were obtained for these large delocalized aromatic radicals, which are unique examples of organic mixed-valence systems. The radical cations and anions of butadiyne-bridged diporphyrins such as 3 display characteristic electronic absorption bands in the near-IR region, which have been successfully predicted with use of these computational methods. The radicals are clearly of the “fully delocalized” or Class III type. The key spectral features of the neutral and dianionic states were also reproduced, although due to the large size of these molecules, quantitative agreement of energies with observations is not as good in the blue end of the visible region. The TDDFT calculations are largely in accord with a previous empirical model for the spectra, which was based simplistically on one-electron transitions among the eight key frontier orbitals of the C4 (1,4-butadiyne) linked diporphyrins.