Use of remote sensing in the context of cage aquaculture

Remote sensing has much to offer to the cage aquaculture industry. First, it is helpful in the choice of sites for development of cage culture. Here, the opposing requirements of dispersion of waste products and shelter for the cages have to be balanced. The ability of remote sensing to deliver spatially-extensive data at high resolution is important, especially when complemented by numerical modelling. The most favourable period for cage deployments of less than one year can also be assessed from the seasonal wind field. Once the sites are selected, remote sensing continues to be beneficial in operational culture by providing early indications of the advent of water masses that are potentially antagonistic, either because of their physical properties or their microflora. Such warning gives producers the option to harvest their fish early and thereby minimize potential losses

Characterising anamolous events using change-point correlation on unsolicited network traffic

Monitoring unused or dark IP addresses offers opportunities to extract useful information about both on-going and new attack patterns. In recent years, different techniques have been used to analyze such traffic including sequential analysis where a change in traffic behavior, for example change in mean, is used as an indication of malicious activity. Change points themselves say little about detected change; further data processing is necessary for the extraction of useful information and to identify the exact cause of the detected change which is limited due to the size and nature of observed traffic. In this paper, we address the problem of analyzing a large volume of such traffic by correlating change points identified in different traffic parameters. The significance of the proposed technique is two-fold. Firstly, automatic extraction of information related to change points by correlating change points detected across multiple traffic parameters. Secondly, validation of the detected change point by the simultaneous presence of another change point in a different parameter. Using a real network trace collected from unused IP addresses, we demonstrate that the proposed technique enables us to not only validate the change point but also extract useful information about the causes of change points

On the energy-efficiency of a packet-level FEC based bufferless core optical network

F1 - Full Written Papers Referee

Greening Router Line-Cards via Dynamic Management of Packet Memory

Continued scaling of switching capacity in the Internet core is threatened by power considerations. Internet service providers face increased carbon footprint and operational costs, while router manufacturers encounter upper limits on switching capacity per rack. This paper studies the role of packet buffer memory on the power consumption of backbone routers. Our first contribution is to estimate from published datasheets the energy costs of static RAM/dynamic RAM packet-buffer memory, showing that it accounts for over 10% of power consumption in a typical router line-card; we then show, using empirical data from core and enterprise networks, that much of this memory is used for only a small fraction of time. Our second contribution is to develop a simple yet practical algorithm for putting much of the memory components to sleep and waking them as needed, while being able to control resulting traffic performance degradation in the form of packet loss during transient congestion. Finally, we conduct a comprehensive evaluation of our scheme, via analytical models pertaining to long-range-dependent traffic, using simulations of offline traffic traces taken from carrier/enterprise networks as well as online Transmission Control Protocol flows in ns2, and by implementing our scheme on a programmable-router test bed. This paper is the first to show the feasibility of, and energy savings from, dynamic management of packet buffer memory in core routers in the market today

A Statistical Approach to Anomaly Detection in Interdomain Routing

Abstract — A number of events such as hurricanes, earthquakes, power outages can cause large-scale failures in the Internet. These in turn cause anomalies in the interdomain routing process. The policy-based nature of Border Gateway protocol (BGP) further aggravates the effect of these anomalies causing severe, long lasting route fluctuations. In this work we propose an architecture for anomaly detection that can be implemented on individual routers. We use statistical pattern recognition techniques for extracting meaningful features from the BGP update message data. A time-series segmentation algorithm is then carried out on the feature traces to detect the onset of an instability event. The performance of the proposed algorithm is evaluated using real Internet trace data. We show that instabilities triggered by events like router mis-configurations, infrastructure failures and worm attacks can be detected with a false alarm rate as low as 0.0083 alarms per hour. We also show that our learning based mechanism is highly robust as compared to methods like Exponentially Weighted Moving Average (EWMA) based detection. I

Queuing Delays in Randomized Load Balanced Networks

(RLB), also promoted under the name ‘two-phase routing’, has previously been shown to provide a cost-effective way of implementing overlay networks that are robust to dynamically changing demand patterns. RLB is accomplished in two steps; in the first step, traffic is randomly distributed across the network, and in the second step traffic is routed to the final destination. One of the benefits of RLB is that packets experience only a single stage of routing, thus reducing queueing delays associated with multi-hop architectures. In this paper, we study the queuing performance of RLB, both through analytical methods and packet-level simulations using ns2 on three representative carrier networks. We show that purely random traffic splitting in the randomization step of RLB leads to higher queuing delays than pseudo-random splitting using, e.g., a round-robin schedule. Furthermore, we show that, for pseudo-random scheduling, queuing delays depend significantly on the degree of uniformity of the offered demand patterns, with uniform demand matrices representing a provably worst-case scenario. These results are independent of whether RLB employs priority mechanisms between traffic from step one over step two. A comparison with multi-hop shortest-path routing reveals that RLB eliminates the occurrence of demand-specific hot spots in the network. I