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This paper studies whether a packet will ultimately succeed in reaching a given destination, how long this will take and how much energy may be expended, in the context of a network with imperfect routing tables and non-homogeneous network characteristics. It also investigates the effect of non-cooperative routers that may actually choose to drop certain packets if they view them to be dangerous for destination nodes, as when packets may be carrying worms, viruses or malware, and when certain packets have been identified as being part of a Denial of Service attack. The approach we take is to construct a probability model for packet travel from a source to destination node in a large non-homogeneous multiple hop network. The randomness models the lack of precise routing information at each of the network hops, and randomness in routing can also be used to model networks where one wishes to explore alternate paths in a network to discover the more reliable paths, or those that may have other desirable characteristics such as lower delay or lower packet loss. We assume that each packet has the same time out: when the time-out elapses, the packet is dropped if it has not yet reached the destination, and some time later the source will retransmit a duplicate packet. A numerical–analytical solution is developed to compute the average travel time of the packet from source to destination and to estimate its total energy consumption. Two applications of these results are then presented. In the first one, the packet is an ‘attack’ packet (e.g. a Denial of Service packet, or some malware) and as it approaches the destination node it is being frequently inspected by routers that may decide to drop it if they correctly detect that it is a threat. The second example considers a wireless network where areas which are remote from the source and destination nodes have poorer wireless coverage so that packet losses become more frequent as the packet ‘unknowingly’ (due to poor routing tables errors) meanders away from the main coverage area. Other applications in wireless networks are also provided and a simulation study is performed to validate the analytical model. [ABSTRACT FROM AUTHOR]

This paper reviews computation in natural systems, focusing mainly on biology and citing examples of the computation that is inherent in chemistry, natural selection, gene regulatory networks, and neuronal systems. [ABSTRACT FROM PUBLISHER]

Although there is great interest in reducing energy consumption for all areas of human activity, many of the proposed approaches such as the smart home or the smart grid are actually prone to an increase in the use of Information and Communication Technologies (ICT) which itself is a big consumer of energy. It is remarkable that ICT's carbon imprint is of the order of 2% of the world total, comparable to the carbon imprint of air travel. Thus, it is imperative to address energy savings in ICT and, in particular, in data centres and networks. This paper follows up on our previous work that seeks novel ways to reduce the energy consumption in packet networks which constitute the backbone of the Internet and of the information society as a whole. Here we discuss the use of routing control as a means to reduce energy consumption while remaining aware of QoS considerations, and propose a method that uses a queueing theoretic analysis and optimization technique to distribute traffic so as to reduce a cost function that comprises both energy and QoS. A method using G-networks is developed to incorporate both the effect of user traffic and the overhead in QoS and energy consumption introduced by the control traffic that will be needed to carry out the re-routing decisions. For an N-node network, we show that this approach results in an algorithm which has O(N3) time complexity. Because this approach may be too costly in computational overhead and delays, we also propose another approach that uses load balancing and which would be much simpler to implement. [ABSTRACT FROM AUTHOR]

We investigate the assignment of assets to tasks where each asset can potentially execute any of the tasks, but assets execute tasks with a probabilistic outcome of success. There is a cost associated with each possible assignment of an asset to a task, and if a task is not executed, there is also a cost associated with the non-execution of the task. Thus, any assignment of assets to tasks will result in an expected overall cost which we wish to minimize. We formulate the allocation of assets to tasks in order to minimize this expected cost, as a nonlinear combinatorial optimization problem. A neural network approach for its approximate solution is proposed based on selecting parameters of a random neural network (RNN), solving the network in equilibrium, and then identifying the assignment by selecting the neurons whose probability of being active is the highest. Evaluations of the proposed approach are conducted by comparison with the optimum (enumerative) solution as well as with a greedy approach over a large number of randomly generated test cases. The evaluation indicates that the proposed RNN-based algorithm is better in terms of performance than the greedy heuristic, consistently achieving on average results within 5% of the cost obtained by the optimal solution for all problem cases considered. The RNN-based approach is fast and is of low polynomial complexity in the size of the problem, while it can be used for decentralized decision making. [ABSTRACT FROM PUBLISHER]

Sensor networks (SNs) consist of spatially distributed sensors which monitor an environment, and which are connected to some sinks or backbone system to which the sensor data is being forwarded. In many cases, the sensor nodes themselves can serve as intermediate nodes for data coming from other nodes, on the way to the sinks. Much of the traffic carried by SNs will originate from routine measurements or observations by sensors that monitor a particular situation, such as the temperature and humidity in a room or the infrared observation of the perimeter of a house, so that the volume of routine traffic resulting from such observations may be quite high. When important and unusual events occur, such as a sudden fire breaking out or the arrival of an intruder, it will be necessary to convey this new information very urgently through the network to a designated set of sink nodes where this information can be processed and dealt with. This paper addresses the important challenge by avoiding the routine background traffic from creating delays or bottlenecks that impede the rapid delivery of high priority traffic resulting from the unusual events. Specifically we propose a novel technique, the ‘Randomized Re-Routing Algorithm (RRR)’, which detects the presence of novel events in a distributed manner, and dynamically disperses the background traffic towards secondary paths in the network, while creating a ‘fast track path’ which provides better delay and better quality of service (QoS) for the high priority traffic which is carrying the new information. When the surge of new information has subsided, this is again detected by the nodes and the nodes progressively revert to best QoS or shortest-path routing for all the ongoing traffic. The proposed technique is evaluated using a mathematical model as well as simulations, and is also compared with a standard node by a node priority scheduling technique. [ABSTRACT FROM PUBLISHER]

Energy efficiency is increasingly important for future information and communication technologies (ICT), because the increased usage of ICT, together with increasing energy costs and the need to reduce green house gas emissions call for energy-efficient technologies that decrease the overall energy consumption of computation, storage and communications. Cloud computing has recently received considerable attention, as a promising approach for delivering ICT services by improving the utilization of data centre resources. In principle, cloud computing can be an inherently energy-efficient technology for ICT provided that its potential for significant energy savings that have so far focused on hardware aspects, can be fully explored with respect to system operation and networking aspects. Thus this paper, in the context of cloud computing, reviews the usage of methods and technologies currently used for energy-efficient operation of computer hardware and network infrastructure. After surveying some of the current best practice and relevant literature in this area, this paper identifies some of the remaining key research challenges that arise when such energy-saving techniques are extended for use in cloud computing environments. [ABSTRACT FROM PUBLISHER]

The article presents comments on a lecture by Peter Harrison of Imperial College London in England on computer system performance analysis. It is asserted that any scientific endeavor in computer system performance will require several major parts, including discovery of new properties or natural or mathematical systems and more compact or more abstract constructs and formalisms, like those proposed by Harrison. The reversed process proposed by Harrison is obtained through the application of the Reversed Compound Agent Theorem (RCAT).

The article discusses the history and accomplishments of "The Computer Journal."

We present two spatially distributed computing systems that operate in a building and provide intelligent navigation services to people for evacuation purposes. These systems adapt to changing conditions by monitoring the building and using local communication and computation for determining the best evacuation paths. The first system, called distributed evacuation system (DES), comprises a network of decision nodes (DNs) positioned at specific locations inside the building. DNs provide people with directions regarding the best available exit. The second system, called opportunistic emergency support system (OESS), consists of mobile communication nodes (CNs) carried by people. CNs form an opportunistic network in order to exchange information regarding the hazard and to direct the evacuees towards the safest exit. Both DES and OESS employ sensor nodes deployed at fixed locations for monitoring the hazard. We evaluate the spatial systems using simulation experiments with a purpose-built emergency simulator called DBES. We show how parameters such as the frequency of information exchange and communication range affect the system performance and evacuation outcome. [ABSTRACT FROM AUTHOR]

We describe a distributed simulation tool which addresses the unique needs for the simulation of emergency response scenarios. The simulation tool adopts the multi-agent paradigm, so as to facilitate the modelling of diverse and autonomous agents, and it provides mechanisms for the interaction of the entities that are being simulated. It operates in a distributed fashion to reduce the simulation time required for such large-scale systems. The simulation tool represents the individuals that need to be evacuated, the resources that contribute to the evacuation including human rescuers, and other active resources and entities which may include robots and which can autonomously interact with the environment and with each other and take individual or collaborative decisions. We illustrate the tool with an application and compare the results for both centralized and distributed execution. Our results also show the significant reduction in execution time that is achieved for different degrees of distribution of the simulator on multiple servers. [ABSTRACT FROM PUBLISHER]