Ricardo Lent

Design and performance of cognitive packet networks

Abstract We discuss a packet network architecture called a cognitive packet network (CPN), in which intelligent capabilities for routing and flow control are moved towards the packets, rather than being concentrated in the nodes and protocols. Our architecture contains “smart” and “dumb” packets, as well as acknowledgement packets. Smart CPN packets route themselves, and learn to avoid congestion and losses from their own observations about the network and from the experience of other packets. They use a reinforcement learning algorithm to route themselves based on a goal function which has been assigned to them for each connection. Dumb CPN packets of a specific quality of service (QoS) class use routes which have been selected by the smart packets (SPs) of that class. Acknowledgement (ACK) packets are generated by the destination when an SP arrives there; the ACK heads back to the source of the SP along the inverse route and is used to update mailboxes in CPN routers, as well as to provide source routing information for dumb packets. We first summarize the basic concepts behind CPN, and present simulations illustrating their performance for different QoS goals, and analytical results for best and worst case performance. We then describe a test-bed network we have designed and implemented in order to demonstrate these ideas. We provide measurement data on the test-bed to illustrate the capacity of the network to adapt to changes in traffic load and to failures of links. Finally, we use measurements to evaluate the impact of the ratio of smart to dumb packets on the end-to-end delay experienced by all of the packets.

Measurement and performance of a cognitive packet network

Abstract As the size of the Internet grows by orders of magnitude both in terms of users, number of IP addresses, and number of routers, and as the links we use (be they wired, optical or wireless) continuously evolve and provide varying reliability and quality of service, the IP based network architecture that we know so well will have to evolve and change. Both scalability and QoS have become key issues. We are currently conducting a research project that revisits the IP routing architecture issues and proposes new designs for routers. As part of this effort, this paper discusses a packet network architecture called a cognitive packet network (CPN), in which intelligent capabilities for routing and flow control are moved towards the packets, rather than being concentrated in the nodes. In this paper we outline the design of the CPN architecture, and discuss the quality-of-service based routing algorithm that we have designed and implemented. We then present our test-bed and report on extensive measurement experiments that we have conducted.

Power-aware ad hoc cognitive packet networks

Abstract This paper proposes a new energy efficient algorithm to find and maintain routes in mobile ad hoc networks. The proposal borrows the notion of learning from a previous research on cognitive packet networks (CPN) to create a robust routing protocol. Our idea uses smart packets that exploit the use of unicasts and broadcasts to search for routes. Because unicasts impose lower overall overhead, their use is preferred. Smart packets learn how to make good unicast routing decisions by employing a combined goal function which considers both the energy stored in the nodes and path delay. The end result is a dynamic discovery of paths that offer an equilibrium between low-delay routes and an efficient use of network resources that extends the working lifetime of the network.

Towards Networks with Cognitive Packets

We discuss packet networks in which intelligent capabilities for routing and flow control are concentrated in the packets, rather than in the nodes and protocols. This paper describes a possible test-bed to test and evaluate their capabilities, and presents an analytical model for the worst and best case performance of such systems.

Cloud computing and its interest in saving energy: the use case of a private cloud

BackgroundCloud computing data centres, due to their housing of powerful ICT equipment, are high energy consumers and therefore accountable for large quantities of emissions. Therefore, energy saving strategies applicable to such data centres are a very promising research direction both from the economical and environmental stand point.ResultsIn this paper, we study the case of private cloud computing environments from the perspective of energy saving incentives. However, the proposed approach can also be applied to any computing style: cloud (both public and private), traditional and supercomputing. To this end, we provide a generic conceptual description for ICT resources of a data centre and identify their corresponding energy-related attributes. Furthermore, we give power consumption prediction models for servers, storage devices and network equipment. We show that by applying appropriate energy optimisation policies guided through accurate power consumption prediction models, it is possible to save about 20% of energy consumption when typical single-site private cloud data centres are considered.ConclusionMinimising the data centre’s energy consumption, on one hand acknowledges the potential of ICT for saving energy across many segments of the economy, on the other hand helps ICT sector to show the way for the rest of the economy by reducing its own carbon footprint. In this paper, we show that it is possible to save energy by studying the case of a single-site private cloud data centres. We believe that through the federation of several cloud data centres (both private and public), it is possible to minimise both the energy consumption as well as CO2 emissions.

Energy Efficient Resource Allocation Strategy for Cloud Data Centres

Cloud computing data centres are emerging as new candidates for replacing traditional data centres. Cloud data centres are growing rapidly in both number and capacity to meet the increasing demands for highly-responsive computing and massive storage. Making the data centre more energy efficient is a necessary task. In this paper, we focus on the organisation’s internal Infrastructure as a Service (IaaS) data centre type. An internal IaaS cloud data centre has many distinguished features with heterogeneous hardware, single application, stable load distribution, lived load migration and highly automated administration. This paper will propose a way of saving energy for IaaS cloud data centre considering all stated constraints. The basic idea is rearranging the allocation in a way that saving energy. The simulation results show the efficiency of the method.

MyAds: A system for adaptive pervasive advertisements

In this paper we show how pervasive technologies can be employed on a public-display advertisement scenario to enable behavioral self-adaptation of content. We show this through MyAds, a system capable of exploiting pervasive technologies to autonomously adapt the advertisement process to the trends of interests detected among the audience in a venue. After describing the rationale, the architecture and the prototype of MyAds, we describe the advantages brought by the use of such a system, in terms of impact on the audience and economic efficiency. The comparison of MyAds performances with different advertisement selection techniques confirms the validity of our advertisement model, and our prototype in particular, as a means for maximising product awareness in an audience and for enhancing economic efficiency.

Energy–QoS Trade-Offs in Mobile Service Selection

An attractive advantage of mobile networks is that their users can gain easy access to different services. In some cases, equivalent services could be fulfilled by different providers, which brings the question of how to rationally select the best provider among all possibilities. In this paper, we investigate an answer to this question from both quality-of-service (QoS) and energy perspectives by formulating an optimisation problem. We illustrate the theoretical results with examples from experimental measurements of the resulting energy and performance.

Information Sciences and Systems 2013

This paper deals with the problem of finding an average of several curves subject to qualitative constraints and restrictions on the curves. The unknown average curve is the solution of a weighted least squares problem involving the deviation between the given curves and the unknown curve. The qualitative constraints are that some curves are preferred compared to other curves. The qualitative information is converted into constraints on the weights in the least squares problem defining the average curve. The model defining the curves is parameterized and restrictions on the curves are defined in terms of restrictions on the parameters. We give an example where the curves determined from three data sets are required to be monotone and convex. We also show that one curve being preferred restricts the set of possible curves that can be an average curve.

A sensor network to profile the electrical power consumption of computer networks

We introduce a sensor network to measure the individual power consumption of a large number of network elements in near-real time. A main application of this system is to obtain profiles of the power usage of network elements, i.e., a parameterization of power consumption with respect to workload. We discuss the design and implementation of both hardware and software aspects of the system as well as the application of the measurement results to achieve power-efficient routing.