Publications
9 results found
Wang L, 2017, The random neural network for cognitive traffic routing and task allocation in networks and the cloud, Probability in the Engineering and Informational Sciences, Vol: 31, Pages: 540-560, ISSN: 0269-9648
© Cambridge University Press 2017. G-Network queueing network models, and in particular the random neural network (RNN), are useful tools for decision making in complex systems, due to their ability to learn from measurements in real time, and in turn provide real-Time decisions regarding resource and task allocation. In particular, the RNN has led to the design of the cognitive packet network (CPN) decision tool for the routing of packets in the Internet, and for task allocation in the Cloud. Thus in this paper, we present recent research on how to dynamically create the means for quality of service (QoS) to end users of the Internet and in the Cloud. The approach is based on adapting the decisions so as to benefit users as the conditions in the Internet and in Cloud servers vary due to changing traffic and workload. We present an overview of the algorithms that were designed based on the RNN, and also detail the experimental results that were obtained in three areas: (i) traffic routing for real-Time applications, which have strict QoS constraints; (ii) routing approaches, which operate at the overlay level without affecting the Internet infrastructure; and (iii) the routing of tasks across servers in the Cloud through the Internet.
Wang L, Brun O, Gelenbe E, 2017, Adaptive workload distribution for local and remote clouds, IEEE International Conference on Systems, Man, and Cybernetics (SMC), Publisher: IEEE, Pages: 3984-3988, ISSN: 1062-922X
Cloud systems include both locally based servers at user premises and remote servers and multiple Clouds that can be reached over the Internet. This paper describes a smart distributed system that combines local and remote Cloud facilities. It operates with a task allocation system that takes decisions to allocate tasks dynamically to the service that offers the best overall Quality of Service and a routing overlay which optimizes network delay for data transfer between clouds. Internet-scale experiments exhibit the effectiveness of our approach in adaptively distributing workload across multiple clouds.
IEEE, 2016, Publisher’s Information, 2016 IEEE 24th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems (MASCOTS), Publisher: IEEE
Gelenbe E, Wang L, 2016, Real-Time Traffic over the Cognitive Packet Network, International Science Conference on Computer Networks 2016, Publisher: Springer International Publishing, Pages: 3-21, ISSN: 1865-0929
Real-Time services over IP (RTIP) have been increasingly significant due to the convergence of data networks worldwide around the IP standard, and the popularisation of the Internet. Real-Time applications have strict Quality of Service (QoS) constraint, which poses a major challenge to IP networks. The Cognitive Packet Network (CPN) has been designed as a QoS-driven protocol that addresses user-oriented QoS demands by adaptively routing packets based on online sensing and measurement, and in this paper we design and experimentally evaluate the“Real-Time (RT) over CPN” protocol which uses QoS goals that match the needs of real-time packet delivery in the presence of other background traffic under varied traffic conditions. The resulting design is evaluated via measurements of packet delay, delay variation (jitter) and packet loss ratio.
Brun O, Wang L, Gelenbe E, 2016, Big data for autonomic intercontinental overlays, IEEE Journal on Selected Areas in Communications, Vol: 34, Pages: 575-583, ISSN: 1558-0008
This paper uses big data and machine learning for the real-time management of Internet scale quality-of-service (QoS) route optimisation with an overlay network. Based on the collection of data sampled every 2 min over a large number of source-destinations pairs, we show that intercontinental Internet protocol (IP) paths are far from optimal with respect to QoS metrics such as end-to-end round-trip delay. We, therefore, develop a machine learning-based scheme that exploits large scale data collected from communicating node pairs in a multihop overlay network that uses IP between the overlay nodes, and selects paths that provide substantially better QoS than IP. Inspired from cognitive packet network protocol, it uses random neural networks with reinforcement learning based on the massive data that is collected, to select intermediate overlay hops. The routing scheme is illustrated on a 20-node intercontinental overlay network that collects some 2 × 106 measurements per week, and makes scalable distributed routing decisions. Experimental results show that this approach improves QoS significantly and efficiently.
Wang L, Gelenbe E, 2015, Demonstrating voice over an autonomic network, 2015 IEEE International Conference on Autonomic Computing, Publisher: IEEE, Pages: 139-140
We demonstrate experimentally how an Autonomic Network based on the CPN protocol can provide the Quality of Service (QoS) required by voice communications. The implementation uses Reinforcement Learning to dynamically seek paths that meet the quality requirements of voice communications. Measurements of packet delay, jitter, and loss illustrate the performance obtained from the system.
Gelenbe E, Wang L, 2015, Adaptive Dispatching of Tasks in the Cloud, IEEE Transactions on Cloud Computing, Pages: 1-1, ISSN: 2168-7161
The increasingly wide application of Cloud Computing enables the consolidation of tens of thousands of applications in shared infrastructures. Thus, meeting the QoS requirements of so many diverse applications in such shared resource environments has become a real challenge, especially since the characteristics and workload of applications differ widely and may change over time. This paper presents an experimental system that can exploit a variety of online QoS aware adaptive task allocation schemes, and three such schemes are designed and compared. These are a measurement driven algorithm that uses reinforcement learning, secondly a “sensible” allocation algorithm that assigns tasks to sub-systems that are observed to provide a lower response time, and then an algorithm that splits the task arrival stream into sub-streams at rates computed from the hosts’ processing capabilities. All of these schemes are compared via measurements among themselves and with a simple round-robin scheduler, on two experimental test-beds with homogenous and heterogenous hosts having different processing capacities.
Wang L, Gelenbe E, 2015, Experiments with Smart Workload Allocation to Cloud Servers, 4th IEEE Symposium on Network Cloud Computing and Applications (NCCA), Publisher: IEEE, Pages: 31-35, ISSN: 2333-2549
- Author Web Link
- Cite
- Citations: 8
, 2009, Proceedings of MASCOTS 2009, 17th Annual Meeting of the IEEE/ACM International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, MASCOTS 2009, International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, Imperial College London, Publisher: IEEE Computer Society Press
Message from the Programme Committee Chairs\r\n\r\nOn behalf of the Organising and Programme Committee, it is our pleasure to present to you the proceedings of MASCOTS 2009, the IEEE Computer SocietyÆs 17th International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunications Systems, which will be held in London. Our society is ever more dependent on the reliable and high performance operation of complex combinations of computer and communication technologies. As existing technologies evolve and new ones emerge, it remains critical to understand, predict and enhance system reliability and performance using stochastic models, simulation and analytical methods. Experimental studies are also needed to parameterise, calibrate and validate models against real-world observations. These are precisely the themes of the MASCOTS conference series. We are very pleased that this yearÆs conference attracted 162 submissions fromall over the world, many of which were of the highest quality. Such a large number of submissions implied a correspondingly high reviewing load, and we are very grateful to the Programme Committee members and many external reviewers who provided between three and six reviews for each submission.\r\n\r\nBased on the critical reviews of the reviewers and discussions in the Programme Committee, we accepted 32 extended papers of the highest quality, 22 high-quality regular papers and 21 posters. The accepted submissions were from 25 countries spanning five continents, included submissions with industrial co-authors from 7 different companies, and covered a diverse set of research areas (e.g. workload modelling, load management and scheduling, performance optimisation and reliability/availability modeling), and diverse application contexts (e.g. parallel and multicore systems, wireless networks and storage systems). The conference programme has been organised broadly to reflect these themes and includes invited keynote tal
This data is extracted from the Web of Science and reproduced under a licence from Thomson Reuters. You may not copy or re-distribute this data in whole or in part without the written consent of the Science business of Thomson Reuters.