Efficient memory management in video on demand servers

In this article we present, analyse and evaluate a new memory management technique for video-on-demand servers. Our proposal, Memory Reservation Per Storage Device (MRPSD), relies on the allocation of a fixed, small number of memory buffers per storage device. Selecting adequate scheduling algorithms, information storage strategies and admission control mechanisms, we demonstrate that MRPSD is suited for the deterministic service of variable bit rate streams to intolerant clients. MRPSD allows large memory savings compared to traditional memory management techniques, based on the allocation of a certain amount of memory per client served, without a significant performance penaltyPublicad

Cross-Layer Peer-to-Peer Track Identification and Optimization Based on Active Networking

P2P applications appear to emerge as ultimate killer applications due to their ability to construct highly dynamic overlay topologies with rapidly-varying and unpredictable traffic dynamics, which can constitute a serious challenge even for significantly over-provisioned IP networks. As a result, ISPs are facing new, severe network management problems that are not guaranteed to be addressed by statically deployed network engineering mechanisms. As a first step to a more complete solution to these problems, this paper proposes a P2P measurement, identification and optimisation architecture, designed to cope with the dynamicity and unpredictability of existing, well-known and future, unknown P2P systems. The purpose of this architecture is to provide to the ISPs an effective and scalable approach to control and optimise the traffic produced by P2P applications in their networks. This can be achieved through a combination of different application and network-level programmable techniques, leading to a crosslayer identification and optimisation process. These techniques can be applied using Active Networking platforms, which are able to quickly and easily deploy architectural components on demand. This flexibility of the optimisation architecture is essential to address the rapid development of new P2P protocols and the variation of known protocols

Dynamic bandwidth allocation in ATM networks

Includes bibliographical references.This thesis investigates bandwidth allocation methodologies to transport new emerging bursty traffic types in ATM networks. However, existing ATM traffic management solutions are not readily able to handle the inevitable problem of congestion as result of the bursty traffic from the new emerging services. This research basically addresses bandwidth allocation issues for bursty traffic by proposing and exploring the concept of dynamic bandwidth allocation and comparing it to the traditional static bandwidth allocation schemes

Accurate and Resource-Efficient Monitoring for Future Networks

Monitoring functionality is a key component of any network management system. It is essential for profiling network resource usage, detecting attacks, and capturing the performance of a multitude of services using the network. Traditional monitoring solutions operate on long timescales producing periodic reports, which are mostly used for manual and infrequent network management tasks. However, these practices have been recently questioned by the advent of Software Defined Networking (SDN). By empowering management applications with the right tools to perform automatic, frequent, and fine-grained network reconfigurations, SDN has made these applications more dependent than before on the accuracy and timeliness of monitoring reports. As a result, monitoring systems are required to collect considerable amounts of heterogeneous measurement data, process them in real-time, and expose the resulting knowledge in short timescales to network decision-making processes. Satisfying these requirements is extremely challenging given today’s larger network scales, massive and dynamic traffic volumes, and the stringent constraints on time availability and hardware resources. This PhD thesis tackles this important challenge by investigating how an accurate and resource-efficient monitoring function can be realised in the context of future, software-defined networks. Novel monitoring methodologies, designs, and frameworks are provided in this thesis, which scale with increasing network sizes and automatically adjust to changes in the operating conditions. These achieve the goal of efficient measurement collection and reporting, lightweight measurement- data processing, and timely monitoring knowledge delivery

The evaluation of an active networking approach for supporting the QOS requirements of distributed virtual environments

This paper describes work that is part of a more general investigation into how Active Network ideas might benefit large scale Distributed-Virtual-Environments (DVEs). Active Network approaches have been shown to offer improved solutions to the Scalable Reliable Multicast problem, and this is in a sense the lowest level at which Active Networks might benefit DVEs in supporting the peer-to-peer architectures considered most promising for large scale DVEs. To go further than this, the key benefit of Active Networking is the ability to take away from the application the need to understand the network topology and delegate the execution of certain actions, for example intelligent message pruning, to the network itself. The need to exchange geometrical information results in a type of traffic that can place occasional, short-lived, but heavy loads on the network. However, the Level of Detail (LoD) concept provides the potential to reduce this loading in certain circumstances. This paper introduces the performance modelling approach being used to evaluate the effectiveness of active network approaches for supporting DVEs and presents an evaluation of messages filtering mechanisms, which are based on the (LoD) concept. It describes the simulation experiment used to carry out the evaluation, presents its results and discusses plans for future work

Some aspects of traffic control and performance evaluation of ATM networks

The emerging high-speed Asynchronous Transfer Mode (ATM) networks are expected to integrate through statistical multiplexing large numbers of traffic sources having a broad range of statistical characteristics and different Quality of Service (QOS) requirements. To achieve high utilisation of network resources while maintaining the QOS, efficient traffic management strategies have to be developed. This thesis considers the problem of traffic control for ATM networks. The thesis studies the application of neural networks to various ATM traffic control issues such as feedback congestion control, traffic characterization, bandwidth estimation, and Call Admission Control (CAC). A novel adaptive congestion control approach based on a neural network that uses reinforcement learning is developed. It is shown that the neural controller is very effective in providing general QOS control. A Finite Impulse Response (FIR) neural network is proposed to adaptively predict the traffic arrival process by learning the relationship between the past and future traffic variations. On the basis of this prediction, a feedback flow control scheme at input access nodes of the network is presented. Simulation results demonstrate significant performance improvement over conventional control mechanisms. In addition, an accurate yet computationally efficient approach to effective bandwidth estimation for multiplexed connections is investigated. In this method, a feed forward neural network is employed to model the nonlinear relationship between the effective bandwidth and the traffic situations and a QOS measure. Applications of this approach to admission control, bandwidth allocation and dynamic routing are also discussed. A detailed investigation has indicated that CAC schemes based on effective bandwidth approximation can be very conservative and prevent optimal use of network resources. A modified effective bandwidth CAC approach is therefore proposed to overcome the drawback of conventional methods. Considering statistical multiplexing between traffic sources, we directly calculate the effective bandwidth of the aggregate traffic which is modelled by a two-state Markov modulated Poisson process via matching four important statistics. We use the theory of large deviations to provide a unified description of effective bandwidths for various traffic sources and the associated ATM multiplexer queueing performance approximations, illustrating their strengths and limitations. In addition, a more accurate estimation method for ATM QOS parameters based on the Bahadur-Rao theorem is proposed, which is a refinement of the original effective bandwidth approximation and can lead to higher link utilisation

What’s Happening Around the World? A Survey and Framework on Event Detection Techniques on Twitter

© 2019, Springer Nature B.V. In the last few years, Twitter has become a popular platform for sharing opinions, experiences, news, and views in real-time. Twitter presents an interesting opportunity for detecting events happening around the world. The content (tweets) published on Twitter are short and pose diverse challenges for detecting and interpreting event-related information. This article provides insights into ongoing research and helps in understanding recent research trends and techniques used for event detection using Twitter data. We classify techniques and methodologies according to event types, orientation of content, event detection tasks, their evaluation, and common practices. We highlight the limitations of existing techniques and accordingly propose solutions to address the shortcomings. We propose a framework called EDoT based on the research trends, common practices, and techniques used for detecting events on Twitter. EDoT can serve as a guideline for developing event detection methods, especially for researchers who are new in this area. We also describe and compare data collection techniques, the effectiveness and shortcomings of various Twitter and non-Twitter-based features, and discuss various evaluation measures and benchmarking methodologies. Finally, we discuss the trends, limitations, and future directions for detecting events on Twitter

Revenue maximization problems in commercial data centers

PhD ThesisAs IT systems are becoming more important everyday, one of the main concerns is that users may face major problems and eventually incur major costs if computing systems do not meet the expected performance requirements: customers expect reliability and performance guarantees, while underperforming systems loose revenues. Even with the adoption of data centers as the hub of IT organizations and provider of business efficiencies the problems are not over because it is extremely difficult for service providers to meet the promised performance guarantees in the face of unpredictable demand. One possible approach is the adoption of Service Level Agreements (SLAs), contracts that specify a level of performance that must be met and compensations in case of failure. In this thesis I will address some of the performance problems arising when IT companies sell the service of running ‘jobs’ subject to Quality of Service (QoS) constraints. In particular, the aim is to improve the efficiency of service provisioning systems by allowing them to adapt to changing demand conditions. First, I will define the problem in terms of an utility function to maximize. Two different models are analyzed, one for single jobs and the other useful to deal with session-based traffic. Then, I will introduce an autonomic model for service provision. The architecture consists of a set of hosted applications that share a certain number of servers. The system collects demand and performance statistics and estimates traffic parameters. These estimates are used by management policies which implement dynamic resource allocation and admission algorithms. Results from a number of experiments show that the performance of these heuristics is close to optimal.QoSP (Quality of Service Provisioning)British Teleco