215 research outputs found

    A four-state Markov model for modelling bursty traffic and benchmarking of random early detection

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    Active Queue Management (AQM) techniques are crucial for managing packet transmission efficiently, maintaining network performance, and preventing congestion in routers. However, achieving these objectives demands precise traffic modeling and simulations in extreme and unstable conditions. The internet traffic has distinct characteristics, such as aggregation, burstiness, and correlation. This paper presents an innovative approach for modeling internet traffic, addressing the limitations of conventional modeling and conventional AQM methods' development, which are primarily designed to stabilize the network traffic. The proposed model leverages the power of multiple Markov Modulated Bernoulli Processes (MMBPs) to tackle the challenges of traffic modeling and AQM development. Multiple states with varying probabilities are used to model packet arrivals, thus capturing the burstiness inherent in internet traffic. Yet, the overall probability is maintained identical, irrespective of the number of states (one, two, or four), by solving linear equations with multiple variables. Random Early Detection (RED) was used as a case study method with different packet arrival probabilities based on MMBPs with one, two, and four states. The results showed that the proposed model influences the outcomes of AQM methods. Furthermore, it was found that RED might not effectively address network burstiness due to its relatively slow reaction time. As a result, it can be concluded that RED performs optimally only with a single-state model

    On distributed ledger technology for the internet of things: design and applications

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    Distributed ledger technology (DLT) can used to store information in such a way that no individual or organisation can compromise its veracity, contrary to a traditional centralised ledger. This nascent technology has received a great deal of attention from both researchers and practitioners in recent years due to the vast array of open questions related to its design and the assortment novel applications it unlocks. In this thesis, we are especially interested in the design of DLTs suitable for application in the domain of the internet of things (IoT), where factors such as efficiency, performance and scalability are of paramount importance. This work confronts the challenges of designing IoT-oriented distributed ledgers through analysis of ledger properties, development of design tools and the design of a number of core protocol components. We begin by introducing a class of DLTs whose data structures consist of directed acyclic graphs (DAGs) and which possess properties that make them particularly well suited to IoT applications. With a focus on the DAG structure, we then present analysis through mathematical modelling and simulations which provides new insights to the properties of this class of ledgers and allows us to propose novel security enhancements. Next, we shift our focus away from the DAG structure itself to another open problem for DAG-based distributed ledgers, that of access control. Specifically, we present a networking approach which removes the need for an expensive and inefficient mechanism known as Proof of Work, solving an open problem for IoT-oriented distributed ledgers. We then draw upon our analysis of the DAG structure to integrate and test our new access control with other core components of the DLT. Finally, we present a mechanism for orchestrating the interaction between users of a DLT and its operators, seeking to improves the usability of DLTs for IoT applications. In the appendix, we present two projects also carried out during this PhD which showcase applications of this technology in the IoT domain.Open Acces

    Empowering Cloud Data Centers with Network Programmability

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    Cloud data centers are a critical infrastructure for modern Internet services such as web search, social networking and e-commerce. However, the gradual slow-down of Moore’s law has put a burden on the growth of data centers’ performance and energy efficiency. In addition, the increasing of millisecond-scale and microsecond-scale tasks also bring higher requirements to the throughput and latency for the cloud applications. Today’s server-based solutions are hard to meet the performance requirements in many scenarios like resource management, scheduling, high-speed traffic monitoring and testing. In this dissertation, we study these problems from a network perspective. We investigate a new architecture that leverages the programmability of new-generation network switches to improve the performance and reliability of clouds. As programmable switches only provide very limited memory and functionalities, we exploit compact data structures and deeply co-design software and hardware to best utilize the resource. More specifically, this dissertation presents four systems: (i) NetLock: A new centralized lock management architecture that co-designs programmable switches and servers to simultaneously achieve high performance and rich policy support. It provides orders-of-magnitude higher throughput than existing systems with microsecond-level latency, and supports many commonly-used policies such as performance isolation. (ii) HCSFQ: A scalable and practical solution to implement hierarchical fair queueing on commodity hardware at line rate. Instead of relying on a hierarchy of queues with complex queue management, HCSFQ does not keep per-flow states and uses only one queue to achieve hierarchical fair queueing. (iii) AIFO: A new approach for programmable packet scheduling that only uses a single FIFO queue. AIFO utilizes an admission control mechanism to approximate PIFO which is theoretically ideal but hard to implement with commodity devices. (iv) Lumina: A tool that enables fine-grained analysis of hardware network stack. By exploiting network programmability to emulate various network scenarios, Lumina is able to help users understand the micro-behaviors of hardware network stacks

    Adaptive Active Queue Management based on Queue Ratio of Set-point Weighting

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    Presently, active queue management (AQM) is one of the important considerations in communication networks. The challenge is to make it simple and robust in bursty traffic and uncertain network conditions. This paper proposes a new AQM scheme, an adaptive ratio proportional integral (ARPI), for adaptively controlling network congestion in dynamic network traffic conditions. First, AQM was designed by adding a set-point weighting structure to a proportional integral (PI) controller to reduce the burstiness of network traffic. Second, an adaptive set-point weighting based on the ratio of instantaneous queue length to the set-point queue and the buffer size was proposed to improve the robustness of a non-linear network. The proposed design integrates the aforementioned expectations into one function and needs only one parameter change to adapt to fluctuating network condition. Hence, this scheme provides lightweight computation and simple software and hardware implementation. This approach was analyzed and compared with the PI AQM scheme. Evaluation results demonstrated that our proposed AQM can regulate queue length with a fast response, good stability under any traffic conditions, and small queuing delay

    Performance Evaluation of Transition-based Systems with Applications to Communication Networks

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    Since the beginning of the twenty-first century, communication systems have witnessed a revolution in terms of their hardware capabilities. This transformation has enabled modern networks to stand up to the diversity and the scale of the requirements of the applications that they support. Compared to their predecessors that primarily consisted of a handful of homogeneous devices communicating via a single communication technology, today's networks connect myriads of systems that are intrinsically different in their functioning and purpose. In addition, many of these devices communicate via different technologies or a combination of them at a time. All these developments, coupled with the geographical disparity of the physical infrastructure, give rise to network environments that are inherently dynamic and unpredictable. To cope with heterogeneous environments and the growing demands, network units have taken a leap from the paradigm of static functioning to that of adaptivity. In this thesis, we refer to adaptive network units as transition-based systems (TBSs) and the act of adapting is termed as transition. We note that TBSs not only reside in diverse environment conditions, their need to adapt also arises following different phenomena. Such phenomena are referred to as triggers and they can occur at different time scales. We additionally observe that the nature of a transition is dictated by the specified performance objective of the relevant TBS and we seek to build an analytical framework that helps us derive a policy for performance optimization. As the state of the art lacks a unified approach to modelling the diverse functioning of the TBSs and their varied performance objectives, we first propose a general framework based on the theory of Markov Decision Processes. This framework facilitates optimal policy derivation in TBSs in a principled manner. In addition, we note the importance of bespoke analyses in specific classes of TBSs where the general formulation leads to a high-dimensional optimization problem. Specifically, we consider performance optimization in open systems employing parallelism and closed systems exploiting the benefits of service batching. In these examples, we resort to approximation techniques such as a mean-field limit for the state evolution whenever the underlying TBS deals with a large number of entities. Our formulation enables calculation of optimal policies and provides tangible alternatives to existing frameworks for Quality of Service evaluation. Compared to the state of the art, the derived policies facilitate transitions in Communication Systems that yield superior performance as shown through extensive evaluations in this thesis

    Markov and Semi-markov Chains, Processes, Systems and Emerging Related Fields

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    This book covers a broad range of research results in the field of Markov and Semi-Markov chains, processes, systems and related emerging fields. The authors of the included research papers are well-known researchers in their field. The book presents the state-of-the-art and ideas for further research for theorists in the fields. Nonetheless, it also provides straightforwardly applicable results for diverse areas of practitioners

    PI Stabilization for Congestion Control of AQM Routers with Tuning Parameter Optimization

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    In this paper, we consider the problem of stabilizing network using a new proportional- integral (PI) based congestion controller in active queue management (AQM) router; with appropriate model approximation in the first order delay systems, we seek a stability region of the controller by using the Hermite- Biehler theorem, which isapplicable to quasipolynomials. A Genetic Algorithm technique is employed to derive optimal or near optimal PI controller parameters

    Tools and Algorithms for the Construction and Analysis of Systems

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    This book is Open Access under a CC BY licence. The LNCS 11427 and 11428 proceedings set constitutes the proceedings of the 25th International Conference on Tools and Algorithms for the Construction and Analysis of Systems, TACAS 2019, which took place in Prague, Czech Republic, in April 2019, held as part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2019. The total of 42 full and 8 short tool demo papers presented in these volumes was carefully reviewed and selected from 164 submissions. The papers are organized in topical sections as follows: Part I: SAT and SMT, SAT solving and theorem proving; verification and analysis; model checking; tool demo; and machine learning. Part II: concurrent and distributed systems; monitoring and runtime verification; hybrid and stochastic systems; synthesis; symbolic verification; and safety and fault-tolerant systems
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