Modelling and Analyzing Cost-Effective Dependability in Passive Optical Networks
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Lately, the proliferation of new applications and services as well as the penetration of the Internet has created a growing need for the deployment of broadband access networks. Mostly due to the high bandwidth they offer, fiber-based solutions are well regarded to implement both present and future broadband access networks. Hence, not only operators and providers are upgrading their access networks to fiber, but also regulators and governmental institutions are supporting its deployment to meet the digital demands of our society. Particularly, Passive Optical Networks (PONs) are one of the most important types of fixed fiber-based access systems today, being widely deployed worldwide. Besides, PONs are also key to enable Fiber-Wireless (FiWi) integration, serving as backhaul networks. Being fiber-based, PONs offer high bandwidth, but also a set of features, such as low-power consumption or easy management, that make them very attractive as broadband access networks. Nevertheless, real telecommunication networks are not failure free. With society, industry and individuals becoming more dependent on telecommunications networks, users also demand these networks to provide dependable access to applications and services. In a dynamic business ecosystem, being able to meet user demands is key to achieve a successful network business. Further, in this context, dependability plays a major role in the overall economics of a business. First, achieving dependability entails a cost, typically due to the investment in fault tolerance. This is especially a concern in access networks, which are more cost sensitive than other parts of the network, due to the lower number of served users. On the other hand, poor dependability has consequences that negatively impact a business, from direct cost of failures and repairs, to bad publicity and loss of reputation. It is then a challenge for operators to increase the dependability of their access networks, hence reducing the consequences of dependability, at an affordable cost. Consequently, this thesis aims at addressing the trade-off between the cost of achieving dependability and its consequences, i.e. cost-effective dependability, in PON deployments. As a first step, this work focuses on modelling the PON deployment area, which affects both sides of the trade-off, due to infrastructure sharing, client clustering and design decisions. Especially, this suggestion builds on a network geometric model, i.e. the Manhattan model, to develop a closed formulation for the Capital Expenditures (CAPEX), accounting for the cost of achieving dependability. Additionally, this formulation also allows for calculating the failure impact (the number of clients affected by a failure) and thus capturing failure dependencies among clients. With the physical framework provided by the Manhattan model and the related equations, the CAPEX, asymptotic availability and failure impact of both unprotected and protected PONs can be analyzed. A central part of the thesis focuses on how to model the consequences of dependability, covering penalties due to breached Service Level Agreements (SLAs), cost of maintenance, buying of spare parts for repair and loss of reputation. Based on the constraints of the Manhattan model, a first approach proposes the use of Markov cost models to estimate dependability consequences fully in monetary units, i.e. as Operational Expenditures (OPEX). By developing the necessary equations, the objective is to express dependability attributes (asymptotic availability and failure impact) as expected OPEX. Thus, by combining CAPEX and OPEX, a first analysis of cost-efficient dependability with respect to hardware failures is performed, aiming at reducing the total cost. Also, an important part of this thesis deals with introducing software failures into the modelling of PON dependability. To do that, the software failure intensity of a PON Optical Line Termination (OLT) is estimated from empirical data by employing Duane’s model for software reliability growth. Different types of software failures are characterized, and integrated with hardware failures in the Markovian cost model. Further, how to model hardware-software interaction with respect to imperfect recovery of hardware faults due to software is also presented. Then, with the insight provided in software failures, the approach to model the consequences of dependability is further refined. Namely, a risk assessment approach, considering the probability distribution of the interval availability during a finite time frame, and thus the risk it represents, is followed. Under this approach, the full probability distribution of the OPEX can be computed, yet loss of reputation is modelled through client dissatisfaction and large outages. Hence, the probability mass function of the number of dissatisfied clients, as well as scatter plots of the down time versus the failure impact. This approach not only gives knowledge about the stochastic behaviour of these three aspects, but also shows how different fault tolerance mechanisms modify this behaviour and its associated risks. Lastly, a final consideration of this work proposes how to protect against software failures and assign available capacity to improve the interval availability of a PON client. In essence, a policy to assign available capacity to clients depending on their accumulated down time is suggested. This policy modifies the distribution of the interval availability, improving its expectation and reducing its variability, while allowing for the implementation of differentiated dependability.