Today's home network usually involves connecting multiple PCs and peripheral devices, such as printers and scanners, together in a network. This provides the benefit of allowing the PCs in the network to share Internet access and other resources. However, it is expected in the future, the home area network (HAN) will grow and extend to other home devices such as home entertainment systems (including digital TV, hi-fi stereo, etc.), appliances, webcam, security alarm system, etc. Connecting other home devices to a HAN provides users with many benefits not available in today's home networks. For example, home devices capable of connecting to the future HAN are able to share the content downloaded from broadband access anywhere in the home. Users can also have remote access and control of their home devices. To extend the home area network to all these different home devices, however, means that the traffic between the ISP and future HAN will be very different from the traffic generated by today’s home network. In today's home network, which consists mainly of multiple PCs, a best-effort approach is able to satisfy the need, since most of the traffic generated by PCs is not real-time in nature. However, in future HANs, it is anticipated that traffic generated from home devices requiring real-time applications such as multimedia entertainment systems, teleconferencing, etc. will occupy a large proportion of the traffic between the ISP and future HANs. In addition, given the variety of home devices that could potentially be added to future HANs, the amount and variety of traffic between the ISP and a future HAN will certainly be very different from today's home network that is dominated by Internet/data traffic. To allow HAN users of these real-time applications and various types of home devices to continue enjoying seamless experiences in using their home devices without noticing significant delays or unnecessary interruptions, it is important for the ISP to be able to effectively manage the channel to the home so that it can provide sufficient bandwidth to ensure high QoS for home applications. The aim of this thesis is to understand the types of traffic that will be expected and to develop an analytical model that will represent the traffic behaviour between the ISP and future HANs to understand how to manage the channel to provide high QoS. In this thesis, we use the continuous-time PH/M/n/m preemptive priority queue to model the traffic behaviour between the ISP and a future HAN. Three classes of traffic are defined in this model: real-time, interactive, and unclassified. Each of these three traffic classes receives a unique priority level. From the model one can approximate the amount of bandwidth required to be allocated for each traffic class for each household so that the total bandwidth required is minimized while the QoS requirements (delay and blocking probability) of the traffic generated by the home devices are met. Thus this model could potentially be used as a network planning tool for ISPs to estimate how much bandwidth they need to provide per household for homes that use home area network. Alternatively, it could also be used to estimate what quality of service (e.g. what is the mean delay and blocking probability expected) given a certain amount of bandwidth per household. / October 2006

Today's home network usually involves connecting multiple PCs and peripheral devices, such as printers and scanners, together in a network. This provides the benefit of allowing the PCs in the network to share Internet access and other resources. However, it is expected in the future, the home area network (HAN) will grow and extend to other home devices such as home entertainment systems (including digital TV, hi-fi stereo, etc.), appliances, webcam, security alarm system, etc. Connecting other home devices to a HAN provides users with many benefits not available in today's home networks. For example, home devices capable of connecting to the future HAN are able to share the content downloaded from broadband access anywhere in the home. Users can also have remote access and control of their home devices. To extend the home area network to all these different home devices, however, means that the traffic between the ISP and future HAN will be very different from the traffic generated by today’s home network. In today's home network, which consists mainly of multiple PCs, a best-effort approach is able to satisfy the need, since most of the traffic generated by PCs is not real-time in nature. However, in future HANs, it is anticipated that traffic generated from home devices requiring real-time applications such as multimedia entertainment systems, teleconferencing, etc. will occupy a large proportion of the traffic between the ISP and future HANs. In addition, given the variety of home devices that could potentially be added to future HANs, the amount and variety of traffic between the ISP and a future HAN will certainly be very different from today's home network that is dominated by Internet/data traffic. To allow HAN users of these real-time applications and various types of home devices to continue enjoying seamless experiences in using their home devices without noticing significant delays or unnecessary interruptions, it is important for the ISP to be able to effectively manage the channel to the home so that it can provide sufficient bandwidth to ensure high QoS for home applications. The aim of this thesis is to understand the types of traffic that will be expected and to develop an analytical model that will represent the traffic behaviour between the ISP and future HANs to understand how to manage the channel to provide high QoS. In this thesis, we use the continuous-time PH/M/n/m preemptive priority queue to model the traffic behaviour between the ISP and a future HAN. Three classes of traffic are defined in this model: real-time, interactive, and unclassified. Each of these three traffic classes receives a unique priority level. From the model one can approximate the amount of bandwidth required to be allocated for each traffic class for each household so that the total bandwidth required is minimized while the QoS requirements (delay and blocking probability) of the traffic generated by the home devices are met. Thus this model could potentially be used as a network planning tool for ISPs to estimate how much bandwidth they need to provide per household for homes that use home area network. Alternatively, it could also be used to estimate what quality of service (e.g. what is the mean delay and blocking probability expected) given a certain amount of bandwidth per household.

Today's home network usually involves connecting multiple PCs and peripheral devices, such as printers and scanners, together in a network. This provides the benefit of allowing the PCs in the network to share Internet access and other resources. However, it is expected in the future, the home area network (HAN) will grow and extend to other home devices such as home entertainment systems (including digital TV, hi-fi stereo, etc.), appliances, webcam, security alarm system, etc. Connecting other home devices to a HAN provides users with many benefits not available in today's home networks. For example, home devices capable of connecting to the future HAN are able to share the content downloaded from broadband access anywhere in the home. Users can also have remote access and control of their home devices. To extend the home area network to all these different home devices, however, means that the traffic between the ISP and future HAN will be very different from the traffic generated by today’s home network. In today's home network, which consists mainly of multiple PCs, a best-effort approach is able to satisfy the need, since most of the traffic generated by PCs is not real-time in nature. However, in future HANs, it is anticipated that traffic generated from home devices requiring real-time applications such as multimedia entertainment systems, teleconferencing, etc. will occupy a large proportion of the traffic between the ISP and future HANs. In addition, given the variety of home devices that could potentially be added to future HANs, the amount and variety of traffic between the ISP and a future HAN will certainly be very different from today's home network that is dominated by Internet/data traffic. To allow HAN users of these real-time applications and various types of home devices to continue enjoying seamless experiences in using their home devices without noticing significant delays or unnecessary interruptions, it is important for the ISP to be able to effectively manage the channel to the home so that it can provide sufficient bandwidth to ensure high QoS for home applications. The aim of this thesis is to understand the types of traffic that will be expected and to develop an analytical model that will represent the traffic behaviour between the ISP and future HANs to understand how to manage the channel to provide high QoS. In this thesis, we use the continuous-time PH/M/n/m preemptive priority queue to model the traffic behaviour between the ISP and a future HAN. Three classes of traffic are defined in this model: real-time, interactive, and unclassified. Each of these three traffic classes receives a unique priority level. From the model one can approximate the amount of bandwidth required to be allocated for each traffic class for each household so that the total bandwidth required is minimized while the QoS requirements (delay and blocking probability) of the traffic generated by the home devices are met. Thus this model could potentially be used as a network planning tool for ISPs to estimate how much bandwidth they need to provide per household for homes that use home area network. Alternatively, it could also be used to estimate what quality of service (e.g. what is the mean delay and blocking probability expected) given a certain amount of bandwidth per household.

In this thesis, an approximation is proposed to evaluate the steady-state performance of kanban controlled assembly systems. The approximation is developed for the systems with two components making up an assembly. Then, it is extended to systems with more than two components. A continuous-time Markov model is aggregated keeping the model exact, and this aggregate model is approximated replacing some state-dependent transition rates with constant rates. Decomposition of the approximate aggregate model into submodels guarantees product-form steady-state distribution for each subsystem. Finally, submodels are combined in such a way that the size of the problem becomes independent of the number of kanbans. This brings about the computational advantage in solving the combined model using numerical matrix-geometric solution algorithms. Based on the numerical comparisons with simulation, the exact model, an approximate aggregate model and another approximation in a previous study in the literature, the approximation is observed to be good in terms of accuracy with respect to computational burden and has the potential to be a building block for the analysis of systems that are more complex but closer to real-life applications.

In this dissertation, we study the interplay between a firm’s operations strategy, with regard to its capacity management, and its marketing decision of product differentiation. For this, we study a market comprising heterogeneous customers who differ in their preferences for time and price. Time sensitive customers are willing to pay a price premium for a shorter delivery time, while price sensitive customers are willing to accept a longer delivery time in return for a lower price. Firms exploit this heterogeneity in customers’ preferences, and offer a menu of products/services that differ only in their guaranteed delivery times and prices. From demand perspective, when customers are allowed to self-select according to their preferences, different products act as substitutes, affecting each other’s demand. Customized product for each segment, on the other hand, results in independent demand for each product. On the supply side, a firm may either share the same processing capacity to serve the two market segments, or may dicate capacity for each segment. Our objective is to understand the interaction between product substitution and the firm’s operations strategy (dedicated versus shared capacity), and how they shape the optimal product differentiation strategy. To address the above issue, we first study this problem for a single monopolist firm, which offers two versions of the same basic product: (i) regular product at a lower price but with a longer delivery time, and (ii) express product at a higher price but with a shorter delivery time. Demand for each product arrives according to a Poisson process with a rate that depends both on its price and delivery time. In addition, if the products are substitutable, each product’s demand is also influenced by the price and delivery time of the other product. Demands within each category are served on a first-come-first-serve basis. However, customers for express product are always given priority over the other category when they are served using shared resources. There is a standard delivery time for the regular product, and the firm’s objective is to appropriately price the two products and select the express delivery time so as to maximize its profit rate. The firm simultaneously needs to decide its installed processing capacity so as to meet its promised delivery times with a high degree of reliability. While the problem in a dedicated capacity setting is solved analytically, the same becomes very challenging in a shared capacity setting, especially in the absence of an analytical characterization of the delivery time distribution of regular customers in a priority queue. We develop a solution algorithm, using matrix geometric method in a cutting plane framework, to solve the problem numerically in a shared capacity setting. Our study shows that in a highly capacitated system, if the firm decides to move from a dedicated to a shared capacity setting, it will need to offer more differentiated products, whether the products are substitutable or not. In contrast, when customers are allowed to self-select, such that independent products become substitutable, a more homogeneous pricing scheme results. However, the effect of substitution on optimal delivery time differentiation depends on the firm’s capacity strategy and cost, as well as market characteristics. The optimal response to any change in capacity cost also depends on the firm’s operations strategy. In a dedicated capacity scenario, the optimal response to an increase in capacity cost is always to offer more homogeneous prices and delivery times. In a shared capacity setting, it is again optimal to quote more homogeneous delivery times, but increase or decrease the price differentiation depending on whether the status-quo capacity cost is high or low, respectively. We demonstrate that the above results are corroborated by real-life practices, and provide a number of managerial implications in terms of dealing with issues like volatile fuel prices. We further extend our study to a competitive setting with two firms, each of which may either share its processing capacities for the two products, or may dedicate capacity for each product. The demand faced by each firm for a given product now also depends on the price and delivery time quoted for the same product by the other firm. We observe that the qualitative results of a monopolistic setting also extend to a competitive setting. Specifically, in a highly capacitated system, the equilibrium prices and delivery times are such that they result in more differentiated products when both the firms use shared capacities as compared to the scenario when both the firms use dedicated capacities. When the competing firms are asymmetric, they exploit their distinctive characteristics to differentiate their products. Further, the effects of these asymmetries also depend on the capacity strategy used by the competing firms. Our numerical results suggest that the firm with expensive capacity always offers more homogeneous delivery times. However, its decision on how to differentiate its prices depends on the capacity setting of the two firms as well as the actual level of their capacity costs. On the other hand, the firm with a larger market base always offers more differentiated prices as well as delivery times, irrespective of the capacity setting of the competing firms.

In this dissertation, we study the interplay between a firm’s operations strategy, with regard to its capacity management, and its marketing decision of product differentiation. For this, we study a market comprising heterogeneous customers who differ in their preferences for time and price. Time sensitive customers are willing to pay a price premium for a shorter delivery time, while price sensitive customers are willing to accept a longer delivery time in return for a lower price. Firms exploit this heterogeneity in customers’ preferences, and offer a menu of products/services that differ only in their guaranteed delivery times and prices. From demand perspective, when customers are allowed to self-select according to their preferences, different products act as substitutes, affecting each other’s demand. Customized product for each segment, on the other hand, results in independent demand for each product. On the supply side, a firm may either share the same processing capacity to serve the two market segments, or may dicate capacity for each segment. Our objective is to understand the interaction between product substitution and the firm’s operations strategy (dedicated versus shared capacity), and how they shape the optimal product differentiation strategy. To address the above issue, we first study this problem for a single monopolist firm, which offers two versions of the same basic product: (i) regular product at a lower price but with a longer delivery time, and (ii) express product at a higher price but with a shorter delivery time. Demand for each product arrives according to a Poisson process with a rate that depends both on its price and delivery time. In addition, if the products are substitutable, each product’s demand is also influenced by the price and delivery time of the other product. Demands within each category are served on a first-come-first-serve basis. However, customers for express product are always given priority over the other category when they are served using shared resources. There is a standard delivery time for the regular product, and the firm’s objective is to appropriately price the two products and select the express delivery time so as to maximize its profit rate. The firm simultaneously needs to decide its installed processing capacity so as to meet its promised delivery times with a high degree of reliability. While the problem in a dedicated capacity setting is solved analytically, the same becomes very challenging in a shared capacity setting, especially in the absence of an analytical characterization of the delivery time distribution of regular customers in a priority queue. We develop a solution algorithm, using matrix geometric method in a cutting plane framework, to solve the problem numerically in a shared capacity setting. Our study shows that in a highly capacitated system, if the firm decides to move from a dedicated to a shared capacity setting, it will need to offer more differentiated products, whether the products are substitutable or not. In contrast, when customers are allowed to self-select, such that independent products become substitutable, a more homogeneous pricing scheme results. However, the effect of substitution on optimal delivery time differentiation depends on the firm’s capacity strategy and cost, as well as market characteristics. The optimal response to any change in capacity cost also depends on the firm’s operations strategy. In a dedicated capacity scenario, the optimal response to an increase in capacity cost is always to offer more homogeneous prices and delivery times. In a shared capacity setting, it is again optimal to quote more homogeneous delivery times, but increase or decrease the price differentiation depending on whether the status-quo capacity cost is high or low, respectively. We demonstrate that the above results are corroborated by real-life practices, and provide a number of managerial implications in terms of dealing with issues like volatile fuel prices. We further extend our study to a competitive setting with two firms, each of which may either share its processing capacities for the two products, or may dedicate capacity for each product. The demand faced by each firm for a given product now also depends on the price and delivery time quoted for the same product by the other firm. We observe that the qualitative results of a monopolistic setting also extend to a competitive setting. Specifically, in a highly capacitated system, the equilibrium prices and delivery times are such that they result in more differentiated products when both the firms use shared capacities as compared to the scenario when both the firms use dedicated capacities. When the competing firms are asymmetric, they exploit their distinctive characteristics to differentiate their products. Further, the effects of these asymmetries also depend on the capacity strategy used by the competing firms. Our numerical results suggest that the firm with expensive capacity always offers more homogeneous delivery times. However, its decision on how to differentiate its prices depends on the capacity setting of the two firms as well as the actual level of their capacity costs. On the other hand, the firm with a larger market base always offers more differentiated prices as well as delivery times, irrespective of the capacity setting of the competing firms.

An ever increasing demand for instant and reliable information on modern communication networks forces codewords to operate in a non-asymptotic regime. To achieve reliability for imperfect channels in this regime, codewords need to be retransmitted from receiver to the transmit buffer, aided by a fast feedback mechanism. Large occupancy of this buffer results in longer communication delays. Therefore, codewords need to be designed carefully to reduce transmit queue-length and thus the delay experienced in this buffer. We first study the consequences of physical layer decisions on the transmit buffer occupancy. We develop an analytical framework to relate physical layer channel to the transmit buffer occupancy. We compute the optimal code-rate for finite-length codewords operating over a correlated channel, under certain communication service guarantees. We show that channel memory has a significant impact on this optimal code-rate. Next, we study the delay in small ad-hoc networks. In particular, we find out what rates can be supported on a small network, when each flow has a certain end-to-end service guarantee. To this end, service guarantee at each intermediate link is characterized. These results are applied to study the potential benefits of setting up a network suitable for network coding in multicast. In particular, we quantify the gains of network coding over classic routing for service provisioned multicast communication over butterfly networks. In the wireless setting, we study the trade-off between communications gains achieved by network coding and the cost to set-up a network enabling network coding. In particular, we show existence of scenarios where one should not attempt to create a network suitable for coding. Insights obtained from these studies are applied to design a distributed rate control algorithm in a large network. This algorithm maximizes sum-utility of all flows, while satisfying per-flow end-to-end service guarantees. We introduce a notion of effective-capacity per communication link that captures the service requirements of flows sharing this link. Each link maintains a price and effective-capacity, and each flow maintains rate and dissatisfaction. Flows and links update their respective variables locally, and we show that their decisions drive the system to an optimal point. We implemented our algorithm on a network simulator and studied its convergence behavior on few networks of practical interest.