In design and optimization of intensive care unit (ICU) networks, one common practice is to prioritize the treatment for patients of higher emergency levels, while ensuring fairness to other patients by guaranteeing a certain Quality of Service (QoS) level. One common approach to realize such priority arrangement is bed reservation policy, which designates a certain number of last occupied beds in each hospital to be exclusively used by certain patient classes. In this paper, we propose an approach that can significantly improve the computational efficiency in obtaining the optimal reservation thresholds for each patient class, given their respective requirements, in a non-hierarchical ICU model (where the external emergency patients can possibly be allocated to any ICU hospital), which has been shown to be computationally challenging in performance evaluation and optimization. Specifically, we apply the Information Exchange Surrogate Approximation (IESA) to analytically approximate the key QoS metrics under given reservation thresholds, and the integer Particle Swarm Optimization (PSO) algorithm to search for the optimal threshold based on the approximation results by IESA. We demonstrate numerically, with real data from ICUs in Hong Kong, that IESA can obtain reasonably accurate results for QoS metrics, and thus lead to accurate optimal reservation thresholds. In addition, our proposed approach combining IESA and PSO can significantly reduce the computation time by more than four orders of magnitude compared to the state-of-the-art evaluation and optimization methods in existing research for similar problems, especially for ICU networks with practical sizes.

Although millimeter wave (mmWave) communications can well support high-data-rate transmissions, the inherent shortcomings, e.g., high path loss and sensitivity to blockage, may cause severe outage problems if the network is not configured properly. This paper aims to minimize the long-term outage probability of an mmWave communication network by optimizing the base station (BS) deployment and user association. For the BS deployment problem, existing works usually assumed that the positions of users are fixed and formulated it as a deterministic optimization problem. With the time-varying nature of positions of user equipments (UEs) taken into account, we establish a stochastic optimization framework for BS deployment optimization. The objective is to maximize the average number of physically accessible BSs of each UE under an inaccessible probability constraint, and a cooperative stochastic approximation (CSA)-based algorithm is developed to effectively search the optimal positions of BSs. For user association, our focus is to properly associate UEs with BSs to minimize the outage probability with balanced workloads among BSs. Combined with the proposed user association scheme, the proposed BS deployment scheme can significantly improve the network outage probability in the long term, especially when the aggregation degree of UEs is large.

We present an enhancement to the problem of beam alignment in millimeter wave (mmWave) multiple-input multiple-output (MIMO) systems, based on a modification of the machine learning-based approach, called Kolmogorov model (KM). Unlike the previous KM, whose computational complexity is not scalable with the size of the problem, a new approach, centered on discrete monotonic optimization (DMO), is proposed, leading to significantly reduced complexity. We also present a Kolmogorov-Smirnov (KS) criterion for the advanced hypothesis testing, which does not require any subjective threshold setting compared to the frequency estimation (FE) method developed for the conventional KM. Simulation results that demonstrate the efficacy of the proposed KM learning for mmWave MIMO beam alignment are presented.

We propose a versatile cross-layer framework to analyze performance metrics for mobile traffic in fifth-generation (5G) millimeter wave (mmWave) networks. Our proposed framework is based on stochastic geometry, teletraffic models, and the classical Erlang Fixed Point Approximation method, with the objective of evaluating blocking probability, mean service time of user requests, and utilization rate of base stations by taking into account practical concerns in mmWave networks including blockages encountered by mmWaves in the physical layer, capacity constraints in the network layer, and the stochastic nature of mobile traffic. We demonstrate by numerical results that our analytical method is accurate and computationally-efficient.

Abstract Dual paraboloid mapping is an approach for environment mapping. Its major advantage is its fast map generation speed. For graphics applications, when filtering is needed, the filtering tool would naturally be mipmapping. However, directly applying mipmapping to dual paraboloid mapping would give us three problems. They are the discontinuity across the dual paraboloid map boundary, the non‐uniform sampling problem and the depth testing issue. We propose three approaches to solve these problems. Our approaches are based on some closed form equations derived via theoretical analysis. Using these equations, we modify the coordinates involved during the rendering process. In other words, these problems are handled just by using dual paraboloid maps and mipmaps differently, instead of fundamentally altering their data structures. Consequently, we are fixing the problems without damaging the map generation speed advantage. Applying all three approaches, we improve the rendering quality of dual paraboloid map mipmaps to a level equivalent to that of cubemap mipmaps, while preserving its fast map generation speed advantage. This gives dual paraboloid map mipmaps the potential to be a better choice than cubemap mipmaps for the devices with less computational power. The effectiveness and the efficiency of the proposed approaches are demonstrated using a glossy reflection application and an omnidirectional soft shadow generation application.

Non-hierarchical overflow loss systems (NH-OLSs) with mutual overflow have applications in many telecommunications and service systems, e.g., cellular networks, video-on-demand, emergency healthcare, or cloud services. In this paper, we make fundamental contributions in teletraffic modeling for blocking probability evaluation in NH-OLSs. These are based on the development of the well-known decomposition methodology, which decomposes an NH-OLS into independent subsystems and applies a suitable node model to each subsystem. In particular, we provide new both theoretical and numerical results involving both new and existing approximation methods. We show that these results cover a broad range of NH-OLSs, including those with heterogeneous arrival processes, server group size, and/or routing, with both Poisson and non-Poisson arrivals of fresh requests. Our theoretical results include the first scalable asymptotic exactness results for NH-OLSs. Our new approximations include the first computationally efficient and fairly accurate approximation for NH-OLSs with both mutual overflow and non-Poisson input with asymptotic exactness properties.

Energy efficiency and Quality of Service (QoS) are two important considerations for the design and planning of cellular networks. One effective approach to handle the tradeoff between power consumption and QoS is to switch some of the Base Stations (BSs) to sleep mode when traffic load is low. In this paper, we model each BS as a processor-sharing queue with vacations, and investigate the performance of three BS sleeping schemes, namely the isolated scheme in which each BS switches mode based on its own load, the cooperative scheme in which traffic is allowed to overflow from sleeping BSs to neighboring active BSs, and a combination of both schemes. We propose a robust, scalable and computationally efficient analytical method to evaluate QoS metrics (including mean delay and blocking probability) and power consumption for each scheme and validate their accuracy by simulations. We also demonstrate the power consumption and QoS trade-off by extensive and statistically reliable experiments, and compare the performance of the three schemes under different network conditions.

We study the problem of job assignment in a large-scale realistically dimensioned server farm comprising multiple processor-sharing servers with different service rates, energy consumption rates, and buffer sizes. Our aim is to optimize the energy efficiency of such a server farm by effectively controlling carried load on networked servers. To this end, we propose a job assignment policy, called Most energy-efficient available server first Accounting for Idle Power (MAIP), which is both scalable and near optimal. MAIP focuses on reducing the productive power used to support the processing service rate. Using the framework of semi-Markov decision process, we show that, with exponentially distributed job sizes, MAIP is equivalent to the well-known Whittle's index policy. This equivalence and the methodology of Weber and Weiss enable us to prove that, in server farms where a loss of jobs happens if and only if all buffers are full, MAIP is asymptotically optimal, as the number of servers tends to infinity under certain conditions associated with the large number of servers, as we have in a real server farm. Through extensive numerical simulations, we demonstrate the effectiveness of MAIP and its robustness to different job-size distributions, and observe that significant improvement in energy efficiency can be achieved by utilizing the knowledge of energy consumption rate of idle servers.

Channel borrowing has been widely adopted in mobile cellular networks as a compromise between fixed channel assignment and dynamic channel assignment. Various channel borrowing schemes have been proposed in the literature with their performance, typically in terms of blocking probability, obtained by computer simulations. However, analytical approximation methods are much more computationally effective and practically useful for performance analysis of mobile cellular networks. In this paper, we model mobile cellular networks with channel borrowing as overflow loss systems. We present two methods for approximation of blocking probabilities in such systems, one based on the classical Erlang fixed point approximation (EFPA) approach, and the other derived from the recently established information exchange surrogate approximation (IESA) framework. Comprehensive results of both EFPA and IESA are provided and compared against simulation results. We demonstrate for a wide range of scenarios that IESA is more accurate in evaluating blocking probabilities than EFPA. To the best of our knowledge, our proposed approximation framework is the first workable approach in terms of accuracy and computational efficiency for cellular networks with consideration to mobility of calls and a channel borrowing mechanism.

Overflow loss systems have wide applications in telecommunications and multimedia systems. In this paper, we consider an overflow loss system consisting of a set of finite-buffer processor-sharing (PS) queues, and develop effective methods for evaluation of its blocking probability. For such a problem, an existing approximation of the blocking probability is based on decomposition of the system into independent PS queues. We provide a new approximation which instead performs decomposition on a surrogate model of the original system, and demonstrate via extensive numerical results that our new approximation is more accurate and robust than the existing approach. We also examine the sensitivity of the blocking probability to the service time distribution, and demonstrate that an exponential distribution is a good approximation for a wide range of service time distributions.

Energy efficiency of server farms is an important design consideration of data centers. One effective approach is to optimize energy consumption by controlling carried load on the networked servers. In this paper, we propose a robust heuristic policy for job assignment in a server farm, aiming to improve the energy efficiency by maximizing the ratio of the long-run average throughput to the expected energy consumption. Our model of the server farm considers parallel processor-sharing queues with finite buffer sizes, heterogeneous server speeds, and an arbitrary energy consumption function. We devise the new energy-efficient (EE) policy in a way that the state distribution of the system depends on the service requirement distribution only through the mean. We show that the state-of-the-art slowest server first (SSF) policy can be obtained as a special case of EE and both policies have the same computational complexity. We provide a rigorous analysis of EE and derive conditions under which EE is guaranteed to outperform SSF in terms of energy efficiency. Extensive numerical results are presented and demonstrate that, in comparison with SSF, EE yields a consistently better system throughput and yet improves the energy efficiency by up to 70%.

Energy efficiency of server farms is an important design consideration of the green datacenter initiative. One effective approach is to optimize power consumption of server farms by controlling the carried load on the networked servers. In this paper, we propose a robust heuristic policy called E* for stochastic job assignment in a server farm, aiming to improve the energy efficiency by maximizing the ratio of job throughput to power consumption. Our model of the server farm considers a parallel system of finite-buffer processor-sharing queues with heterogeneous server speeds and energy consumption rates. We devise E* as an insensitive policy so that the stationary distribution of the number of jobs in the system depends on the job size distribution only through its mean. We provide a rigorous analysis of E* and compare it with a baseline approach, known as most energy-efficient server first (MEESF), that greedily chooses the most energy-efficient servers for job assignment. We show that E* has always a higher job throughput than that of MEESF, and derive realistic conditions under which E* is guaranteed to outperform MEESF in energy efficiency. Extensive numerical results are presented and demonstrate that E* can improve the energy efficiency by up to 100%.

We study the problem of stochastic job assignment in a server farm comprising multiple processor-sharing servers with various speeds and finite buffer sizes. We consider two types of assignment policies: without jockeying, where an arriving job is assigned only once to an available server, and with jockeying, where a job may be reassigned at any time. We also require that the underlying Markov process under each policy is insensitive. Namely, the stationary distribution of the number of jobs in the system is independent of the job size distribution except for its mean. For the case without jockeying, we derive two insensitive heuristic policies: One aims at maximizing job throughput, and the other trades off job throughput for energy efficiency. For the case with jockeying, we formulate the optimal assignment problem as a semi-Markov decision process and derive optimal policies with respect to various optimization criteria. We further derive two simple insensitive heuristic policies with jockeying: One maximizes job throughput, and the other aims at maximizing energy efficiency. Numerical examples demonstrate that, under a wide range of system parameters, the latter policy performs very close to the optimal policy. Numerical examples also demonstrate energy/throughput tradeoffs for the various policies and, in the case with jockeying, they show a potential of substantial energy savings relative to a policy that optimizes throughput.

The six papers in this special issue are devoted to the topic of next generation spectrum efficient and elastic optical transport systems.

The emergence of Internet Protocol Television (IPTV) has brought potentials to revolutionize personal entertainment. Streaming TV content over the highly pervasive wireless networks allows easy access to personalized entertainment. Focusing on wireless home entertainment which is one of the main driving forces of IPTV development, we develop a Markovian framework that investigates several important issues related to network capacity and streaming quality in an IEEE 802.11e enabled wireless home network. The Markovian framework captures not only the IEEE 802.11e MAC protocol performance, but also the statistical characteristics of IPTV media streams. The inclusion of these two key descriptions allows our model to be practically used in wireless home network planning and design. To deal with the complexity in the model, we apply the efficient Matrix Geometric approach to obtain numerical results. We further perform simulations with real IPTV traffic to not only validate our analytical results, but also obtain further insight to the performance.

To compute link blocking rates in an optical burst/packet switched network, it has been shown that the Engset formula should be used with a reduced packet arrival rate to account for packet dumping. This reduction is realized by lengthening the off-time during which a source remains idle, which is referred to as the lengthened off-time approximation (LOA). It is known that if the blocking probability is small or the number of sources (input links) is large, the LOA is redundant. We show here that the LOA is also redundant for certain state independent approximations and prove that they are equivalent to the Engset formula if the blocking probability is evaluated as a function of utilization. We consider a new LOA whereby the interval by which the off-time is lengthened depends on the number of busy channels. We demonstrate that this new state-dependent LOA (sdLOA) yields higher accuracy irrespective of whether blocking probability is evaluated as a function of either utilization which is a system-dependent measure, or system independent measures such as the intended arrival rate.

We observe that an effect of ldquodisk resource sharingrdquo of multi-copy movie traffic has great impact on the blocking performance of a video-on-demand system. This observation leads us to establish a conjecture on how to balance the movie traffic load among ldquocombinationrdquo groups of disks to maximize the level of disk resource sharing. For a given file replication instance, the conjecture predicts in general an effective lower bound on the blocking performance of the system. It motivates the design of a numerical index that measures quantitatively the goodness of disk resource sharing on allocation of multi-copy movie files. It also motivates the design of a greedy file allocation method that decides a good quality heuristic solution for each feasible file replication instance. We further develop analytical formulas to obtain approximate results for the bound fast and accurately. These techniques can be utilized by an optimization program to find near-optimal file assignment solutions for the system computationally efficiently.

We present a genetic algorithm for tackling a file assignment problem for a large-scale video-on-demand system. The file assignment problem is to find the optimal replication and allocation of movie files to disks so that the request blocking probability is minimized subject to capacity constraints. We adopt a divide-and-conquer strategy, where the entire solution space of file assignments is divided into subspaces. Each subspace is an exclusive set of solutions sharing a common file replication instance. This allows us to utilize a greedy file allocation method for finding a good-quality heuristic solution within each subspace. We further design two performance indices to measure the quality of the heuristic solution on 1.) its assignment of multicopy movies and 2.) its assignment of single-copy movies. We demonstrate that these techniques, together with ad hoc population handling methods, enable genetic algorithms to operate in a significantly reduced search space and achieve good-quality file assignments in a computationally efficient way.

The designers of a large scale video-on-demand system face an optimization problem of deciding how to assign movies to multiple disks (servers) such that the request blocking probability is minimized subject to capacity constraints. To solve this problem, it is essential to develop scalable and accurate analytical means to evaluate the blocking performance of the system for a given file assignment. The performance analysis is made more complicated by the fact that the request blocking probability depends also on how disks are selected to serve user requests for multicopy movies. In this paper, we analyze several efficient resource selection schemes. Numerical results demonstrate that our analysis is scalable and sufficiently accurate to support the task of file assignment optimization in such a system.

In this paper, we consider the problem of estimating steady-state blocking probability for circuit-switched networks with alternate routing. In particular, we focus on networks protected by trunk reservation. A new blocking probability estimate, the so-called overflow priority classification approximation (OPCA), is considered as an alternative to the currently used the Erlang fixed-point approximation (EFPA). OPCA is applied to the case of arbitrary circuit-switched networks with trunk reservation. We show through empirical results that OPCA yields a superior estimate for blocking probability compared to EFPA for circuit-switched networks with alternate routing that are reasonably protected against instability by trunk reservation.

This paper studies the blocking performance of optical burst switching (OBS) networks using a sequential office control (SOC) state-independent deflection routing policy. We show that unprotected deflection routing may destabilize OBS resulting in higher blocking probabilities than if bursts were not deflected but simply blocked. This study was motivated by the well-known destabilizing effect that alternative routing has on circuit switching in classical telephony networks. We propose two forms of protection to guard against destabilization: 1) wavelength reservation, which is analogous to trunk reservation in circuit switching; and, 2) preemptive priority, which is a new form of protection where bursts that have not been deflected are given preemptive priority over bursts that have been deflected. Our main contribution is a one-moment reduced-load approximation to evaluate the blocking performance of OBS networks using deflection routing protected by either wavelength reservation or preemptive priority. Our reduced-load approximation relies on the usual assumptions of link independence and Poisson distributed link arrivals. We quantify the error admitted in making these two assumptions via simulation. Using our reduced-load approximation, we evaluate the blocking performance of protected and unprotected deflection routing in several randomly generated networks. The chief conclusion of our study is that deflection routing in OBS should be given some form of protection to avoid destabilization resulting from upward load variations, and in terms of blocking performance, preemptive priority is the best form of protection for OBS. We use simulation to verify that our conclusions remain valid for a realistic traffic scenario.

We consider a simple model for distributed video on demand (VoD) systems. The model is analyzed by the Erlang fixed point (EFP) approximation and by a new method referred to as overflow priority classification (OPC). The OPC method imposes a preemptive priority regime in a non-priority system to capture the traffic load dependence among VoD servers. Comparison between OPC and EFP reveals that OPC is more accurate. The comparison is made with respect to two quality of service (QoS) measures, the loss probability and the probability that a video request is not served by the preferred VoD server given that it is not lost.

Little and Venkatesh conjectured that, for an interactive VoD system with a single random trial resource selection scheme, the blocking probability of a user's request is minimized when the overall movie traffic load is spread uniformly on each disk in the system. In this paper, we generalize this conjecture to the situation where there can be repeated random trials or where a least busy fit resource selection scheme is used. We support our conjecture with a simulation of a realistic system, and propose a metric following the idea of our conjecture to assess the goodness of movie assignment in the system.

Limited wavelength conversion and deflection routing are considered as viable methods to reduce the burst blocking probability in optical burst switched (OBS) networks. The question of which of the two methods is most effective has yet to be resolved. In this paper, an analytical model is derived that provides an accurate and quick means to evaluate and compare the effectiveness of limited wavelength conversion and deflection routing. First, an overflow load fixed point approximation model is derived for a single link in isolation. Then the single link model is integrated into a reduced load fixed point approximation model for a general network topology with deflection routing. The accuracy of the analytical model is verified through simulation.

Methods to resolve wavelength contention are needed to improve the performance of optical burst switched (OBS) networks. Network simulations and Markovian queuing models for nodes in isolation have suggested that deflection routing (alternate routing) may be a viable method to resolve wavelength contention. However, we show that deflection routing may destabilise OBS networks operating at high loads. To prevent the destabilising effect of deflection routing, we propose and analyse a technique called wavelength reservation to intentionally limit the amount of deflection at high loads. Wavelength reservation is analogous to trunk reservation in circuit switched networks. This paper is the first to present a new reduced load Erlang fixed point analysis of OBS networks with deflection routing and wavelength reservation. We apply the new analysis to evaluate the benefit of deploying deflection routing and wavelength reservation in a sample OBS network.

We consider a video-on-demand system in which multiple copies of each movie file are kept in separate disks. Various schemes for file allocation and retrieval are considered and compared. We have introduced a certain easy-to-implement scheme that requires 15% more storage than another certain harder-to-implement scheme.

Resource-demanding services such as multimedia-on-demand (MOD) become possible as broadband Internet is getting more popular. However, as the size of multimedia files grows rapidly, storage of such large files becomes a problem. Since multimedia contents will generally become less popular with time, it is desirable to design a prediction algorithm so that the multimedia content can be unloaded from the server when it becomes unpopular. In this paper, we have two objectives: 1) analyse the MOD viewing trend in order to understand the viewing behaviour of users; 2) predict the viewing trend based on the knowledge obtained from the trend analysis. For trend analysis, we study three traditional regression models, including linear regression, exponential regression, and power regression, and propose two additive regression models, exponential-exponential-sum (EES) and exponential-power-sum (EPS), to improve the goodness of fit. Then, the most fitted models are used in trend prediction. Four prediction approaches, fixed regression selecting (FRS), continuous regression updating (CRU), historical updating (HU), and continuous regression with historical updating (CRHU) are proposed. From the numerical results, we find that CRHU, which is constructed by considering historical trends and new incoming viewing request data, is in general the best method in forecasting MOD trend.

As broadband networks keep increasing in transmission speed, multimedia-on-demand (MOD) is expected to be one of the most popular broadband services in the near future. MOD systems can provide two types of services: interactive service and batch service. The interactive service provides each user with one dedicated stream. With it, the user can use the VCR function controls such as pause, forward and rewind. However, the service is expensive and not scalable to the system size. With the batch service, the users who request the same multimedia content within a batch period will be grouped together and served by one dedicated stream. Hence, the system can either use fewer resources or serve more customers. However, batch users must wait for programs to begin and are unable to perform VCR function control. We take advantage of both services by considering an MOD system that provides both services. Also, we propose two methods to optimize their coexistence. In addition, an analytical model is derived for performance evaluation. Numerical results show that the proposed hybrid system really outperforms both individual systems. The hybrid system provides a superior solution to both the service providers and the users. Specifically, given a limited storage resource, the service provider receives more revenue by serving more users concurrently, while the users have a satisfactory viewing experience due to higher availability of programs and the low average waiting time.

A video-on-demand (VoD) system allows a viewer to choose a video of his choice such as movies, electronic encyclopedia, or educational videos, which is delivered to him real time via a network. This paper is a performance study of a distributed video server system in a fully connected backbone network. Under the assumptions of uniform loading and symmetrical network, analytical models are proposed for two server selection strategies (random and least loaded) and for two reservation schemes (the strict reservation and the residual reservation). The models can be used to determine the call blocking probability and the requirements of network bandwidth given the capacity of video servers. In addition, our models provide a useful tool for VoD operators to design and dimension their systems.

Dynamic routing has been adopted in circuit-switched networks in many parts of the world. Most of the routing algorithms used are least loaded routing (LLR) based for its simplicity and efficiency. Rerouting is the practice of routing calls on alternate paths back to direct paths or to other less congested alternate paths. It allows the continuous redistribution of network loads for the relief of the congestion on direct paths. In this paper, we present an original analysis of an LLR-based rerouting scheme. Through numerical examples and confirmation by computer simulation, the throughput gain of rerouting is established.

Fair packet discarding (FPD) is a mechanism which provides incentives to users for participation in congestion control, such that ATM networks can operate in a more efficient manner. In this paper, we propose a simple congestion control framework consisting of FPD and ATM Forum's explicit-rate flow control scheme. By simulations, we show that this framework enables the packet-level throughput of each connection converging to the max-min fair apportionment in a distributed manner. With the built-in policing capability of FPD, this framework also eliminates the need of usage parameter control for ABR service.

Dynamic routing has been adopted in many circuit switched networks in many parts of the world. A number of dynamic routing schemes have been designed and studied with the aim of maximizing the network throughput. The least loaded routing (LLR) is simple and efficient, while other more elaborate routing schemes can only provide marginal throughput gain over that of LLR. Re-routing is the practice of routing calls on alternate paths to direct paths or other less congested alternate paths. It allows the continuous redistribution of network loads so that the congestion on direct paths can be relieved. We study a re-routing scheme based on LLR. An original analysis of re-routing is performed and numerical examples confirm the significant throughput gain over LLR routing.

The asynchronous transfer mode (ATM) Forum has chosen rate-based control as the flow control scheme for the available bit-rate (ABR) service. However, rate-based schemes can achieve congestion control only if all users act in a cooperative manner. Even a limited number of uncooperative users can cause congestion collapse. We propose a mechanism called fair packet discarding to provide incentives to users to participate in network congestion control so that the network can operate in a more efficient manner.

The implementation of optical packet-switched networks requires that the problems of resource contention, signalling and local and global synchronization be resolved. A possible optical solution to resource contention is based on the use of switching matrices suitably connected with optical delay lines. Signalling could be dealt with using subcarrier multiplexing of packet headers. Synchronization could take advantage of clock tone multiplexing techniques, digital processing for ultra-fast clock recovery, and new distributed techniques for global packet-slot alignment. To explore the practical feasibility and effectiveness of these key techniques, a consortium was formed among the University of Massachusetts, Stanford University, and GTE Laboratories. The consortium, funded by ARPA, has three main goals: investigating networking issues involved in optical contention resolution (University of Massachusetts), constructing an experimental contention-resolution optical (CRO) device (GTE Laboratories), and building a packet-switched optical network prototype employing a CRO and novel signaling/synchronization techniques (Stanford University). This paper describes the details of the project and provides an overview of the main results obtained so far.

An interactive video-on-demand (VoD) system allows users to access video services, such as movies, electronic encyclopedia, interactive games, and educational videos from video servers on a broadband network. This paper develops a performance evaluation tool for the system design. In particular, a user activity model is developed to describe the usage of system resources, i.e., network bandwidth and video server usage, by a user as it interacts with the service. In addition, we allow batching of user requests, and the effect of such batching is captured in a batching model. Our proposed queueing model integrates both the user activity and the batching model. This model can be used to determine the requirements of network bandwidth and video server and, hence, the trade-off in communication and storage costs for different system resource configurations.

This protocol is fully distributed and no onboard processing is required for the satellite. A control parameter f is used to adaptively control the packet transmission rate such that maximum system capacity can be attained and the average delay is always minimized for a given throughput. The controlled protocol is found to give a smaller average delay than slotted ALOHA even when the throughput is as low as 0.05. On the other hand, under heavy traffic conditions, it can provide a throughput close to unity and an average delay not much more than one round-trip propagation delay. The system performance is also robust, in the sense that a 15% error in throughput estimation results in no more than a 3% increase of the overall average packet delay.>

An analysis is made of an alternate-path routing rule called maximum free circuit routing (MFCR). In the use of MFCR, a call is routed to the alternate path that has the maximum number of free circuits when the direct path is blocked. Analytical results show that in conjunction with trunk reservation, this routing rule can offer a stable throughput at high traffic conditions and can increase the call carrying capacity by about 20% (compared to direct path routing) under a blocking requirement of 10/sup -2/ on a fully connected symmetrical nonhierarchical network.>

An improvement of the announced retransmission random access (ARRA) protocol for packet satellite communication, called the scheduled retransmission multiaccess protocol, is introduced. Besides avoiding collision between new and retransmitted packets, the improved protocol eliminates reservation conflicts between different slots. Explicit acknowledgement is used, so that traffic to other zones served by the same satellite can also be accommodated. Assuming that 3% of the channel capacity is used for retransmission reservation, fixed frame and dynamic-frame SRMAs achieve effective maximum throughputs of 0.65 and 0.89, respectively. Both protocols give average delays considerably lower than slotted ALOHA, even when the throughput is as low as 0.2.>