The objective of congestion control is to maximize network bandwidth utilization and minimize the average flow completion time in data center networks. Key performance indicators required to achieve this objective are high throughput and low packet latency. Existing approaches have proposed various methods to control packet delivery based on various network events or parameters such as packet loss, bottleneck bandwidth, RTT, and queue length. However, these methods often result in suboptimal packet transmission states that compromise throughput or latency. We provide a method to overcome this drawback. To this end, we introduce a congestion control method called PRTT (Predicted RTT) that leverages predicted RTT for congestion control. By using accurately predicted RTT values, PRTT dynamically adjusts the packet delivery rate to control the number of in-flight packets. This enables the transmission to approach states where the buffer holds only a few packets while fully utilizing the link bandwidth. Experimental results show that PRTT achieves higher throughput, lower latency, and shorter flow completion times than other state-of-the-art methods, particularly under bursty traffic. These results demonstrate that PRTT offers a promising solution for congestion control.

This paper addresses network slicing in a large-scale Multi-Access Edge Computing (MEC)-enabled Radio Access Network (RAN) comprising heterogeneous edge nodes with varying computing and storage resource capacities. These resources are dynamically allocated to slice requests and released when the service of a slice request is completed. Our objective is to optimize the resource allocation for each admitted arriving slice request, considering its demands for computing and storage resources, to maximize the long-run average Earning Before Interest and Taxes (EBIT) of the MEC slicing system. We formulate the optimization problem as a Restless Multi-Armed Bandit (RMAB)-based resource allocation problem with a nonlinear cost rate function. To solve this, we introduce a new policy called Prioritizing-the-Future-Approximated earning per request (PFA) where for each admitted slice request, we always prioritize the allocation of the resource combination that gives the highest achievable earning, considering the future effects of this allocation. PFA is designed to be scalable and applicable to large-scale networks. We numerically demonstrate the superior performance of PFA in maximizing long-run average EBIT through simulations, comparing it with two baseline policies, at various cases of parameter values. Moreover, our findings offer insights for network operators in resource allocation policy selection.

Virtual Network Embedding (VNE) is an important problem in network virtualization, involving the optimal allocation of resources from substrate networks to service requests in the form of Virtual Networks (VNs). This paper addresses a specific VNE problem in the context of Composable/Disaggregated Data Center (DDC) networks, characterized by the decoupling and reassembly of different resources into resource pools. Existing research on the VNE problem within Data Center (DC) networks primarily focuses on the Server-based DC (SDC) architecture. In the VNE problem within SDCs, a virtual node is typically mapped to a single server to fulfill its requirements for various resources. However, in the case of DDCs, a virtual node needs to be mapped to different resource nodes for different resources. We aim to design an optimization method to achieve the most efficient VNE within DDCs. To this end, we provide an embedding scheme that acts on each arriving VN request to embed the VN with minimized power consumption. Through this scheme, we demonstrate that we also achieve a high long-term acceptance ratio. We provide Mixed Integer Linear Programming (MILP) and scalable greedy algorithms to implement this scheme. We validate the efficiency of our greedy algorithms by comparing their performance against the MILP for small problems and demonstrate their superiority over baseline algorithms through comprehensive evaluations using both synthetic simulations and real-world Google cluster traces.

Music, a traditional element in human entertainment, has been extensively studied. We propose a novel method for music generation from human dance motions. We employ Residual Vector Quantization (RVQ) for music feature tokenization, using RVQ indices as the music representation, which reduces the learning complexity. A cross-modal generation model integrating LSTM and attention mechanisms is designed to generate RVQ codebook indices from dance motions. Finally, the indices can be converted back to music waveforms through a music decoder. Experimental results demonstrate the feasibility of generating coherent music that aligns with dance dynamics, providing a new approach for cross-modal entertainment content creation. ©2025 IEEE.

The paper addresses the challenge of scheduling multiple output priority queues of a switch in a real-world setting characterized by bursty traffic and diverse traffic priorities. Existing queue scheduling methods primarily employ two types of strategies: priority-based scheduling and weight-based scheduling. However, there is a lack of scheduling methods that can simultaneously handle bursty traffic in a timely manner and maintain priority-based scheduling. This paper addresses this issue by formulating the problem as a restless multi-armed bandit problem, and a queue scheduling method is proposed to balance priority service provisioning and bursty traffic processing. The proposed queue scheduling method operates efficiently in a timely manner based on instant queue length and utilizes the Whittle index method to achieve an asymptotically optimal solution. This design facilitates packet forwarding by considering the instant states of queues, offering improvements over existing methods. Experimental results demonstrate that the proposed method achieves a more efficient balance between priority service provisioning and bursty traffic processing compared to other state-of-the-art methods. Additionally, the proposed method results in better balanced network performance metrics, such as queue length, packet delay, and packet loss, thus efficiently supporting various applications that generate bursty traffic randomly.

Submarine optical fiber communication cables (subsequently referred to as submarine cables) form the backbone of the Internet’s infrastructure. Damage to these cables can precipitate Internet outages with far-reaching socio-economic impacts. The prevailing practice of manual cable routing is laborious, considering the thousands of kilometers these cables span. It also fails to strike an optimal balance between cost and risk due to its lack of scalability and precision. The Fast Marching Method (FMM), a non-iterative, precise numerical approach capable of solving the Eikonal equation, offers a viable alternative by optimizing the required path between source and destination, considering a summary objective function of costs and risk factors. An interpretation of its solution signifies the optimum value of the objective function between a starting point and all other points. However, the sequential nature of the FMM algorithm suffers from computational limitations and impedes direct parallelization. In this study, we introduce an Adaptive Parallel FMM (APFMM), an innovative approach utilizing adaptive domain decomposition and dynamic multi-resolution analysis. This scalable and widely applicable method can overcome the limitations of existing methods and achieve planning of high-precision, ultra-long-distance (over 14,000 km) submarine cable routes over the Earth’s surface. Simulated experiment results corroborate that APFMM effectively overcomes the computational challenges posed by the sequential FMM when dealing with large datasets. Additionally, it reduces the running time by more than 81% compared to the traditional parallel FMM. This marks a substantial advancement in facilitating efficient, automated, high-precision planning for long-distance submarine cable paths. Note to Practitioners—This paper introduces APFMM, a novel technique based on adaptive domain decomposition and multi-resolution analysis, facilitating high-precision, ultra-long-distance (over 14,000 km) submarine cable path planning. Results from simulated experiments show that APFMM not only overcomes the computational constraints associated with the sequential FMM for large datasets but also cuts the running time by more than 81% relative to the conventional parallel FMM. This breakthrough improvement holds significant implications for practitioners in submarine cable design and construction. With the use of APFMM, designers can plan and optimize cable paths more efficiently, thereby lowering cabling costs and enhancing network resilience. Furthermore, the application of APFMM is not limited to submarine cable path planning and can be employed in other domains involving large-scale data processing and complex path planning, such as electricity cables, gas pipelines, and transportation route planning. While our focus in this paper is primarily on submarine cable path planning, we anticipate practitioners extending the application of APFMM to other use cases, realizing broader utility and benefits.

Composable or disaggregated architectures have emerged as a solution to address the drawbacks of server-based architectures in data centers, such as resource inefficiency and limited scalability. This article focuses on the workload consolidation problem where we aim to consolidate workloads that spread over many underutilized (resource) nodes onto fewer ones, with the two objectives of minimizing the number of active nodes and workload migrations, thereby enhancing energy efficiency and resource utilization. To address this problem, we propose a Q-learning-based reinforcement learning method that yields an approximate Pareto front, providing a set of (approximate) optimal solutions catering to different preferences for the two objectives. The performance of the proposed method is validated by comparing it to integer linear programming, simulated annealing, first fit, and first fit decreasing algorithms.

This paper introduces two new algorithms for fault-tolerant design of multi-layered networks, both of which extend the previously published multi-layered market algorithm (MMA), by including provision of additional resources to be used during network failure events. The new algorithms are called resilient MMA (RMMA) and failure-traffic MMA (FTMMA). RMMA runs MMA iteratively and independently for each failure scenario. FTMMA treats each failure event as a type of traffic, which enables more efficient sharing of network resources. Both RMMA and FTMMA consider a range of single physical link failures and aim to maximize earnings before interest and tax (EBIT). The costs considered in the EBIT evaluation include amortized capital and operational expenditures and penalties (compensation to the customers when the service is degraded). They both focus on optimizing resource provisioning, in particular, capacity assignment, for fault-tolerant and cost-effective design of multi-layered networks. The novel aspects of RMMA and FTMMA include the incorporation of variable bit rate traffic streams in fault-tolerant multilayered network design, together with the aim to maximize EBIT. RMMA and FTMMA are validated by comparing designs with those produced by an integer linear programming benchmark for small-size networks. Numerical results show that FTMMA can more efficiently allocate capacity for failures by sharing these resources across different failure events.

Generating choreography from music poses a significant challenge. Conventional dance generation methods are limited by only being able to match specific dance movements to music with corresponding rhythms, restricting the utilization of existing dance sequences. To address this limitation, we propose a method that generates a label, based on a probability distribution function derived from music features, that can be applied to music segments of varying lengths. By using the Kullback-Leibler divergence, we assess the similarity between music segments based on these labels. To ensure adaptability to different musical rhythms, we employ a cubic spline method to represent dance movements. This approach allows us to control the speed of a dance sequence by resampling it, enabling adaptation to varying rhythms based on the tempo of newly input music. To evaluate the effectiveness of our method, we compared the dances generated by our approach with those generated by other neural network-based and conventional methods. Quantitative evaluations demonstrated that our method outperforms these alternatives in terms of dance quality and fidelity.

The Internet of Things (IoT) has emerged with Distributed Ledger Technology (DLT) to address existing scalability challenges and improve the trustworthiness of machine-to-machine communication. Among the numerous potential benefits of combining IoT and DLT, Blockchain, a subset of DLT, is a crucial enabler to accelerate secure IoT adoption. Appending a new block to a blockchain, especially in a blockchain-based IoT ecosystem, requires more delay than expected. This delay is one of several issues limiting the broader adoption of blockchain within the IoT domain. To assess this delay, we develop a new comprehensive model to estimate the time required to generate a new block in a blockchain-enabled IoT system. To this end, we develop sub-computation models and compare time consumption associated with the block generation process by conducting an extensive analysis of the following selected IoT layers: device layer, cluster head layer, fog/edge layer, and cloud layer. Our study identifies potential time-consuming steps in adding a new block to a network. Our results demonstrate that the type of blockchain framework and data encryption algorithms could affect the block generation time and that Avalanche, Conflux, Algorand, Polkadot Hyperledger Fabric outperforms Ethereum in terms of block generation time in IoT networks. On the other hand, the blockchain framework does not play a significant role in block generation time for smaller data packets. We also observed the benefit of using 256-bit ECC (elliptic curve cryptography) encryption and the fog layer in IoT networks to enhance the scalability of the block generation process. All in all, our results indicate that the total block generation time varies depending on the selected IoT framework, data encryption algorithm, blockchain type, and key functions of the layers. However, we found that time delays associated with queuing or block size are negligible relative to the other key components of block generation time.

Federated Learning (FL) is a distributed machine learning method that ensures the privacy and security of participants’ data by avoiding direct data upload to a central node for training. However, the traditional FL typically applies a star structure with cloud servers as the central aggregator for the model parameters from different terminals, leading to problems such as central failure, malicious tampering and malicious participants, resulting in training errors or system crashes. To address these issues, a permissioned blockchain is used to build a secure and reliable data-sharing platform among participating terminals, replacing the central aggregator in the traditional FL called blockchain-based federated learning. However, the block generation method of the blockchain system may introduce significant latency in the federated learning where distributed model parameters upload randomly, resulting in low efficiency of the federated learning. To overcome this, we propose a block generation strategy that groups terminals and generates a block for each group, which minimizes the latency of a single round of federated learning, and an optimal block generation algorithm that considers data distribution, terminal resources, and network resources is provided. The analysis shows that the proposed algorithm can effectively obtain the optimal solution of block generation to minimize the authentication time, and we conduct extensive experiments that demonstrate the time efficiency of the proposed algorithm.

We focus on energy-efficient offloading strategies in a slicing-enabled large-scale edge network, or an "edge slicing" system, with different computing/storage components, on which the service capacities are dynamically released and reused by the incoming user requests. The offloading problem is challenged by its large problem size and the heterogeneity of the service components and user requests, leading to the high-dimensional state space of the underlying stochastic process. We formulate the problem in the manner of the restless-bandit-based (RB-based) resource allocation problem and generalize unrealistic previously made assumptions on specific forms of the power functions, such as linearity, convexity, and taking only binary power states. We adapt the RB-based resource allocation technique to the offloading problem. We quantify actions of selecting certain service components to serve requests through marginal rewards, which take consideration of both the history and future effects of the corresponding action. The marginal rewards exist in closed forms when assuming linear power functions, but it remains an open question in the non-linear case. We approximate the marginal rewards by introducing state-dependent coefficients that compensate for the undesirable effects of non-linearity. We propose a scheduling policy that always prioritizes the service components with the highest marginal rewards, which is simple and applicable in the large-scale case. In the special case with linear power functions, the policy becomes asymptotically optimal - it approaches optimality when the number of components tends to infinity. We numerically demonstrate the effectiveness and robustness of the proposed policy in practical situations with respect to energy efficiency.

By overcoming the “server box” barrier, resource disaggregation in data centers (DCs) can significantly improve resource utilization. This may then provide a more cost-efficient approach for resource upgrade and expansion. The advantages of resource disaggregation have been explored in earlier research to improve the efficiency of resource usage. This paper investigates the potential benefits of resource disaggregation from the aspect of reliability, which has not been considered before. Resource disaggregation gives rise to a new failure pattern. For example, in a conventional server, the failure of one type of resource leads to the failure of the entire server, so that other types of resources in the same server also become unavailable. After disaggregating, the failure of different types of resources becomes more isolated so that other resources are still available. In this paper, we model the reliability of a resource allocation request in a server-based or disaggregated DC based on whether the request is allocated with only working resources or is also provisioned with backup resources. We then consider a resource allocation problem to maximize the number of requests accepted with guaranteed reliability. This is formulated as an integer linear programming (ILP) problem, and a more straightforward heuristic approach is also proposed. Our numerical studies demonstrate that it may be possible to significantly improve service reliability with this resource disaggregation approach.

Network function virtualization (NFV) achieves cost-effective network service provisioning through exploitation of virtualization and automation by decoupling network functions (software) from dedicated hardware. The software of the various devices can then be hosted by low-cost general computation devices rather than by more expensive dedicated devices. To obtain a specific network service, the traffic flow is steered to go through a specific order of network functions that are hosted by cloud computing, and this network function sequence is known as a service function chaining (SFC). To allocate computation resources for network functions and bandwidth resources between network functions in a physical network is the SFC embedding problem. In this article, we consider the SFC embedding problem in multi-domain networks, where no domain information, like domain topology and network resource, is disclosed among domains. We propose a new optimization algorithm based on column generation method to solve this problem, which is distributedly computed in each domain. To further improve the scalability, we also provide two heuristic algorithms. We selected two networks one large (158 nodes) and one small (18 nodes) to numerically validate the proposed algorithms and demonstrate that the acceptance ratio obtained by the heuristic algorithms is close (within 5.6 percent) to that of the optimal algorithm.

Hardware disaggregation decouples resources (e.g., processors and memory) from monolithic servers, potentially improving service reliability. However, from another perspective, directly exposing resource modules to a shared network may adversely affect service reliability. In this article, we study a reliable resource allocation problem in disaggregated data centers, considering network impact and different disaggregation scales. We provide a mixed-integer linear programming formulation and a resource allocation framework named Radar for this problem. The numerical results demonstrate that the benefits of hardware disaggregation may be adversely affected by an imperfect network. It also shows that both the hardware backup and a proposed migration-based restoration can be applied to overcome this potential adverse effect.

Network function virtualization (NFV) is a new networking paradigm based on decoupling network functions from dedicated hardware, so these network functions can be run as pieces of software on general-purpose computation servers, which are called virtual network functions. In addition to guarantee the service qualities provided by NFV networks comparable to those provided by traditional telecommunication networks, energy consumption becomes one of the challenges faced by NFV. This is due to a large number of general computation servers that consume a significant amount of energy. We address here the problem of how to provide an energy-aware service function chaining (SFC) embedding in NFV networks with a hierarchical resource allocation, where an SFC has a set of virtual network functions to be executed in a specific sequential order providing a specific network service. Assuming a dynamic traffic scenario, we introduce for this new problem an integer linear programming (ILP) and three polynomial heuristic algorithms for resource allocation. All three heuristic algorithms achieve energy savings by shutting down idle devices and balance the tradeoff between energy cost and SFC request acceptance ratio. Numerical results demonstrate the quality of the proposed heuristic algorithms in terms of acceptance ratio by comparing them with the ILP method and a method extended from an exiting algorithm despite the fact that they save energy.

Edge computing is an indispensable technology that overcomes delay limitations of cloud computing. In edge computing, computational resources are deployed at the network edge, and computational tasks and data of end terminals can be efficiently processed by edge nodes. Considering the computational resource limitations of edge nodes, collaborative edge computing integrates computational resources of edge nodes and provides more efficient computing services for end terminals. This article considers a computation offloading problem in collaborative edge computing networks, where computation offloading and resource allocation are optimized by means of a collaborative load shedding approach: a terminal can offload a computing task to an edge node, which either can process the task with its computing resource or further offload the task to other edge nodes. Long-term objectives and long-term constraints are considered, and Lyapunov optimization is applied to convert the original nonconvex computation offloading problem into a second problem that approximate the original problem and it is still nonconvex but has a special structure, which gives rise to a new distributed algorithm that optimally solves the second problem. Finally, the performance and provable bound of the distributed algorithm is theoretically analyzed. Numerical results demonstrate that the distributed algorithm can achieve a guaranteed long-term performance, and also demonstrate the improvement in performance achieved over the case of computation offloading without collaborating edge nodes.

This paper optimizes path planning for a trunk-and-branch topology network in an irregular 2-dimensional manifold embedded in 3-dimensional Euclidean space with application to submarine cable network planning. We go beyond our earlier focus on the weighted costs of cables (cable laying cost, resilience, design level and repair rate) to include the cost of branching units (BUs), including material and labor, as well as submarine cable landing stations (CLSs). This optimization also includes choices of locations of BUs and CLSs. These are important issues for the economics of cable laying and significantly change the model and the optimization process. We pose the problem as a variant of the Steiner tree problem, but one in which the Steiner nodes can vary in number, while incurring a penalty. We refer to it as the weighted Steiner node problem. It differs from the Euclidean Steiner tree problem, where Steiner points are forced to have degree three; this is no longer the case, in general, when nodes incur a cost. We are able to prove that our algorithm is applicable to Steiner nodes with degree greater than three, enabling optimization of network costs in this context. The optimal solution is achieved in polynomial-time using dynamic programming.

The improved efficiency achieved by virtualizing data centers (DCs) has been well established. In this paper, we propose a mixed Virtual Data Center (VDC) capable of supporting both unicast and multicast services. We provide a new method to realize the embedding of these VDCs. We also provide a Mixed Integer Linear Programming (MILP) formulation and a scalable heuristic algorithm for efficiently embedding its demands. Numerical results show that mixed VDC embedding supporting both unicast and multicast services performs significantly better than existing embedding methods in terms of system cost, power consumption, link capacity utilization, and VDC acceptance ratio.

We study the path planning of submarine cable systems with trunk-and-branch tree topology on the surface of the earth. Existing work on path planning represents the earth's surface by triangulated manifolds and takes account of laying cost of the cable including material, labor, alternative protection levels, terrain slope and survivability of the cable. Survivability issues include the risk of future cable break associated with laying the cable through sensitive and risky areas, such as, in particular, earthquake-prone regions. The key novelty of this paper is an examination and solution of the path planning of submarine cable systems with trunk-and-branch tree topology. We formulate the problem as a Steiner minimal tree problem on irregular 2D manifolds in R3. For a given Steiner topology, we propose a polynomial time computational complexity numerical method based on the dynamic programming principle. If the topology is unknown, a branch and bound algorithm is adopted. Simulations are performed on real-world three-dimensional geographical data.

Edge computing is a promising technology that offers data analysis and computing for Internet of Things (IoT) services at the network edge. It has the potential to significantly reduce the latency and improve the reliability of IoT services by allowing computation workloads and local data generated by IoT devices to be offloaded to edge nodes. This paper aims to develop algorithms for efficient provision of both job assignment and resource allocation for edge computing networks. The main objective is to minimize the long-term average of the response time delay subject to constraints on computation resources and power consumption. We apply a drift-plus-penalty based Lyapunov optimization approach to convert the original problem into an upper bound optimization problem. We then relax the latter to a convex optimization problem. Finally, a distributed algorithm based on branch-and-bound approach is provided and the gap between the distributed algorithm solution and the optimal solution of the original problem is theoretically analyzed. Numerical results based on extensive experiments have demonstrated that our distributed algorithm can achieve the required performance of edge computing that supports IoT systems, under static traffic conditions as well as under dynamic environments with time-varying traffic.

Wireless network slicing is a promising technology for next-generation networks to provide tailored on-demand services to mobile users. We consider a scheduling policy for wireless network slicing with the aim to maximize the energy efficiency of the network defined as the ratio of long-run average throughput of user requests to the long- run average power consumption. This gives rise to a problem of extremely high computational complexity which prevents direct application of conventional optimization techniques. We propose a scalable priority-based policy, referred to as the Most Energy-Efficient Resource First (MEERF). MEERF is proved to be asymptotically optimal in the special case appropriate for a local wireless environment with highly dense user population and exponentially distributed service time requirement. The robustness of MEERF to different service time distributions is demonstrated by extensive simulations. We present numerically the effectiveness of MEERF %balancing the QoS and relevant power consumption by comparing it with benchmark policies in a more general network with potentially geographically distributed users and infrastructures. The results show that MEERF outperforms the benchmark policies in most of our experiments and achieves up to 52% improvement in terms of energy efficiency.

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.

This paper focuses on an important and fundamental problem of connecting two points by a cable, subject to a tradeoff between cost and earthquake survivability. In particular, we address the problem of selecting a route for laying a cable under arbitrary topography, based on earthquake data. First, we derive a semi-supervised probability density estimation model for the likelihood of earthquake disaster. Based on this probabilistic model, we generate a nearest neighbor graph. The graph represents each data point with a four-dimensional space formed by the three-dimensional undersea coordinates and the one-dimensional data of earthquake disaster level. It then forms the weight on graph between any positions. The data used in this study are all real data of undersea topography and earthquake information of the Taiwan Strait. As a result, both the undersea topology and the earthquake level can be transferred into a distance for shortest route finding. Finally, Dijkstra's algorithm is used for finding the optimal shortest route for cabling between the two given points on the graph. Extensive simulations based on a synthetic dataset and the Taiwan Strait real-world dataset corroborate the effectiveness of the proposed method.

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.

Whether it be a road, railway track, pipeline, or fiber-optic cable, achieving a low probability of “cable” damage is important for the proper operation of modern infrastructure. This paper considers the fundamental problem of how to connect two points with a cable that crosses an earthquake fault line. We first develop a model, under certain general assumptions, for the cable break probability. Then, we formulate a multiobjective optimization problem, with cable cost and probability of cable break being the two objectives. For two important and meaningful sets of cable shape alternatives, the Pareto front is determined for these two objectives. All the analytical results are verified by simulations.

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%.

In this paper, we consider an integrated service system, providing multirate guaranteed services and homogeneous best-effort services. The total system capacity is shared by both service types, while the guaranteed service customers are treated as high priority and are allocated fixed data rate bandwidth units. The best-effort service customers are supported by the remaining capacity leftover by the guaranteed services, in a processor-sharing manner. Admission control on the best-effort service customers is adopted to provide a certain level of quality of service (not guaranteed) to avoid the effect of repeated attempts. The best-effort customer blocking probability is an important metric for network dimensioning. In this integrated service system, we observe that the blocking probability of the best-effort customers is not insensitive to the shapes of their flow-size distributions and the guaranteed customers' holding-time distributions. We obtain here by light computation, for the best-effort customers, blocking probability bounds, and approximations, which possess the insensitivity property. Considering that the distributions for customers' holding times and flow sizes may be unknown, the proposed insensitive bounds and approximations will facilitate system design and network dimensioning with predictable and acceptable performance.

Today, video-on-demand (VoD) systems are challenged by a growing number of users, growing sizes of libraries, and increasing video streaming rates. Therefore, scalability and the bandwidth efficiency of VoD systems have become important considerations. In this paper, we propose a scalable and bandwidth efficient delivery scheme for VoD systems, which optimally exploits the storage and multicast capabilities to reduce the consumption of server capacity resources. Our proposed scheme, which we call prepopulation assisted batching with multicast patching (PAB-MP), facilitates video multicast from the server by strategic preplacement of initial segments of videos at the end-users' devices. Using an analytical approach, we show how the parameters for the PAB-MP scheme can be selected to achieve optimal performance. Moreover, we propose methods to replicate and place the initial video segments (IVSs) across the end-users' devices such that load arising from IVSs is evenly distributed among user-nodes. Using simulations, we show that our proposed scheme effectively reduces the load on the server especially under high load, while imposing less burden on individual user nodes. Simulation results indicate that our proposed PAB-MP scheme is significantly more scalable than the other popular approaches that exploit multicast and storage.

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.

For integrated service systems supporting real-time (RT) and non-real-time (NRT) traffic, evaluating the performance of the NRT traffic is often challenging. An approximation based on a fluid model - the so-called Fluid Approximation (FA) - can be used when RT traffic has much larger successful call arrival rate than the NRT traffic, while otherwise it may introduce errors. In this paper, we propose a Generalized Fluid Approximation (GFA), extending the use and improving the accuracy of FA. It can also be used to trade off the computational complexity and the accuracy. Benefits achieved by GFA are demonstrated by extensive numerical examples.

Net ML is a system which runs in a browser - with help from "the cloud'" which can be used to analyse and design networks. Versions of Net ML have existed for 10 years however the version now available is the first to include full graphical editing as well as a rich collection of network design and analysis tools. Net ML provides a way to navigate through views of one network, and through a network of networks. The software itself is networked and provides the means for network researchers to jointly develop networks and algorithms for research and educational purposes. The tools provided in Net ML include layered and single-layer network design, robust network design, availability analysis, and loss analysis. Networks can also be transformed by means of XSLT scripts.

This paper provides a new formula for dimensioning of a link fed by fractional Brownian input. This formula is obtained based on another new approximate result for the stationary workload distribution of a queue loaded by fractional Brownian input. An efficient approach to simulate such a queue is also presented. Agreement between the analytical and the simulation results has been demonstrated numerically.

This paper considers a two-tier femto-macro overlay network architecture, where groups of femtocells are overlaid by macrocells. The capacity of the femtocells is limited, thus redirection of blocked femto calls to the overlaid macrocell is used as a QoS assurance strategy. Similar overflow approaches have been used in telephony; however, extension of this approach to capacity-constrained femto-macro overlay networks poses significant challenges due to the wide variety of bandwidth requirement of Internet services, the large number of femtocells overlaid by a macrocell, and the need for fast computations imposed by practical requirements of dynamic real-time resource allocation. We propose a new computationally efficient numerical method for the loss probability approximation in such two-tier femto-macro overlay networks. The accuracy of the method is verified by exact Markov-chain solutions for small systems and by simulations for larger systems.

This letter establishes the similarity between the sensor scheduling problem and the handoff (i.e., base station assignment) problem in cellular networks. A mobile user behavior is then modelled by a Hidden Markov Model (HMM). The handoff problem is formulated as an optimization problem of base station scheduling that minimizes a cost function that involves the HMM state estimation error and base station measurement costs. The optimization problem can be solved using algorithms known as partially observed Markov decision processes.

This paper proposes a multicast traffic grooming scheme for efficient resource utilization in wavelength- division multiplexing (WDM) mesh networks. This Light-Tree Division -Adjacent Node Component based Grooming scheme (LTD-ANCG) is based on the idea of dividing a light-tree into smaller sub-light-trees. It improves the efficiency of resource utilization and lowers the optical- electronic-optical (OEO) conversion overhead. We use computer simulations to evaluate the performance of the scheme. Our simulations demonstrate that compared with existing algorithms, the new scheme significantly reduces the request blocking probability but can be implemented with very reasonable electronic processing.

The use of passive optical networks (PONs) enables access rates of multi-Gbit/sec bandwidth and provision of quality of service for high definition multimedia services. In this paper, we consider and analyse a generic multi-priority dynamic bandwidth allocation (DBA) algorithm for TDM PONs serving multimedia traffic in an upstream link. PON traffic is served strictly according to its priority. We consider this DBA algorithm using two approaches: (i) the algorithm assigns a fixed service quantum to each priority service and (ii) different service quanta are assigned to different priority services. The mean message delay is evaluated using a multiqueue processor sharing (MPS) model and an MPS with Heterogeneous Traffic (MPS-HT)model for the two approaches respectively. The MPS model is a classical processor sharing model limited by the critical assumption that there is egalitarian service sharing among all users, which is inefficient for multimedia applications in PONs. We extend the MPS model to a general MPS-HT model that enables the analysis of message delay performance in the case where the service quanta may be different for different services.

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 consider an important performance measure of wireless sensor networks, namely, the least number of nodes, N, required to facilitate routing between any pair of nodes, allowing other nodes to remain in sleep mode in order to conserve energy. We derive the expected value and the distribution of N for single dimensional dense networks.

In this paper, we propose an adaptive algorithm called Adaptive REM (AREM) that improves the performance of random exponential marking (REM) in two ways. Firstly, it adaptively controls the parameter alpha to achieve fast response, and secondly, it introduces a new method to evaluate dropping/marking probability with each packet arrival to reduce the queue length jitter. We demonstrate by extensive simulation results that AREM maintains queue length stability independently of traffic loads, round trip propagation delay, and bottleneck capacity. We also demonstrate that AREM is robust to non-responsive UDP traffic and HTTP traffic, and it is effective for networks with multiple bottlenecks. Comparison with REM demonstrates the superiority of AREM in achieving faster convergence to queue length target, smaller queue length jitter, lower packet loss rate, and higher link utilization.

Worldwide interoperability for microwave access (WiMAX) is based on the IEEE 802.16 Standard with mobility support from the 802.16e amendment and it enables convergence of mobile and fixed broadband wireless networks covering metropolitan and rural areas. WiMAX traffic management aims at providing efficient delivery of multimedia applications with a range of QoS requirements. Focussing on the point-to-multipoint mode, we propose a priority-based fair scheduling algorithm for subscriber stations to serve a mixture of uplink traffic from different scheduling services and provide an analytical model for evaluating user-perceived delay performance under this scheduling scheme. The model is supported and validated by a simulation study. We present numerical results to illustrate the effect of traffic load and other design parameters on WiMAX message delay.

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.

The paper introduces a new algorithm for rate allocation in a multi-service Internet. The algorithm meets delay requirements of different services and provides fairness and congestion control. A performance comparison with other leading protocols is also carried out.

We consider a telecommunication competition model involving an incumbent and one or more new entrants who only choose to serve the most profitable traffic. The contribution of this paper is a new analysis that studies the effect of traffic shifts, which are common in the Internet era, on the distribution of market share. Introducing and using a new concept of a shifting network process, we analyze an incumbent network provider adapting its network to this shifting network process with a mixed integer linear program (MILP). In addition, a second MILP is developed to determine the maximum market share obtainable by a single competitor, engaging in so-called "cream-skimming". Further, we investigate the situation wherein multiple competitors compete on specific parts of the network. Numerical results are provided to demonstrate the various market share effects that may occur.

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.

Active queue management (AQM) is an effective method to provide an early notification of network congestion by pro-actively dropping or marking packets. In this paper, we propose a novel algorithm called fuzzy control RED (FCRED) that overcomes the drawbacks of the original RED. FCRED uses a fuzzy controller to adjust the maximum drop probability to stabilize the average queue length around the target queue length. We demonstrate by simulation results that FCRED maintains its performance independent of traffic loads, round trip propagation delay, and bottleneck capacity. We also demonstrate that FCRED is robust to non-responsive UDP traffic and HTTP traffic, and it is effective for networks with multiple bottlenecks. Comparison with other well-known AQM algorithms like PI, REM and ARED demonstrates the superiority of FCRED in achieving faster convergence to queue length target, and smaller queue length jitter.

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.

A new approach for modeling and performance analysis of the IEEE 802.11 medium access control (MAC) protocol is presented. The approach is based on the so-called system approximation technique, where the protocol service time distribution of the IEEE 802.11 MAC protocol is studied and approximated by an appropriate phase-type distribution, leading to the construction of a versatile queueing model which is amenable to analysis and, at the same time, general enough to allow for bursty arrival process as well as key statistical characteristics of the protocol operations. The versatility of the model is demonstrated by considering Markov modulated and on/off arrival processes as well as various data frame size distributions. The accuracy of the analytical results is verified by simulation.

A novel hybrid ARQ (HARQ) scheme using a concatenated two-state trellis-coded modulation (CT-TCM) code is proposed for improving wireless TCP throughput. A distinguished feature of the proposed scheme is that the heavily punctured TCM codes are used for retransmissions of the corrupted data block, which are combined at the receiver with the previously received sequences of the same data block for decoding. By this method, significantly improved coding gain and efficient spectrum utilization can be achieved with very low complexity. A Markov model is developed to evaluate TCP throughput over the proposed HARQ in wireless link. By both analysis and simulation, we demonstrate that compared with other existing TCM-based ARQ schemes, significant improvement of TCP throughput over wireless links is achieved by the proposed CT-TCM HARQ while smaller buffer size is required at the access point.

This paper presents a framework to minimize energy consumption in the medium access control (MAC) layer for wireless sensor networks. While satisfying a range of quality of service (QoS) requirements, such as the packet transmission success rate and maximum delay constraint, we optimally choose the lengths of periods in which sensors are active and inactive, such that the energy consumption per unit time in the entire network is minimized. We first use our framework to optimize the values of the MAC attributes macBeaconOrder and macSuperframeOrder in an IEEE 802.15.4 beacon-enabled star network. Then we consider a much simpler protocol, which we call "select-and-transmit" (S&T), and the same framework is applied to find the optimal lengths of the active and inactive portions. Finally, we compare the minimal energy consumption of the IEEE 802.15.4 MAC and S&T under the same QoS requirements and show that the IEEE 802.15.4 MAC outperforms S&T in most cases. However, the S&T MAC performs better than the standard under our framework in certain scenarios, e.g., event-driven sensor networks where the packet transmission success rate is usually low.

Multipath routing is a key technique to support video transmission over wireless ad hoc networks (WANETs). In WANETs, the lifetime of a node is related to its residual energy, current traffic conditions and the required energy consumed for sending a packet to its next hop in the path. In this paper, we propose a new adaptive routing scheme termed lifetime-aware multipath optimal routing (LAMOR) for supporting high-speed real time video transmission in WANETs, which is optimized in terms of lifetime and analyze its characteristics. Both theoretical analysis and simulation results demonstrate that LAMOR indeed extends network lifetime and improves the transmission quality of video streams

Push-to-Talk (PTT) is a new mobile service which enables fast-access two-way communication between two or more users. This paper provides an approximate evaluation model for PTT delay performance using the General Packet Radio Service (GPRS) over Global System for Mobile Communications (GSM) networks and taking account of GSM and GPRS traffic loading. Cases with and without provision of strict priority for PTT traffic over GPRS data are considered. Our PTT/GPRS/GSM model is based on the following three modelling assumptions: (1) a quasistationary assumption on the behaviour of GPRS under GSM voice loading, (2) a M/G/1 processor sharing (PS) queueing model to analyse PTT packet queueing delay performance, and (3) independence between retransmission traffic and original traffic. The model is validated by simulation. We present numerical and simulation mean delay results to illustrate the effect of traffic load and various design parameters on PTT packet delay.

This paper proposes a new call quality measure based on mobile signal strength measurements to evaluate performance of handoff algorithms in wireless cellular networks. The proposed measure allows the quantification of the impact of the handoff algorithms of performance. Using the proposed measure we compare existing handoff algorithms to identify the trade-off between signal quality and required number of handoffs. Our results indicate that a handoff method based on a threshold with 2 dB hysteresis provides better performance compared to the conventional wisdom of 3 dB hysteresis. We provide a benchmark value for handoff algorithms based on an off-line heuristic method using the new measure. Our benchmark shows that there is substantial room for improvement of the existing handoff algorithm

This paper proposes a new optical burst switching (OBS) paradigm known as dynamic two-way reservation OBS (DTWR/OBS), in which the burst length is dynamically determined at an ingress router, according to the minimum length reservation period available on each wavelength channel within a lightpath, from the ingress router to the egress router. The dynamic nature of DTWR/OBS ensures the packet transmission rate is controlled at the ingress routers, based on the level of network congestion. This increases network utilization and eliminates burst blocking due to wavelength contention. An analytical model for a single isolated optical cross-connect (OXC) is derived to determine the waiting time distribution of an arbitrary IP packet at the ingress router using the proposed DTWR/OBS. The accuracy of the analytical model is confirmed by simulations.

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.

We propose a new low-complexity MAC solution for multimedia CDMA cellular networks considering packet reservation and scheduling jointly. The proposed MAC, which we call joint PRMA and packet scheduling (JPPS), treats real-time traffic and non-real-time traffic separately. Once a real-time traffic flow (a continuous packet burst) is admitted to the system, its code channel(s) will be implicitly reserved until its last packet is transmitted, as in PRMA. Real-time traffic has delay priority over non-real-time, and we also prioritize within various non-real-time traffic flows. Packet scheduling techniques are then used to schedule all the real-time and non-real-time packets. Furthermore, an optimal channel access scheme based on a simple linear program is proposed. Simulation results comparing the performance of JPPS versus the well-known MAC protocol WISPER demonstrate lower delay for real time traffic and lower overall packet loss ratio for JPPS

We obtain detailed analytic formulas for the density and probability distribution of the waiting time in a time-division multiple-access (TDMA) model with a finite buffer and state-dependent service. On successive intervals of length equal to the duration of a slot, the density is expressed as a linear combination of beta densities with positive coefficients. A recursive scheme, obtained by a matrix-analytic derivation, allows for the highly efficient computations of the coefficient sequences. An expression for the mean waiting time is derived using the classical queueing formula L=/spl lambda/W. We also demonstrate that our methodology provides a concise treatment of various special cases that have been studied over the past half century.

This paper proposes a new measure for network performance evaluation called topology lifetime. The measure provides insight into which one of a set of topologies is likely to last the longest before more capacity must be installed. The lifetime measure is not single valued, but considers growth as a function of a set of demand shifts (perturbation). One network may be better able to support a uniform growth in the traffic, while another may support more growth when unexpected shifts in the load occur. The ability of a network to support unexpected changes in load is becoming more important because of: 1) current practices for installing fiber optics cables; 2) recent advances in dense wavelength division multiplexing; and 3) the increasing popularity of the Internet. The lifetime measure is applied to several topologies; a dual ring, a chordal ring, a Manhattan Street network and an hierarchical network. We also apply the measure to a realistic US IP Backbone network. In this paper, our objective is to show how to apply the measure to different networks, and to explain certain implications for comparisons between networks. We expect this measure to be useful both in the construction of new networks and in selecting between new links that may be added to an existing network.

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.

Using a simple model of saturated, synchronized and homogeneous sources of TCP Reno with drop-tail queue management and a discrete-time framework, we derive formulae for stationary as well as transient queueing behavior that shed light on the relationship between large buffers and work conservation (queue never empties). Using simulations, the relevance of the results for the case of non-synchronized sources is demonstrated. In particular, we demonstrate that a certain simple lower bound for the stationary queue length applies also to the case where the sources are non-stationary.

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.

This paper proposes an alternate solution for Ethernet passive optical networks. Our solution uses a novel protocol named full utilization local loop request contention multiple-access protocol to efficiently provide communications in passive optical networks. We study the physical layer implementation, as well as medium access control (MAC) layer protocol performance to illustrate the feasibility and benefit of our solution. The performance studies show that the MAC protocol is capable of offering 95% channel utilization under heavy load conditions. The performance results also indicate that delivery of multimedia traffic with a high quality-of-service can be achieved with our solution.

We propose a new optical hybrid switching system that takes advantage of both optical burst switching (OBS) and optical circuit switching (OCS) technologies. This system classifies incoming IP traffic flows into short-lived and long-lived flows. We model the system as a single server queue in a Markovian environment. The burst generation process is assumed to follow a two-state Markov modulated Poisson process (MMPP), and the service rate fluctuates based on the number of concurrent OCS sessions. Results for the mean delay and queue size are derived.

This paper describes a proportional-differential (PD) control algorithm as a new active queue management (AQM) scheme for TCP/IP congestion control. From the viewpoint of the control theory, TCP congestion control system can be regarded as a feedback regulating system. In this paper, a robust AQM called PD-controller is proposed. The design principles of PD-controller are presented in details. Its performance is extensively evaluated by simulations. The results demonstrate that the PD-controller AQM is stable and robust against traffic load fluctuations, UDP and HTTP disturbances. Its superiority over other AQMs is also demonstrated.

This paper provides and efficient for the derivation of the delay distribution for a TDMA system with a finite buffer. Assuming successive intervals of length equal to the duration of a slot, the density is expressed as (finite) linear combinations of gamma densities and beta densities with positive coefficients. The results are verified by simulations. Comparison with a previously proposed solution demonstrates that the solution presented here is more efficient.

In this paper we provide an analytical framework for studying the performance of optical burst switching (OBS) networks using burst segmentation. We first consider a single link model to evaluate the blocking probability of OBS using burst segmentation and confirm it by simulation. We use this analysis to demonstrate the benefit of burst segmentation over the well-known just-enough-time (JET) policy for the single link case. We then extend these models to a network scenario using a reduced load fixed point approximation to evaluate blocking probabilities and to show the advantages provided by burst segmentation for OBS networks.

We introduce a new congestion control architecture called MaxNet, which, like the Internet, is a stateless network where sources transmit information at rates according to their utility function. We prove MaxNet can achieve max-min fairness with no global information or per-flow state or processing in the link for general homogenous utility functions. Simulations are performed to study the transient behaviour of MaxNet. The interoperation of MaxNet with an existing IP network is investigated.

This paper investigates the implementation of a class of active queue management algorithm (AQM) whose measure of congestion includes packet arrival rate, such as REM and GREEN, in a combined input and output queued (CIOQ) switch. We propose a structure with one AQM per output port and we analyze the constraints on the switch fabric speed that this imposes. A two queue model of a CIOQ switch is developed and simulated to validate our design and compare its performance with a tail-drop queue switch.

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.

Detailed analytic formulas for the density and probability distribution of the waiting time in a TDMA model with a finite buffer is obtained. On successive intervals of length equal to the duration of a slot, the density is expressed as (infinite) linear combinations of beta densities with positive coefficients. A recursive scheme, obtained by a matrix-analytic derivation, allows for the highly efficient computations of the coefficient sequences.

This paper provides a framework for analysis and performance evaluation of Optical Burst Switching (OBS) networks. In particular, a new reduced load fixed point approximation model to evaluate blocking probabilities in OBS networks is introduced. The model is versatile enough to cover known OBS reservation policies such as Just-Enough-Time (JET), Just-In-Time (JIT), Burst Segmentation and Route-dependent Priorities. The accuracy of the model is confirmed by simulation and the various policies are compared.

This paper presents the Poisson Pareto burst process (PPBP) as a simple but accurate model for Internet traffic. It presents formulae relating the parameters of the PPBP to measurable traffic statistics, and describes a technique for fitting the PPBP to a given traffic stream. The PPBP is shown to accurately predict the queueing performance of a sample trace of aggregated Internet traffic. We predict that in few years, natural growth and statistical multiplexing will lead to an efficient optical Internet.

This paper provides a scalable framework for analysis and performance evaluation of optical burst-switching (OBS) networks. In particular, a new reduced load fixed point approximation model to evaluate blocking probabilities in OBS networks is introduced. The model is versatile enough to cover known OBS reservation policies such as just-enough-time, just-in-time, burst segmentation, and route-dependent priorities. The accuracy of the model is confirmed by simulation and the various policies are compared.

A new technique is proposed for performance evaluation of random access protocols. This technique is based on the idea that the service probability distribution of a particular random access protocol can be described by a phase-type distribution. By modeling the service probability distribution of a protocol into a phase-type distribution, the performance of the protocol can be evaluated using an equivalent continuous time Markov chain. We demonstrate the applicability of the technique to evaluate the performance of the IEEE 802.3 and the IEEE 802.11 MAC protocols. Furthermore, we provide an example of performance evaluation of a MAC protocol subject to bursty traffic models such as the Markov modulated Poisson process. The accuracy of the results is verified by simulation.

In this paper we introduce a new active queue management (AQM) algorithm called GREEN. GREEN provides high link utilization whilst maintaining low delay and packet loss. GREEN enables low latency interactive applications such as telephony and network games. GREEN is shown to outperform the current AQM algorithms. Certain performance problems with current AQMs are discussed.

We analyze our previously proposed protocol, CSMA/RI, and its closest related protocol, CSMA/CD. Our models include the widely used retransmission algorithm-the truncated binary exponential backoff (BEB) algorithm. The analysis is performed under two realistic traffic scenarios-the saturation and the disaster scenarios. The performance comparison of CSMA/RI and CSMA/CD is also presented. The analytic results are verified by simulations and are found to be accurate.

We present a new perspective on effective bandwidths which allows us to squeeze the most from a network while still meeting QoS constraints. We show that if buffer space requirements are also considered, the effective bandwidth of a given stream can be considerably reduced. This reduction in capacity is shown to have little or no impact on the ability of the network to meet the QoS constraints of the stream. We also show that even with a fixed buffer threshold, Gaussian traffic streams can provide a multiplexing gain not predicted by traditional effective bandwidths.

This paper proposes to use a programmable network to offer a new type of wholesale product. By augmenting the wholesale of carriage with the wholesale of network-based computation, the need for server overlay deployment to support retail service providers is reduced or eliminated. The paper develops an efficient algorithm for allocation and management of processing and transport resources. This algorithm is designed to optimize a large network supporting many retail service providers. The algorithm is based on the Lagrangian relaxation method and provides error bounds so that the accuracy of the solution can be ascertained. Numerical results are presented and the benefit of the optimization for a simple multiservice scenario is assessed.

We introduce a new framework for various measurement-based connection admission control (MBCAC) schemes for a multiservice network. Then, using heterogeneous bursty multimedia traffic traces, efficiency and simplicity tradeoffs are obtained by simulations. For all our MBCAC schemes, we use a procedure of 'available bandwidth' evaluation based on online measurements and an adaptive feedback mechanism. The online measurement concept is based on the use of different 'traffic histograms' that maintain records of the aggregated traffic in a link over a range of time-scales. The most complex MBCAC scheme involves measuring and then storing of all traffic information for every connection, the use of a 'warming up period' technique, and updating the histograms when a connection departs. However, for the other simpler MBCAC schemes, various aspects of the most complex scheme are eliminated or simplified. We also consider two model-based CAC schemes, i.e., Gaussian and effective bandwidth, where a priori statistical knowledge of the connections are known in advance. Given that the traffic is known beforehand, the performance of such schemes will be better than if the statistics are not exactly known. A comparison between such model-based CAC schemes with our MBCAC schemes provides a benchmark that gives the best efficiency and QoS a model-based CAC may achieve. Simulation results demonstrate that while the best efficiency achievable by the model-based gives efficiency of 80%, the most complex MBCAC schemes achieved 81% efficiency, and the simplest MBCAC schemes obtained 76% value.

This paper explores the potential benefits of programmable networks for telecommunication carriers. It particularly focuses on using a programmable network to offer a new type of wholesale product by adding value to the wholesale of pure carriage. By augmenting the wholesale of carriage with the wholesale of network-based computation, the need for server overlay deployment to support retail service providers is reduced or eliminated. The paper develops an efficient algorithm for allocation and management of computation and transport resources within the network infrastructure. This algorithm is designed to optimize a large network supporting many retail service providers and can be used by the wholesaler for the optimal management of scarce computational and transport resources. The algorithm is based on the Lagrangian relaxation method and provides error bounds so that the accuracy of the solution can be ascertained. Numerical results are presented and the benefit of the optimization for a simple multi-service scenario is assessed.

This paper proposes a new measure for network performance evaluation called topology lifetime. The measure provides insight into which one of a set of topologies is likely to last the longest before more capacity must be installed. The lifetime measure is not single valued, but considers growth as a function of a set of demand shifts. The lifetime measure is applied to several topologies: a dual ring, a chordal ring, a Manhattan Street network and an hierarchical network.

Enforcement of max-min fairness encourages end users to implement adaptive end-to-end flow control. This pushes back the responsibility of controlling congestion to users and guarantees reliable and efficient network operation. Unfortunately, there could be malicious users who are indifferent to congestion and intentionally cause performance degradation to other users. The commonly used max-min fairness definition might allocate more resources to malicious users than they deserve and hence cause network inefficiency. This paper proposes a modified max-min definition according to which strict priority is given to cooperative users over malicious ones. We demonstrate that implementation of such two-priority fairness in local network bottlenecks will achieve controlled congestion as well as global fairness, even in the presence of malicious users. This paper motivates the need for traffic measurements to identify malicious users.

This paper proposes a new carrier sense multiple access (CSMA) protocol, called CSMA with reservations by interruptions (CSMA/RI). This new protocol uses a novel approach to reserve capacity for multiple users. The performance of the new protocol is studied by simulations under realistic (long range dependent) traffic conditions and compared with CSMA/CD as well as with its work conserving G/D/1 queue. It is demonstrated that CSMA/RI always offers better performance than CSMA/CD and under certain realistic assumptions regarding packet size, the performance of CSMA/RI can be very close to that of its G/D/1 benchmark.

This paper proposes an enhancement for the carrier sense multiple access with collision detection (CSMA/CD) protocol, called CSMA with reservations by interruptions (CSMA/RI). This new protocol uses a novel approach to reserve capacity by interrupting an ongoing packet transmission. The performance of the protocol is studied by simulations under realistic (long range dependent) traffic conditions and compared to the CSMA/CD, token ring protocols, as well as with the work conserving G/D/1 queue. It is demonstrated that CSMA/RI always offers better performance than CSMA/CD, and under certain realistic assumptions regarding packet size, the performance of CSMA/RI can be very close to that of token-ring and G/D/1.

Medium access protocols for HFC and wireless ATM networks often use a collision based capacity request signalling channel which may rely on the slotted Aloha multiaccess principle. This paper studies the performance of a p-persistence slotted Aloha contention resolution algorithm (CRA), subject to extreme interstation correlation, by means of a discrete-time Markov chain analysis. We examine in detail the conditions leading to a deadlock-a situation where the time to collision resolution becomes unacceptably high and the system is practically unstable. We analyze two disaster scenario deadlock models, and study the effect of channel error probability, signalling traffic load, and the contention resolution algorithm used. We show that the key factor of the CRA is the collision rate and not channel errors. We propose and test three signalling channel capacity allocation schemes. We identify the best-performing of these three schemes as the cyclic contention mini-slot (CMS) sharing employing multiple CMSs per data slot. Finally, we demonstrate the need for implementation of an added scheme, which dynamically adjusts the p-persistence parameter.

This paper presents steps towards creating a general model for aggregated traffic streams. We show that fitting the mean, variance and Hurst parameter is insufficient to consistently model a long range dependent traffic stream. A fourth parameter, the "level of aggregation", is required. We also show that with increased aggregation, the behaviour of a traffic stream tends towards that of a fractal Gaussian process.

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.

Modern multi-service medium access protocols use a collision based capacity request signalling channel. Such signalling channels may be based on the slotted Aloha multiaccess principle. This paper studies the performance of slotted Aloha subject to extreme inter-station correlation by means of a discrete-time Markov chain analysis. We study conditions whereby the time to collision resolution becomes unacceptably high (defined as deadlock). Three signalling channel management schemes for alleviating the deadlock problem are evaluated. Of these, the cyclic contention mini-slot (CMS) sharing technique employing multiple CMSs per data slot is the one that extends the protocol's useable load region the furthest. We find that implementation of a scheme, which dynamically adjusts the p-persistence parameter towards its optimal value, is desirable. Both error free and error prone conditions are studied. The results highlight the fact that the critical signalling load is largely unaffected by the presence of errors, so that even in extremely error prone environments, the limiting performance factor is still the collision rate.

In this paper, the performance of channel allocation schemes for TDMA type digital mobile cellular systems are studied. We study by simulation the effect of traffic loading and characteristics, including new call arrivals and inter-cell handovers, on blocking and drop out probabilities. The channel allocation schemes under examination includes: (1) fixed channel allocation (FCA) where the number of frequency carriers in each cell is fixed (2) dynamic channel allocation where the number of frequency carriers in each cell is adaptive and dependent on the load, and (3) dynamic frequency/time channel allocation where the number of channels is adaptive (based on the load), allowing two different time division channels of the same frequency carrier to be used in two neighbouring cells. We also study the possible benefit of a simple channel reservation scheme on these systems. We demonstrate that the effects of arrival rate on blocking and drop our probabilities are as expected. As the arrival rate increases, the blocking as well as the drop out probabilities increases. We demonstrate that the implementation of channel reservation is suitable for FCA but not suitable with schemes that is capable of dynamic channel allocation as it may actually worsen drop out rates in such schemes. We also demonstrate that under overload situations, there is no significant benefit in the dynamic schemes and in certain situations they may even perform worse than FCA.

We study a new scheme for provision of priorities within the framework of a MAC protocol which is a potential candidate of the (as yet unpublished) IEEE 802.14 standard. A comprehensive simulation study, based on real traffic traces, shows that this new method provides better protection for the high priority traffic than an earlier proposed scheme. Except for physical and MAC layer overheads, the scheme exhibits comparable behaviour to that of an ideal multiplexer, for traces with very diverse correlation structures. The significance of using realistic (correlated) traffic streams for modelling purposes is demonstrated and discussed.

The paper provides insight into the performance of a candidate IEEE 802.14 MAC protocol for hybrid fibre/coax (HFC) networks, by simulation. The simulations make use of two types of input traffic: measured traffic traces, and randomly generated memoryless traffic. It is confirmed that the protocol does not reach congestion collapse under any traffic load, and this is found to hold regardless of traffic type. The paper demonstrates that memoryless traffic streams cannot be used for dimensioning guidelines as they underestimate delay as compared to real traffic traces. The paper also compares between the performance of the protocol loaded by traffic traces with that of a benchmark based on an ideal multiplexer under the same traffic conditions. It is demonstrated that both the throughput and delay performance of the protocol converge to that of an ideal multiplexer, with an increasing number of active stations.

In this paper we compare, by simulation based on real traffic traces, the EFCI (explicit forward congestion indication) and the ERF (explicit rate feedback) rate-based flow control algorithms for the available bit rate (ABR) service in ATM networks. The factors under consideration are: (1) distance from source to destination and (2) rate of resource management (RM) cells. Although no significant fairness throughput problem has been identified in both EFCI and ERF, sources with high end-to-end propagation delay will experience higher queueing delay and loss probability. In this paper, we have also shown that although EFCI can lead to considerably lower utilisation than ERF if the parameters are set arbitrarily, it can perform as good as or close to ERF if there were complementary software which can compute the optimal parameters for EFCI based on the on-line measurement of traffic streams.

A new adaptive connection admission control scheme is proposed. The aggregate statistics of all calls in progress are continuously measured and new calls are considered as CBR at their peak during certain warming up period. The main features of our proposed CAC scheme are (1) an adaptive mechanism which updates the warming up period based on the traffic conditions, (2) an accurate mechanism to predict cell loss, and (3) a simple and conservative way to consider call departures. We demonstrate by simulation that the scheme is more efficient than several other schemes.

We consider the P/sub i/-persistent protocol and we derive approximate analytical results for the unfinished work distribution at each node on the bus under the assumption that the arrival process at each node follows a discrete time Gaussian process. A deterministic version of the protocol, which reduces the variance of the service process and therefore improves queueing performance, is also considered. The solution is simple and fast to compute and is not overly sensitive to the number of nodes. Simulation results indicate that the results are accurate so long as the performance can be accurately approximated based on the dominant effect of an exponential tail in the distribution of queued load. The results are also applicable to a single server Gaussian queue with priorities.

A queueing network structured as a directed tree, where only transmissions towards its head are allowed, is considered. Input to each queue is assumed to follow a discrete-time Gaussian process. To compute loss probabilities, each queue is considered independently and its input statistics are derived by approximating output statistics of its immediate sources. Performance statistics of a single Gaussian queue can be accurately approximated as a function of three parameters: the mean and variance of the arrival process, and a parameter related to the asymptotic rate of the long term variance. Of these three parameters only the variance changes when the traffic is filtered through an infinite buffer queue. The authors derive an approximation of the variance of the output process. Comparisons with simulations show that the approximation is accurate. Having the parameters of the output process, the single queue analysis is extended to a tree type network of queues. A small example of a network of three queues is simulated and the performance results are found to be close to results obtained analytically.>

Congestion controls are a key factor in achieving the robust performance required of common channel signaling (CCS) networks in the face of partial network failures and extreme traffic loads, especially as networks become large and carry high traffic volume. The CCITT recommendations define a number of types of congestion control, and the parameters of the controls must be well set in order to ensure their efficacy under transient and sustained signalling network overload. The objective of this paper is to present a modeling approach to the determination of the network parameters that govern the performance of the SS7 congestion controls under sustained overload. Results of the investigation by simulation are presented and discussed.>

This paper provides a method for approximating the probability distributions of stationary statistics in FIFO single server queues. The method is based on the Wiener-Hopf factorization technique, and is applied to semi-Markov queues where the underlying state space is of unlimited size. It has been established that the tail of the distribution of the waiting time or unfinished work in such queues is negative exponential, and in this paper we estimate the parameters of that exponential term. A particularly important case, which is treated here, is a model for a statistical multiplexer where the net input process forms a stationary ergodic Gaussian discrete-time stochastic process. In this case, it is possible to derive analytically a simple formula in a closed form for the approximation. The formula is in terms of three parameters of the net input process: the mean, the variance, and the autocovariance sum. This provides a solid theoretical basis for traffic characterization by these parameters. Comparison with simulation results show that the method is accurate. Also presented is a result for a special case where the arrival process is autoregressive.>

A method for approximating the probability distributions of stationary statistics in first-in, first-out (FIFO) single server queues is provided. The method is based on the Wiener-Hopf factorization technique and is applied to semi-Markov queues with the underlying state space of unlimited size. A particularly important case is a model for a statistical multiplexer where the net input process forms a stationary ergodic Gaussian discrete-time stochastic process.>

A generic flow control (GFC) protocol is proposed to regulate multiple terminals within the broadband-ISDN customer premises network. The GFC protocol is based on multipriority distributed queuing preceded by a traffic shaping function, and meets the requirements for flexible allocation of service parameters, control of jitter and guarantee of bandwidth. This GFC protocol provides the necessary flexibility to match the constant bit rate (CBR) jitter performance and variable bit rate (VBR) service requirements to the particular service type. It integrates well with usage parameter control without imposing excessive burstiness on each virtual connection cell stream delivered to the network. By allowing connectionless users to access at the lowest priority, only when no higher priority users are queued, there is no reduction in guaranteed capacity available for other VBR and CBR users.>

The authors propose a fairness criterion for sharing spare (unallocated) capacity among different nonguaranteed bursty data sources with different relative usage values. Based on this criterion, the throughput of some sources may be controlled in the case where the total average offered traffic within a certain small time interval is greater than the total available spare capacity. It is proposed that all controlled sources will enjoy a share of capacity which is proportional to their relative usage values, and that no uncontrolled source will enjoy higher relative throughput than a controlled source. This criterion uniquely defines a set of throughputs for all sources. A method to compute these throughput values which has been demonstrated by several examples has been presented.>

The authors consider a multipriority queuing system which involves several distributed local queues and a central server which performs round-robin processor sharing (RRPS). This queuing model, referred to as multiqueue processor sharing (MPS), is an extension of Kleinrock's classical processor sharing model with generalization to multiple priorities and the addition of a buffer at each traffic source for each priority to buffer additional packets queued at that source. The authors present an exact analysis of the MPS model which leads to a closed-form result for the mean packet delay as a function of the packet length for each priority. It is demonstrated by simulation that the result obtained is applicable for accurately computing the average packet delay for a DQDB (distributed queue dual bus) subnetwork, given that stations transmitting on the same bus are not widely separated.>

Consideration is given to a hybrid switch which provides integrated packet and circuit switching. The statistical characteristics of the bandwidth utilized by bursty isochronous traffic are derived. The derivation takes into consideration the waste inherent in the framing structure. Bursty traffic due to circuit allocation requests is modeled by a switched Poisson process. The analysis is performed for two circuit allocation policies, namely, repacking and first-fit with static numbering. Numerical results are presented to demonstrate the effect of traffic burstiness on the capacity utilized by the isochronous traffic.>

We consider a discrete single-server queueing/communication system, in which the arrival distribution is changing stochastically with time. The distribution of the number of messages to arrive within a time slot depends on a traffic activity process of modes. This process of modes is a two-state Markov chain: mode 0 and mode 1, which represent light and heavy traffic conditions, respectively. The transition probabilities of this Markov chain [herein called mode duration parameters (MDP's)] determine the distribution of the duration of time the process stays in each mode. Expressions for thez-transform, expectation, and variance are obtained for the steady-state queue size and delay. A special consideration is given to the effect of the MDP's on the system performance, and the limiting cases (where the MDP's approach zero) are studied.