While the electric vehicle (EV) industry is facing some challenges concerning its refueling, its rapid growth in popularity is increasing these difficulties. In this paper, we demonstrate the gravity of the problems that EVs may experience for charging,both now and in the near future, and show how establishing new charging stations can be challenging. We also present the challenges in optimizing the use of charging stations by EV users. Then, we envisage opportunities for the rise of alternative charging options, such as distributed generation, crowdsourced, wireless and mobile charging stations. Additionally, we explain directions on how route and charging stations' location planning can cater to optimizing the charging infrastructure.

Cloud data centers consume a large amount of energy that leads to a high carbon footprint. Taking into account a carbon tax imposed on the emitted carbon makes energy and carbon cost play a major role in data centers' operational costs. To address this challenge, we investigate parameters that have the biggest effect on energy and carbon footprint cost to propose more efficient VM placement approaches. We formulate the total energy cost as a function of the energy consumed by servers plus overhead energy, which is computed through power usage effectiveness (PUE) metric as a function of IT load and outside temperature. Furthermore, we consider that data center sites have access to renewable energy sources. This helps to reduce their reliance on “brown” electricity delivered by off-site providers, which is typically drawn from polluting sources. We then propose multiple VM placement approaches to evaluate their performance and identify the parameters with the greatest impact on the total renewable and brown energy consumption, carbon footprint, and cost. The results show that the approach which considers dynamic PUE, renewable energy sources, and changes in the total energy consumption outperforms the others while still meeting cloud users' service level agreements.

It has been recently discovered that heavy-tailed completion times can result from protocol interaction even when file sizes are light-tailed. A key to this phenomenon is the use of a restart policy where if the file is interrupted before it is completed, it needs to restart from the beginning. In this paper, we show that fragmenting a file into pieces whose sizes are either bounded or independently chosen after each interruption guarantees light-tailed completion time as long as the file size is light-tailed; i.e., in this case, heavy-tailed completion time can only originate from heavy-tailed file sizes. If the file size is heavy-tailed, then the completion time is necessarily heavy-tailed. For this case, we show that when the file size distribution is regularly varying, then under independent or bounded fragmentation, the completion time tail distribution function is asymptotically bounded above by that of the original file size stretched by a constant factor. We then prove that if the distribution of times between interruptions has nondecreasing failure rate, the expected completion time is minimized by dividing the file into equal-sized fragments; this optimal fragment size is unique but depends on the file size. We also present a simple blind fragmentation policy where the fragment sizes are constant and independent of the file size and prove that it is asymptotically optimal. Both these policies are also shown to have desirable completion time tail behavior. Finally, we bound the error in expected completion time due to error in modeling of the failure process.

Making use of predictions is a crucial, but under-explored, area of online algorithms. This paper studies a class of online optimization problems where we have external noisy predictions available. We propose a stochastic prediction error model that generalizes prior models in the learning and stochastic control communities, incorporates correlation among prediction errors, and captures the fact that predictions improve as time passes. We prove that achieving sublinear regret and constant competitive ratio for online algorithms requires the use of an unbounded prediction window in adversarial settings, but that under more realistic stochastic prediction error models it is possible to use Averaging Fixed Horizon Control (AFHC) to simultaneously achieve sublinear regret and constant competitive ratio in expectation using only a constant-sized prediction window. Furthermore, we show that the performance of AFHC is tightly concentrated around its mean.

Energy expenditure has become a significant fraction of data center operating costs. Recently, “geographical load balancing” has been proposed to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use. We explore whether the geographical diversity of Internet-scale systems can also provide environmental gains. Specifically, we explore whether geographical load balancing can encourage use of “green” renewable energy and reduce use of “brown” fossil fuel energy. We make two contributions. First, we derive three distributed algorithms for achieving optimal geographical load balancing. Second, we show that if the price of electricity is proportional to the instantaneous fraction of the total energy that is brown, then geographical load balancing significantly reduces brown energy use. However, the benefits depend strongly on dynamic energy pricing and the form of pricing used.

The pool of unused routable IPv4 prefixes is dwindling, with less than 4% remaining for allocation at the end of June 2014. Yet the adoption of IPv6 remains slow. We demonstrate a new capture-recapture technique for improved estimation of the size of "IPv4 reserves" (allocated yet unused IPv4 addresses or routable prefixes) from multiple incomplete data sources. A key contribution of our approach is the plausible estimation of both observed and unobserved-yet-active (ghost) IPv4 address space. This significantly improves our community's understanding of IPv4 address space exhaustion and likely pressure for IPv6 adoption. Using "ping scans", network traces and server logs we estimate that 6.3 million /24 subnets and 1.2 billion IPv4 addresses are currently in use (roughly 60% and 45% of the publicly routed space respectively). We also show how utilisation has changed over the last 2--3 years and provide an up-to-date estimate of potentially-usable remaining IPv4 space.

Efficient management of supercomputing facilities requires estimates of future workload based on past user behaviour. For supercomputers with large numbers of users, aggregate user behaviour is commonly assumed to be best in prediction of future workloads, however for systems with smaller numbers of users the question arises as to whether it is still suitable or if benefits can be derived from monitoring individual user behaviour to predict future workload. We compare using individual user behaviour, aggregate user behaviour and a hybrid approach where we track heavy users individually and cluster aggregate light users into a small number of clusters. We find that the hybrid approach produces the best results in both mean absolute error and mean squared error. However, treating all users separately provides slightly worse predictions. We also introduce a new approach to prediction based on the hazard function which is a significant improvement on previously used schemes based on autoregressive models. The schemes are investigated numerically using a two-year workload trace from a supercomputer with a population of 136 users.

We consider algorithms for “smoothed online convex optimization” problems, a variant of the class of online convex optimization problems that is strongly related to metrical task systems. Prior literature on these problems has focused on two performance metrics: regret and the competitive ratio. There exist known algorithms with sublinear regret and known algorithms with constant competitive ratios; however, no known algorithm achieves both simultaneously. We show that this is due to a fundamental incompatibility between these two metrics - no algorithm (deterministic or randomized) can achieve sublinear regret and a constant competitive ratio, even in the case when the objective functions are linear. However, we also exhibit an algorithm that, for the important special case of one dimensional decision spaces, provides sublinear regret while maintaining a competitive ratio that grows arbitrarily slowly.

We investigate three ways WLANs can use two channels to carry TCP traffic. Using simulation and a simple model, we show that load balancing over both channels outperforms the others while using a single double-width channel is the worst.

To achieve high system efficiency with increasing speeds, recent WiFi standards, such as IEEE 802.11e/n, allow burst transmissions with block acknowledgements, provided the initial packet is successfully received. Consequently, a user can sometimes improve its throughput by sending the initial packet at a lower rate than other users. We model such a system as a game. Our results show that the socially optimal strategy is to send the initial packet at a lower rate than the rest of the burst. Such a strategy results in a better Nash Equilibrium than using the same rate for the entire burst. Moreover, we show that using the rate that maximizes the per-packet throughput, as commonly done, can result in performance that is far from the social optimum.

No abstract available.

Wireless LANs carry a mixture of traffic, with different delay and throughput requirements. The usual way to provide low-delay services is to give priority to such traffic. However, this creates an incentive for throughput sensitive traffic also to use this service, which degrades overall network performance. We show, analytically and by simulation, that the performance of both delay and throughput sensitive traffic can be improved by scaling IEEE 802.11's $(CW_{{\rm min}})$ and TXOP limit parameters in equal proportion. This reduces, but does not eliminate, the incentive for bulk data users to use the low-delay service. We further show that this incentive can be removed, while still giving improved performance to both classes, by reducing the $(CW_{{\rm min}})$ of the high throughput class by a constant that is independent of the traffic load.

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of low load. This paper investigates how much can be saved by dynamically “right-sizing” the data center by turning off servers during such periods and how to achieve that saving via an online algorithm. We propose a very general model and prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new “lazy” online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data-center workloads and show that significant cost savings are possible. Additionally, we contrast this new algorithm with the more traditional approach of receding horizon control.

Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much gain knowing future workload information can bring to dynamic provisioning. In particular, we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the offline dynamic provisioning problem, which allows us to characterize the optimal solution in a “divide-andconquer” manner. We then exploit this insight to design two online algorithms with competitive ratios 2 - α and e/(e - 1 + α), respectively, where 0 ≤ α ≤ 1 is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to α = 1) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world traces.

Despite the predicted exhaustion of unallocated IPv4 addresses between 2012 and 2014, it remains unclear how many current clients can use its successor, IPv6, to access the Internet. We propose a refinement of previous measurement studies that mitigates intrinsic measurement biases, and demonstrate a novel web-based technique using Google ads to perform IPv6 capability testing on a wider range of clients. After applying our sampling error reduction, we find that 6% of world-wide connections are from IPv6-capable clients, but only 1--2% of connections preferred IPv6 in dual-stack (dual-stack failure rates less than 1%). Except for an uptick around IPv6-day 2011 these proportions were relatively constant, while the percentage of connections with IPv6-capable DNS resolvers has increased to nearly 60%. The percentage of connections from clients with native IPv6 using happy eyeballs has risen to over 20%.

Many congestion control protocols use explicit feedback from the network to achieve high performance. Most of these either require more bits for feedback than are available in the IP header or incur performance limitations due to inaccurate congestion feedback. There has been recent interest in protocols that obtain high-resolution estimates of congestion by combining the explicit congestion notification (ECN) marks of multiple packets, and using this to guide multiplicative increase, additive increase, multiplicative decrease (MI-AI-MD) window adaptation. This paper studies the potential of such approaches, both analytically and by simulation. The evaluation focuses on a new protocol called Binary Marking Congestion Control (BMCC). It is shown that these schemes can quickly acquire unused capacity, quickly approach a fair rate distribution, and have relatively smooth sending rates, even on high bandwidth-delay product networks. This is achieved while maintaining low average queue length and negligible packet loss. Using extensive simulations, we show that BMCC outperforms XCP, VCP, MLCP, CUBIC, CTCP, SACK, and in some cases RCP, in terms of average flow completion times. Suggestions are also given for the incremental deployment of BMCC.

Power consumption imposes a significant cost for data centers implementing cloud services, yet much of that power is used to maintain excess service capacity during periods of predictably low load. This paper investigates how much can be saved by dynamically `right-sizing' the data center by turning off servers during such periods, and how to achieve that saving via an online algorithm. We prove that the optimal offline algorithm for dynamic right-sizing has a simple structure when viewed in reverse time, and this structure is exploited to develop a new `lazy' online algorithm, which is proven to be 3-competitive. We validate the algorithm using traces from two real data center workloads and show that significant cost-savings are possible.

Wireless LANs carry a mixture of traffic, with different delay and throughput requirements. The usual way to provide low-delay services is to give priority to such traffic. However this creates an incentive for throughput sensitive traffic also to use this service, which degrades overall network performance. We propose to allow applications to trade off delay for throughput, without giving preference to one class over another, by simultaneously scaling IEEE 802.11's CWminand TXOP limit parameters. We provide a model of this scheme with two traffic classes, and show that increasing CWminand TXOP limit in equal proportion reduces, but does not eliminate, the incentive for bulk data users to use the low-delay service. We show that subtracting a small constant from CWmineliminates this incentive, while still giving improved performance to both classes.

Energy expenditure has become a significant fraction of data center operating costs. Recently, "geographical load balancing" has been suggested to reduce energy cost by exploiting the electricity price differences across regions. However, this reduction of cost can paradoxically increase total energy use.

It has been recently discovered that heavy-tailed file completion time can result from protocol interaction even when file sizes are light-tailed. A key to this phenomenon is the RESTART feature where if a file transfer is interrupted before it is completed, the transfer needs to restart from the beginning. In this paper, we show that independent or bounded fragmentation produces light-tailed file completion time as long as the file size is light-tailed, i.e., in this case, heavy-tailed file completion time can only originate from heavy-tailed file sizes. If the file size is heavy-tailed, then the file completion time is clearly heavy-tailed. For this case, we show that when the file size distribution is regularly varying, then under independent or bounded fragmentation, the completion time tail distribution function is asymptotically upper bounded by that of the original file size stretched by a constant factor. We then prove that if the failure distribution has non-decreasing failure rate, the expected completion time is minimized by dividing the file into equal sized fragments; this optimal fragment size is unique but depends on the file size. We also present a simple blind fragmentation policy where the fragment sizes are constant and independent of the file size and prove that it is asymptotically optimal. Finally, we bound the error in expected completion time due to error in modeling of the failure process.

This work examines fundamental tradeoffs incurred by a speed scaler seeking to minimize the sum of expected response time and energy use per job. We prove that a popular speed scaler is 2-competitive for this objective and no "natural" speed scaler can do better. Additionally, we prove that energy-proportional speed scaling works well for both Shortest Remaining Processing Time (SRPT) and Processor Sharing (PS) and we show that under both SRPT and PS, gated-static speed scaling is nearly optimal when the mean workload is known, but that dynamic speed scaling provides robustness against uncertain workloads. Finally, we prove that speed scaling magnifies unfairness under SRPT but that PS remains fair under speed scaling. These results show that these speed scalers can achieve any two, but only two, of optimality, fairness, and robustness.

Wireless local area networks (WLANs) support a wide range of applications, with various packet sizes. This diversity is set to increase in 802.11e WLANs which effectively allow very large packets controlled by a transmission opportunity (TxOP) parameter. This paper demonstrates a new phenomenon which occurs as a result of this diversity: When a network carries some large packets and many small packets, the collision probability after a large packet is much larger than predicted by previous models. This can be important because collision probability determines the number of packet transmissions, and hence the energy consumption. We propose a candidate model which captures this effect.

This paper develops a new model that describes the queueing process of a communication network when data sources use window flow control. The model takes into account the burstiness in sub-round-trip time (RTT) timescales and the instantaneous rate differences of a flow at different links. It is generic and independent of actual source flow control algorithms. Basic properties of the model and its relation to existing work are discussed. In particular, for a general network with multiple links, it is demonstrated that spatial interaction of oscillations allows queue instability to occur even when all flows have the same RTTs and maintain constant windows. The model is used to study the dynamics of delay-based congestion control algorithms. It is found that the ratios of RTTs are critical to the stability of such systems, and previously unknown modes of instability are identified. Packet-level simulations and testbed measurements are provided to verify the model and its predictions.

Peer-to-peer (P2P) file distribution is a scalable way to disseminate content to a wide audience. For a P2P network, one fundamental performance metric is the average time needed to deliver a certain file to all peers, which in general depends on the topology of the network and the scheduling of transmissions. Despite its apparent importance, how to minimize average finish time remains an open question even for a fully- connected network. This is mainly due to the analytical challenges that come with the combinatorial structures of the problem. In this paper, by using the water-filling technique, we determine how each peer should use its capacity to sequentially minimize the file download times in an upload-constrained P2P network. Furthermore, it is argued that this scheduling also potentially minimizes average finish time for the network. This result not only provides fundamental insight to scheduling in such P2P systems, but also can serve as a benchmark to evaluate practical algorithms and illustrate the scalability of P2P networks.

This paper proposes a framework for congestion control, called binary marking congestion control (BMCC) for high bandwidth-delay product networks. The basic components of BMCC are i) a packet marking scheme for obtaining high resolution congestion estimates using the existing bits available in the IP header for explicit congestion notification (ECN) and ii) a set of load-dependent control laws that use these congestion estimates to achieve efficient and fair bandwidth allocations on high bandwidth-delay product networks, while maintaining a low persistent queue length and negligible packet loss rate. We present analytical models that predict and provide insights into the convergence properties of the protocol. Using extensive packet-level simulations, we assess the efficacy of BMCC and perform comparisons with several proposed schemes. BMCC outperforms VCP, MLCP, XCP, SACK+RED/ECN and in some cases RCP, in terms of average flow completion times for typical Internet flow sizes.

Energy use of computer communication systems has quickly become a vital design consideration. One effective method for reducing energy consumption is dynamic speed scaling, which adapts the processing speed to the current load. This paper studies how to optimally scale speed to balance mean response time and mean energy consumption under processor sharing scheduling. Both bounds and asymptotics for the optimal speed scaling scheme are provided. These results show that a simple scheme that halts when the system is idle and uses a static rate while the system is busy provides nearly the same performance as the optimal dynamic speed scaling. However, the results also highlight that dynamic speed scaling provides at least one key benefit - significantly improved robustness to bursty traffic and mis-estimation of workload parameters.

This paper considers the problem of minimizing outage probabilities in the downlink of a multiuser, multicell orthogonal frequency division multiple access (OFDMA) cellular network with frequency selective fading, imperfect channel state information, and frequency hopping. The task is to determine the allocation of powers and subcarriers for users to ensure that the user outage probabilities are as low as possible. We formulate a min-max outage probability problem and solve it under the constraint that the transmit power spectrum at each base station is flat. In particular, we obtain a subchannel allocation algorithm that has complexityO(LlogL) inL, the number of users in the cell. We also consider suboptimal but implementable approaches with and without the flat transmit power spectrum constraint. We conclude that the flat transmit spectrum approach has merit, and warrants further study.

MaxNet TCP is a congestion control protocol that uses explicit multi-bit signalling from routers to achieve desirable properties such as high throughput and low latency. In this paper we present an implementation of an extended version of MaxNet. Our contributions are threefold. First, we extend the original algorithm to give both provable stability and rate fairness. Second, we introduce the MaxStart algorithm which allows new MaxNet connections to reach their fair rates quickly. Third, we provide a Linux kernel implementation of the protocol. With no overhead but 24-bit price signals, our implementation scales from 32 bit/s to 1 peta-bit/s with a 0.001% rate accuracy. We confirm the theoretically predicted properties by performing a range of experiments at speeds up to 1 Gbit/sec and delays up to 180 ms on the WAN-in-Lab facility.

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.

A novel continuous time fluid flow model of the dynamics of the interaction between ACK-clocking and the link buffer is presented. A fundamental integral equation relating the instantaneous flow rate and the window dynamics is derived. Properties of the model, such as well-posedness and stability, are investigated. Packet level experiments verify that this new model is more accurate than existing models, correctly predicting qualitatively different behaviors, for example when round trip delays are heterogeneous.

This paper studies window flow control focusing on bridging the gap between microscopic factors such as burstiness in sub-RTT timescales, and observable macroscopic properties such as steady state bandwidth sharing and flow level stability. Using new models, we analytically capture notable effects of microscopic behavior on macroscopic quantities. For loss-based protocols, we calculate the loss synchronization rate for different flows and use it to quantitatively explain the unfair bandwidth sharing between paced and unpaced TCP flows. For delay-based protocols, we show that the ratios of round trip delays are critical to the stability of the system. These results deepen the fundamental understanding of congestion control systems. Packet level simulations are used to verify our theoretical claims.

This paper investigates the interaction between end-to-end flow control and medium access control (MAC)-layer scheduling on wireless links. We consider a wireless network with multiple users receiving information from a common access point; each user suffers fading and a scheduler allocates the channel based on channel quality but is subject to fairness and latency considerations. We show that the fairness property of the scheduler is compromised by the transport-layer flow control of transmission control protocol (TCP) New Reno. We provide a receiver-side control algorithm, CLAMP, that remedies this situation. CLAMP works at a receiver to control a TCP sender by setting the TCP receiver's advertised window limit, and this allows the scheduler to allocate bandwidth fairly between the users.

This paper presents a link model which captures the queue dynamics when congestion windows of TCP sources change. By considering both the self-clocking and the link integrator effects, the model is a generalization of existing models and is shown to be more accurate by both open loop and closed loop packet level simulations. It reduces to the known static link model when flows' round trip delays are similar, and approximates the standard integrator link model when the heterogeneity of round trip delays is significant. We then apply this model to the stability analysis of FAST TCP. It is shown that FAST TCP flows over a single link are always linearly stable regardless of delay distribution. This result resolves the notable discrepancy between empirical observations and previous theoretical predictions. The analysis highlights the critical role of self-clocking in TCP stability and the scalability of FAST TCP with respect to delay. The proof technique is new and less conservative than the existing ones.

We propose a jointly opportunistic source coding and opportunistic routing (OSCOR) protocol for correlated data gathering in wireless sensor networks. OSCOR improves data gathering efficiency by exploiting opportunistic data compression and cooperative diversity associated with wireless broadcast advantage. The design of OSCOR involves several challenging issues across different network protocol layers. At the MAC layer, sensor nodes need to coordinate wireless transmission and packet forwarding to exploit multiuser diversity in packet reception. At the network layer, in order to achieve high diversity and compression gains, routing must be based on a metric that is dependent on not only link-quality but also compression opportunities. At the application layer, sensor nodes need a distributed source coding algorithm that has low coordination overhead and does not require the source distributions to be known. OSCOR provides practical solutions to these challenges incorporating a slightly modified 802.11 MAC, a distributed source coding scheme based on network coding and Lempel-Ziv coding, and a node compression ratio dependent metric combined with a modified Dijkstra's algorithm for path selection. We evaluate the performance of OSCOR through simulations, and show that OSCOR can potentially reduce power consumption by over 30% compared with an existing greedy scheme, routing driven compression, in a 4 × 4 grid network.

In the present paper, we consider the problem of joint bandwidth (subcarriers) and power allocation for the downlink of a multi-user multi-cell OFDM cellular network. This resource allocation problem is formulated as a power minimization problem, subject to meeting the target rates of all users in the network. We develop a distributed solution to find the globally optimal allocation which determines the subcarrier and power allocation dynamically. In addition, we investigate the impact of reducing the complexity by reducing the number of degrees of freedom available in the optimization. In particular, we consider a static bandwidth allocation scheme, and a static power allocation scheme. The numerical results show that the penalty on network performance due to the reduction in the available degrees of freedom is not significant.

We prove that the XCP equilibrium solves a constrained max-min fairness problem by identifying it with the unique solution of a hierarchy of optimization problems, namely those solved by max-min fair allocation, but solved by XCP under an additional constraint. We describe an algorithm to compute this equilibrium and derive a lower and upper bound on link utilization. While XCP reduces to max-min allocation at a single link, in a network the additional constraint can cause a flow to receive an arbitrarily small fraction of its max-min allocation. We present simulation results to confirm our analytical findings.

Multiband code division multiple access (sometimes called multicarrier CDMA) is a promising approach to increasing the capacity of CDMA networks, while maintaining compatibility with existing systems. This paper investigates a family of algorithms for allocating new calls to bands based on measured path gains, or alternatively, on estimates of the users' positions. By separating strong and weak users into separate bands, this approach reduces the other-cell interference on the uplink. This is shown to reduce the number of calls in soft handoff, which reduces the hardware requirements at the base stations. Under a range of conditions, it also provides significantly lower outage than alternative algorithms. An additional benefit of this approach is a reduction in the dynamic range required for uplink power control.

In ad-hoc networks, the optimal hop size is a trade-off between the transmission errors and the number of hops required. This paper investigates the optimal hop size and transmission strategy in networks with overlaid base stations. The objective is to maximize the minimum throughput any user achieves, excluding traffic that it relays. The optimum depends on the routing algorithm used, and a specific receiver-based algorithm is proposed. Optimal parameters are derived using a simplified model, and are shown by simulation to out-perform systems with the optimal position-invariant hop size. For this choice of objective, improvement is obtained by using one hop size for the base station and another for relays.

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.

This paper considers the estimation of blocking probabilities of circuit-switched WDM networks with no wavelength converters and with fixed routing. It presents an importance sampling simulation technique for determining whether or not such a network meets a specific grade of service requirement, in the sense of all routes having blocking below a given threshold. It is especially efficient for networks with high grades of service, which take a long time to simulate using conventional methods.

The paper presents an improved version of CLAMP, a system that controls the behavior of TCP to enhance the performance of wireless access points. It only requires modifications to be made to the access network, and is totally compatible with TCP senders. We demonstrate its performance by simulation, and provide insight into the stability of the algorithm via analysis.

The paper presents a solution for providing differentiated capacity allocation in an access network. The system is based on CLAMP (curtailing the large TCP advertised window to maximize performance), an algorithm that can differentiate between flows sharing the same FIFO queue. The system is suitable for access networks, such as those based on DSL and HFC modems and wireless LAN access points. The deployment of CLAMP is completely contained within the access network; no changes to the remainder of the network are required. CLAMP provides the opportunity to enforce local policies on TCP flows that originate from sources distributed globally. The performance of CLAMP is verified by both simulation and analysis.

In multihop cellular networks, mobiles with no good path to any base station may instead relay their calls through other mobiles with better propagation conditions. This can improve coverage and capacity, and reduce the required total transmission power, but Its effectiveness depends greatly on the routing strategy used. This paper investigates the minimum possible aggregate transmit power in the presence of interference in a single-cell multihop cellular network. The new concept of interference-sensitive link costs is introduced, and is shown to perform substantially better than routing based solely on path loss, which is optimal in the noise-limited case.

Wireless local area networks have developed into a promising solution to support advanced data services in untethered environments. Selection of an efficient packet-scheduling scheme is important for managing the bandwidth while satisfying QoS requirements of active sessions having diverse traffic characteristics. The key difficulty is the distributed nature of the queues in the uplink, resulting in the scheduler having to trade off polling greedy stations against wasting resources by polling potentially idle stations. In order to address this, we propose a novel scheduling scheme, "embedded round robin", which dynamically classifies stations as "busy" and "clear". We then extend the previously proposed dual queue scheduling discipline to the case of wireless networks.

Multiband (or multi-carrier) CDMA is a promising approach to increasing the capacity of CDMA systems, while maintaining compatibility with existing systems. This paper proposes an algorithm for allocating new calls to bands based on measured path gains, or alternatively, on estimates of the mobile stations' positions. By separating strong and weak users into separate bands, this algorithm reduces the other-cell interference on the uplink. It is shown to provide significantly better performance than alternative algorithms when hard handoff is used. An additional benefit of this algorithm is a reduction in the dynamic range required for uplink power control.

In this paper we focus on real-time scheduling of "soft" real-time data services such as multimedia data, MPEG video streaming and IP telephony, which can tolerate a small degree of loss or delay. We argue that network operators and service providers should be able to select from a range of quality of service objectives, including maximizing the number of customers receiving good service. Further, we argue that scheduling disciplines such as fair queueing are unable to achieve such goals and hence there is a need for alternative approaches. We propose a new scheduling scheme, which we call the dual queue discipline. We show that the dual queue has the flexibility to satisfy a variety of QoS objectives, ranging from existing notions of fairness through to maximizing the number of customers receiving good service. In addition, even the simplest dual queue implementation outperforms fair queueing, is scalable in the number of active sessions, and can be made fair, if desired, over moderate to long time scales.

Wireless local area networks are a promising solution to support advanced data services in mobile environments. The IEEE 802.11 wireless LAN standard is emerging as a mature technology to support delay sensitive network services. In order to support these services the standard has proposed the use of a polling scheme; however, existing polling schemes require high communication overheads or suffer from unfairness. In this paper, we propose a distributed fair queueing algorithm "distributed deficit round robin", which is compatible with the 802.11 medium access control rules. Software simulation of this scheme shows that it can manage a heterogeneous mix of delay sensitive traffic.

This paper presents expressions for the optimal step length to use when training a vector quantizer by stochastic approximation. By treating each update as an estimation problem, it provides a unified framework covering both batch and incremental training, which were previously treated separately, and extends existing results to the semibatch case. In addition, the new results presented provide a measurable improvement over results which were previously thought to be optimal.

This note propose an alternative to a neural network for designing scaler quantizers proposed by Van Hulle and Martinez (ibid., vol.5, p.498-501, May 1994). It also points out that the performance measure used is of limited applicability.