Recent advancements in multimodal large language models (MLLMs) have garnered significant attention, offering a promising pathway toward artificial general intelligence (AGI).Among the essential capabilities required for AGI, creativity has emerged as a critical trait for MLLMs, with association serving as its foundation.Association reflects a model's ability to think creatively, making it vital to evaluate and understand.While several frameworks have been proposed to assess associative ability, they often overlook the inherent ambiguity in association tasks, which arises from the divergent nature of associations and undermines the reliability of evaluations.To address this issue, we decompose ambiguity into two types-internal ambiguity and external ambiguity-and introduce AssoCiAm, a benchmark designed to evaluate associative ability while circumventing the ambiguity through a hybrid computational method.We then conduct extensive experiments on MLLMs, revealing a strong positive correlation between cognition and association.Additionally, we observe that the presence of ambiguity in the evaluation process causes MLLMs' behavior to become more random-like.Finally, we validate the effectiveness of our method in ensuring more accurate and reliable evaluations.See Project Page for the data and codes.

The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling process in an end-to-end manner, focusing primarily on word-level interactions while often overlooking sentence-level representations. This limitation hampers the model’s ability to capture global contextual information, particularly when dealing with multi-word aspect and opinion terms in complex sentences. To address these issues, we propose boundary-driven table-filling with cross-granularity contrastive learning (BTF-CCL) to enhance the semantic consistency between sentence-level representations and word-level representations. By constructing positive and negative sample pairs, the model is forced to learn the associations at both the sentence level and the word level. Additionally, a multi-scale, multi-granularity convolutional method is proposed to capture rich semantic information better. Our approach can capture sentence-level contextual information more effectively while maintaining sensitivity to local details. Experimental results show that the proposed method achieves state-of-the-art performance on public benchmarks according to the F1 score.

Learning causal relationships in directed acyclic graphs (DAGs) from multi-type event sequences is a challenging task, especially in large-scale telecommunication networks. Existing methods struggle with the exponentially growing search space and lack global exploration. Gradient-based approaches are limited by their reliance on local information and often fail to generalize. To address these issues, we propose TCCD, a framework that combines Monte Carlo Tree Search (MCTS) with continuous gradient optimization. TCCD balances global exploration and local optimization, overcoming the shortcomings of purely gradient-based methods and enhancing generalization. By unifying various causal structure learning approaches, TCCD offers a scalable and efficient solution for causal inference in complex networks. Extensive experiments validate its superior performance on both synthetic and real-world datasets. Code and Appendix are available at https://github.com/jzephyrl/TCCD.

With feature size shrinking and design complexity increasing, detailed routing has become a crucial challenge in VLSI design. Although detailed routers have been proposed to judiciously handle hard-to-access pins and various design rules, their performances are sensitive to the order of nets to be routed, especially for those sequential routers with ripup-and-reroute scheme. In the published literature, net ordering strategies mainly rely on experts' knowledge to design heuristics to guarantee their performances. In this paper, we propose a novel transformer-based reinforcement learning framework for net ordering in detailed routing, aiming at automatically gaining failure/success routing experiences and building net order policies to guide detailed routing. Our experimental results show that our framework can effectively reduce the number of design rule violations and routing cost with comparable wirelength and via count, with comparison to state-of-the-art approaches.

In multimodal recommender systems (MRS), integrating various modalities helps to model user preferences and item characteristics more accurately, thereby assisting users in discovering items that match their interests. Although the introduction of multimodal information offers opportunities for performance improvement, it will increase the risks of inherent noise and information redundancy, posing challenges to the robustness of MRS. Many existing methods typically address these two issues separately either by introducing perturbations at the model input for robust training to handle noise or by designing complex network structures to filter out redundant information. In contrast, we propose the DVIB framework to simultaneously address both issues in a simple manner. We found that moving the perturbations from the input layer to the hidden layer, combined with feature self-distillation, can mitigate noise and handle information redundancy without altering the original network architecture. Additionally, we also provide theoretical evidence for the effectiveness of DVIB, demonstrating that the framework not only explicitly enhances the robustness of model training but also implicitly exhibits an information bottleneck effect, which effectively reduces redundant information during multimodal fusion and improves feature extraction quality. Extensive experiments show that DVIB consistently improves the performance of MRS across different datasets and model settings, and it can complement existing robust training methods, representing a promising new paradigm in MRS. See code at https://github.com/MarshmallowLight/DVIB.git.

In the forthcoming era of 6G networks, delay-sensitive applications for Internet of Things (IoT) are poised to become the prevailing services with ultra-reliable and low-latency (URLLC) requirements. Unlike traditional video caching, IoT-based edge caching faces unique challenges due to diverse data types, update frequencies, and computational needs, requiring integrated storage and computational resource management. To support the more stringent requirements for these innovative applications, mobile edge computing (MEC) is introduced to enhance the service reliability of delay-sensitive applications in the 6G era. However, task offloading, as an indispensable procedure in MEC, would encounter many challenges, such as network jitter and resource insufficiency, possibly leading to unpredictable queuing delays and other negative issues. To ensure reliable services in a dynamical MEC environment, the caching-enabled MEC network has emerged as a novel architecture, placing computing and storage resources in the edge network. In this paper, we investigate the caching-enabled MEC to support reliable task offloading for delay-sensitive applications, with a focus on IoT scenarios. In our system model, we formulate the task process as a two-hop tandem queuing system with limited capacity, including task transmission and computation queues. The Martingale theory is leveraged to analyze the delay violation probability in this system, demonstrating how the offloading and caching decisions affect the end-to-end (E2E) delay. Besides, task offloading and resource allocation policies are integrated to reduce high system costs, including energy consumption and cache resource rental costs. Based on the delay analysis of martingale theory, we propose an advanced deep reinforcement learning (DRL) algorithm called Dynamic Request Aware Soft Actor-Critic (DRA-SAC) algorithm to achieve minimal system costs by obtaining the optimal task offloading and resource allocation policies, including caching and computation resources. We conduct some illustrative studies to evaluate the proposed scheme. The algorithm we have put forward outperforms benchmark algorithms regarding both cache hit ratio and system cost.

The increasing demand for connectivity in 5G networks has led to a focus on massive machine-type communication (mMTC) in mobile edge computing (MEC) for IoTs. However, the proliferation of IoT devices has resulted in densely deployed networks and led to a high volume of task offloading to the same edge servers simultaneously. As a consequence, mMTC applications may experience service congestion, negatively impacting service reliability. To enhance the service reliability of latency-sensitive applications, task replication with resource allocation is proposed in MEC, in which a task can be sent simultaneously to multiple computing nodes. Task replication can reduce task latency and improve service reliability at the cost of consuming more computation resources. However, unconstrained task replication may result in too many uploading links, leading to severe costs in network operation. To handle the above challenge, we propose a constrained stochastic optimization problem by task replication with wireless resource block (RB) allocation and edge server queue management. To ensure queue stability while minimizing cost, we design one strategy based on the Lyapunov optimization framework. Accordingly, we further model RB allocation as a mean-field game (MFG) due to the intensive coupling of the RB pool for massive users. Tractable partial differential equations are used to analyze MFG equilibrium, and we derive the optimal edge server queue management based on a given task replication strategy and RB allocation scheme. Our theoretical analysis demonstrates that our algorithm closely approaches the optimal overall costs within a small gap, and simulation results show that our strategy generates a significantly lower cumulative cost than other alternative strategies.

Chain-of-Thought (CoT) [2], [3] guides large language models (LLMs) to reason step-by-step, and can motivate their logical reasoning ability. While effective for logi-cal tasks, CoT is not conducive to creative problem-solving which often requires out-of-box thoughts and is crucial for innovation advancements. In this paper, we explore the Leap-of-Thought (LoT) abilities within LLMs - a non-sequential, creative paradigm involving strong associations and knowledge leaps. To this end, we study LLMs on the popular Oogiri game which needs participants to have good creativity and strong associative thinking for responding unexpectedly and humorously to the given image, text, or both, and thus is suitable for LoT study. Then to investi-gate LLMs' LoT ability in the Oogiri game, we first build a multimodal and multilingual Oogiri-GO dataset which contains over 130,000 samples from the Oogiri game, and observe the insufficient LoT ability or failures of most existing LLMs on the Oogiri game. Accordingly, we introduce a creative Leap-of-Thought (CLoT) paradigm to improve LLM's LoT ability. CLoT first formulates the Oogiri-GO dataset into LoT-oriented instruction tuning data to train pre-trained LLM for achieving certain LoT humor generation and discrimination abilities. Then CLoT designs an ex-plorative self-refinement that encourages the LLM to gener-ate more creative LoT data via exploring parallels between seemingly unrelated concepts and selects high-quality data to train itself for self-refinement. CLoT not only excels in humor generation in the Oogiri game as shown in Fig. 1 but also boosts creative abilities in various tasks like “cloud guessing game” and “divergent association task”. These findings advance our understanding and offer a pathway to improve LLMs' creative capacities for innovative applications across domains. The dataset, code, and models have been released online: https://zhongshsh.github.io/CLoT.

Multimodal recommender systems utilize various types of information to model user preferences and item features, helping users discover items aligned with their interests. The integration of multimodal information mitigates the inherent challenges in recommender systems, e.g., the data sparsity problem and cold-start issues. However, it simultaneously magnifies certain risks from multimodal information inputs, such as information adjustment risk and inherent noise risk. These risks pose crucial challenges to the robustness of recommendation models. In this paper, we analyze multimodal recommender systems from the novel perspective of flat local minima and propose a concise yet effective gradient strategy called Mirror Gradient (MG). This strategy can implicitly enhance the model's robustness during the optimization process, mitigating instability risks arising from multimodal information inputs. We also provide strong theoretical evidence and conduct extensive empirical experiments to show the superiority of MG across various multimodal recommendation models and benchmarks. Furthermore, we find that the proposed MG can complement existing robust training methods and be easily extended to diverse advanced recommendation models, making it a promising new and fundamental paradigm for training multimodal recommender systems. The code is released at https://github.com/Qrange-group/Mirror-Gradient.

Vision Transformer (ViT)-based image super-resolution (SR) methods have achieved impressive performance and surpassed CNN-based SR methods by utilizing Multi-Head Self-Attention (MHSA) to model long-range dependencies. However, the quadratic complexity of MHSA and the inefficiency of non-parallelized window partition seriously affect the inference speed, hindering these SR methods from being applied to application scenarios requiring speed and quality. To address this issue, we propose an Asymmetric Large-kernel Attention Network (ALAN) utilizing a stage-to-block design paradigm inspired by ViT. In the ALAN, the core block named Asymmetric Large Kernel Convolution Block (ALKCB) adopts a similar structure to the Swin Transformer Layer but replaces the MHSA with our proposed Asymmetric Depth-Wise Convolution Attention (ADWCA) to enhance both the SR quality and inference speed. The proposed ADWCA, with linear complexity, uses large kernel depth-wise dilation convolution and Hadamard product as the attention map. The structural re-parameterization technique to strengthen the kernel skeletons with asymmetric convolution is also explored. Experimental results demonstrate that ALAN achieves state-of-the-art performance with faster inference speed than ViT-based models and smaller parameters than CNN-based models. Specifically, the tiny size of ALAN (ALAN-T) is$3\times$smaller than ShuffleMixer with similar performance, and ALAN is$4\times$faster than SwinIR-S with 0.1 dB gain in PSNR.

Within the paradigm of forthcoming 6G network infrastructures, unmanned aerial vehicles (UAVs), functioning as principal conveyances, are projected to emerge as pivotal enablers in the nascent domain of the low-altitude economy. UAVs are poised to embrace various innovative applications, including latency-sensitive and compute-intensive services. However, UAVs are constrained by their energy capacity and computational resources, rendering them insufficient for fulfilling the increasingly rigorous service demands in the future. To address these challenges, our investigation focuses on the innovative UAV-based Vehicular Edge Computing (UVEC) framework, incorporating Vehicular Edge Computing (VEC) in UAV systems to bolster service reliability. A UAV can enhance its mission duration by dynamically selecting suitable vehicles for computation offloading and adaptively adjusting the task offloading ratio between vehicles and the edge server. By integrating vehicle selection and task offloading scheduling in the UVEC framework, we investigate the optimization of energy efficiency while satisfying the statistical delay and the buffer constraints for UAVs. To deal with the proposed problem, a distributed algorithm is designed by jointly considering the vehicle selection for task offloading radio to vehicles and the edge server. The stochastic network calculus (SNC) is employed to derive performance bounds for the statistical delay and constraints, enabling robust analysis and optimization of network performance. After that, we leverage linear transformation techniques to reformulate the original problem into a linear framework, enabling the application of the Alternating Direction Method of Multipliers (ADMM) algorithm to efficiently solve the transformed problem. Theoretical analysis and simulation results show that our algorithm converges while effectively satisfying service reliability constraints within the desired targets, outperforming benchmark schemes in terms of efficiency while meeting task delay and error-rate bounded constraints.

With the proliferation of 5G networks, mobile edge computing (MEC) has emerged as a promising technology to fulfill the stringent requirements for reliability-aware services in the Internet of Things (IoT). However, in such networks, the wireless channel states and the task arrivals are stochastic and hard to predict well. Under this scenario, tasks generated from mobile devices would pile up in the transmission queue and edge computing queue when offloading to the edge cloud via a 5G network, resulting in quality degradation for reliability-aware services. To tackle the above challenges, we introduce non-orthogonal multiple access (NOMA) in MEC to meet the requirements of ultra-reliable and low-latency communications (URLLC), in which task queuing delay violation probability and transmission error probability are both considered. Furthermore, we explore the closed-form expression based on the effective capacity (EC) to derive the performance boundary of service reliability under a general model that multiple data sources are from different IoT devices and tasks are offloaded through two-stage transmission-computing tandem queues. Based on the above mathematical analysis for service reliability, we propose an efficient strategy combining power allocation and task offloading to reduce energy consumption for all devices in NOMA-enabled MEC. Extensive simulation studies are further conducted to validate the advantage of our strategy and show the significant performance gain of nearly up to 20% over other alternatives.

In computer vision, the performance of deep neural networks (DNNs) is highly related to the feature extraction ability, i.e., the ability to recognize and focus on key pixel regions in an image. However, in this paper, we quantitatively and statistically illustrate that DNNs have a serious attention bias problem on many samples from some popular datasets: (1) Position bias: DNNs fully focus on label-independent regions; (2) Range bias: The focused regions from DNN are not completely contained in the ideal region. Moreover, we find that the existing self-attention modules can alleviate these biases to a certain extent, but the biases are still non-negligible. To further mitigate them, we propose a lightweight sub-attention strategy (LSAS), which utilizes high-order sub-attention modules to improve the original self-attention modules. The effectiveness of LSAS is demonstrated by extensive experiments on widely-used benchmark datasets and popular attention networks. We release our code to help other researchers to reproduce the results of LSAS1

Diffusion models, which have emerged to become popular text-to-image generation models, can produce high-quality and content-rich images guided by textual prompts. However, there are limitations to semantic understanding and commonsense reasoning in existing models when the input prompts are concise narrative, resulting in low-quality image generation. To improve the capacities for narrative prompts, we propose a simple-yet-effective parameter-efficient fine-tuning approach called the Semantic Understanding and Reasoning adapter (SUR-adapter) for pre-trained diffusion models. To reach this goal, we first collect and annotate a new dataset SURD which consists of more than 57,000 semantically corrected multi-modal samples. Each sample contains a simple narrative prompt, a complex keyword-based prompt, and a high-quality image. Then, we align the semantic representation of narrative prompts to the complex prompts and transfer knowledge of large language models (LLMs) to our SUR-adapter via knowledge distillation so that it can acquire the powerful semantic understanding and reasoning capabilities to build a high-quality textual semantic representation for text-to-image generation. We conduct experiments by integrating multiple LLMs and popular pre-trained diffusion models to show the effectiveness of our approach in enabling diffusion models to understand and reason concise natural language without image quality degradation. Our approach can make text-to-image diffusion models easier to use with better user experience, which demonstrates our approach has the potential for further advancing the development of user-friendly text-to-image generation models by bridging the semantic gap between simple narrative prompts and complex keyword-based prompts. The code is released at https://github.com/Qrange-group/SUR-adapter.

Task offloading can scale the service capacity of IoT devices. However, IoT devices should go through wireless networks to connect with edge computing servers. The wireless network’s performance can not be guaranteed in the dynamic scenario of the mobile environment. Devices would obtain diverse channel quality in frequency, time, and space, which is affected by many factors, such as selective channel fading and path loss fading. The network experience varies significantly between devices, even allocating the same amount of resources for all devices. Besides, too many tasks offloaded to one edge server simultaneously could exhaust the network resources between devices and base station and computing resources in the edge server. So, allocating the communication resource and determining task offloading among devices is a critical issue that should be considered appropriately and comprehensively. Aiming at this problem, we propose a QoS-aware task offloading strategy by decomposing the original problem into two sub-problems: bandwidth resource block allocation and task offloading scheduling. In our approach, the bandwidth resource allocation from the 5G network and task offloading scheduling between multiple edge servers in one edge cloud are jointly considered in two successive phases. Our strategy enables the acceleration of task computation by fine-grained management of network resources in real-time. Simulation results show that our algorithm significantly improves task offloading utility and improves the utilization of network symbol resource.

With the rapid development of computing power and artificial intelligence, IoT devices equipped with ubiquitous sensors are gradually installed with intelligence. People can enjoy many conveniences with intelligent devices, such as face recognition, video understanding, and motion estimation. Currently, deep neural networks are the mainstream technology in intelligent mobile applications. Inspired by DNN model partition schemes, the paradigm of edge computing could be utilized collaboratively to improve the effectiveness of intelligent task execution in IoT devices. However, due to the dynamics of the wireless network environment and the increasing number of IoT devices, a DNN partition policy without adequate consideration would pose a significant challenge to the efficiency of task inference. Moreover, the shortage and high rental cost of edge computing resources make the optimization of DNN-based task execution more difficult. To cope with those situations, we propose a joint method by a self-adaptive DNN partition with cost-effective resource allocation to facilitate collaborative computation between IoT devices and edge servers. Our proposed online algorithm can be proved to ensure the overall rental cost within an upper bound above the optimal solution while guaranteeing the latency for DNN-based task inference. To evaluate the performance of our strategy, we conduct extensive trace-driven illustrative studies and show that the proposed method can achieve sub-optimal results and outperforms other alternative methods.

Software defect prediction constructs a model from the previous version of a software project to predict defects in the current version, which can help software testers to focus on software modules with more defects in the current version. Most existing methods construct defect prediction models through minimizing the defect prediction error measures. Some researchers proposed model construction approaches that directly optimized the ranking performance in order to achieve an accurate order. In some situations, the model complexity is also considered. Therefore, defect prediction can be seen as a multi-objective optimization problem and should be solved by multi-objective approaches. And hence, in this paper, we employ an existing multi-objective evolutionary algorithm and propose a new multi-objective learning method based on it, to construct defect prediction models by simultaneously optimizing more than one goal. Experimental results over 30 sets of cross-version data show the effectiveness of the proposed multi-objective approaches.

Recently, deep learning based single image super-resolution(SR) approaches have achieved great development. The state-of-the-art SR methods usually adopt a feed-forward pipeline to establish a non-linear mapping between low-res(LR) and high-res(HR) images. However, due to treating all image regions equally without considering the difficulty diversity, these approaches meet an upper bound for optimization. To address this issue, we propose a novel SR approach that discriminately processes each image region within an image by its difficulty. Specifically, we propose a dual-way SR network that one way is trained to focus on easy image regions and another is trained to handle hard image regions. To identify whether a region is easy or hard, we propose a novel image difficulty recognition network based on PSNR prior. Our SR approach that uses the region mask to adaptively enforce the dual-way SR network yields superior results. Extensive experiments on several standard benchmarks (e.g., Set5, Set14, BSD100, and Urban100) show that our approach achieves state-of-the-art performance.

Software defect prediction can help software testers to focus on software modules with more defects.Many ensemble methods have been proposed for software defect prediction to divide software modules into defect-prone and defect-free, and these ensemble methods have been proved to be more effective than single learning algorithms.A few ensemble approaches have been applied to predict the number of defects in software modules, and they also perform well in most cases.The good performance of ensemble approaches implies that ensemble algorithms might not only improve the accuracy of software defect classification models, but also improve the performance of defect ranking models.Therefore, we propose an ensemble method based on Yang et al.'s learning-to-rank approach in this paper.Experimental results show that the learning-to-rank-based ensemble approach performs better than the single learningto-rank approach, which means that the idea of ensemble can improve the performance of the learning-to-rank approach to sort modules in order of defect count.We also conduct a comparison study of ensemble approaches for cross-version defect prediction over 30 sets of cross-version data, which indicates that the ensemble technique of random subspace is more appropriate than boosting over these experimental data sets.

Empowered by today's rich media generating devices and convenient Internet access, crowdsourced live streaming (CSLS) service has developed rapidly and become one of the most popular Internet services. Large crowdsourced live streaming providers (CSLSPs) are migrating their services to geo-distributed cloud platforms (GDCPs) for lower costs and higher availability. A CSLSP may rent compute and network resources from cloud providers for video transcoding, video delivering, user-requests handling, and other related tasks. However, due to dynamic requests by viewers and widely spread locations of broadcasters and viewers, it is still challenging for a CSLSP to serve demands of users with reasonable resources from the cloud-based geo-distributed data centers. To overcome this challenge cost-effectively, we propose an online algorithm to save operational costs for CSLSPs by jointly and dynamically choosing right data centers for broadcasters and viewers. Mathematical analysis is presented and proves that our proposed online algorithm can ensure operational costs to be within an upper bound above the optimal solution, while guaranteeing the QoE for viewers. We conduct extensive trace-driven illustrative studies and show that the proposed method can achieve suboptimal results and outperforms other alternative methods.

Mobile edge computing (MEC) is a promising paradigm to integrate computing and communication resources in mobile networks. MEC can improve mobile service quality and enhance Quality of Experience (QoE) by offloading computation tasks to MEC servers. However, a MEC server only can provide limited computational resources for users. In this paper, we consider a mobile edge computing system that provides three offloading policies that are: (i) executing tasks in local device, (ii) offloading tasks to servers in a local region, (iii)offloading tasks to servers in a nearby region. In the policy (iii), mobile user equipment can utilize computational resources of MEC servers in nearby regions to solve the problem of insufficient computational resources in local region servers. We formulate the computation offloading problem as a potential game and propose a Distributed Offloading strategy based on Jacobi algorithm (DOJ) for solving the computation offloading problem in a short period. The simulation results show that our proposed algorithm can reduce overall system costs and guarantee the QoE of users.

Dynamic requests of viewers from sparse and dispersed locations for crowdsourced-live-streaming (CSLS) service make current cloud service providers (CSPs) inadequate to provide sufficient quality of experience (QoE). To solve this issue, we propose a multi-CDN-assisted-CSLS (MCACLS) architecture, a novel cloud architecture complemented by multiple content delivery networks (Multi-CDNs). MCACLS architecture can enhance a CSP's capacity of video distribution service and improve the quality of CSLS service for end-users while reducing the overall operational cost. MCACLS adaptively adjusts resources between a CSP and its leased CDN service in a fine granularity to deal with the volatility of user requests. However, scheduling resources cost-effectively in response to user requests from different regions is a critical issue that must be addressed. We formulate the above problem into a constrained stochastic optimization problem and propose an algorithm based on the Nash bargaining solution. Our proposed algorithm makes tradeoff between QoE of users and the overall operational cost for CSPs. Illustrative studies validate the advantages of MCACLS and show that it is more cost-effective, reducing the overall operational cost by up to 15% compared with other alternatives while achieving sufficient QoE for viewers.

Sorting software modules in order of defect count can help testers to focus on software modules with more defects. One of the most popular methods for sorting modules is generalized linear regression. However, our previous study showed the poor performance of these regression models, which might be caused by severe multicollinearity. Ridge regression (RR) can improve the prediction performance for multicollinearity problems. Lasso regression (LAR) is a worthy competitor to RR. Therefore, we investigate both RR and LAR models for cross-version defect prediction. Cross-version defect prediction is an approximate to real applications. It constructs prediction models from a previous version of projects and predicts defects in the next version. Experimental results based on 11 projects from the PROMISE repository consisting of 41 different versions show that: 1) there exist severe multicollinearity problems in the experimental datasets; 2) both RR and LAR models perform better than linear regression and negative binomial regression for cross-version defect prediction; and 3) compared with two best methods in our previous study for sorting software modules according to the predicted number of defects, RR has comparable performance and less model construction time.

This paper investigates a generalized protection framework for availability-guaranteed connection provisioning in an optical wavelength-division multiplexing (WDM) mesh network. We develop a link-state-modeling mechanism to form a dynamic link-state parameter, called link and resource availability (LRA). Such link-state information can be used by a standard link-state routing protocol to efficiently provision reliable connections. Based on the LRA parameter, we then propose a connection-provisioning algorithm which can guarantee customers' availability requirements. A new generalized protection model is developed through our dynamic LRA-based provisioning. Numerical results demonstrate the performance of the proposed provisioning approach to be promising.

In near video-on-demand (near-VoD), requests for a video title are grouped together (i.e. batched) and are served with a single multicast stream, thereby increasing the number of concurrent users which can be supported by the system. Since users may not be able to tolerate the delay incurred by batching and hence cancel their requests, a batching policy should be designed so as to achieve low user loss and high revenue (given by the total pay-per-view collected over a long period of time across all movies). We propose an adaptive batching policy which offers users low delay at low arrival rate, and gates the allocation of the channels at high rate. Such adaptivity is achieved by the use of a simple "token-tray" (TT) scheme which governs when a stream may be allocated to a movie. In assigning a movie to a stream, we propose a weight function which depends on the user queuing-time and its pay-per-view (hence the term "weighted queuing-time") (WQT). By comparing our batching policy (TT/WQT) with a number of traditional ones (FCFS, forced-wait, batch-size-based scheme, etc.), our scheme is shown to achieve the highest revenue and lowest loss rate even when the arrival rate changes, with the user loss rate across the movies being fairly uniform, and the user delay being fairly low even at high arrival rate.

We propose a WDM client-server network architecture based on a passive-star-coupler-based broadcast-and-select network. In this architecture, all down-stream data traffic uses WDM data channels and all up-stream requests, up-stream control messages, and down-stream control messages use an Ethernet channel (control channel). This architecture is broadcast and multicast capable. We propose a detailed point-to-point connection-setup procedure for this architecture. The system's performance is analyzed in terms of whether the control channel or the data channels are the bottleneck. We also analyze the request delay, request's channel-holding time and system throughput following the model description. We conclude that the control channel is not a bottleneck in the system. When the user request rate is high, the server-channel scheduler is the most important adjustable factor to reduce the user response time. An illustrative analysis of FCFS scheduling policy for the system is provided.

Layered video is a video compression technique to encode video data in multiple layers. It typically consists of a base layer and additional layers that provide enhanced video quality. The multicasting operation of a layered video may need to satisfy: (i) bounded end-to-end delay from a source to each receiver, (ii) minimum total cost, and (iii) minimum delay jitter between the various video streams received by the receivers. Because different nodes may request different video quality and because of limited bandwidth on the network's links, different layers of video data may reach their destinations over different distribution trees, and not all receivers may receive all of their requested layers. The problem of computing such data distribution paths is NP-complete, which means that no optimal solution method is available. This paper presents a new heuristic algorithm called LVMSR. With O(Rn/sup 2/) time complexity and O(R/sup 2/) message complexity, where n is the number of nodes in the network and R is the receiver group size. Our simulation results show that the multicast data paths computed by our algorithm can always satisfy the delay constraint with reasonably small total cost.