The management of large-scale events poses significant resilience challenges to urban traffic systems, where classical computational methods often prove inadequate for the resulting complex, dynamic optimization problems. This paper introduces a quantum-enhanced hybrid high-performance computing framework designed to improve traffic resilience during such events. The framework leverages a Quantum Annealing (QA) algorithm to solve a Quadratic Unconstrained Binary Optimization (QUBO) model of key traffic optimization tasks, integrated with classical computing for data processing and workflow control. Using the traffic management for a major concert at Hefei Luogang Park as a case study, we focus on modeling and applying this framework to two core resilience problems: Dynamic Evacuation Route Optimization (DERO) and Emergency Resource Allocation (ERA). Our simulation results provide empirical validation of the framework's effectiveness, demonstrating that the proposed quantumenhanced approach reduces total evacuation time by 20.0 % and average emergency response time by 32.2 % compared to welldefined greedy heuristics. These findings highlight the potential of quantum computing as an emerging high-performance computing paradigm for addressing the intricate challenges of traffic resilience.

The proliferation of Unmanned Aerial Vehicles (UAVs) for urban public safety is critically hindered by operational risks inherent in complex environments. To address these challenges, this paper introduces a “UAV-as-a-Service” (UaaS) paradigm, an application paradigm innovation that leverages the core infrastructure of telecom operators. Our primary contribution is a closed-loop intelligent method, representing an algorithmic innovation, centered on a 3D Dynamic Risk Map (DRM). The DRM is generated in real-time by a Dynamic Bayesian Network (DBN) and explicitly informs both a risk-aware Multi-Agent Reinforcement Learning (MARL) dispatcher and a hybrid Rapidly-exploring Random Tree Star (RRT*)-Model Predictive Control (MPC) path planner. This tight coupling ensures that tactical decisions are grounded in a holistic understanding of risk. Simulation results demonstrate that the proposed UaaS model substantially enhances operational outcomes, reducing the time to achieve critical situational awareness by over 75 % and decreasing firefighter risk exposure by$\mathbf{7 8 \%}$. These technical advancements validate a novel and viable Business-to-Government (B2G) service model, demonstrating significant technologycommercial synergy.

The escalating adoption of electric vehicles (EVs) presents a critical challenge for urban planning: the strategic placement of charging infrastructure, especially when integrating intermittent renewable energy. Existing approaches to this problem often rely on time-series models that neglect spatial dependencies, or they employ simplistic weighted-sum optimizations that fail to resolve the conflicting interests of diverse stakeholders. To overcome these limitations, we introduce a novel Hierarchical Spatio-Temporal Graph Neural Network (H-STGNN) framework. This framework integrates two core components: (1) a Spatio-Temporal Graph Neural Network (ST-GNN) that leverages fine-grained mobile operator data to accurately forecast charging demand by capturing complex spatio-temporal correlations across a city-wide graph. (2) a hierarchical multi-objective optimization model, solved by the NSGA-II algorithm, to generate Pareto-optimal placement strategies. Our model prioritizes maximizing renewable energy consumption and grid stability at a strategic level, while optimizing user convenience and operator profitability at an operational level. Experiments on a large-scale, real-world dataset from Hefei, China, show that our ST-GNN reduces prediction error (MAE) by over 25% compared to state-of-theart baselines. The resulting placement strategies increase renewable energy utilization by over 18% and reduce user generalized costs by 12%, offering a robust and scalable solution for green urban transportation planning.

Electricity is one of most widely used energies and encouraged to be saved by scientific management and new technologies such as Time-of-Use policy. Batch scheduling can significantly improve production efficiency and is used in many high electricity consumption and high technology industries. This paper investigates a new bi-objective single machine batch scheduling problem with TOU policy. The first objective is to improve productivity and the second aims to minimize the total electricity cost. For the problem, a bi-objective mixed integer nonlinear programming model is formulated. Its corresponding single objective optimization problems are linearized by analyzed properties such that the multiobjective ε-constraint method can be used to obtain Pareto solutions.

In this paper, we analyze virtual machine (VM) scalability on multi-core systems for compute-, memory-, and network I/O-intensive workloads. The VM scalability evaluation under these three workloads will help cloud users to understand the performance impact of underlying system and network architectures. We demonstrate that VMs on the state-of-the-art multi-core processor based systems scale as well as multiple threads on native SMP kernel for CPU and memory intensive workloads. Intra-VM communication of network I/O intensive TCP message workload has a lower overhead compared to multiple threads when VMs are pinned to specific cores. However, VM scalability is severely limited for such workloads for across-VM communication on a single host due to virtual bridges. For across local and wide area network communication, the network bandwidth is the limiting factor. Unlike previous studies that use workload mixes, we apply a single workload type at a time to clearly attribute VM scalability bottlenecks to system and network architectures or virtualization itself.

L7-filter is a significant component in Linux's QoS framework that classifies network traffic based on application layer data. It enables subsequent distribution of network resources in respect to the priority of applications. Considerable research has been reported to deploy multi-core architectures for computationally intensive applications. Unfortunately, the proliferation of multi-core architectures has not helped fast packet processing due to: 1) the lack of efficient parallelism in legacy network programs, and 2) the non-trivial configuration for scalable utilization on multi-core servers.In this paper, we propose a highly scalable parallelized L7-filter system architecture with affinity-based scheduling on a multi-core server. We start with an analytical study of the system architecture based on an offline design. Similar to Receive Side Scaling (RSS) in the NIC, we develop a model to explore the connection level parallelism in L7-filter and propose an affinity-based scheduler to optimize system scalability. Performance results show that our optimized L7-filter has superior scalability over the naive multithreaded version. It improves system performance by about 50% when all the cores are deployed.

Stream-based simultaneous XPath processing plays a critical role in service oriented networking, where the processing must scale well in terms of concurrent input streams and number of XPath queries. However, there are no benchmarks or evaluation methodology in existing literatures that benchmark stream-based XPath engines supporting simultaneous queries. In this paper, we describe a novel benchmarking methodology for evaluating XPath engines which handle simultaneous queries on streaming traffics. With structured data model, query model, and control model in our benchmark, we conduct well controlled experiments to assess and isolate various performance factors. We also demonstrate that our structured, quantified approach with wide data set coverage, enables accurate performance measurements, and easy bottleneck isolations of a real-world XPath engine implementation.

Cisco Systems application oriented networking (AON) product is an important network element towards building next generation service oriented intelligent information network (IIN). AON processes application-level content and moves far beyond a conventional content-aware Web switch. It creates a novel content delivery platform and allows more sophisticated load balancing schemes to be deployed in a switch among back-end servers to reduce user-perceived response time. In this paper, we investigate different scheduling techniques for an AON system to maximize overall throughput and minimize latency per message for a heterogeneous server cluster consisting of different application servers. Based on a thorough evaluation of the three existing load balancing algorithms of AON, we propose a novel message type based service adaptive scheduling algorithm that makes AON more efficient and more intelligent. Systematic performance measurements, analyses, and comparisons are conducted to demonstrate the superiority of our intelligent message scheduling technique.

Cisco systems' application oriented networking (AON) is an important network element towards building next generation service oriented network. AON processes application- level content and moves far beyond a conventional content-aware Web switch. It creates a novel content delivery platform and allows more sophisticated message-level load balancing mechanisms to be deployed in a switch/router in front of back-end servers. In addition to the three existing load balancing algorithms of current AON system, round robin (RR), weighted round robin (WRR) and Adpative (ADP), we propose a new intelligent message-type-based adaptive scheduling algorithm that can maximize system performance with minimal user configuration complexity. We implemented this novel scheduler into AON system and conducted performance studies on all four scheduling schemes in terms of overall throughput, average latency, and server utilization. Experimental results show that our proposed scheduler outperforms the three existing schemes in all evaluation cases.

There is a growing trend to insert application intelligence into network devices. Processors in this type of application-oriented networking (AON) devices are required to handle both packet-level network I/O intensive operations as well as XML message-level CPU intensive operations. In this paper, we investigate performance effect of dual processing via (1) hyperthreading, (2) uni-processor to dual- processor, and (3) single-core to dual-core, on both packet-level and XML message-level traffic. We analyze and cross-examine the dual processing effect from both high-level performance as well as processor microarchitectural perspectives. We employ on-chip performance counters to measure cycles per instruction, cache misses, bus utilization, and branch miss predictions for this work. Our results show a significant improvement in dual-core Pentium M processor over Hyperthreaded Xeon processor for AON workload. These results will not only provide insight to processor designers, but also help architects of AON devices to select from alternative processors with restrictions to use one or two physical CPUs due to space and power consumption limitations.

In this paper, we characterize the performance of several business and technical benchmarks on a Pentium Pro processor based system. Various architectural data are collected using a performance monitoring counter tool. Results show that the Pentium Pro processor achieves significantly lower cycles per instruction than the Pentium processor due to its out of order and speculative execution, and non-blocking cache and memory system. Its higher clock frequency also contributes to even higher performance.

This paper presents a unified model for analyzing multistage interconnection networks with multi-queue buffered strategies. Buffering strategies include SAFC (statically allocated fully connected), SAMQ (statically allocated multi-queue), DAMQ (dynamically allocated multi-queue), and DAFC (dynamically allocated fully connected) schemes. We develop a unified model to evaluate the performance of all these buffer allocation schemes under both uniform and nonuniform traffic patterns. The analytical model is validated through extensive simulations. Using the unified model, we conducted performance comparisons on the four buffer allocation schemes. It is shown that the DAFC scheme has the best performance over all the four buffer allocation schemes under both uniform and nonuniform loads.

Proposes an analysis technique for a class of Multistage Interconnection Networks (MIN's) that have finite buffers at their switch inputs and operate in a synchronous packet-switched mode. The authors examine the issue of clock period in design and analysis of synchronous MIN's and propose a model based on small clock periods. Then they analyze their "small cycle" design and compare the results with those obtained from the standard "big cycle" model that is currently used. The significant performance improvement of their model is shown based on various clock width, data width, and buffer length.>

In this paper, we propose an adaptive scan (AS) strategy for submesh allocation. The earlier frame sliding (FS) strategy allocates submeshes based on fixed orientations of incoming faska. It also slides fiunaes om mesh planes by fdzed strides. Our AS a1Iocation strategy differs from the FS strategy in the following two ways: (1) it does not fiz the orientations of incoming tasks; (2) it scans on mesh planes adapfively. Experimental studies show that our AS strategy outperforms the FS strategy in terms of external fragmentation, completion time, and processor uitilizaiion.