Precise quantum expectation values are crucial for quantum algorithm development, but noise in real-world systems can degrade these estimations. While quantum error correction is resource-intensive, error mitigation strategies offer a practical alternative. This paper presents a framework that combines Virtual Channel Purification (VCP) technique with Conditional Value-at-Risk (CVaR) optimization to improve expectation value estimations in noisy quantum circuits. Our contributions are twofold: first, we derive conditions to compare CVaR values from different probability distributions, offering insights into the reliability of quantum estimations under noise. Second, we apply this framework to VCP, providing analytical bounds that establish its effectiveness in improving expectation values, both when the overhead VCP circuit is ideal (error-free) and when it adds additional noise. By introducing CVaR into the analysis of VCP, we offer a general noise-characterization method that guarantees improved expectation values for any quantum observable. We demonstrate the practical utility of our approach with numerical examples, highlighting how our bounds guide VCP implementation in noisy quantum systems.

The Quantum Alternating Operator Ansatz (QAOA) is a prominent variational quantum algorithm for solving combinatorial optimization problems. Its effectiveness depends on identifying input parameters that yield high-quality solutions. However, understanding the complexity of training QAOA remains an under-explored area. Previous results have given analytical performance guarantees for a small, fixed number of parameters. At the opposite end of the spectrum, barren plateaus are likely to emerge at Ω (n) parameters for n qubits. In this work, we study the difficulty of training in the intermediate regime, which is the focus of most current numerical studies and near-term hardware implementations. Through extensive numerical analysis of the quality and quantity of local minima, we argue that QAOA landscapes can exhibit a superpolynomial growth in the number of low-quality local minima even when the number of parameters scales logarithmically with n. This means that the common technique of gradient descent from randomly initialized parameters is doomed to fail beyond small n, and emphasizes the need for good initial guesses of the optimal parameters.

Abstract We propose an efficient distributed out-of-memory implementation of the non-negative matrix factorization (NMF) algorithm for heterogeneous high-performance-computing systems. The proposed implementation is based on prior work on NMFk, which can perform automatic model selection and extract latent variables and patterns from data. In this work, we extend NMFk by adding support for dense and sparse matrix operation on multi-node, multi-GPU systems. The resulting algorithm is optimized for out-of-memory problems where the memory required to factorize a given matrix is greater than the available GPU memory. Memory complexity is reduced by batching/tiling strategies, and sparse and dense matrix operations are significantly accelerated with GPU cores (or tensor cores when available). Input/output latency associated with batch copies between host and device is hidden using CUDA streams to overlap data transfers and compute asynchronously, and latency associated with collective communications (both intra-node and inter-node) is reduced using optimized NVIDIA Collective Communication Library (NCCL) based communicators. Benchmark results show significant improvement, from 32X to 76x speedup, with the new implementation using GPUs over the CPU-based NMFk. Good weak scaling was demonstrated on up to 4096 multi-GPU cluster nodes with approximately 25,000 GPUs when decomposing a dense 340 Terabyte-size matrix and an 11 Exabyte-size sparse matrix of density $$10^{-6}$$ 10 - 6 .

Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is important for benchmarking and application considerations. Unfortunately, exact fidelity measurements quickly become prohibitively expensive, as they scale exponentially as O (3 N for N -qubit states, using full state tomography with measurements in all Pauli bases combinations. However, Somma et al.established that the complexity could be drastically reduced when looking at fidelity lower bounds for states that exhibit symmetries, such as Dicke states and GHZ states. These bounds must still be tight enough for larger states to provide reasonable estimations on NISQ devices. For the first time and more than 15 years after the theoretical introduction, we report meaningful lower bounds for the state preparation fidelity of all Dicke states up to N =10 and all GHZ states up to N =20 on Quantinuum H1 ion-trap systems using efficient implementations of recently proposed scalable circuits for these states. Our achieved lower bounds match or exceed previously reported exact fidelities on superconducting systems for much smaller states. Furthermore, we provide evidence that for large Dicke states \(\left|\smash{D_{N/2}^{N}} \right\rangle\) , we may resort to a GHZ-based approximate state preparation to achieve better fidelity. This work provides a path forward to benchmarking entanglement as NISQ devices improve in size and quality.

Quantum volume is a full-stack benchmark for near-term quantum computers. It quantifies the largest size of a square circuit which can be executed on the target device with reasonable fidelity. Error mitigation is a set of techniques intended to remove the effects of noise present in the computation of noisy quantum computers when computing an expectation value of interest. Effective quantum volume is a proposed metric that applies error mitigation to the quantum volume protocol to evaluate the effectiveness not only of the target device but also of the error mitigation algorithm. Digital zero-noise extrapolation is an error mitigation technique that estimates the noiseless expectation value using circuit folding to amplify errors by known scale factors and then extrapolating computed expectation values to the zero-noise limit. Here we demonstrate that zero-noise extrapolation, with global and local unitary folding with fractional scale factors, in conjunction with dynamical decoupling, can increase the effective quantum volume over the vendor-measured quantum volume. Specifically, we measure the effective quantum volume of four IBM Quantum superconducting processor units, obtaining values that are larger than the vendor-measured quantum volume on each device. This is the first such increase reported.

Estimating the state preparation fidelity of highly entangled states on noisy intermediate-scale quantum (NISQ) devices is an important task for benchmarking and application considerations. Unfortunately, exact fidelity measurements quickly become prohibitively expensive, as they scale exponentially as O(3N) for N-qubit states, using full state tomography with measurements in all Pauli bases combinations. However, Somma et al. [20] established that the complexity could be drastically reduced when looking at fidelity lower bounds for states that exhibit symmetries, such as Dicke States and GHZ States. For larger states, these bounds still need to be tight enough to provide reasonable estimations on NISQ devices.

Recent years have seen the adoption of Machine Learning (ML) techniques to predict the performance of large-scale applications, mostly at a coarse level. In contrast, we propose to use ML techniques for performance prediction at a much finer granularity, namely at the Basic Block (BB) level, which are single entry, single exit code blocks that are used for analysis by the compilers to break down a large code into manageable pieces. Utilizing ML and BB analysis together can enable scalable hardware-software co-design beyond the current state of the art. In this work, we extrapolate the basic block execution counts of GPU applications and use it for predicting the performance for large input sizes from the counts of smaller input sizes.We trained a Poisson Neural Network (PNN) model using random input values as well as the lowest input values of the application to learn the relationship between inputs and basic block counts. Experimental results show that the model can accurately predict the basic block execution counts of 16 GPU benchmarks. We achieved an accuracy of 93.5% for extrapolating the basic block counts for large input sets when the model is trained using smaller input sets. Additionally, the model shows an accuracy of 97.7% for predicting basic block counts on random instances. In a significant case study, we applied the ML model to CUDA GPU benchmarks for performance prediction across a spectrum of applications, spanning linear algebra to machine learning benchmarks. We employed a diverse set of metrics for evaluation, including global memory requests, tensor cores’ active cycles, and the active cycles of ALU and FMA units. The results from the case study demonstrate that the model is capable of predicting the performance of large datasets with high accuracy. For example, The average error rates for global and shared memory requests are 0.85% and 0.17%, respectively. Furthermore, to address the utilization of the main functional units in Ampere architecture GPUs, we calculated the active cycles for units like tensor cores, ALU, FMA, and FP64 units. Our predictions for the active cycles show an average error of 2.3% for the ALU and 10.66% for the FMA units, while the maximum observed error across all tested applications and units reaches 18.5%.

We study the status of fair sampling on Noisy Intermediate Scale Quantum (NISQ) devices, in particular the IBM Q family of backends. Using the recently introduced Grover Mixer-QAOA algorithm for discrete optimization, we generate fair sampling circuits to solve six problems of varying difficulty, each with several optimal solutions, which we then run on twenty backends across the IBM Q system. For a given circuit evaluated on a specific set of qubits, we evaluate: how frequently the qubits return an optimal solution to the problem, the fairness with which the qubits sample from all optimal solutions, and the reported hardware error rate of the qubits. To quantify fairness, we define a novel metric based on Pearson’s χ 2 test. We find that fairness is relatively high for circuits with small and large error rates, but drops for circuits with medium error rates. This indicates that structured errors dominate in this regime, while unstructured errors, which are random and thus inherently fair, dominate in noisier qubits and longer circuits. Our results show that fairness can be a powerful tool for understanding the intricate web of errors affecting current NISQ hardware.

As quantum computers become available to the general public, the need has arisen to train a cohort of quantum programmers, many of whom have been developing classical computer programs for most of their careers. While currently available quantum computers have less than 100 qubits, quantum computing hardware is widely expected to grow in terms of qubit count, quality, and connectivity. This review aims at explaining the principles of quantum programming, which are quite different from classical programming, with straightforward algebra that makes understanding of the underlying fascinating quantum mechanical principles optional. We give an introduction to quantum computing algorithms and their implementation on real quantum hardware. We survey 20 different quantum algorithms, attempting to describe each in a succinct and self-contained fashion. We show how these algorithms can be implemented on IBM’s quantum computer, and in each case, we discuss the results of the implementation with respect to differences between the simulator and the actual hardware runs. This article introduces computer scientists, physicists, and engineers to quantum algorithms and provides a blueprint for their implementations.

Traditional Parallel Discrete Event Simulation (PDES) systems employ a monolithic approach for choosing their thread synchronization protocol. They either implement a Time Window-based conservative synchronization or an optimistic event processing capability based on the Time Warp synchronization. In this paper, we show that this binary choice is suboptimal and unnecessary, particularly in the realistic situation where the load distribution across the simulation domain changes over time. We thus propose a new PDES synchronization scheme, called Hybrid PDES, that dynamically switches between conservative and optimistic synchronization protocols based on the simulation run time characteristics.

In this paper, we present PPT-GPU, a scalable performance prediction toolkit for GPUs. PPT-GPU achieves scalability through a hybrid high-level modeling approach where some computations are extrapolated and multiple parts of the model are parallelized. The tool primary prediction models use pre-collected memory and instructions traces of the workloads to accurately capture the dynamic behavior of the kernels.

GPUs are prevalent in modern computing systems at all scales. They consume a significant fraction of the energy in these systems. However, vendors do not publish the actual cost of the power/energy overhead of their internal microarchitecture. In this paper, we accurately measure the energy consumption of various PTX instructions found in modern NVIDIA GPUs. We provide an exhaustive comparison of more than 40 instructions for four high-end NVIDIA GPUs from four different generations (Maxwell, Pascal, Volta, and Turing). Furthermore, we show the effect of the CUDA compiler optimizations on the energy consumption of each instruction. We use three different software techniques to read the GPU on-chip power sensors, which use NVIDIA's NVML API and provide an in-depth comparison between these techniques. Additionally, we verified the software measurement techniques against a custom-designed hardware power measurement. The results show that Volta GPUs have the best energy efficiency of all the other generations for the different categories of the instructions. This work should aid in understanding NVIDIA GPUs' microarchitecture. It should also make energy measurements of any GPU kernel both efficient and accurate.

In this paper, we introduce an accurate and scalable memory modeling framework for General Purpose Graphics Processor units (GPGPUs), PPT-GPU-Mem. That is Performance Prediction Tool-Kit for GPUs Cache Memories. PPT-GPU-Mem predicts the performance of different GPUs' cache memory hierarchy (L1 & L2) based on reuse profiles. We extract a memory trace for each GPU kernel once in its lifetime using the recently released binary instrumentation tool, NVBIT. The memory trace extraction is architecture-independent and can be done on any available NVIDIA GPU. PPT-GPU-Mem can then model any NVIDIA GPU caches given their parameters and the extracted memory trace. We model Volta Tesla V100 and Turing TITAN RTX and validate our framework using different kernels from Polybench and Rodinia benchmark suites in addition to two deep learning applications from Tango DNN benchmark suite. We provide two models, MBRDP (Multiple Block Reuse Distance Profile) and OBRDP (One Block Reuse Distance Profile), with varying assumptions, accuracy, and speed. Our accuracy ranges from 92% to 99% for the different cache levels compared to real hardware while maintaining the scalability in producing the results. Finally, we illustrate that PPT-GPU-Mem can be used for design space exploration and for predicting the cache performance of future GPUs.

In this work, we accurately measure the energy consumption of the different instructions that can be executed in modern NVIDIA GPGPUs. We use three different software techniques to read the GPU on-chip power sensors, which use NVIDIA's NVML API and provide an in-depth comparison between these techniques. Additionally, we verified the software measurement techniques against a custom-designed hardware power measurement. The results show that Volta GPUs have the best energy efficiency of all the other generations for the different categories of the instructions. This work should give GPU architects and developers a more concrete understanding of these representative NVIDIA GPUs' microarchitecture. It should also make energy measurements of any GPU kernel both efficient and accurate.

In this work, we propose a very low overhead and portable analysis for exposing the hidden latency of each individual instruction executing in the pipeline and different access latencies of the various memory hierarchies at the microarchitecture level. We also show the impact of the possible optimizations a CUDA compiler have over the various latencies. We run our evaluation on seven different high-end NVIDIA GPUs from five different generations/architectures namely: Kepler, Maxwell, Pascal, Volta, and Turing.

GPU architects have introduced on-chip memories in GPUs to provide local storage nearby processing to reduce the traffic to the device global memory. From then on-wards, modeling to predict the cache performance has been an active area of research. However, due to the complexities found in this highly parallel hardware, this has not been a straightforward task. In this paper, we propose a memory model to predict the entire cache performance (L1 & L2 caches) in GPUs. Our model is based on reuse distance. We use an analytical probabilistic measure of the reuse distance distributions from the memory traces of an application to predict the hit rates. The application’s memory trace is extracted using NVIDIA’s SASSI instrumentation tool. We use 20 different kernels from Polybench and Rodinia benchmark suites and compare our model to the real hardware. The results show that the average prediction accuracy of the model over all the kernels is 86.7% compared to the real device with higher accuracy for the L2 (95.26%) cache than the L1. Furthermore, extracting the application’s memory trace is on average 4. 9x slower compared to the kernels running without instrumentation. This overhead is much smaller than other published results. Furthermore, our model is very flexible where it takes into account the different cache parameters thus it can be used for design space exploration and sensitivity analysis.

We present the Analytical Memory Model with Pipelines (AMMP) of the Performance Prediction Toolkit (PPT). PPT-AMMP takes high-level source code and hardware architecture parameters as input, predicts runtime of that code on the target hardware platform, which is defined in the input parameters. PPT-AMMP transforms the code to an (architecture-independent) intermediate representation, then (i) analyzes the basic block structure of the code, (ii) processes architecture-independent virtual memory access patterns that it uses to build memory reuse distance distribution models for each basic block, (iii) runs detailed basic-block level simulations to determine hardware pipeline usage. Further, PPT-AMMP uses machine learning and regression techniques to build the prediction models based on small instances of the input code, then integrates into a higher-order discrete-event simulation model of PPT running on Simian PDES engine. We validate PPT-AMMP on four standard computational physics benchmarks, finally present a use case of hardware parameter sensitivity analysis to identify bottleneck hardware resources on different code inputs.

This paper is devoted to the online dominating set problem and its variants. We believe the paper represents the first systematic study of the effect of two limitations of online algorithms: making irrevocable decisions while not knowing the future, and being incremental, i.e., having to maintain solutions to all prefixes of the input. This is quantified through competitive analyses of online algorithms against two optimal algorithms, both knowing the entire input, but only one having to be incremental. We also consider the competitive ratio of the weaker of the two optimal algorithms against the other. We consider important graph classes, distinguishing between connected and not necessarily connected graphs. For the classic graph classes of trees, bipartite, planar, and general graphs, we obtain tight results in almost all cases. We also derive upper and lower bounds for the class of bounded-degree graphs. From these analyses, we get detailed information regarding the significance of the necessary requirement that online algorithms be incremental. In some cases, having to be incremental fully accounts for the online algorithm’s disadvantage.

The capture of data about the events executed by a discrete event simulation can easily lead to very large trace data files. While disk space is relatively inexpensive and mostly capable of storing these large trace files, the manipulation and analysis of these large trace files can prove difficult. Furthermore, some types of analysis must be performed in-core and they cannot be performed with the trace data exceeds the size of the physical RAM where the analysis is performed. Because of these limits, it is often necessary to strictly limit the simulation run time to satisfy the analysis time memory limits. Experience with the DESMetrics tool suite (a collection of tools to analyze event trace files), demonstrates that our in-memory analysis tools are limited to trace files on the order of 10GB (on a machine with 24GB of RAM). Furthermore, even when it is possible to analyze large trace files, the run time costs of performing this analysis can take several days to complete. While high performance analysis of traces data is not strictly necessary, the results should be available within some reasonably bounded time frame. This paper explores techniques to overcome the limits of analyzing very large event trace files. While explorations for out-out-core analysis have been examined as part of this work, the run time costs for out-of-core processing can increase processing time 10-fold. As a result, the work reported here will focus on an approach to capture and analyze small samples from the event trace file. The work reported in this paper will examine how closely the analysis from sampling matches the analysis from a full trace file. Two techniques for comparison are presented. First a visual comparison of analysis results between the full trace and a trace sample are presented. Second, numerical quantification of the different analysis results (between the full trace and trace sample) will be reported using the Wasserstein, Directed Hausdorff, and Kolmogorov-Smirnov distance metrics. Finally, the ability to process trace samples from a very large trace file of 80GB is demonstrated.

Parallel application performance models provide valuable insight about the performance in real systems. Capable tools providing fast, accurate, and comprehensive prediction and evaluation of high-performance computing (HPC) applications and system architectures have important value. This paper presents PyPassT, an analysis based modeling framework built on static program analysis and integrated simulation of the target HPC architectures. More specifically, the framework analyzes application source code written in C with OpenACC directives and transforms it into an application model describing its computation and communication behavior (including CPU and GPU workloads, memory accesses, and message-passing transactions). The application model is then executed on a simulated HPC architecture for performance analysis. Preliminary experiments demonstrate that the proposed framework can represent the runtime behavior of benchmark applications with good accuracy.

As the US Department of Energy (DOE) invests in exascale computing, scalable performance modeling of physics codes on CPUs remains a hard challenge in computational codesign due to advanced design features of processors such as the memory hierarchy, instruction pipelining, and speculative execution. Reuse distance is a powerful (but unscalable) characteristic that helps to predict cache hit-rates. We propose, Analytical Memory Model (AMM), a novel hardware model based on cache memory hierarchies. AMM efficiently computes close approximations of reuse distance distributions through a combination of static analysis of basic code blocks and sampling from very small code instances. The results show that AMM accurately predicts reuse profiles of scientific mini-applications (for example, matrix multiplication). Coupling AMM with the Performance Prediction Toolkit (PPT), we further show a scalable runtime prediction of scientific codes on Intel Xeon.

Branch prediction is crucial in improving the throughput of microprocessors. It reduces branching stalls in the pipeline, which helps to maintain the instruction execution flow. Of these instructions, conditional branches are non-trivial in determining the microprocessor performance and throughput. Modern microprocessors accurately predict the branches using advanced branch prediction techniques. Appropriately estimating the branch mis-predictions benefits to improve the overall performance of an application through effectively saving the CPU cycles. In general, collecting branch prediction statistics using state-of-the-art simulators is time consuming and not scalable. We present a novel Monte Carlo simulation framework that predicts branch mis-prediction rate. Our framework produces results that suggest that the mis-prediction rates on three scientific applications are similar (with an average difference of 0.3%) to that of a Markov model of a 2-bit saturating branch predictor.

Conditional branch instructions have a significant effect on the microprocessor performance and throughput. Accurate branch prediction is crucial in reducing control hazards and improving microprocessor performance. Modern microprocessors accurately predict the branch outcomes using advanced prediction techniques. Estimating branch mis-prediction rates accurately helps to improve the overall performance by saving CPU cycles and power. In general, we run the application programs on cycle accurate hardware simulators such as GEM5 [4], to collect the branch prediction statistics. This method comes out to be time consuming and is also not scalable. We present a novel Monte Carlo simulation framework that produces the branch prediction rate statically, without actually running the application on the hardware. Our framework mimics the execution behavior of the real hardware. It uses one of the three different branch prediction schemes to calculate the branch prediction statistics. It also comments on the branch prediction rates of individual branches. Results suggest that the conditional prediction rates for four scientific applications are similar to that of results from the GEM5 [4] simulator.

Interconnection network is a critical component of high-performance computing architecture and application co-design. For many scientific applications, the increasing communication complexity poses a serious concern as it may hinder the scaling properties of these applications on novel architectures. It is apparent that a scalable, efficient, and accurate interconnect model would be essential for performance evaluation studies. In this paper, we present an interconnect model for predicting the performance of large-scale applications on high-performance architectures. In particular, we present a sufficiently detailed interconnect model for Cray's Gemini 3-D torus network. The model has been integrated with an implementation of the Message-Passing Interface (MPI) that can mimic most of its functions with packet-level accuracy on the target platform. Extensive experiments show that our integrated model provides good accuracy for predicting the network behavior, while at the same time allowing for good parallel scaling performance.

Graphs are widely used to characterize relationships or information flows among entities in large networks or distributed systems. In this work, we propose a systematic framework that leverages temporal similarity inherent in dynamic graphs for anomaly detection. This framework relies on the Neyman-Pearson criterion to choose similarity measures with high discriminative power for online anomaly detection in dynamic graphs. We formulate the problem rigorously, and after establishing its inapproximibility result, we develop a greedy algorithm for similarity measure selection. We apply this framework to dynamic graphs generated from email communications among thousands of employees in a large research institution and demonstrate that it works effectively on a set of more than 100 candidate graph similarity measures.

This paper presents findings from a study of the email network of a large scientific research organization, focusing on methods for visualizing and modeling organizational hierarchies within large, complex network datasets. In the first part of the paper, we find that visualization and interpretation of complex organizational network data is facilitated by integration of network data with information on formal organizational divisions and levels. By aggregating and visualizing email traffic between organizational units at various levels, we derive several insights into how large subdivisions of the organization interact with each other and with outside organizations. In the second part of the paper, we propose a power law model for predicting degree distribution of organizational email traffic based on hierarchical relationships between managers and employees. This model considers the influence of global email announcements sent from managers to all employees under their supervision, and the role support staff play in generating email traffic, acting as agents for managers.

We present the design and implementation of FileSim, a simulation framework with detailed models of parallel file systems, capable of reproducing the complex I/O behavior at scale. FileSim aims to support comprehensive and accurate end-to-end I/O performance prediction and evaluation of exascale high-end computing systems. To this end, FileSim provides several key features, including detailed, pluggable models of contemporary parallel file systems, the support of trace-driven simulation, and the capability of running large-scale I/O systems using parallel and distributed simulation.We conducted extensive validation and performance studies, through which we show that the simulator is capable of reproducing important I/O system behaviors comparable to those measured from the real systems. We demonstrate the capabilities of FileSim as a tool for exploring the parameter space and design alternatives of large-scale parallel file systems.

We revisit the problem of computing the path with the minimum cost in terms of the expected number of link layer transmissions (including retransmissions) in wireless mesh networks. Unlike previous efforts, such as the popular ETX, we account for the fact that MAC protocols (including the IEEE 802.11 MAC) incorporate a finite number of transmission attempts per packet. This in turn leads to our key observation: the performance of a path depends not only on the number of the links on the path and the quality of its links, but also, on the relative positions of the links on the path. Based on this observation, we propose ETOP, a path metric that accurately captures the expected number of link layer transmissions required for reliable end-to-end packet delivery. We analytically compute ETOP, which is not trivial, since ETOP is a noncommutative function of the link success probabilities. Although ETOP is a more involved metric, we show that the problem of computing paths with the minimum ETOP cost can be solved by a greedy algorithm. We implement and evaluate a routing approach based on ETOP on a 25-node indoor mesh network. Our experiments show that the path selection with ETOP consistently results in superior TCP goodput (by over 50 percent in many cases) compared to path selection based on ETX. We also perform an in-depth analysis of the measurements to better understand why the paths selected by ETOP improve the TCP performance.

Recently, tuning the clear channel assessment (CCA) threshold in conjunction with power control has been considered for improving the performance of WLANs. However, we show that, CCA tuning can be exploited by selfish nodes to obtain an unfair share of the available bandwidth. Specifically, a selfish entity can manipulate the CCA threshold to ignore ongoing transmissions; this increases the probability of accessing the medium and provides the entity a higher, unfair share of the bandwidth. We experiment on our 802.11 testbed to characterize the effects of CCA tuning on both isolated links and in 802.11 WLAN configurations. We focus on AP-client(s) configurations, proposing a novel approach to detect this misbehavior. A misbehaving client is unlikely to recognize low power receptions as legitimate packets; by intelligently sending low power probe messages, an AP can efficiently detect a misbehaving node. Our key contributions are: 1) We are the first to quantify the impact of selfish CCA tuning via extensive experimentation on various 802.11 configurations. 2) We propose a lightweight scheme for detecting selfish nodes that inappropriately increase their CCAs. 3) We extensively evaluate our system on our testbed; its accuracy is 95 percent while the false positive rate is less than 5 percent.

Online social networks, which have been expanding at a blistering speed recently, have emerged as a popular communication infrastructure for Internet users. Meanwhile, malware that specifically target these online social networks are also on the rise. In this work, we aim to investigate the characteristics of malware propagation in online social networks. Our study is based on a dataset collected from a real-world location-based online social network, which includes not only the social graph formed by its users but also the users' activity events. We analyze the social structure and user activity patterns of this network, and confirm that it is a typical online social network, suggesting that conclusions drawn from this specific network can be translated to other online social networks. We use extensive trace-driven simulation to study the impact of initial infection, user click probability, social structure, and activity patterns on malware propagation in online social networks. We also investigate the performance of a few user-oriented and server-oriented defense schemes against malware spreading in online social networks and identify key factors that affect their effectiveness. We believe that this comprehensive study has deepened our understanding of the nature of online social network malware and also shed light on how to defend against them effectively.

In this paper, we study some geographic aspects of the Internet. We base our analysis on a large set of geolocated IP hop-level session data (including about 300, 000 backbone routers, 130 million end hosts, and one billion sessions) that we synthesized from a variety of different input sources such as US census data, computer usage statistics, Internet market share data, IP geolocation data sets, CAIDA's Skitter data set for backbone connectivity, and BGP routing tables. We use this model to perform a nationwide and statewide geographic analysis of the Internet. Our main observations are: (1) There is a dominant coast-to-coast pattern in the US Internet traffic. In fact, in many instances even if the end-devices are not near either coast, still the traffic between them takes a long detour through the coasts. (2) More than half of the Internet paths are inflated by 100% or more compared to their corresponding geometric straight-line distance. This circuitousness makes the average ratio between the routing distance and geometric distance big (around 10). (3) The weighted mean hop count is around 5, but the hop counts are very loosely correlated with the distances. The weighted mean AS count (number of ASes traversed) is around 3.

Botnets are one of the most serious security threats to the Internet and its end users. In recent years, utilizing P2P as a Command and Control (C&C) protocol has become popular due to its decentralized nature that can help hide the botmaster's identity. Most bot detection approaches targeting P2P botnets either rely on behavior monitoring or traffic flow and packet analysis, requiring fine-grained information collected locally. This requirement limits the scale of detection. In this paper, we consider detection of P2P botnets at a high-level - the infrastructure level-by exploiting their structural properties from a graph analysis perspective. Using three different P2P overlay structures, we measure the effectiveness of detecting each structure at various locations (the Autonomous System (AS), the Point of Presence (PoP), and the router rendezvous) in the Internet infrastructure.

Recently, peer-to-peer (P2P) networks have emerged as a covert communication platform for malicious programs known as bots. As popular distributed systems, they allow bots to communicate easily while protecting the botmaster from being discovered. Existing work on P2P-based botnets mainly focuses on measurement-based studies of botnet behaviors. In this work, through simulation, we study extensively the structure of P2P networks running Kademlia, one of a few widely used P2P protocols in practice. Our simulation testbed not only incorporates the actual code of a real Kademlia client software to achieve high realism, but also applies distributed event-driven simulation techniques to achieve high scalability. Using this testbed, we analyze the scaling, clustering, reachability, and various centrality properties of P2P-based botnets from a graph-theoretical perspective. We further demonstrate experimentally and theoretically that monitoring bot activities in a P2P network is difficult, suggesting that the P2P mechanism indeed helps botnets hide their communication effectively. Finally, we evaluate the effectiveness of some potential mitigation techniques, such as content poisoning, sybil-based and eclipse-based mitigation. Conclusions drawn from this work shed light on the structure of P2P botnets, how to monitor bot activities in P2P networks, and how to mitigate botnet operations effectively.

The rising popularity of mobile devices, such as cellular phones and PDAs, has made them a lucrative playground for mobile malware propagation. One common infection vector exploited by these mobile malware is Bluetooth. In this paper, we propose an architecture called Blue-Watchdog that detects Bluetooth worm propagation in public areas based on statistical methods. To achieve fast and accurate Bluetooth worm detection, Blue-Watchdog monitors abrupt changes of average paging rate per Bluetooth device from both temporal and temporal-spatial perspectives. The temporal scheme relies on the CUSUM (Cumulative Sum) sequential test together with the generalized likelihood ratio (GLR), and the temporal-spatial scheme aims to identify spatial regions with abnormally frequent paging attempts. Experimental results show that Blue-Watchdog not only has low false alarm rates, but also effectively detects Bluetooth worms that spread quickly in areas where Bluetooth devices are greatly mixed due to high mobility and also those that propagate relatively slowly in a spatially constrained fashion.

We describe the various aspects involved in building FastTrans, a scalable, parallel microsimulator for transportation networks that can simulate and route tens of millions of vehicles on real-world road networks in a fraction of real time. Vehicular trips are generated using agent-based simulations that provide realistic, daily activity schedules for a synthetic population of millions of intelligent agents. We use parallel discrete-event simulation techniques and distributed-memory algorithms to scale these simulations to over one thousand compute nodes. We present various optimizations for speeding up simulation execution times, including (i) a set of routing algorithms such as variations of Dijkstra's shortest path algorithm and heuristic-based A* search, and (ii) a number of different partitioning schemes for load balancing, including geographic partitioning (that assigns simulation entities that are geographically close by to the same processor) and scattering (that assigns geographically close by entities to different processors). Our main findings include: (i) A* significantly outperforms other routing algorithms while computing near-optimal paths; (ii) surprisingly, scattering outperforms more sophisticated partitioning schemes by achieving near-perfect load-balancing. With optimized routing and partitioning, FastTrans is able to simulate a full 24 hour work-day in New York - involving over one million road links and approximately 25 million vehicular trips - in less than one hour of wall-clock time on a 512-node cluster.

Recently, tuning the clear channel assessment (CCA) threshold in conjunction with power control has been considered for improving the performance of Wireless LANs. However, CCA tuning can be exploited by selfish nodes in order to obtain an unfair share of the available bandwidth. In particular, by increasing the CCA threshold, a selfish client can manipulate the carrier sensing mechanism to ignore the presence of other transmissions on the medium; consequently, it increases the probability of accessing the medium and therefore obtains a higher, unfair share of the available bandwidth. In this paper, we propose a novel approach to detect this misbehavior in WLANs. A key insight that leads to our approach is that a misbehaving node that has increased its CCA is unlikely to recognize low power receptions as legitimate packets; by intelligently sending low power probe messages, an AP can detect a misbehaving node with high probability. In a nutshell, our contributions are as follows: (a) We are the first to quantify the impact of selfish CCA tuning via extensive experimentation (b) We propose a novel lightweight scheme for detecting selfish nodes that inappropriately increase their CCA thresholds; we call our scheme CMD (for carrier sensing misbehavior detection) (c) We perform extensive evaluations on an indoor 802.11 WLAN testbed to demonstrate that CMD detects misbehaving users with very high accuracy (approximately 95 % of the time). Furthermore, it only incurs a false positive rate of less than 5 %.

Recent years have witnessed widespread use of mobile devices such as cell phones, laptops, and PDAs. In this paper, we propose an architecture called Mobi-Watchdog to detect mobility anomalies of mobile devices in wireless networks that track their locations regularly. Given the past mobility records of a mobile device, Mobi-Watchdog uses clustering techniques to identify the high-level structure of its mobility and then trains a HHMM (hierarchical hidden Markov model). Mobi-Watchdog raises an alert by requesting the device holder to reauthenticate himself when it finds an observed mobility trace significantly deviates from the trained model. The time complexity of the original generalized Baum-Welch algorithm, which is used for HHMM parameter reestimation, scales linearly with T3, where T is the number of locations in an observed sequence. Such a high computational cost can significantly impede deployment of Mobi-Watchdog in large-scale wireless networks in practice. To achieve better scalability, we modify this algorithm to make it scale linearly with T instead. Experimental results with realistic mobility traces demonstrate that Mobi-Watchdog detects mobility anomalies with high probability and reasonably low false alarm rates. We also show that Mobi-Watchdog has very low computational overhead, which makes it a viable candidate for mobility anomaly detection in large wireless networks.

In the last few years, the growing popularity of mobile devices has made them attractive to virus and worm writers. One communication channel often exploited by mobile malware is the Bluetooth interface. In this paper, we present a detailed analytical model that characterizes the propagation dynamics of Bluetooth worms. Our model captures not only the behavior of the Bluetooth protocol but also the impact of mobility patterns on the Bluetooth worm propagation. Validation experiments against a detailed discrete-event Bluetooth worm simulator reveal that our model predicts the propagation dynamics of Bluetooth worms with high accuracy. We further use our model to efficiently predict the propagation curve of Bluetooth worms in big cities such as Los Angeles. Our model not only sheds light on the propagation dynamics of Bluetooth worms, but also allows to predict spreading curves of Bluetooth worm propagation in large areas without the high computational cost of discrete-event simulation.

We present PedSims, a suite of mobility models spanning the entire range from the classic random waypoint (RWP), to correlated movement, to trace-level-quality social-activity-based mobility. Instrumenting PedSims at various levels of structural complexity we assess and quantify the impact of real world mobility patterns, such as common interests or synchronous behavior, on ad hoc network performance. Thus, PedSims provides not only a flexible platform for mobility simulation but also a means to strike a trade-off between a tolerable error and computational complexity. Notably, as our study of DSR, AODV and SAFARI finds, neglecting to capture mobility patterns may result in inversion of the ranking of routing protocols as well as in over- and under-estimation of network performance. Further contributions include a bias correction for the sampling of node-node connection time.

We revisit the problem of computing the path with the minimum cost in terms of the expected number of link layer transmissions (including retransmissions) in wireless mesh networks. Unlike previous efforts, such as the popular ETX, we account for the fact that MAC protocols (including the IEEE 802.11 MAC) incorporate a finite number of transmission attempts per packet. This in turn leads to our key observation: the performance of a path depends not only on the number of the links on the path and the quality of its links, but also, on the relative positions of the links on the path. Based on this observation, we propose ETOP, a path metric that accurately captures the expected number of link layer transmissions required for reliable end-to-end packet delivery. We analytically compute ETOP, which is not trivial, since ETOP is a noncommutative function of the link success probabilities. Although ETOP is a more involved metric, we show that the problem of computing paths with the minimum ETOP cost can be solved by a greedy algorithm. We implement and evaluate a routing approach based on ETOP on a 25-node indoor mesh network. Our experiments show that the path selection with ETOP consistently results in superior TCP goodput (by over 50% in many cases) compared to path selection based on ETX. We also perform an in-depth analysis of the measurements to better understand why the paths selected by ETOP improve the TCP performance.

Firewalls are widely deployed nowadays to enforce security policies of enterprise networks. While having played crucial roles in securing these networks, firewalls themselves are subject to performance limitations. An overloaded firewall can cause severe damage to the protected enterprise network, because any legitimate communication through it is either degraded or even completely severed. In this paper, we address how to dynamically balance packet filtering workloads on distributed firewalls efficiently in large enterprise networks. We model dynamic load balancing on distributed firewalls as a minimax optimization problem, and show that it is strongly NP-complete even if we eliminate all precedence relationships among policy rules by rule rewriting. Accordingly, we propose a light-weight rule distribution scheme that quickly balances workloads among all firewalls. Our scheme is adaptive to incoming traffic. Moreover, dynamically placing and ordering policy rules on distributed firewalls reduces the probability that attackers successfully infer the rule distribution. Experimental results show that using a commodity PC, our approach can reduce the peak firewall workload in distributed firewall systems by 40% within less than five minutes, compared against alternative solutions that only optimize rule ordering on individual firewalls.

Multicast streaming is gaining increasing importance in wireless ad hoc networks, in part because ad hoc scenarios often include team activities and the requirement for distribution of audio, video and situation awareness to the members. At the network level, techniques for routing the multimedia streams are quite mature. Much more challenging is the allocation of resources, the fair sharing among streams and the control of congestion.

We consider the problem of establishing a route and sending packets between a source/destination pair in ad hoc networks composed of rational selfish nodes whose purpose is to maximize their own utility. In order to motivate nodes to follow the protocol specification, we use side payments that are made to the forwarding nodes. Our goal is to design a fully distributed algorithm such that (1) a node is always better off participating in the protocol execution (individual rationality), (2) a node is always better off behaving according to the protocol specification (truthfulness), (3) messages are routed along the most energy-efficient (least cost) path, and (4) the message complexity is reasonably low. We introduce the COMMIT protocol for individually rational, truthful, and energy-efficient routing in ad hoc networks. To the best of our knowledge, this is the first ad hoc routing protocol with these features. COMMIT is based on the VCG payment scheme in conjunction with a novel game-theoretic technique to achieve truthfulness for the sender node. By means of simulation, we show that the inevitable economic inefficiency is small. As an aside, our work demonstrates the advantage of using a cross-layer approach to solving problems: Leveraging the existence of an underlying topology control protocol, we are able to simplify the design and analysis of our routing protocol and reduce its message complexity. On the other hand, our investigation of the routing problem in the presence of selfish nodes disclosed a new metric under which topology control protocols can be evaluated: the cost of cooperation.

The alarm that worms start to spread on increasingly popular mobile devices calls for an in-depth investigation of their propagation dynamics. In this paper, we study how mobility patterns affect Bluetooth worm spreading speeds. We find that the impact of mobility patterns is substantial over a large set of of changing Bluetooth and worm parameters. For instance, a mobility model under which devices move among a fixed set of activity locations can result in worm propagation speeds four times faster than a classical mobility model such as the random walk model. Our investigation reveals that the key factors affecting Bluetooth worm propagation speeds include spatial distributions of nodes, link duration distributions, degrees to which devices are mixed together, and even the burstiness of successive links.

In this paper, we present queue management algorithms with low implementation complexity that can partition the bandwidth of an outgoing link among flows in a high speed packet switch. These algorithms belong to a family of queue management schemes called sending rate estimate based queue management schemes (SREQM), which try to prevent congestion by effectively limiting flows based on their estimated sending rates. We also present techniques based on sampling and Bloom filters to further reduce the implementation overhead. The capability of the algorithms to protect responsive flows from non-responsive flows are confirmed by exhaustive analysis and simulations.

We propose a rigorous mathematical interpretation of a novel family of active queue management schemes, called sending rate estimate based queue management (SREQM) scheme, that aims to provide fair bandwidth allocation to all the flows in a router by estimating the flow sending rates, while maintaining only minimal per-flow state information. We propose an optimized implementation of SREQM, called fair sending rate estimate based queue management (FSREQM) scheme, and show through comparative simulation that FRESQM is the only scheme among those tested that successfully prevents bandwidth abuse while maintaining high link utilization.

The growing popularity of mobile devices in the last few years has made them attractive to virus and worm writers. One communication channel exploited by mobile malware is the Bluetooth interface. In this paper, we present a detailed analytical model that characterizes the propagation dynamics of Bluetooth worms. Our model captures not only the behavior of the Bluetooth protocol but also the impact of mobility patterns on the Bluetooth worm propagation. Validation experiments against a detailed discrete-event Bluetooth worm simulator reveal that our model predicts the propagation dynamics of Bluetooth worms with high accuracy.

Scalable and reliable routing is a critical issue in sensor network deployment. A number of approaches have been proposed for sensor network routing, but sensor field implementation tends to be lacking in the literature. In our study, the problems of scalability and reliability in sensor network routing are addressed through a simple but powerful scheme implemented on Mica2 motes running TinyOS along with other, more widely-used routing protocols. Motes are tested in an outdoor sensor field, and detailed experiments are carried out for performance analysis. This paper presents the implementation details and the results obtained from head-to-head comparison of routing protocols. The proposed protocol delivers 93% of packets injected at a rate of one packet per second in networks with end to end hop distances of over 10 hops-a result which significantly improves upon results from the standard TinyOS routing implementation of MINTRoute. The promising results can be explained by the key protocol properties of reliability (via multi-path redundancy), scalability (with efficiently contained flooding), and flexibility (source-tunable per-packet priority) which are achieved without adding protocol complexity or resource consumption. These strengths enable the protocol to outperform even sophisticated link estimation based protocols especially in adverse outdoor sensor field environments.

We revisit the problem of computing the path with the minimum cost in terms of the expected number of link layer retransmissions in wireless mesh networks. Unlike previous efforts (such as the popular ETX) we account for the fact that link layer protocols (such as the IEEE 802.11 MAC) incorporate a non-zero but finite number of retransmission attempts per packet. A key observation that motivates this work is that the performance of a path depends not only on the number of links on the path and their qualities, but also on the relative positions of the links on the path. In particular, the closer a lossy link to the destination, the higher is its impact on the performance of that path. We design a new path metricthat captures all of the above factors and we call this metric ETOP. In this paper, we provide a synopsis of the analytical computation of ETOP. We also implement a routing strategy based on ETOP on a 25-node experimental testbed and provide sample results to showcase the performance with ETOP.

The occurrences of mobile worms like Cabir, Mabir and CommWarrior have created growing concerns over the security of data stored on mobile devices such as cell phones and PDAs. These worms have in common that they all use Bluetooth communication as their infection channel. In order to prepare effective defense strategies against such worms, we study the nature, characteristics, and spreading dynamics of Bluetooth worms in the safe environment of simulation. Our key findings are: (i) mobility may not boost the Bluetooth worm propagation; instead, link instability owing to it has negative impact on the worm spreading speed; (ii) the inherent capacity constraints imposed by the wireless channel (e.g. interference) and the specifics of the Bluetooth protocol can significantly slow down the Bluetooth worm propagation; (iii) intelligently designed worms can improve their propagation speed to a noticeable degree by strategically selecting worm model parameters or exploiting out-of-band propagation capabilities

Discrete event simulation is an indispensable tool to understand the dynamics of communication networks and evaluate their performance. As the scale and complexity of these networks increases, simulation itself becomes a computationally prohibitive undertaking. Among all possible solutions, improving the performance of event manipulation operations is an important one. In this paper, we discover that in network simulation events are often inserted into the simulation kernel in their timestamp order. Based on this observation, we make some simple modifications on the conventional calendar queue. Experiments show that the new data structure can achieve two orders of execution speedup against the conventional calendar queue in some wireline network simulation and in wireless network simulation, the speedup scales well with the network size.

We propose novel solutions for unicast routing in wireless networks consisted of selfish terminals: in order to alleviate the inevitable over-payment problem (and thus economic inefficiency) of the VCG (Vickrey-Clark-Groves) mechanism, we design a mechanism that results in Nash equilibria rather than the traditional strate-gyproofness (using weakly dominant strategy). In addition, we systematically study the unicast routing system in which both the relay terminals and the service requestor (either the source or the destination nodes or both) could be selfish. To the best of our knowledge, this is the first paper that presents social efficient unicast routing systems with proved performance guarantee. Thus, we call the proposed systems: Optimal Unicast Routing Systems (OURS).Our main contributions of OURS are as follows. (1) For the principal model where the service requestor is not selfish, we propose a mechanism that provably creates incentives for intermediate terminals to cooperate in forwarding packets for others. Our mechanism substantially reduces the overpayment by using Nash equilibrium solutions as opposed to strategyproof solutions. We then study a more realistic case where the service requestor can act selfishly. (2) We first show that if we insist on the requirement of strategyproofness for the relay terminals, then no system can guarantee that the central authority can retrieve at least 1overn of the total payment. (3) We then present a strategyproof unicast system that collects 1over2n of the total payment, which is thus asymptotically optimum. (4) By only requiring Nash Equilibrium solutions, we propose a system that creates incentives for the service requestor and intermediate terminals to correctly follow the prescribed protocol. More importantly, the central authority can retrieve at least half the total payment. We verify the economic efficiency of our systems through simulations that are based on very realistic terminal distributions.

Smart antennas have gained significant importance in multi-hop wireless networks in recent years, because of their sophisticated signal processing capabilities that hold the potential for increased data rates and reliability. In this work, we consider the problem of communication in multi-hop wireless networks with smart antennas (specifically digital adaptive arrays). These smart antennas provide degrees of freedom (DOFs) that can be used to suppress co-existing communication links, thereby increasing spatial reuse in the network. Thus, the communication problem comprises of not just determining a channel access mechanism to be used by the communication links, but also involves the determination of the communication pattern (usage of DOFs) to be used by each node during channel access. To the best of our knowledge, our work is the first step towards addressing this problem.We first consider the problem of determining the communication pattern to be used by the nodes and formulate it combinatorially with the goal of optimizing network performance through interference minimization. We present efficient centralized and distributed algorithms that are within a factor of ¾ and ½ of the optimum solution respectively. We then extend the distributed algorithm to incorporate TDMA-based scheduling in a purely localized manner. The distributed algorithms are then evaluated through simulations in ns2 and insights are drawn into the potential performance benefits of smart antennas in multi-hop wireless networks.

We introduce a game-theoretic setting for routing in a mobile ad hoc network that consists of greedy, selfish agents who accept payments for forwarding data for other agents if the payments cover their individual costs incurred by forwarding data. In this setting, we propose Ad hoc-VCG, a reactive routing protocol that achieves the design objectives of truthfulness (i.e., it is in the agents' best interest to reveal their true costs for forwarding data) and cost-efficiency (i.e., it guarantees that routing is done along the most cost-efficient path) in a game-theoretic sense by paying to the intermediate nodes a premium over their actual costs for forwarding data packets. We show that the total overpayment (i.e., the sum of all premiums paid) is relatively small by giving a theoretical upper bound and by providing experimental evidence. Our routing protocol implements a variation of the well-known mechanism by Vickrey, Clarke, and Groves in a mobile network setting. Finally, we analyze a very natural routing protocol that is an adaptation of the Packet Purse Model [8] with auctions in our setting and show that, unfortunately, it does not achieve cost-efficiency or truthfulness.

The problem MINIMUM CONVEX COVER of covering a given polygon with a minimum number of (possibly overlapping) convex polygons is known to be NP-hard, even for polygons without holes [J. C. Culberson and R. A. Reckhow, J. Algorithms, 17 (1994), pp. 2--44]. We propose a polynomial-time approximation algorithm for this problem for polygons with or without holes that achieves an approximation ratio of O(log n), where n is the number of vertices in the input polygon. To obtain this result, we first show that an optimum solution of a restricted version of this problem, where the vertices of the convex polygons may lie only on a certain grid, contains at most three times as many convex polygons as the optimum solution of the unrestricted problem. As a second step, we use dynamic programming to obtain a convex polygon which is maximum with respect to the number of "basic triangles" that are not yet covered by another convex polygon. We obtain a solution that is at most a logarithmic factor off the optimum by iteratively applying our dynamic programming algorithm. Furthermore, we show that MINIMUM CONVEX COVER is APX-hard; i.e., there exists a constant $\delta >0$ such that no polynomial-time algorithm can achieve an approximation ratio of $1+\delta$. We obtain this result by analyzing and slightly modifying an already existing reduction [J. C. Culberson and R. A. Reckhow, J. Algorithms, 17 (1994), pp. 2--44].

We present a prototype system for a simple version of electromagnetic wave propagation prediction in rural areas, using a real time exploration environment for very large topographic scenes. The wave propagation prediction algorithm is based on a simple line of sight approach, realized with a modified hidden surface removal algorithm. The system serves as a transmitter management tool for interactively placing transmitters and studying the effects. Improvements according to the propagation prediction and automatic optimized placement of transmitters are current subjects of our research.