IEEE INFOCOM 2024

Program at a Glance

IEEE INFOCOM 2024

Session Break-3-1: Coffee Break Session Break-3-2: Coffee Break

Session A-8: A-8: Mobile Networks and Applications Session A-9: A-9: Crowdsourcing and crowdsensing Session A-10: A-10: RF and Physical Layer Session A-11: A-11: Topics in Wireless and Edge Networks

Session B-8: B-8: Streaming Systems Session B-9: B-9: Localization and Tracking Session B-10: B-10: Network Verification and Tomography Session B-11: B-11: Topics in Secure and Reliable Networks

Session C-8: C-8: Staleness and Age of Information (AoI) Session C-9: C-9: Internet of Things (IoT) Networks Session C-10: C-10: Network Security and Privacy Session C-11: C-11: User experience, Orchestration, and Telemetry

Session D-8: D-8: Backscatter Networking Session D-9: D-9: RFID and Wireless Charging Session D-10: D-10: High Speed Networking Session D-11: D-11: Network Computing and Offloading

Session E-8: E-8: Machine Learning 2 Session E-9: E-9: Machine Learning 3 Session E-10: E-10: Machine Learning 4 Session E-11: E-11: Machine Learning 5

Session F-8: F-8: Internet Architectures and Protocols Session F-9: F-9: Hashing, Clustering, and Optimization Session F-10: F-10: Spectrum Access and Sensing

Session Lunch-3: Conference Lunch (for Registered Attendees)

Session G-8: G-8: Ethereum Networks and Smart Contracts Session G-9: G-9: Modeling and Optimization Session G-10: G-10: Edge Networks

LightCross: Sharding with Lightweight Cross-Shard Execution for Smart Contracts

Xiaodong Qi and Yi Li (Nanyang Technological University, Singapore)

Sharding is a prevailing solution to enhance the scalability of current blockchain systems. However, the cross-shard commit protocols adopted in these systems to commit cross-shard transactions commonly incur multi-round shard-to-shard communication, leading to low performance. Furthermore, most solutions only focus on simple transfer transactions without supporting complex smart contracts, preventing sharding from widespread applications. In this paper, we propose LightCross, a novel blockchain sharding system that enables the efficient execution of complex cross-shard smart contracts. First, LightCross offloads the execution of cross-shard transactions into off-chain executors equipped with the TEE hardware, which can accommodate execution for arbitrarily complex contracts. Second, we design a lightweight cross-shard commit protocol to commit cross-shard transactions without multi-round shard-to-shard communication between shards. Last, LightCross lowers the cross-shard transaction ratio by dynamically changing the distribution of contracts according to historical transactions. We implemented the LightCross prototype based on the FISCO-BCOS project and evaluated it in real-world blockchain environments, showing that LightCross can achieve 2.6x more throughput than state-of-the-art sharding systems.

Speaker

Speaker biography is not available.

ConFuzz: Towards Large Scale Fuzz Testing of Smart Contracts in Ethereum

Taiyu Wong, Chao Zhang and Yuandong Ni (Institute for Network Sciences and Cyberspace, Tsinghua University, China); Mingsen Luo (University of Electronic Science and Technology of China, China); HeYing Chen (University of Science and Technology of China, China); Yufei Yu (Tsinghua University, China); Weilin Li (University of Science and Technology of China, China); Xiapu Luo (The Hong Kong Polytechnic University, Hong Kong); Haoyu Wang (Huazhong University of Science and Technology, China)

Fuzzing is effective at finding vulnerabilities in traditional applications and has been adapted to smart contracts. However, existing fuzzing solutions for smart contracts are not smart enough and can hardly be applied to large-scale testing since they heavily rely on source code or ABI. In this paper, we propose a fuzzing solution ConFuzz applicable to large-scale testing, especially for bytecode-only contracts. ConFuzz adopts Adaptive Interface Recovery (AIR) and Function Information Collection (FIC) algorithms to automatically recover the function interfaces and information, supporting fuzzing smart contracts without source code or ABI. Furthermore, ConFuzz employs a Dependence-based Transaction Sequence Generation (DTSG) algorithm to infer dependencies of transactions and generate high-quality sequences to trigger the vulnerabilities. Lastly, ConFuzz utilizes taint analysis and function information to help detect harmful vulnerabilities and reduce false positives. The experiment shows that ConFuzz can accurately recover over 99.7% of function interfaces and reports more vulnerabilities than state-of-the-art solutions with 98.89% precision and 93.69% accuracy. On all 1.4M unique contracts from Ethereum, ConFuzz found over 11.92% vulnerable contracts. To the best of our knowledge, ConFuzz is the first efficient and scalable solution to test all smart contracts deployed in Ethereum.

Speaker Taiyu Wong(Tsinghua University)

Blockchain engineer at Tsinghua University

Deanonymizing Ethereum Users behind Third-Party RPC Services

Shan Wang, Ming Yang, Wenxuan Dai and Yu Liu (Southeast University, China); Yue Zhang (Drexel University, USA); Xinwen Fu (University of Massachusetts Lowell, USA)

Third-party RPC services have become the mainstream way for users to access Ethereum. In this paper, we present a novel deanonymization attack that can link an Ethereum address to a real-world identity such as IP address of a user who accesses Ethereum via a third-party RPC service. We find that RPC API calls result in distinguishable sizes of encrypted TCP packets. An attacker can then find when a user sends a transaction to a RPC provider and immediately send a beacon transaction after the user transaction. By exploiting the differences in the distributions of inter-arrival time intervals of normal transactions and two simultaneously initiated transactions, the attacker can identify the victim transaction in the Ethereum network. This enables the attacker to correlate the Ethereum address of the victim transaction's initiator with the source IP address of TCP packets from a victim user. We model the attack through empirical measurements and conduct extensive real-world experiments to validate the effectiveness of our attack. With three optimization strategies, the correlation accuracy can reach to 98.70% and 96.60% respectively in Ethereum testnet and mainnet. We are the first to study the deanonymization of Ethereum users behind third-party RPC services.

Speaker Shan Wang

Shan Wang is currently a Postdoctoral Fellow in the Department of Computing, The Hong Kong Polytechnic University. She obtained her Ph.D. degree from Southeast University. In the period from Sep. 2019 to May 2023, she studied at UMass Lowell, USA as a visiting scholar. Her past work mainly focuses on the security problems in permissioned blockchain. Currently, she is working on application of crypto in blockchain and de-anonymization in public blockchain.

DEthna: Accurate Ethereum Network Topology Discovery with Marked Transactions

Chonghe Zhao (Shenzhen University, China); Yipeng Zhou (Macquarie University, Australia); Shengli Zhang and Taotao Wang (Shenzhen University, China); Quan Z. Sheng (Macquarie University, Australia); Song Guo (The Hong Kong University of Science and Technology, Hong Kong)

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism. Consequently, existing measuring schemes cannot accurately infer the Ethereum network topology with a low cost. To address this challenge, we propose the Distributed Ethereum Network Analyzer (DEthna) tool, which can accurately and efficiently measure the Ethereum network topology. In DEthna, a novel parallel measurement model is proposed that can generate marked transactions to infer link connections based on the transaction replacement and propagation mechanism in Ethereum. Moreover, a workload offloading scheme is designed so that DEthna can be deployed on multiple probing nodes so as to measure a large-scale Ethereum network at a low cost. We run DEthna on Goerli (the most popular Ethereum test network) to evaluate its capability in discovering network topology. The experimental results demonstrate that DEthna significantly outperforms the state-of-the-art baselines. Based on DEthna, we further analyze characteristics of the Ethereum blockchain network which reveals that there exist more than 50% low-degree Ethereum nodes weakening network robustness.

Speaker

Speaker biography is not available.

Session Chair

Wenhai Sun (Purdue University, USA)

Enter Zoom

AnalyticalDF: Analytical Model for Blocking Probabilities Considering Spectrum Defragmentation in Spectrally-Spatially Elastic Optical Networks

Imran Ahmed and Roshan Kumar Rai (South Asian University, India); Eiji Oki (Kyoto University, Japan); Bijoy Chand Chatterjee (South Asian University, India)

Recently, multi-core and multi-mode fibres (MCMMFs) have been considered to overcome physical limitations and increase transport capacity. They are combined with elastic optical networks (EONs) to form spectrally-spatially elastic optical networks (SS-EONs), an emerging technology. Fragmentation and crosstalk (XT) are well-known drawbacks of SS-EONs that increase blocking probability; evaluating blocking probability analytically is difficult due to additional constraints. When calculating blocking probabilities in MCMMFs-based SS-EONs, it is unsurprising that all current studies either employ simulation-based techniques or do not consider defragmentation of their analytical models. This paper proposes AnalyticalDF, an exact analytical continuous-time Markov chain model for blocking probabilities in SS-EONs, which considers defragmentation and the XT-avoided approach. AnalyticalDF generates all possible states and transitions while avoiding inter-core and inter-mode XTs for single-class and multi-class requests. Single-class requests utilize the same number of slots, whereas multi-class requests adopt varying numbers of slots to accommodate client needs. We introduce an iterative approximation model for a single-hop link when AnalyticalDF is not tractable due to scalability. We extend the single-hop model for multi-hop networks further. We evaluate AnalyticalDF, the iterative approximate model, and simulation studies for a single-hop link. The numerical results indicate that AnalyticalDF outperforms a non-defragmentation-aware benchmark model.

Speaker

Speaker biography is not available.

Modeling Average False Positive Rates of Recycling Bloom Filters

Kahlil A Dozier, Loqman Salamatian and Dan Rubenstein (Columbia University, USA)

Bloom Filters are a space-efficient data structure used for testing membership in a set that errs only in the false positive direction. However, the standard analysis that measures this false positive rate provides a form of worst case bound that is overly conservative for the majority of network applications that utilize Bloom Filters, and reduces accuracy by not taking into account the Bloom Filter state (number of bits set) after each arrival. In this paper, we more accurately characterize the false positive dynamics of Bloom Filters as they are commonly used in networking applications. Network applications often utilize a Bloom Filter that "recycles": it repeatedly fills, empties and fills again. Users of a Recycling Bloom Filter are often best served by the average false positive rates as opposed to the worst case. We efficiently compute the average false positive rate of a recycling Bloom Filter as a Markov model and derive exact expressions for the long-term false positive rate. We apply our model to the standard Bloom Filter and a "two-phase" variant, verify model accuracy with simulations, and find that the previous worst-case formulation leads to a reduction in the efficiency of Bloom Filter when applied in network applications.

Speaker

Speaker biography is not available.

On Ultra-Sharp Queueing Bounds

Florin Ciucu and Sima Mehri (University of Warwick, United Kingdom (Great Britain)); Amr Rizk (University of Duisburg-Essen, Germany)

We present a robust method to analyze a broad range of classical queueing models, e.g., the $GI/G/1$ queue with renewal arrivals, an $AR/G/1$ queue with alternating renewals (AR), as a special class of Semi-Markovian processes, and Markovian fluids queues. At the core of the method lies a standard change-of-measure argument to reverse the sign of the \textit{negative} drift in the underlying random walks. Combined with a suitable representation of the overshoot, we obtain exact results in terms of series. Closed-form and computationally fast bounds follow by taking the series' first terms, which are the dominant ones because of the \textit{positive} drift under the new probability measure. The obtained bounds generalize the state-of-the-art class of martingale bounds and can be much sharper by orders of magnitude.

Speaker

Speaker biography is not available.

Optimization of Offloading Policies for Accuracy-Delay Tradeoffs in Hierarchical Inference

Hasan Burhan Beytur, Ahmet Gunhan Aydin, Gustavo de Veciana and Haris Vikalo (The University of Texas at Austin, USA)

We consider a hierarchical inference system with multiple clients connected to a server via a shared communication resource. When necessary, clients with low-accuracy machine learning models can offload classification tasks to a server for processing on a high-accuracy model. We propose a distributed online offloading algorithm which maximizes the accuracy subject to a shared resource utilization constraint thus indirectly realizing accuracy-delay tradeoffs possible given an underlying network scheduler. The proposed algorithm, named Lyapunov-EXP4, introduces a loss structure based on Lyapunov-drift minimization techniques to the bandits with expert advice framework. We prove that the algorithm converges to a near-optimal threshold policy on the confidence of the clients' local inference without prior knowledge of the system's statistics and efficiently solves a constrained bandit problem with sublinear regret. We further consider settings where clients may employ multiple thresholds, allowing more aggressive optimization of overall accuracy at a possible loss in fairness. Extensive simulation results on real and synthetic data demonstrate convergence of Lyapunov-EXP4, and show the accuracy-delay-fairness trade-offs achievable in such systems.

Speaker Hasan Burhan Beytur (The University of Texas at Austin)

Session Chair

Ningning Ding (Northwestern University, USA)

Enter Zoom

Minimizing Latency for Multi-DNN Inference on Resource-Limited CPU-Only Edge Devices

Tao Wang (Tianjin University, China); Tuo Shi (City University of Hong Kong, Hong Kong); Xiulong Liu (Tianjin University, China); Jianping Wang (City University of Hong Kong, Hong Kong); Bin Liu (Tsinghua University, China); Yingshu Li (Georgia State University, USA); Yechao She (City University of Hong Kong, Hong Kong)

Despite the rapid development of specialized hardware, a vast number of IoT edge devices remain powered by traditional CPUs. As the number of IoT users expands, performing multiple Deep Neural Networks inference on these resource-constrained, CPU-only edge devices presents significant challenges. Existing solutions such as model compression, hardware acceleration, and model partitioning either compromise inference accuracy, are not applicable due to hardware specificity, or result in inefficient resource utilization. To address these issues, this paper introduces L-PIC (Latency Minimized Parallel Inference on CPU), a framework that is specifically designed to optimize resource allocation, minimize inference latency, and maintain result accuracy on CPU-only edge devices. Comprehensive experiments validate the superior efficiency and effectiveness of the L-PIC framework compared to existing methods.

Speaker Tao Wang (Tianjin University)

Tao Wang received his BE degree in Computer Science and Technology from Ocean University of China, China. He is currently working toward the master’s degree in the College of Intelligence and Computing, Tianjin University, China. His research interests include edge computing and DNN Inference.

M3OFF: Module-Compositional Model-Free Computation Offloading in Multi-Environment MEC

Tao Ren (Institute of Software Chinese Academy of Sciences, China); Zheyuan Hu, Jianwei Niu and Weikun Feng (Beihang University, China); Hang He (Hangzhou Innovation Institute, Beihang University & Beihang University, China)

Computation offloading is one of the key issues in mobile edge computing (MEC) that alleviates the tension between user equipment's limited capabilities and mobile application's high requirements. To achieve model-free computation offloading when reliable MEC dynamics are unavailable, deep reinforcement learning (DRL) has become a popular methodology. However, most existing DRL-based offloading approaches are developed for a single MEC environment, with invariant system bandwidth, edge capability, task types, etc., while realistic MEC scenarios tend to be of high diversity. Unfortunately, in multi-MEC environments, DRL-based offloading faces at least two challenges, learning inefficiency and interference of offloading experiences. To address the challenges, we propose a DRL-based Multi-environmental Module-compositional Model-free computation OFFloading (M3OFF) framework. M3OFF generates offloading policies using module composition instead of a single DRL network so that learning efficiency could be improved by reusing the same modules and learning interference could be reduced by composing different modules. Furthermore, we design multiple module composition-specific training methods for M3OFF, including alternate modules-and-composer updates to improve training stability, loss-regularization to avoid module degeneration, and module-dropout to mitigate overfitting. Extensive experimental results on both simulation and testbed demonstrate that M3OFF outperforms state-of-the-art by more than 16.7% in multi-MEC and reaches performances close to single-MEC.

Speaker Zheyuan Hu (Beihang University, China)

Zheyuan Hu received the B.S. degree in computer science and engineering from Northeastern University, Shenyang, China, in 2017, and the M.S. degree in computer science and engineering from Beihang University, Beijing, China, in 2021. He is currently pursuing the Ph.D. degree with the School of Computer Science and Engineering, Beihang University, Beijing, China. His research interests include mobile edge computing and distributed computing system.

On Efficient Zygote Container Planning and Task Scheduling for Edge Native Application Acceleration

Yuepeng Li (China University of Geosciences, China); Lin Gu (Huazhong University of Science and Technology, China); Zhihao Qu (Hohai University, China); Lifeng Tian and Deze Zeng (China University of Geosciences, China)

Edge native applications usually consist of several dependent tasks encapsulated in containers and started on-demand in the edge cloud. Unfortunately, the application performance is deeply affected by the notorious cold startup problem of containers. Pre-warming Zygote container pre-imported certain common packages has been proven as an effective startup acceleration solution. Since a Zygote can be shared among co-located tasks that require identical common packages, not only the Zygote planning but also the task scheduling decisions shall be carefully made to maximize the benefit of the Zygotes pre-warmed in limited memory. Additionally, task dependency necessitates co-locating highly dependent tasks on the same server, naturally raising a dilemma in task scheduling. To this end, in this paper, we investigate the problem of how to plan Zygote and schedule tasks for application completion time minimization, which is proved to be NP-hard. We further propose a Priority and Popularity (P&P) based edge native application acceleration algorithm. Both theoretical analysis and extensive experiments demonstrate the effectiveness of our proposed algorithm. The experiment results show that P&P can reduce the application completion time by 11.7%.

Speaker Yuepeng Li (China University of Geosciences, Wuhan)

Yuepeng Li received the B.S. and the M.S. degrees from the School of Computer Science, China University of Geosciences, Wuhan, China, in 2016 and 2019, respectively. His current research interests mainly focus on edge computing, and related technologies like task scheduling, and Trusted Execution Environment.

Optimization for the Metaverse over Mobile Edge Computing with Play to Earn

Chang Liu, Terence Jie Chua and Jun Zhao (Nanyang Technological University, Singapore)

The concept of the Metaverse has garnered growing interest from both academic and industry circles. The decentralization of both the integrity and security of digital items has spurred the popularity of play-to-earn (P2E) games, where players are entitled to earn and own digital assets which they may trade for physical-world currencies. However, these computationally-intensive games are hardly playable on resource-limited mobile devices and the computation tasks have to be offloaded to an edge server. Through mobile edge computing (MEC), users can upload data to the Metaverse Service Provider (MSP) edge servers for computing. Nevertheless, there is a trade-off between user-perceived in-game latency and user visual experience. The downlink transmission of lower-resolution videos lowers user-perceived latency while lowering the visual fidelity and consequently, earnings of users. In this paper, we design a method to enhance the Metaverse-based MAR in-game user experience. Specifically, we formulate and solve a multi-objective optimization problem. Given the inherent NP-hardness of the problem, we present a low-complexity algorithm to address it, mitigating the trade-off between delay and earnings. The experiment results show that our method can effectively balance the user-perceived latency and profitability, thus improving the performance of Metaverse-based MAR systems.

Speaker Chang Liu (Nanyang Technological University, Singapore)

Chang Liu is a Ph.D. student at Nanyang Technological University, Singapore. His research interests include edge computing, federated learning and Metaverse.

Session Chair

Junaid Ahmed Khan (Western Washington University, USA)

Enter Zoom

Program at a Glance