arXiv subjects

Shaileshh Bojja Venkatakrishnan

Publications and source records attributed to Shaileshh Bojja Venkatakrishnan.

At least 19 recordsLinked to original sources

arXiv2026

Rethinking Collaborative Trust for Verifiably Decentralized Blockchain Systems

Despite the promise of decentralization, measurement studies have identified a conspicuous lack of decentralization in blockchains. Centralization has been observed in almost all layers of the blockchain, in decentralized applications, and in decentralized autonomous organizations. In many cases, it is practically impossible to definitively determine the extent of centralization in the system. While multiple works have proposed methods to decrease centralization, by and large blockchains continue to be significantly centralized. In this paper, we develop a general framework for building verifiably decentralized blockchain systems. Our framework is motivated by the core observation that the richness and diversity of collaborative interactions between users -- rather than resource uniformity -- captures the essence and extent of decentralization in a blockchain system. Existing blockchains do not have any incentive mechanisms to encourage inter-coalition collaboration, which directly contributes to centralization. We propose a novel reward design that incentivizes users to collaborate with other users without forming isolated coalitions. Technically, our method uses a Sybil-resistant asymmetric Shapley value for reward attribution within a collaboration group, and the theory of expander graphs for measuring and enforcing decentralization. Our framework is general and can be adapted to alleviate centralization in any layer, application, or decentralized organization. It also has important implications beyond the topic of centralization. For example, we show that our solution can naturally address the blockchain scalability problem. We also identify a new class of decentralized collaborative applications that have hitherto been unexplored in blockchains.

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arXiv2026

Starfield: Demand-Aware Satellite Topology Design for Low-Earth Orbit Mega Constellations

Low-Earth orbit (LEO) mega-constellations are emerging as high-capacity backbones for next-generation Internet. Deployment of laser terminals enables high-bandwidth, low-latency inter-satellite links (ISLs); however, their limited number, slow acquisition, and instability make forming a stable satellite topology difficult. Existing patterns like +Grid and Motif ignore regional traffic, ground station placement, and constellation geometry. Given sparse population distribution on Earth and the isolation of rural areas, traffic patterns are inherently non-uniform, providing an opportunity to orient inter-satellite links (ISLs) according to these traffic patterns. In this paper, we propose Starfield, a novel demand-aware satellite topology design heuristic algorithm supported by mathematical analysis. We first formulate a vector field on the constellation's shell according to traffic flows and define a corresponding Riemannian metric on the spherical manifold of the shell. The metric, combined with the spatial geometry, is used to assign a distance to each potential ISL, which we then aggregate over all demand flows to generate a heuristic for each satellite's link selection. Inspired by +Grid, each satellite selects the link with the minimum Riemannian heuristic along with its corresponding angular links. To evaluate Starfield, we developed a custom, link-aware, and link-configurable packet-level simulator, comparing it against +Grid and Random topologies. For the Phase 1 Starlink, simulation results show up to a 30% reduction in hop count and a 15% improvement in stretch factor across multiple traffic distributions. Moreover, static Starfield, an inter-orbital link matching modification of Starfield, achieves a 20% improvement in stretch factor under realistic traffic patterns compared to +Grid. Experiments further demonstrate Starfield's robustness under traffic demand perturbations.

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arXiv2025

Honeybee: Byzantine Tolerant Decentralized Peer Sampling with Verifiable Random Walks

Popular blockchains today have hundreds of thousands of nodes and need to be able to support sophisticated scaling solutions$\unicode{x2013}$such as sharding, data availability sampling, and layer-2 methods. Designing secure and efficient peer-to-peer (p2p) networking protocols at these scales to support the tight demands of the upper layer crypto-economic primitives is a highly non-trivial endeavor. We identify decentralized, uniform random sampling of nodes as a fundamental capability necessary for building robust p2p networks in emerging blockchain networks. Sampling algorithms used in practice today (primarily for address discovery) rely on either distributed hash tables (e.g., Kademlia) or sharing addresses with neighbors (e.g., GossipSub), and are not secure in a Sybil setting. We present Honeybee, a decentralized algorithm for sampling nodes that uses verifiable random walks and table consistency checks. Honeybee is secure against attacks even in the presence of an overwhelming number of Byzantine nodes (e.g., $\geq50\%$ of the network). We evaluate Honeybee through experiments and show that the quality of sampling achieved by Honeybee is significantly better compared to the state-of-the-art. Our proposed algorithm has implications for network design in both full nodes and light nodes.

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arXiv2024

Flashback: Enhancing Proposer-Builder Design with Future-Block Auctions in Proof-of-Stake Ethereum

Maximal extractable value (MEV) in which block proposers unethically gain profits by manipulating the order in which transactions are included within a block, is a key challenge facing blockchains such as Ethereum today. Left unchecked, MEV can lead to a centralization of stake distribution thereby ultimately compromising the security of blockchain consensus. To preserve proposer decentralization (and hence security) of the blockchain, Ethereum has advocated for a proposer-builder separation (PBS) in which the functionality of transaction ordering is separated from proposers and assigned to separate entities called builders. Builders accept transaction bundles from searchers, who compete to find the most profitable bundles. Builders then bid completed blocks to proposers, who accept the most profitable blocks for publication. The auction mechanisms used between searchers, builders and proposers are crucial to the overall health of the blockchain. In this paper, we consider PBS design in Ethereum as a game between searchers, builders and proposers. A key novelty in our design is the inclusion of future block proposers, as all proposers of an epoch are decided ahead of time in proof-of-stake (PoS) Ethereum within the game model. Our analysis shows the existence of alternative auction mechanisms that result in a better (more profitable) equilibrium to players compared to state-of-the-art. Experimental evaluations based on synthetic and real-world data traces corroborate the analysis. Our results highlight that a rethinking of auction mechanism designs is necessary in PoS Ethereum to prevent disruption.

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arXiv2023

Topiary: Fast, Scalable Publish/Subscribe for Peer-to-Peer (D)Apps

The emergence of blockchain technology has fostered the development of numerous decentralized applications (dapps) in recent years Pub/sub (publish/subscribe) systems play a crucial role by associating messages with specific topics and propagating them from publishers to subscribers across the network. Decentralized pub/sub aims to provide this functionality without relying on centralized control or global network state information, enabling message propagation among nodes in a coordinated manner. Efficiency in pub/sub services entails ensuring that subscribers receive published messages promptly. We introduce Topiary, a rapid and scalable protocol designed for decentralized applications' pub/sub systems. Topiary autonomously learns an efficient peer-to-peer (p2p) topology tailored to the publish/subscribe network. It does so by analyzing peers' interactions with their neighbors. Inspired by concepts from the multi-armed bandit problem, Topiary strikes an optimal balance between maintaining connections with well-connected neighbors and exploring new connections within the network, based on their topical needs. Through experimental evaluations, Topiary has shown a 50% reduction in broadcast latency while achieving an interested topic coverage of over 98%, marking it as a promising solution for efficient decentralized pub/sub networks.

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arXiv2023

Masquerade: Simple and Lightweight Transaction Reordering Mitigation in Blockchains

Blockchains offer strong security gurarantees, but cannot protect users against the ordering of transactions. Players such as miners, bots and validators can reorder various transactions and reap significant profits, called the Maximal Extractable Value (MEV). In this paper, we propose an MEV aware protocol design called Masquerade, and show that it will increase user satisfaction and confidence in the system. We propose a strict per-transaction level of ordering to ensure that a transaction is committed either way even if it is revealed. In this protocol, we introduce the notion of a "token" to mitigate the actions taken by an adversary in an attack scenario. Such tokens can be purchased voluntarily by users, who can then choose to include the token numbers in their transactions. If the users include the token in their transactions, then our protocol requires the block-builder to order the transactions strictly according to token numbers. We show through extensive simulations that this reduces the probability that the adversaries can benefit from MEV transactions as compared to existing current practices.

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arXiv2023

Cobalt: Optimizing Mining Rewards in Proof-of-Work Network Games

Mining in proof-of-work blockchains has become an expensive affair requiring specialized hardware capable of executing several megahashes per second at huge electricity costs. Miners earn a reward each time they mine a block within the longest chain, which helps offset their mining costs. It is therefore of interest to miners to maximize the number of mined blocks in the blockchain and increase revenue. A key factor affecting mining rewards earned is the connectivity between miners in the peer-to-peer network. To maximize rewards a miner must choose its network connections carefully, ensuring existence of paths to other miners that are on average of a lower latency compared to paths between other miners. We formulate the problem of deciding whom to connect to for miners as a combinatorial bandit problem. Each node picks its neighbors strategically to minimize the latency to reach 90\% of the hash power of the network relative to the 90-th percentile latency from other nodes. A key contribution of our work is the use of a network coordinates based model for learning the network structure within the bandit algorithm. Experimentally we show our proposed algorithm outperforming or matching baselines on diverse network settings.

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arXiv2023

PolicyClusterGCN: Identifying Efficient Clusters for Training Graph Convolutional Networks

Graph convolutional networks (GCNs) have achieved huge success in several machine learning (ML) tasks on graph-structured data. Recently, several sampling techniques have been proposed for the efficient training of GCNs and to improve the performance of GCNs on ML tasks. Specifically, the subgraph-based sampling approaches such as ClusterGCN and GraphSAINT have achieved state-of-the-art performance on the node classification tasks. These subgraph-based sampling approaches rely on heuristics -- such as graph partitioning via edge cuts -- to identify clusters that are then treated as minibatches during GCN training. In this work, we hypothesize that rather than relying on such heuristics, one can learn a reinforcement learning (RL) policy to compute efficient clusters that lead to effective GCN performance. To that end, we propose PolicyClusterGCN, an online RL framework that can identify good clusters for GCN training. We develop a novel Markov Decision Process (MDP) formulation that allows the policy network to predict ``importance" weights on the edges which are then utilized by a clustering algorithm (Graclus) to compute the clusters. We train the policy network using a standard policy gradient algorithm where the rewards are computed from the classification accuracies while training GCN using clusters given by the policy. Experiments on six real-world datasets and several synthetic datasets show that PolicyClusterGCN outperforms existing state-of-the-art models on node classification task.

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arXiv2023

Goldfish: Peer selection using Matrix completion in unstructured P2P network

Peer-to-peer (P2P) networks underlie a variety of decentralized paradigms including blockchains, distributed file storage and decentralized domain name systems. A central primitive in P2P networks is the peer selection algorithm, which decides how a node should select a fixed number of neighbors to connect with. In this paper, we consider the design of a peer-selection algorithm for unstructured P2P networks with the goal of minimizing the broadcast latency. We propose Goldfish, a novel solution that dynamically decides the neighbor set by exploiting the past experiences as well as exploring new neighbors. The key technical contributions come from bringing ideas of matrix completion for estimating message delivery times for every possible message for every peer ever connected, and a streaming algorithm to efficiently perform the estimation while achieving good performance. The matrix completion interpolates the delivery times to all virtual connections in order to select the best combination of neighbors. Goldfish employs a streaming algorithm that only uses a short recent memory to finish matrix interpolation. When the number of publishing source is equal to a node's maximal number of connections, Goldfish found the global optimal solution with 92.7% probability by exploring every node only once. In more complex situations where nodes are publishing based on exponential distribution and adjusting connection in real time, we compare Goldfish with a baseline peer selection system, and show Goldfish saves approximately 14.5% less time under real world geolocation and propagation latency.

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arXiv2023

Kadabra: Adapting Kademlia for the Decentralized Web

Blockchains have become the catalyst for a growing movement to create a more decentralized Internet. A fundamental operation of applications in a decentralized Internet is data storage and retrieval. As today's blockchains are limited in their storage functionalities, in recent years a number of peer-to-peer data storage networks have emerged based on the Kademlia distributed hash table protocol. However, existing Kademlia implementations are not efficient enough to support fast data storage and retrieval operations necessary for (decentralized) Web applications. In this paper, we present Kadabra, a decentralized protocol for computing the routing table entries in Kademlia to accelerate lookups. Kadabra is motivated by the multi-armed bandit problem, and can automatically adapt to heterogeneity and dynamism in the network. Experimental results show Kadabra achieving between 15-50% lower lookup latencies compared to state-of-the-art baselines.

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arXiv2022

DecVi: Adaptive Video Conferencing on Open Peer-to-Peer Networks

Video conferencing has become the preferred way of interacting virtually. Current video conferencing applications, like Zoom, Teams or WebEx, are centralized, cloud-based platforms whose performance crucially depends on the proximity of clients to their data centers. Clients from low-income countries are particularly affected as most data centers from major cloud providers are located in economically advanced nations. Centralized conferencing applications also suffer from occasional outages and are embattled by serious privacy violation allegations. In recent years, decentralized video conferencing applications built over p2p networks and incentivized through blockchain are becoming popular. A key characteristic of these networks is their openness: anyone can host a media server on the network and gain reward for providing service. Strong economic incentives combined with lower entry barrier to join the network, makes increasing server coverage to even remote regions of the world. These reasons, however, also lead to a security problem: a server may obfuscate its true location in order to gain an unfair business advantage. In this paper, we consider the problem of multicast tree construction for video conferencing sessions in open p2p conferencing applications. We propose DecVi, a decentralized multicast tree construction protocol that adaptively discovers efficient tree structures based on an exploration-exploitation framework. DecVi is motivated by the combinatorial multi-armed bandit problem and uses a succinct learning model to compute effective actions. Despite operating in a multi-agent setting with each server having only limited knowledge of the global network and without cooperation among servers, experimentally we show DecVi achieves similar quality-of-experience compared to a centralized globally optimal algorithm while achieving higher reliability and flexibility.

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arXiv2022

Less is More: Fairness in Wide-Area Proof-of-Work Blockchain Networks

Blockchain is rapidly emerging as an important class of network application, with a unique set of trust, security and transparency properties. In a blockchain system, participants record and update the `server-side' state of an application as blocks of a replicated, immutable ledger using a consensus protocol over the Internet. Mining blocks has become lucrative in recent years; e.g., a miner receives over USD 200,000 per mined block in Bitcoin today. A key factor affecting mining rewards, is the latency of broadcasting blocks over the network. In this paper, we consider the problem of topology design for optimizing mining rewards in a wide-area blockchain network that uses a Proof-of-Work protocol for consensus. Contrary to general wisdom that a faster network is always better for miners, we show a counter intuitive result where a slower network is actually beneficial to some miners. This is because competing miners must choose neighbors that not only decrease their own latency to others, but also ensure that the latency between other miners do not decrease because of itself. We formalize this problem, and provide both theoretical analysis and experimental results to support our claim.

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arXiv2021

The Effect of Network Topology on Credit Network Throughput

Credit networks rely on decentralized, pairwise trust relationships (channels) to exchange money or goods. Credit networks arise naturally in many financial systems, including the recent construct of payment channel networks in blockchain systems. An important performance metric for these networks is their transaction throughput. However, predicting the throughput of a credit network is nontrivial. Unlike traditional communication channels, credit channels can become imbalanced; they are unable to support more transactions in a given direction once the credit limit has been reached. This potential for imbalance creates a complex dependency between a network's throughput and its topology, path choices, and the credit balances (state) on every channel. Even worse, certain combinations of these factors can lead the credit network to deadlocked states where no transactions can make progress. In this paper, we study the relationship between the throughput of a credit network and its topology and credit state. We show that the presence of deadlocks completely characterizes a network's throughput sensitivity to different credit states. Although we show that identifying deadlocks in an arbitrary topology is NP-hard, we propose a peeling algorithm inspired by decoding algorithms for erasure codes that upper bounds the severity of the deadlock. We use the peeling algorithm as a tool to compare the performance of different topologies as well as to aid in the synthesis of topologies robust to deadlocks.

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arXiv2020

Perigee: Efficient Peer-to-Peer Network Design for Blockchains

A key performance metric in blockchains is the latency between when a transaction is broadcast and when it is confirmed (the so-called, confirmation latency). While improvements in consensus techniques can lead to lower confirmation latency, a fundamental lower bound on confirmation latency is the propagation latency of messages through the underlying peer-to-peer (p2p) network (inBitcoin, the propagation latency is several tens of seconds). The de facto p2p protocol used by Bitcoin and other blockchains is based on random connectivity: each node connects to a random subset of nodes. The induced p2p network topology can be highly suboptimal since it neglects geographical distance, differences in bandwidth, hash-power and computational abilities across peers. We present Perigee, a decentralized algorithm that automatically learns an efficient p2p topology tuned to the aforementioned network heterogeneities, purely based on peers' interactions with their neighbors. Motivated by the literature on the multi-armed bandit problem, Perigee optimally balances the tradeoff between retaining connections to known well-connected neighbors, and exploring new connections to previously-unseen neighbors. Experimental evaluations show that Perigee reduces the latency to broadcast by $33\%$. Lastly Perigee is simple, computationally lightweight, adversary-resistant, and compatible with the selfish interests of peers, making it an attractive p2p protocol for blockchains.

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arXiv2020

High Throughput Cryptocurrency Routing in Payment Channel Networks

Despite growing adoption of cryptocurrencies, making fast payments at scale remains a challenge. Payment channel networks (PCNs) such as the Lightning Network have emerged as a viable scaling solution. However, completing payments on PCNs is challenging: payments must be routed on paths with sufficient funds. As payments flow over a single channel (link) in the same direction, the channel eventually becomes depleted and cannot support further payments in that direction; hence, naive routing schemes like shortest-path routing can deplete key payment channels and paralyze the system. Today's PCNs also route payments atomically, worsening the problem. In this paper, we present Spider, a routing solution that "packetizes" transactions and uses a multi-path transport protocol to achieve high-throughput routing in PCNs. Packetization allows Spider to complete even large transactions on low-capacity payment channels over time, while the multi-path congestion control protocol ensures balanced utilization of channels and fairness across flows. Extensive simulations comparing Spider with state-of-the-art approaches shows that Spider requires less than 25% of the funds to successfully route over 95% of transactions on balanced traffic demands, and offloads 4x more transactions onto the PCN on imbalanced demands.

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arXiv2019

Learning Scheduling Algorithms for Data Processing Clusters

Efficiently scheduling data processing jobs on distributed compute clusters requires complex algorithms. Current systems, however, use simple generalized heuristics and ignore workload characteristics, since developing and tuning a scheduling policy for each workload is infeasible. In this paper, we show that modern machine learning techniques can generate highly-efficient policies automatically. Decima uses reinforcement learning (RL) and neural networks to learn workload-specific scheduling algorithms without any human instruction beyond a high-level objective such as minimizing average job completion time. Off-the-shelf RL techniques, however, cannot handle the complexity and scale of the scheduling problem. To build Decima, we had to develop new representations for jobs' dependency graphs, design scalable RL models, and invent RL training methods for dealing with continuous stochastic job arrivals. Our prototype integration with Spark on a 25-node cluster shows that Decima improves the average job completion time over hand-tuned scheduling heuristics by at least 21%, achieving up to 2x improvement during periods of high cluster load.

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arXiv2019

Placeto: Learning Generalizable Device Placement Algorithms for Distributed Machine Learning

We present Placeto, a reinforcement learning (RL) approach to efficiently find device placements for distributed neural network training. Unlike prior approaches that only find a device placement for a specific computation graph, Placeto can learn generalizable device placement policies that can be applied to any graph. We propose two key ideas in our approach: (1) we represent the policy as performing iterative placement improvements, rather than outputting a placement in one shot; (2) we use graph embeddings to capture relevant information about the structure of the computation graph, without relying on node labels for indexing. These ideas allow Placeto to train efficiently and generalize to unseen graphs. Our experiments show that Placeto requires up to 6.1x fewer training steps to find placements that are on par with or better than the best placements found by prior approaches. Moreover, Placeto is able to learn a generalizable placement policy for any given family of graphs, which can then be used without any retraining to predict optimized placements for unseen graphs from the same family. This eliminates the large overhead incurred by prior RL approaches whose lack of generalizability necessitates re-training from scratch every time a new graph is to be placed.

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arXiv2019

Variance Reduction for Reinforcement Learning in Input-Driven Environments

We consider reinforcement learning in input-driven environments, where an exogenous, stochastic input process affects the dynamics of the system. Input processes arise in many applications, including queuing systems, robotics control with disturbances, and object tracking. Since the state dynamics and rewards depend on the input process, the state alone provides limited information for the expected future returns. Therefore, policy gradient methods with standard state-dependent baselines suffer high variance during training. We derive a bias-free, input-dependent baseline to reduce this variance, and analytically show its benefits over state-dependent baselines. We then propose a meta-learning approach to overcome the complexity of learning a baseline that depends on a long sequence of inputs. Our experimental results show that across environments from queuing systems, computer networks, and MuJoCo robotic locomotion, input-dependent baselines consistently improve training stability and result in better eventual policies.

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