arXiv subjects

Faisal Nawab

Publications and source records attributed to Faisal Nawab.

16 recordsLinked to original sources

arXiv2026

SpendableStore: A UTXO-based Decentralized Data Store

The literature on blockchain-based databases is divided into permissioned blockchains and permissionless account-based blockchains. However, the former is not fully decentralized, and the latter suffers from challenges in scalability and practicality. We propose SpendableStore, a hybrid on/off-chain database that operates on top of permissionless UTXO-based blockchains as a novel approach to the problem of data decentralization. Our design integrates atomic data units into individual UTXOs to create a new blockchain concept called Spendable Data Objects that perform traditional CRUD operations. The integrity, immutability, and ownership of these Spendable Data Objects are safeguarded directly by the blockchain peer nodes, thus constraining the power of database administrators to achieve true data decentralization. We further support database transactions and propose an isolation mechanism called Future Now Snapshot Isolation to reason about transactional correctness in SpendableStore. We performed experiments on a major blockchain's Mainnet and observed up to 16x better throughput compared to a state-of-the-art blockchain-based database.

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arXiv2026

Game-Theoretic Framework for Private Data Sharing in Vehicular Networks

We present a novel game-theoretic framework designed to enhance privacy and scalability in decentralized vehicular data collection systems. The proposed hybrid architecture comprises vehicles that supply sensor data, independent servers that process data via secure multiparty computation, a coordinator node that manages data flow, and data consumers that set economic incentives. Crucially, our framework ensures that only the data consumer can access the fully aggregated data, preventing individual raw data exposure and significantly reducing privacy risks. By integrating principles of the Stackelberg competition from game theory, our approach dynamically balances privacy and economic incentives, enabling vehicles to make participation decisions based on perceived privacy risks and incentives. We empirically validate our framework using real-world vehicular location data, quantifying privacy risks by evaluating the accuracy with which a potential adversary can reconstruct a vehicle's path using only a subset of the shared data. This paper details the development and deployment of a data-trading platform within this framework, introducing a practical and privacy-preserving marketplace for profitable vehicle data sharing. Through experiments and simulations, we evaluate the effectiveness of the system in preserving privacy and explore the dynamics that influence vehicle participation. Our findings highlight the robustness of the proposed framework in preserving privacy while supporting an active data market.

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arXiv2025

Brook-2PL: Tolerating High Contention Workloads with A Deadlock-Free Two-Phase Locking Protocol

The problem of hotspots remains a critical challenge in high-contention workloads for concurrency control (CC) protocols. Traditional concurrency control approaches encounter significant difficulties under high contention, resulting in excessive transaction aborts and deadlocks. In this paper, we propose Brook-2PL, a novel two-phase locking (2PL) protocol that (1) introduces SLW-Graph for deadlock-free transaction execution, and (2) proposes partial transaction chopping for early lock release. Previous methods suffer from transaction aborts that lead to wasted work and can further burden the system due to their cascading effects. Brook-2PL addresses this limitation by statically analyzing a new graph-based dependency structure called SLW-Graph, enabling deadlock-free two-phase locking through predetermined lock acquisition. Brook-2PL also reduces contention by enabling early lock release using partial transaction chopping and static transaction analysis. We overcome the inherent limitations of traditional transaction chopping by providing a more flexible chopping method. Evaluation using both our synthetic online game store workload and the TPC-C benchmark shows that Brook-2PL significantly outperforms state-of-the-art CC protocols. Brook-2PL achieves an average speed-up of 2.86x while reducing tail latency (p95) by 48% in the TPC-C benchmark.

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arXiv2025

Meaningful Data Erasure in the Presence of Dependencies

Data regulations like GDPR require systems to support data erasure but leave the definition of "erasure" open to interpretation. This ambiguity makes compliance challenging, especially in databases where data dependencies can lead to erased data being inferred from remaining data. We formally define a precise notion of data erasure that ensures any inference about deleted data, through dependencies, remains bounded to what could have been inferred before its insertion. We design erasure mechanisms that enforce this guarantee at minimal cost. Additionally, we explore strategies to balance cost and throughput, batch multiple erasures, and proactively compute data retention times when possible. We demonstrate the practicality and scalability of our algorithms using both real and synthetic datasets.

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arXiv2024

PoneglyphDB: Efficient Non-interactive Zero-Knowledge Proofs for Arbitrary SQL-Query Verification

In database applications involving sensitive data, the dual imperatives of data confidentiality and provable query processing are important. This paper introduces PoneglyphDB, a database system that leverages non-interactive zero-knowledge proofs (ZKP) to support both confidentiality and provability. Unlike traditional databases, PoneglyphDB enhances confidentiality by ensuring that raw data remains exclusively with the host, while also enabling verification of the correctness of query responses by providing proofs to clients. The main innovation in this paper is proposing efficient ZKP designs (called circuits) for basic operations in SQL query processing. These basic operation circuits are then combined to form ZKP circuits for larger, more complex queries. PoneglyphDB's circuits are carefully designed to be efficient by utilizing advances in cryptography such as PLONKish-based circuits, recursive proof composition techniques, and designs with low-order polynomial constraints. We demonstrate the performance of PoneglyphDB with the standard TPC-H benchmark. Our experimental results show that PoneglyphDB can efficiently achieve both confidentiality and provability, outperforming existing state-of-the-art ZKP methods.

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arXiv2024

MSF-Model: Queuing-Based Analysis and Prediction of Metastable Failures in Replicated Storage Systems

Metastable failure is a recent abstraction of a pattern of failures that occurs frequently in real-world distributed storage systems. In this paper, we propose a formal analysis and modeling of metastable failures in replicated storage systems. We focus on a foundational problem in distributed systems -- the problem of consensus -- to have an impact on a large class of systems. Our main contribution is the development of a queuing-based analytical model, MSF-Model, that can be used to characterize and predict metastable failures. MSF-Model integrates novel modeling concepts that allow modeling metastable failures which was interactable to model prior to our work. We also perform real experiments to reproduce and validate our model. Our real experiments show that MSF-Model predicts metastable failures with high accuracy by comparing the real experiment with the predictions from the queuing-based model.

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arXiv2024

A Survey of Open Source User Activity Traces with Applications to User Mobility Characterization and Modeling

The current state-of-the-art in user mobility research has extensively relied on open-source mobility traces captured from pedestrian and vehicular activity through a variety of communication technologies as users engage in a wide-range of applications, including connected healthcare, localization, social media, e-commerce, etc. Most of these traces are feature-rich and diverse, not only in the information they provide, but also in how they can be used and leveraged. This diversity poses two main challenges for researchers and practitioners who wish to make use of available mobility datasets. First, it is quite difficult to get a bird's eye view of the available traces without spending considerable time looking them up. Second, once they have found the traces, they still need to figure out whether the traces are adequate to their needs. The purpose of this survey is three-fold. It proposes a taxonomy to classify open-source mobility traces including their mobility mode, data source and collection technology. It then uses the proposed taxonomy to classify existing open-source mobility traces and finally, highlights three case studies using popular publicly available datasets to showcase how our taxonomy can tease out feature sets in traces to help determine their applicability to specific use-cases.

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arXiv2023

Data-CASE: Grounding Data Regulations for Compliant Data Processing Systems

Data regulations, such as GDPR, are increasingly being adopted globally to protect against unsafe data management practices. Such regulations are, often ambiguous (with multiple valid interpretations) when it comes to defining the expected dynamic behavior of data processing systems. This paper argues that it is possible to represent regulations such as GDPR formally as invariants using a (small set of) data processing concepts that capture system behavior. When such concepts are grounded, i.e., they are provided with a single unambiguous interpretation, systems can achieve compliance by demonstrating that the system-actions they implement maintain the invariants (representing the regulations). To illustrate our vision, we propose Data-CASE, a simple yet powerful model that (a) captures key data processing concepts (b) a set of invariants that describe regulations in terms of these concepts. We further illustrate the concept of grounding using "deletion" as an example and highlight several ways in which end-users, companies, and software designers/engineers can use Data-CASE.

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arXiv2023

TransEdge: Supporting Efficient Read Queries Across Untrusted Edge Nodes

We propose Transactional Edge (TransEdge), a distributed transaction processing system for untrusted environments such as edge computing systems. What distinguishes TransEdge is its focus on efficient support for read-only transactions. TransEdge allows reading from different partitions consistently using one round in most cases and no more than two rounds in the worst case. TransEdge design is centered around this dependency tracking scheme including the consensus and transaction processing protocols. Our performance evaluation shows that TransEdge's snapshot read-only transactions achieve an 9-24x speedup compared to current byzantine systems.

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arXiv2022

Reliable Transactions in Serverless-Edge Architecture

Modern edge applications demand novel solutions where edge applications do not have to rely on a single cloud provider (which cannot be in the vicinity of every edge device) or dedicated edge servers (which cannot scale as clouds) for processing compute-intensive tasks. A recent computing philosophy, Sky computing, proposes giving each user ability to select between available cloud providers. In this paper, we present our serverless-edge co-design, which extends the Sky computing vision. In our serverless-edge co-design, we expect edge devices to collaborate and spawn required number of serverless functions. This raises several key challenges: (1) how will this collaboration take place, (2) what if some edge devices are compromised, and (3) what if a selected cloud provider is malicious. Hence, we design ServerlessBFT, the first protocol to guarantee Byzantine fault-tolerant (BFT) transactional flow between edge devices and serverless functions. We present an exhaustive list of attacks and their solutions on our serverless-edge co-design. Further, we extensively benchmark our architecture on a variety of parameters.

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arXiv2022

Efficient Dynamic Clustering: Capturing Patterns from Historical Cluster Evolution

Clustering aims to group unlabeled objects based on similarity inherent among them into clusters. It is important for many tasks such as anomaly detection, database sharding, record linkage, and others. Some clustering methods are taken as batch algorithms that incur a high overhead as they cluster all the objects in the database from scratch or assume an incremental workload. In practice, database objects are updated, added, and removed from databases continuously which makes previous results stale. Running batch algorithms is infeasible in such scenarios as it would incur a significant overhead if performed continuously. This is particularly the case for high-velocity scenarios such as ones in Internet of Things applications. In this paper, we tackle the problem of clustering in high-velocity dynamic scenarios, where the objects are continuously updated, inserted, and deleted. Specifically, we propose a generally dynamic approach to clustering that utilizes previous clustering results. Our system, DynamicC, uses a machine learning model that is augmented with an existing batch algorithm. The DynamicC model trains by observing the clustering decisions made by the batch algorithm. After training, the DynamicC model is usedin cooperation with the batch algorithm to achieve both accurate and fast clustering decisions. The experimental results on four real-world and one synthetic datasets show that our approach has a better performance compared to the state-of-the-art method while achieving similarly accurate clustering results to the baseline batch algorithm.

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arXiv2021

Croesus: Multi-Stage Processing and Transactions for Video-Analytics in Edge-Cloud Systems

Emerging edge applications require both a fast response latency and complex processing. This is infeasible without expensive hardware that can process complex operations -- such as object detection -- within a short time. Many approach this problem by addressing the complexity of the models -- via model compression, pruning and quantization -- or compressing the input. In this paper, we propose a different perspective when addressing the performance challenges. Croesus is a multi-stage approach to edge-cloud systems that provides the ability to find the balance between accuracy and performance. Croesus consists of two stages (that can be generalized to multiple stages): an initial and a final stage. The initial stage performs the computation in real-time using approximate/best-effort computation at the edge. The final stage performs the full computation at the cloud, and uses the results to correct any errors made at the initial stage. In this paper, we demonstrate the implications of such an approach on a video analytics use-case and show how multi-stage processing yields a better balance between accuracy and performance. Moreover, we study the safety of multi-stage transactions via two proposals: multi-stage serializability (MS-SR) and multi-stage invariant confluence with Apologies (MS-IA).

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arXiv2020

WedgeChain: A Trusted Edge-Cloud Store With Asynchronous (Lazy) Trust

We propose WedgeChain, a data store that spans both edge and cloud nodes (an edge-cloud system). WedgeChain consists of a logging layer and a data indexing layer. In this study, we encounter two challenges: (1) edge nodes are untrusted and potentially malicious, and (2) edge-cloud coordination is expensive. WedgeChain tackles these challenges by the following proposals: (1) Lazy (asynchronous) certification: where data is committed at the untrusted edge and then lazily certified at the cloud node. This lazy certification method takes advantage of the observation that an untrusted edge node is unlikely to act maliciously if it knows it will be detected (and punished) eventually. Our lazy certification method guarantees that malicious acts (i.e., lying) are eventually detected. (2) Data-free certification: our lazy certification method only needs to send digests of data to the cloud, instead of sending all data to the cloud, which enables saving network and cloud resources and reduce costs. (3) LSMerkle: we extend a trusted index (mLSM) to enable indexing data at the edge while utilizing lazy and data-free certification.

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arXiv2020

Predict and Write: Using K-Means Clustering to Extend the Lifetime of NVM Storage

Non-volatile memory (NVM) technologies suffer from limited write endurance. To address this challenge, we propose Predict and Write (PNW), a K/V-store that uses a clustering-based machine learning approach to extend the lifetime of NVMs. PNW decreases the number of bit flips for PUT/UPDATE operations by determining the best memory location an updated value should be written to. PNW leverages the indirection level of K/V-stores to freely choose the target memory location for any given write based on its value. PNW organizes NVM addresses in a dynamic address pool clustered by the similarity of the data values they refer to. We show that, by choosing the right target memory location for a given PUT/UPDATE operation, the number of total bit flips and cache lines can be reduced by up to 85% and 56% over the state of the art.

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arXiv2020

Matchmaker Paxos: A Reconfigurable Consensus Protocol [Technical Report]

State machine replication protocols, like MultiPaxos and Raft, are at the heart of nearly every strongly consistent distributed database. To tolerate machine failures, these protocols must replace failed machines with live machines, a process known as reconfiguration. Reconfiguration has become increasingly important over time as the need for frequent reconfiguration has grown. Despite this, reconfiguration has largely been neglected in the literature. In this paper, we present Matchmaker Paxos and Matchmaker MultiPaxos, a reconfigurable consensus and state machine replication protocol respectively. Our protocols can perform a reconfiguration with little to no impact on the latency or throughput of command processing; they can perform a reconfiguration in one round trip (theoretically) and a few milliseconds (empirically); they provide a number of theoretical insights; and they present a framework that can be generalized to other replication protocols in a way that previous reconfiguration techniques can not. We provide proofs of correctness for the protocols and optimizations, and present empirical results from an open source implementation.

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arXiv2012

Serializability, not Serial: Concurrency Control and Availability in Multi-Datacenter Datastores

We present a framework for concurrency control and availability in multi-datacenter datastores. While we consider Google's Megastore as our motivating example, we define general abstractions for key components, making our solution extensible to any system that satisfies the abstraction properties. We first develop and analyze a transaction management and replication protocol based on a straightforward implementation of the Paxos algorithm. Our investigation reveals that this protocol acts as a concurrency prevention mechanism rather than a concurrency control mechanism. We then propose an enhanced protocol called Paxos with Combination and Promotion (Paxos-CP) that provides true transaction concurrency while requiring the same per instance message complexity as the basic Paxos protocol. Finally, we compare the performance of Paxos and Paxos-CP in a multi-datacenter experimental study, and we demonstrate that Paxos-CP results in significantly fewer aborted transactions than basic Paxos.

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