Bertil Chapuis

Scaling and Load Testing Location-Based Publish and Subscribe

The rise of the Internet of things (IoT) poses massive scalability issues for location-based services. More particularly, location-aware publish and subscribe services are struggling to scale out the computation of matches between publications and subscriptions that continuously update their location. In this demonstration paper, we propose a novel distributed and horizontally scalable architecture for location-aware publish and subscribe. Our middleware architecture relies on a multi-step routing mechanism based on consistent hashing and range partitioning. To demonstrate its scalability, we present a traffic data generator, which, in contrast to existing generators, can be used to perform real-time load tests. Finally, we show that our architecture can be deployed on a small 10-node cluster and can process up to 80,000 location updates per second producing 25,000 matches per seconds.

Privacy-Preserving Location-Based Services by using Intel SGX

We are witnessing a rapid proliferation of location-based services, due to the useful context-aware services they provide their users. However, sharing sensitive location traces with untrusted service-providers has many privacy implications. Although, user-data monetization is the core economic model of such services, offering private services to concerned users will be a beneficial functionality in the coming years. Existing solutions include location perturbation, k-anonymity and cryptographic primitives that trade service accuracy or latency for enhanced user privacy. We introduce a novel approach for privacy preserving location-based services by using the Intel Software Guard eXtensions (SGX). We implement a simple location-based service using SGX and gauge its performance in terms of efficiency and effectiveness, in comparison with its bare-metal implementation. Our evaluation results show that SGX contributes a marginal overhead but also provides near-to-the-perfect results in contrast to spatial cloaking with k-anonymity whose performance deteriorates as the degree of desired privacy increases. We show that hardware-based trusted execution-environments are a promising alternative for offering proactive and de-facto location-privacy in the context of location-based services.

Throughput: A Key Performance Measure of Content-Defined Chunking Algorithms

Data deduplication techniques are often used by cloud storage systems to reduce network bandwidth and storage requirements. As a consequence, the current research literature tends to focus most of its algorithmic efforts on improving the Duplicate Elimination Ratio (DER), which reflects the compression achieved using a given algorithm. Yet, the importance of this indicator tends to be overestimated, while another key indicator, namely throughput, tends to be underestimated. To substantiate this claim, we reimplement a selection of popular Content-Defined Chunking algorithms (CDC) and perform a detailed performance analysis. On this basis, we show that the gain brought by algorithms that are aggressively focusing on DER often come at a significant cost in terms of throughput. As a consequence, we advocate for future optimizations taking throughput into account and for making balanced tradeoffs between DER and throughput.