arXiv subjects

Muhammad Shahir Rahman

Publications and source records attributed to Muhammad Shahir Rahman.

3 recordsLinked to original sources

arXiv2024

Alto: Orchestrating Distributed Compound AI Systems with Nested Ancestry

Compound AI applications chain together subcomponents such as generative language models, document retrievers, and embedding models. Applying traditional systems optimizations such as parallelism and pipelining in compound AI systems is difficult because each component has different constraints in terms of the granularity and type of data that it ingests. New data is often generated during intermediate computations, and text streams may be split into smaller, independent fragments (such as documents to sentences) which may then be re-aggregated at later parts of the computation. Due to this complexity, existing systems to serve compound AI queries do not fully take advantage of parallelism and pipelining opportunities. We present Alto, a framework that automatically optimizes execution of compound AI queries through streaming and parallelism. Bento introduces a new abstraction called nested ancestry, a metadata hierarchy that allows the system to correctly track partial outputs and aggregate data across the heterogeneous constraints of the components of compound AI applications. This metadata is automatically inferred from the programming model, allowing developers to express complex dataflow patterns without needing to reason manually about the details of routing and aggregation. Implementations of four applications in Alto outperform or match implementations in LangGraph, a popular existing AI programming framework. Alto implementations match or improve latency by between 10-30%.

cs.AI↗

arXiv2022

WiSwarm: Age-of-Information-based Wireless Networking for Collaborative Teams of UAVs

The Age-of-Information (AoI) metric has been widely studied in the theoretical communication networks and queuing systems literature. However, experimental evaluation of its applicability to complex real-world time-sensitive systems is largely lacking. In this work, we develop, implement, and evaluate an AoI-based application layer middleware that enables the customization of WiFi networks to the needs of time-sensitive applications. By controlling the storage and flow of information in the underlying WiFi network, our middleware can: (i) prevent packet collisions; (ii) discard stale packets that are no longer useful; and (iii) dynamically prioritize the transmission of the most relevant information. To demonstrate the benefits of our middleware, we implement a mobility tracking application using a swarm of UAVs communicating with a central controller via WiFi. Our experimental results show that, when compared to WiFi-UDP/WiFi-TCP, the middleware can improve information freshness by a factor of 109x/48x and tracking accuracy by a factor of 4x/6x, respectively. Most importantly, our results also show that the performance gains of our approach increase as the system scales and/or the traffic load increases.

cs.NI↗

arXiv2020

WiFresh: Age-of-Information from Theory to Implementation

Emerging applications, such as smart factories and fleets of drones, increasingly rely on sharing time-sensitive information for monitoring and control. In such application domains, it is essential to keep information fresh, as outdated information loses its value and can lead to system failures and safety risks. The Age-of-Information is a performance metric that captures how fresh the information is from the perspective of the destination. In this paper, we show that as the congestion in the wireless network increases, the Age-of-Information degrades sharply, leading to outdated information at the destination. Leveraging years of theoretical research, we propose WiFresh: an unconventional architecture that achieves near optimal information freshness in wireless networks of any size, even when the network is overloaded. Our experimental results show that WiFresh can improve information freshness by two orders of magnitude when compared to an equivalent standard WiFi network. We propose and realize two strategies for implementing WiFresh: one at the MAC layer using hardware-level programming and another at the Application layer using Python.

cs.NI↗