Divyanshu Saxena

Selected Publications

1. NSDI ’26

   Towards Performance Robustness for Microservices

   Divyanshu Saxena, Gaurav Vipat, Jiaxin Lin, Jingbo Wang, Isil Dillig, Sanjay Shakkottai, and Aditya Akella

   Microservices are foundational to modern distributed applications, enabling modular design and scalability. However, they face performance variability due to environmental factors like workload burstiness, resource contention, and shared dependencies. Existing microservice controllers, such as autoscalers and admission controllers, struggle to ensure good performance, often causing several Service Level Objective (SLO) violations. We argue that this is because controller decision-making is uninformed, lacking guidance about robustness to environmental factors. We propose the concept of run-time “performance robustness certificates” (PeRC) to address this limitation. A PeRC provides statistical bounds on tail latencies of specific request types under a range of environmental perturbations. We show how to leverage a queueing-theoretic model of microservice performance to quickly derive actionable PeRCs. We introduce Galileo, a framework that integrates PeRCs with two state-of-the-art learned controllers to guide robust actions toward meeting SLOs. Experimental results with real-world benchmarks validate the effectiveness of PeRCs in ensuring robust microservice performance.

2. EuroSys ’26

   Canopy: Property-Driven Learning for Congestion Control

   Chenxi Yang, Divyanshu Saxena, Rohit Dwivedula, Kshiteej Mahajan, Swarat Chaudhuri, and Aditya Akella

   Learning-based congestion controllers offer better adaptability compared to traditional heuristics. However, the unreliability of learning techniques can cause learning-based controllers to behave poorly, creating a need for formal guarantees. While methods for formally verifying learned congestion controllers exist, these methods offer binary feedback that cannot optimize the controller toward better behavior. We improve this state-of-the-art via Canopy, a new property-driven framework that integrates learning with formal reasoning in the learning loop. Canopy uses novel quantitative certification with an abstract interpreter to guide the training process, rewarding models, and evaluating robust and safe model performance on worst-case inputs. Our evaluation demonstrates that unlike state-of-the-art learned controllers, Canopy-trained controllers provide both adaptability and worst-case reliability across a range of network conditions.

3. HotNets ’25

   Man-Made Heuristics Are Dead. Long Live Code Generators!

   Rohit Dwivedula, Divyanshu Saxena, Aditya Akella, Swarat Chaudhuri, and Daehyeok Kim

   Policy design for various systems controllers has conventionally been a manual process, with domain experts carefully tailoring heuristics for the specific instance under which the policy will be deployed. In this paper, we re-imagine policy design via a novel automated search technique fueled by recent advances in generative models, specifically Large Language Model (LLM)-driven code generation. We outline the design and implementation of PolicySmith, a framework that applies LLMs to synthesize instance-optimal heuristics. We apply PolicySmith to two long-standing systems policies - web caching and congestion control, highlighting the opportunities unraveled by this LLM-driven heuristic search. For caching, PolicySmith discovers heuristics that outperform established baselines on standard open-source traces. For congestion control, we show that PolicySmith can generate safe policies that can directly run inside the Linux kernel.

4. HotOS ’25

   How I learned to stop worrying and love learned OS policies

   Divyanshu Saxena, Jiayi Chen, Sujay Yadalam, Yeonju Ro, Rohit Dwivedula, Eric Campbell, Aditya Akella, Christopher Rossbach, and Michael Swift

   While machine learning has been adopted across various fields, its ability to outperform traditional heuristics in operating systems is often met with justified skepticism. Concerns about unsafe decisions, opaque debugging processes, and the challenges of integrating ML into the kernel—given its stringent latency constraints and inherent complexity—make practitioners understandably cautious. This paper introduces "Guardrails for the OS", a framework that allows kernel developers to declaratively specify system-level properties and define corrective actions to address property violations. The framework facilitates the compilation of these guardrails into monitors capable of running within the kernel. In this work, we establish the foundation for Guardrails, detailing its core abstractions, examining the problem space, and exploring potential solutions.

5. EuroSys ’22

   Memory Deduplication for Serverless Computing with Medes.

   Divyanshu Saxena, Tao Ji, Arjun Singhvi, Junaid Khalid, and Aditya Akella

   Serverless platforms today impose rigid trade-offs between resource use and user-perceived performance. Limited controls, provided via toggling sandboxes between warm and cold states and keep-alives, force operators to sacrifice significant resources to achieve good performance. We present a serverless framework, Medes, that breaks the rigid trade-off and allows operators to navigate the trade-off space smoothly. Medes leverages the fact that the warm sandboxes running on serverless platforms have a high fraction of duplication in their memory footprints. We exploit these redundant chunks to develop a new sandbox state, called a dedup state, that is more memory-efficient than the warm state and faster to restore from than the cold state. We develop novel mechanisms to identify memory redundancy at minimal overhead while ensuring that the dedup containers’ memory footprint is small. Finally, we develop a simple sandbox management policy that exposes a narrow, intuitive interface for operators to trade-off performance for memory by jointly controlling warm and dedup sandboxes. Detailed experiments with a protoformat using real-world serverless workloads demonstrate that Medes can provide up to 1X-2.75X improvements in the end-to-end latencies. The benefits of Medes are enhanced in memory pressure situations, where Medes can provide up to 3.8X improvements in end-to-end latencies. Medes achieves this by reducing the number of cold starts incurred by 10-50% against the state-of-the-art baselines.