publications

2025

1. ASPLOS ’25 Conference

   Expressive and Efficient Service Mesh Policies

   Divyanshu Saxena, William Zhang, Shankara Pailoor, Isil Dillig, and Aditya Akella

   In International Conference on Architectural Support for Programming Languages and Operating Systems 2025

2024

1. Arxiv Preprint

   CONGO: Compressive Online Gradient Optimization with Application to Microservices Management

   Jeremy Carleton, Prathik Vijaykumar, Divyanshu Saxena, Dheeraj Narasimha, Srinivas Shakkottai, and Aditya Akella

   2024

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   We address the challenge of online convex optimization where the objective function’s gradient exhibits sparsity, indicating that only a small number of dimensions possess non-zero gradients. Our aim is to leverage this sparsity to obtain useful estimates of the objective function’s gradient even when the only information available is a limited number of function samples. Our motivation stems from distributed queueing systems like microservices-based applications, characterized by request-response workloads. Here, each request type proceeds through a sequence of microservices to produce a response, and the resource allocation across the collection of microservices is controlled to balance end-to-end latency with resource costs. While the number of microservices is substantial, the latency function primarily reacts to resource changes in a few, rendering the gradient sparse. Our proposed method, CONGO (Compressive Online Gradient Optimization), combines simultaneous perturbation with compressive sensing to estimate gradients. We establish analytical bounds on the requisite number of compressive sensing samples per iteration to maintain bounded bias of gradient estimates, ensuring sub-linear regret. By exploiting sparsity, we reduce the samples required per iteration to match the gradient’s sparsity, rather than the problem’s original dimensionality. Numerical experiments and real-world microservices benchmarks demonstrate CONGO’s superiority over multiple stochastic gradient descent approaches, as it quickly converges to performance comparable to policies pre-trained with workload awareness.

2023

1. MLSys at
   NeurIPS ’23 Workshop

   On a Foundation Model for Operating Systems

   Divyanshu Saxena, Nihal Sharma, Donghyun Kim, Rohit Dwivedula, Jiayi Chen, Chenxi Yang, Sriram Ravula, Zichao Hu, Aditya Akella, Sebastian Angel, Joydeep Biswas, Swarat Chaudhuri, Isil Dillig, Alex Dimakis, Brighten P Godfrey, Daehyeok Kim, Christopher Rossbach, and Gang Wang

   In 7th Workshop on Machine Learning for Systems. Held at 37th Conference on Neural Information Processing Systems (NeurIPS 2023). 2023

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   This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are interrelated and that OS traces offer the ideal dataset for a foundation model to grasp the intricacies of diverse OS components and their behavior in varying environments and workloads. We discuss a wide range of possibilities that then arise, from employing foundation models as policy agents to utilizing them as generators and predictors to assist traditional OS control algorithms. Our hope is that this paper spurs further research into OS foundation models and creating the next generation of operating systems for the evolving computing landscape.

2. SoCC ’23 Conference

   Yama: Providing Performance Isolation for Black-Box Offloads

   Tao Ji, Divyanshu Saxena, Brent E. Stephens, and Aditya Akella

   In Proceedings of the 2023 ACM Symposium on Cloud Computing 2023

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   The sharing of clusters with various on-NIC offloads by high-level entities (users, containers, etc.) has become increasingly common. Performance isolation across these entities is desired because the offloads can become bottlenecks due to the limited capacity of hardware. However, the existing works that provide scheduling and resource management to NIC offloads all require customization of the NIC or offloads, while commodity off-the-shelf NICs and offloads with proprietary implementation have been widely deployed in datacenters. This paper presents Yama, the first solution to enable per-entity isolation in the sharing of such black-box NIC offloads. Yama provides a generic framework that captures a common abstraction to the operation of most offloads, which allows operators to incorporate existing offloads. The framework proactively probes for the performance of the offloads with auxiliary workload and enforces isolation at the initiator side. Yama also accommodates chained offloads. Our evaluation shows that 1) Yama achieves per-entity max-min fairness for various types of offloads and in complicated offload chaining scenarios; 2) Yama quickly converges to changes in equilibrium and 3) Yama adds negligible overhead to application workload.

3. Arxiv Preprint

   Dirigo: Self-scaling Stateful Actors For Serverless Real-time Data Processing

   Le Xu, Divyanshu Saxena, Neeraja J. Yadwadkar, Aditya Akella, and Indranil Gupta

   2023

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   We propose Dirigo, a distributed stream processing service built atop virtual actors. Dirigo achieves both a high level of resource efficiency and performance isolation driven by user intent (SLO). To improve resource efficiency, Dirigo adopts a serverless architecture that enables time-sharing of compute resources among streaming operators, both within and across applications. Meanwhile, Dirigo improves performance isolation by inheriting the property of function autoscaling from serverless architecture. Specifically, Dirigo proposes (i) dual-mode actor, an actor abstraction that dynamically provides orderliness guarantee for streaming operator during autoscaling and (ii) a data plane scheduling mechanism, along with its API, that allows scheduling and scaling at the message-level granularity.

4. ApPLIED at
   PODC ’23 Workshop

   Towards Efficient Microservice Communication.

   Divyanshu Saxena, William Zhang, Madhav Tummala, Saksham Goel, and Aditya Akella

   In Proceedings of the 5th Workshop on Advanced Tools, Programming Languages, and PLatforms for Implementing and Evaluating Algorithms for Distributed Systems. Held in conjunction with PODC 2023

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   Distributed applications on the cloud are being developed and deployed as microservices as opposed to the monolithic architecture. Service Meshes have emerged as a way of specifying communication policies between microservices. Service Meshes have the potential to abstract the networking requirements of distributed applications from the application logic. However, current service mesh frameworks introduce significant performance and resource overheads. We study the overheads of service meshes and make a case for redesigning both the control plane and data plane for service meshes. First, we propose the notion of Application Defined Middleboxes, which makes it possible for the mesh control planes to reduce the overheads by optimizing where to implement application policies. Second, we demonstrate preliminary ideas on accelerating the data plane to further reduce the overheads.

5. ACM OSR ’23 Journal

   Navigating Performance-Efficiency Tradeoffs in Serverless Computing: Deduplication to the Rescue!

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

   SIGOPS Operating Systems Review Jun 2023

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   Navigating the performance and efficiency trade-offs is critical for serverless platforms, where the providers ideally want to give the illusion of warm function startups while maintaining low resource costs. Limited controls, provided via toggling sandboxes between warm and cold states and keepalives, force operators to sacrifice significant resources to achieve good performance. We present Medes, a serverless framework, that allows operators to navigate the trade-off space smoothly. Our approach takes advantage of the high duplication in warm sandboxes on serverless platforms 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 use innovative techniques to identify redundancy with minimal overhead, and provide a simple management policy to balance performance and memory. Our evaluation demonstrates that Medes can provide up to 3.8X better end-to-end latencies and reduce the number of cold starts by 10-50% against the state-of-the-art baselines.

6. NSDI ’23 Poster

   Application-tailored Communication with xMesh.

   Divyanshu Saxena, Saksham Goel, William Zhang, Madhav Tummala, and Aditya Akella

   In Poster session of NSDI ’23: 20th USENIX Symposium on Networked Systems Design and Implementation 2023

2022

1. EuroSys ’22 Conference

   Memory Deduplication for Serverless Computing with Medes.

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

   In Proceedings of the Seventeenth European Conference on Computer Systems 2022

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   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.