About
I run AI and Data services in AWS.
I have been awarded (or filed for) more than 250 patents, authored around 40 referred scientific papers and journals, and participate in several academic circles and conferences. In addition to these, I was part of the team that built several AWS Services like CloudFront, Amazon RDS, Amazon S3, Amazon's Paxos based lock service, original Amazon Dynamo etc. I was also one of the main authors for Amazon Dynamo paper (http://bit.ly/1mDs0Yh) along with Werner Vogels. Amazon Dynamo now is the foundation for many other NoSQL systems like Riak, Cassandra and Voldemort.
Articles by Swami
Activity
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Come meet some of the Kiro team in San Francisco and learn how we created spec-driven development and how it’s bringing structure to AI coding!
Come meet some of the Kiro team in San Francisco and learn how we created spec-driven development and how it’s bringing structure to AI coding!
Shared by Swami Sivasubramanian
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What powers an agentic AI application? Marc Brooker breaks down key three components behind scalable agent infrastructure ➡️ https://lnkd.in/grEMikHF…
What powers an agentic AI application? Marc Brooker breaks down key three components behind scalable agent infrastructure ➡️ https://lnkd.in/grEMikHF…
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🚀 We just released our new open-source MCP server for AgentCore—making it easier to start building AI Agents. Whether you're an experienced…
🚀 We just released our new open-source MCP server for AgentCore—making it easier to start building AI Agents. Whether you're an experienced…
Shared by Swami Sivasubramanian
Experience
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Amazon Web Services (AWS)
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Committee Member
National Artificial Intelligence Advisory Committee
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VP, Amazon AI
Amazon Web Services
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Amazon Web Services
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Amazon.com
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Education
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Vrije Universiteit Amsterdam (VU Amsterdam)
Ph.D. Computer Science
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- Published around 30 research papers in ACM/IEEE journals and conferences.
- Won a best research paper award from IEEE Service Computing Technical committee. -
Iowa State University
M.S Computer Engineering
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Activities and Societies: Teaching Assistant for Real-time systems, Research Assistant - worked on design and implementation of new RTOS scheduling algorithms, SITAR - Society for Indian Tradition and ARts
- Passed with dual honors (research excellence and teaching excellence awards). GPA: 4.0/4.0
- Involved in research of real-time scheduling for dynamic real-time systems. Areas focussed: Value-based Scheduling and Feedback-controlled real-time scheduling.
- Published 2 journals and seven conference/workshop papers.
- Awarded Research Excellence Award.
- Taught "Digital Systems Design" and "Real-Time Systems" for undergraduate courses.
- Awarded Teaching Excellence…- Passed with dual honors (research excellence and teaching excellence awards). GPA: 4.0/4.0
- Involved in research of real-time scheduling for dynamic real-time systems. Areas focussed: Value-based Scheduling and Feedback-controlled real-time scheduling.
- Published 2 journals and seven conference/workshop papers.
- Awarded Research Excellence Award.
- Taught "Digital Systems Design" and "Real-Time Systems" for undergraduate courses.
- Awarded Teaching Excellence Award by President of ISU.
- Was involved in building the practical lab exercises for Real-Time Systems Course (CprE 558 ) from scratch -
College of Engineering Guindy, Chennai
B.E. Computer Science & Engineering
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Skills
Publications
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Feedback control for real-time scheduling
American Control Conference
Most real-time scheduling algorithms are open-loop algorithms as the scheduling decisions are based on the worst-case estimates of task parameters. In recent years, the "closed-loop" scheduling has gained importance due to its applicability to many real-world problems wherein the feedback information can be exploited efficiently to adjust task and/or scheduler parameters, thereby improving the system's performance. In this paper, we discuss an open-loop dynamic scheduling algorithm that employs…
Most real-time scheduling algorithms are open-loop algorithms as the scheduling decisions are based on the worst-case estimates of task parameters. In recent years, the "closed-loop" scheduling has gained importance due to its applicability to many real-world problems wherein the feedback information can be exploited efficiently to adjust task and/or scheduler parameters, thereby improving the system's performance. In this paper, we discuss an open-loop dynamic scheduling algorithm that employs a notion of task overlap in the scheduler in order to provide some flexibility in task execution time. Then we present a novel closed-loop approach for dynamically estimating the execution time of tasks based on both deadline miss ratio and task rejection ratio in the system. This approach is highly preferable for firm/soft real-time systems since it provides a firm performance guarantee in terms of deadline misses while achieving a high guarantee ratio. We design the proportional-integral controller and H∞ controller for closed loop scheduling. We evaluate the performance of the open-loop and the closed-loop approaches using simulation studies. We show that the closed-loop dynamic scheduling offers a better performance over the open-loop scheduling under all practical conditions.
Other authorsSee publication -
Amazon Dynamo
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Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components…
Reliability at massive scale is one of the biggest challenges we face at Amazon.com, one of the largest e-commerce operations in the world; even the slightest outage has significant financial consequences and impacts customer trust. The Amazon.com platform, which provides services for many web sites worldwide, is implemented on top of an infrastructure of tens of thousands of servers and network components located in many datacenters around the world. At this scale, small and large components fail continuously and the way persistent state is managed in the face of these failures drives the reliability and scalability of the software systems.
This paper presents the design and implementation of Dynamo, a highly available key-value storage system that some of Amazon’s core services use to provide an “always-on” experience. To achieve this level of availability, Dynamo sacrifices consistency under certain failure scenarios. It makes extensive use of object versioning and application-assisted conflict resolution in a manner that provides a novel interface for developers to use.
Patents
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Dynamic Resource Commitment Management.
Issued US 8,479,211
This Elastic Block Storage (EBS) patent describes the management of the block storage to ensure that resources requested by clients are properly provisioned and available. For example, a client can request a resource. The EBS control module can create a volume, specifying the number of partitions and/or the way in which data is stored across partitions to guarantee the requested resource to the client. In addition, partitions and data storage can be updated dynamically without significantly…
This Elastic Block Storage (EBS) patent describes the management of the block storage to ensure that resources requested by clients are properly provisioned and available. For example, a client can request a resource. The EBS control module can create a volume, specifying the number of partitions and/or the way in which data is stored across partitions to guarantee the requested resource to the client. In addition, partitions and data storage can be updated dynamically without significantly impacting the customer using the volume. The management and update of resources are optimized using various techniques, including striping data, splitting mapping data, and balancing the data in partitions.
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Managing Route Selection in a Communication Network
Issued US 8,472,324
This patent relates to a way to reduce processing load on network routers in our AWS data centers. In particular, the described technology can offload pre-determined route calculations and storage from edge routers to a central processing system in order to increase edge router performance. In some cases, this facilitates the use of lower-capability, cheap commodity-based routers.
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RESOURCE ISOLATION THROUGH REINFORCEMENT LEARNING
Issued US US 8429096 B1
Systems and methods for providing resource isolation in a shared computing environment using reinforcement learning (RL) techniques are disclosed. A resource isolation mechanism may be applied in a shared storage system, or database service, that limits the resource utilization of each namespace to its specified allocation. For example, the resource isolation mechanism may be used to limit the I/O utilization of database applications in a shared computing system (e.g., a system supporting a…
Systems and methods for providing resource isolation in a shared computing environment using reinforcement learning (RL) techniques are disclosed. A resource isolation mechanism may be applied in a shared storage system, or database service, that limits the resource utilization of each namespace to its specified allocation. For example, the resource isolation mechanism may be used to limit the I/O utilization of database applications in a shared computing system (e.g., a system supporting a database service) to a specified limit. In such embodiments, RL techniques may be applied to the system to automatically control the rate of queries made by an application. RL techniques, such as those based on the State-Action-Reward-State-Action (SARSA) method may be effective in controlling the I/O utilization of database applications for different workloads. RL techniques may be applied globally by the service, or may be applied to particular subscribers, applications, shared resources, namespaces, or query types.
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Managing content delivery network service providers
Issued US 12/272,699
A system, method, and computer readable medium for managing CDN service providers are provided. A network storage provider storing one or more resources on behalf of a content provider obtains client computing device requests for content. The network storage provider processes the client computing device requests and determines whether a subsequent request for the resource should be directed to a CDN service provider as a function of the updated or processed by the network storage provider…
A system, method, and computer readable medium for managing CDN service providers are provided. A network storage provider storing one or more resources on behalf of a content provider obtains client computing device requests for content. The network storage provider processes the client computing device requests and determines whether a subsequent request for the resource should be directed to a CDN service provider as a function of the updated or processed by the network storage provider storage component.
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System-aware resource scheduling
US 8,347,302
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Claude Sonnet 4.5—the world’s best coding model per SWE-Bench—just arrived in our Kiro IDE 👻 kiro.dev Devs using Kiro can boost their coding with…
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It was a pleasure to meet Swami Sivasubramanian at TEDAI Vienna 🙏 Snoonu and Amazon Web Services (AWS) have a long track of partnership in AI and we…
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Thrilled about this partnership with the National Basketball Association (NBA)! As an innovation leader and super-fan of the game, I'm excited about…
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We’re ready for tip off! 🏀 AWS is bringing basketball fans (like me!) new advanced stats and in-game developments that unfold in real-time. And…
We’re ready for tip off! 🏀 AWS is bringing basketball fans (like me!) new advanced stats and in-game developments that unfold in real-time. And…
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🚀 Delivering more model choice today for builders—Claude Sonnet 4.5 by Anthropic is available now in Amazon Bedrock ➡️…
🚀 Delivering more model choice today for builders—Claude Sonnet 4.5 by Anthropic is available now in Amazon Bedrock ➡️…
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The next frontier in AI isn't just about making software faster—it’s about making it smarter. Great discussion today at #TedAIVienna with Vladislav…
The next frontier in AI isn't just about making software faster—it’s about making it smarter. Great discussion today at #TedAIVienna with Vladislav…
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✈️ In Vienna, Austria this week for TEDAI Vienna! Looking forward to hearing from other leaders in this space and sharing our vision for the future…
✈️ In Vienna, Austria this week for TEDAI Vienna! Looking forward to hearing from other leaders in this space and sharing our vision for the future…
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I joined Tobias Macey on the Data Engineering Podcast this week to talk about how agents are (and aren't) changing the world of databases and data…
I joined Tobias Macey on the Data Engineering Podcast this week to talk about how agents are (and aren't) changing the world of databases and data…
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