Sudharshan Muralidharan
IBM, Software Engineer
Bengaluru, India
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Sudharshan Muralidharan is a Full Stack Developer specializing in Go, MERN, and Kubernetes. Contributing to open-source projects like Confidential Containers and Kubernetes, focusing on s390x pipelines and multi-architecture optimizations. Passionate about cloud-native technologies, AI-driven workload optimization, and building efficient, cross-architecture solutions for complex production environments.
Area of Expertise
Topics
Creating an In-Cluster Registry for Trusted Execution Environment (TEE) Confidential Environments
Confidential Containers leverage Trusted Execution Environments (TEEs) to enhance security in cloud-native applications by safeguarding data-in-use against external threats. However, security vulnerabilities persist when container images are pulled from third-party registries, as such sources may introduce compromised or malicious images. This paper proposes a comprehensive design for an in-cluster container registry tailored for TEE-enabled environments, detailing its implementation, benefits, and role in strengthening the security posture of confidential workloads.
Empowering Kubernetes Operations: Incluster-Registry for Secure Image Management
In today's dynamic containerized landscape, efficient management and secure deployment of container images within private Kubernetes clusters are paramount. This paper introduces a comprehensive solution, Incluster-Registry, centered around a set of custom Go programs seamlessly integrated with Kubernetes operations. The solution comprises a custom registry, an independent multi-architecture image builder, and a Kubernetes Operator to orchestrate the entire workflow.
AI-Powered Kubernetes Workload Optimization on IBM Z (s390x): Leveraging LLMs for Predictive Scaling
Kubernetes on IBM Z (s390x) presents unique challenges due to hardware constraints (LPARs, shared memory, I/O bottlenecks) and the need for efficient workload placement in Multi-Cloud Platform (MCP) environments. Traditional autoscaling (HPA/VPA) struggles with latency-sensitive workloads (e.g., financial transactions, mainframe-offloaded AI inferencing).
AI-Powered Kubernetes Workload Optimization on s390x: Leveraging LLMs for Predictive Scaling
Kubernetes on IBM Z (s390x) presents unique challenges due to hardware constraints (LPARs, shared memory, I/O bottlenecks) and the need for efficient workload placement in Multi-Cloud Platform (MCP) environments. Traditional autoscaling (HPA/VPA) struggles with latency-sensitive workloads (e.g., financial transactions, mainframe-offloaded AI inferencing).
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