arXiv subjects

Kabilan Kannan

Publications and source records attributed to Kabilan Kannan.

5 recordsLinked to original sources

arXiv2026

Scalable Sequential Recommendation under Latency and Memory Constraints

Sequential recommender systems must model long-range user behavior while operating under strict memory and latency constraints. Transformer-based approaches achieve strong accuracy but suffer from quadratic attention complexity, forcing aggressive truncation of user histories and limiting their practicality for long-horizon modeling. This paper presents HoloMambaRec, a lightweight sequential recommendation architecture that combines holographic reduced representations for attribute-aware embedding with a selective state space encoder for linear-time sequence processing. Item and attribute information are bound using circular convolution, preserving embedding dimensionality while encoding structured metadata. A shallow selective state space backbone, inspired by recent Mamba-style models, enables efficient training and constant-time recurrent inference. Experiments on Amazon Beauty and MovieLens-1M under a 10-epoch budget show that HoloMambaRec surpasses SASRec on both datasets, attains state-of-the-art ranking on MovieLens-1M, and trails only GRU4Rec on Amazon Beauty, all while maintaining substantially lower memory complexity. The design further incorporates forward-compatible mechanisms for temporal bundling and inference-time compression, positioning HoloMambaRec as a practical and extensible alternative for scalable, metadata-aware sequential recommendation.

cs.IR↗

arXiv2025

AI-Driven Cloud Resource Optimization for Multi-Cluster Environments

Modern cloud-native systems increasingly rely on multi-cluster deployments to support scalability, resilience, and geographic distribution. However, existing resource management approaches remain largely reactive and cluster-centric, limiting their ability to optimize system-wide behavior under dynamic workloads. These limitations result in inefficient resource utilization, delayed adaptation, and increased operational overhead across distributed environments. This paper presents an AI-driven framework for adaptive resource optimization in multi-cluster cloud systems. The proposed approach integrates predictive learning, policy-aware decision-making, and continuous feedback to enable proactive and coordinated resource management across clusters. By analyzing cross-cluster telemetry and historical execution patterns, the framework dynamically adjusts resource allocation to balance performance, cost, and reliability objectives. A prototype implementation demonstrates improved resource efficiency, faster stabilization during workload fluctuations, and reduced performance variability compared to conventional reactive approaches. The results highlight the effectiveness of intelligent, self-adaptive infrastructure management as a key enabler for scalable and resilient cloud platforms.

cs.DC↗

arXiv2025

An SLO Driven and Cost-Aware Autoscaling Framework for Kubernetes

Kubernetes provides native autoscaling mechanisms, including the Horizontal Pod Autoscaler, Vertical Pod Autoscaler, and node-level autoscalers, to enable elastic resource management for cloud-native applications. However, production environments frequently experience Service Level Objective violations and cost inefficiencies due to reactive scaling behavior, limited use of application-level signals, and opaque control logic. This paper investigates how Kubernetes autoscaling can be enhanced using AIOps principles to jointly satisfy SLO and cost constraints under diverse workload patterns without compromising safety or operational transparency. We present a gap-driven analysis of existing autoscaling approaches and propose a safe and explainable multi-signal autoscaling framework that integrates SLO-aware and cost-conscious control with lightweight demand forecasting. Experimental evaluation using representative microservice and event-driven workloads shows that the proposed approach reduces SLO violation duration by up to 31 percent, improves scaling response time by 24 percent, and lowers infrastructure cost by 18 percent compared to default and tuned Kubernetes autoscaling baselines, while maintaining stable and auditable control behavior. These results demonstrate that AIOps-driven, SLO-first autoscaling can significantly improve the reliability, efficiency, and operational trustworthiness of Kubernetes-based cloud platforms.

cs.SE↗

arXiv2025

Secure and Governed API Gateway Architectures for Multi-Cluster Cloud Environments

API gateways serve as critical enforcement points for security, governance, and traffic management in cloud-native systems. As organizations increasingly adopt multi-cluster and hybrid cloud deployments, maintaining consistent policy enforcement, predictable performance, and operational stability across heterogeneous gateway environments becomes challenging. Existing approaches typically manage security, governance, and performance as loosely coupled concerns, leading to configuration drift, delayed policy propagation, and unstable runtime behavior under dynamic workloads. This paper presents a governance-aware, intent-driven architecture for coordinated API gateway management in multi-cluster cloud environments. The proposed approach expresses security, governance, and performance objectives as high-level declarative intents, which are systematically translated into enforceable gateway configurations and continuously validated through policy verification and telemetry-driven feedback. By decoupling intent specification from enforcement while enabling bounded, policy-compliant adaptation, the architecture supports heterogeneous gateway implementations without compromising governance guarantees or service-level objectives. A prototype implementation across multiple Kubernetes clusters demonstrates the effectiveness of the proposed design. Experimental results show up to a 42% reduction in policy drift, a 31% improvement in configuration propagation time, and sustained p95 latency overhead below 6% under variable workloads, compared to manual and declarative baseline approaches. These results indicate that governance-aware, intent-driven gateway orchestration provides a scalable and reliable foundation for secure, consistent, and performance-predictable cloud-native platforms.

cs.CR↗

arXiv2025

A Privacy-Preserving Cloud Architecture for Distributed Machine Learning at Scale

Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.

cs.LG↗