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DUAL-BLADE: Dual-Path NVMe-Direct KV-Cache Offloading for Edge LLM Inference Progressive Semantic Communication for Efficient Edge-Cloud Vision-Language Models Efficient, VRAM-Constrained xLM Inference on Clients Folding Tensor and Sequence Parallelism for Memory-Efficient Transformer Training & Inference DORA: A Scalable Asynchronous Reinforcement Learning System for Language Model Training AMMA: A Multi-Chiplet Memory-Centric Architecture for Low-Latency 1M Context Attention Serving RaMP: Runtime-Aware Megakernel Polymorphism for Mixture-of-Experts Spark Policy Toolkit: Semantic Contracts and Scalable Execution for Policy Learning in Spark Internet of Everything in the 6G Era: Paradigms, Enablers, Potentials and Future Directions PolyKV: A Shared Asymmetrically-Compressed KV Cache Pool for Multi-Agent LLM Inference A Survey on Split Learning for LLM Fine-Tuning: Models, Systems, and Privacy Optimizations ITAS: A Multi-Agent Architecture for LLM-Based Intelligent Tutoring Latency and Cost of Multi-Agent Intelligent Tutoring at Scale TACO: Efficient Communication Compression of Intermediate Tensors for Scalable Tensor-Parallel LLM Training FreeScale: Distributed Training for Sequence Recommendation Models with Minimal Scaling Cost CommFuse: Hiding Tail Latency via Communication Decomposition and Fusion for Distributed LLM Training A Taxonomy and Resolution Strategy for Client-Level Disagreements in Federated Learning Usable Agent Discovery for Decentralized AI Systems Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers Data-Free Contribution Estimation in Federated Learning using Gradient von Neumann Entropy Shard the Gradient, Scale the Model: Serverless Federated Aggregation via Gradient Partitioning Promoting Simple Agents: Ensemble Methods for Event-Log Prediction GraphLeap: Decoupling Graph Construction and Convolution for Vision GNN Acceleration on FPGA AGNT2: Autonomous Agent Economies on Interaction-Optimized Layer 2 Infrastructure FedSIR: Spectral Client Identification and Relabeling for Federated Learning with Noisy Labels Stream-CQSA: Avoiding Out-of-Memory in Attention Computation via Flexible Workload Scheduling A Delta-Aware Orchestration Framework for Scalable Multi-Agent Edge Computing Federated Learning over Blockchain-Enabled Cloud Infrastructure Optimal Routing for Federated Learning over Dynamic Satellite Networks: Tractable or Not? Sherpa.ai Privacy-Preserving Multi-Party Entity Alignment without Intersection Disclosure for Noisy Identifiers
Achieving Operational Scalability Using Razee Continuous ...
Srini Bhagavan, Saravanan Balasubramanian, Prasad Reddy Annem, T · 2020-12-19 · via cs.DC updates on arXiv.org

Recent advancements in the cloud computing domain have resulted in huge strides toward simplifying the procurement of hardware and software for diverse needs. By moving enterprise workloads to managed cloud offerings (private, public, hybrid), customers are delegating mundane tasks and labor-intensive maintenance activities related to network connectivity, procurement of cloud resource, application deployment, software patches, and upgrades, etc., This often translates to benefits such as high availability and reduced cost. The popularity of container and micro-services-based deployment has made Kubernetes the de-facto standard to deliver applications. However, even with Kubernetes orchestration, cloud service providers frequently have operational scalability issues due to lack of Continuous Integration and Continuous Deployment (CICD) automation and increased demand for human operators when managing a large number of software deployments across multiple data centers/availability zones. Kubernetes solves this in a novel way by creating and managing custom applications using Operators. Agile methodology advocates incremental CICD which are adopted by cloud providers. However, ironically, it is this same continuous delivery feature of application updates, Kubernetes cluster upgrades, etc., that is also a bane to cloud providers. In this paper, we will demonstrate the use of IBM open-source project Razee as a scalable continuous deployment framework to deploy open-source RStudio and Nginx Operators. We will discuss how IBM Watson SaaS application Operator, Blockchain applications, and Kubernetes resources updates, etc., can be deployed similarly and the use of Operators to perform application life cycle management. We assert that using Razee in conjunction with Operators on Kubernetes simplifies application life cycle management and increases scalability.