惯性聚合 高效追踪和阅读你感兴趣的博客、新闻、科技资讯
阅读原文 在惯性聚合中打开

推荐订阅源

D
Docker
V
V2EX
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
云风的 BLOG
云风的 BLOG
Blog — PlanetScale
Blog — PlanetScale
Recent Announcements
Recent Announcements
Last Week in AI
Last Week in AI
博客园 - Franky
Microsoft Security Blog
Microsoft Security Blog
Hugging Face - Blog
Hugging Face - Blog
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Vercel News
Vercel News
MyScale Blog
MyScale Blog
大猫的无限游戏
大猫的无限游戏
罗磊的独立博客
H
Help Net Security
月光博客
月光博客
Martin Fowler
Martin Fowler
博客园 - 【当耐特】
宝玉的分享
宝玉的分享
P
Proofpoint News Feed
GbyAI
GbyAI
腾讯CDC
freeCodeCamp Programming Tutorials: Python, JavaScript, Git & More

cs.DC updates on arXiv.org

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
Bunk8s: Enabling Easy Integration Testing of Microservice...
Christoph Reile, Mohak Chadha, Valentin Hauner, Anshul Jindal, B · 2022-07-14 · via cs.DC updates on arXiv.org

Microservice architecture is the common choice for cloud applications these days since each individual microservice can be independently modified, replaced, and scaled. However, the complexity of microservice applications requires automated testing with a focus on the interactions between the services. While this is achievable with end-to-end tests, they are error-prone, brittle, expensive to write, time-consuming to run, and require the entire application to be deployed. Integration tests are an alternative to end-to-end tests since they have a smaller test scope and require the deployment of a significantly fewer number of services. The de-facto standard for deploying microservice applications in the cloud is containers with Kubernetes being the most widely used container orchestration platform. To support the integration testing of microservices in Kubernetes, several tools such as Octopus, Istio, and Jenkins exist. However, each of these tools either lack crucial functionality or lead to a substantial increase in the complexity and growth of the tool landscape when introduced into a project. To this end, we present \emph{Bunk8s}, a tool for integration testing of microservice applications in Kubernetes that overcomes the limitations of these existing tools. \emph{Bunk8s} is independent of the test framework used for writing integration tests, independent of the used CI/CD infrastructure, and supports test result publishing. A video demonstrating the functioning of our tool is available from \url{https://www.youtube.com/watch?v=e8wbS25O4Bo}.