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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
Analyzing Query Performance and Attributing Blame for Con...
Prajakta Kalmegh, Shivnath Babu, Sudeepa Roy · 2017-08-29 · via cs.DC updates on arXiv.org

There are many approaches is use today to either prevent or minimize the impact of inter-query interactions on a shared cluster. Despite these measures, performance issues due to concurrent executions of mixed workloads still prevail causing undue waiting times for queries. Analyzing these resource interferences is thus critical in order to answer time sensitive questions like 'who is causing my query to slowdown' in a multi-tenant environment. More importantly, dignosing whether the slowdown of a query is a result of resource contentions caused by other queries or some other external factor can help an admin narrow down the many possibilities of performance degradation. This process of investigating the symptoms of resource contentions and attributing blame to concurrent queries is non-trivial and tedious, and involves hours of manually debugging through a cycle of query interactions. In this paper, we present ProtoXplore - a Proto or first system to eXplore contentions, that helps administrators determine whether the blame for resource bottlenecks can be attributed to concurrent queries, and uses a methodology called Resource Acquire Time Penalty (RATP) to quantify this blame towards contentious sources accurately. Further, ProtoXplore builds on the theory of explanations and enables a step-wise deep exploration of various levels of performance bottlenecks faced by a query during its execution using a multi-level directed acyclic graph called ProtoGraph. Our experimental evaluation uses ProtoXplore to analyze the interactions between TPC-DS queries on Apache Spark to show how ProtoXplore provides explanations that help in diagnosing contention related issues and better managing a changing mixed workload in a shared cluster.