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

人人都是产品经理
人人都是产品经理
量子位
月光博客
月光博客
罗磊的独立博客
宝玉的分享
宝玉的分享
博客园_首页
酷 壳 – CoolShell
酷 壳 – CoolShell
奇客Solidot–传递最新科技情报
奇客Solidot–传递最新科技情报
WordPress大学
WordPress大学
博客园 - 叶小钗
博客园 - 聂微东
阮一峰的网络日志
阮一峰的网络日志
V
V2EX
雷峰网
雷峰网
博客园 - 三生石上(FineUI控件)
Jina AI
Jina AI
OSCHINA 社区最新新闻
OSCHINA 社区最新新闻
博客园 - Franky
美团技术团队
爱范儿
爱范儿
V
Visual Studio Blog
让小产品的独立变现更简单 - ezindie.com
让小产品的独立变现更简单 - ezindie.com
H
Hackread – Cybersecurity News, Data Breaches, AI and More
Y
Y Combinator Blog

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
An NBDMMM Algorithm Based Framework for Allocation of Res...
Mansaf Alam, Kashish Ara Shakil · 2014-12-27 · via cs.DC updates on arXiv.org

Cloud computing is a technological advancement in the arena of computing and has taken the utility vision of computing a step further by providing computing resources such as network, storage, compute capacity and servers, as a service via an internet connection. These services are provided to the users in a pay per use manner subjected to the amount of usage of these resources by the cloud users. Since the usage of these resources is done in an elastic manner thus an on demand provisioning of these resources is the driving force behind the entire cloud computing infrastructure therefore the maintenance of these resources is a decisive task that must be taken into account. Eventually, infrastructure level performance monitoring and enhancement is also important. This paper proposes a framework for allocation of resources in a cloud based environment thereby leading to an infrastructure level enhancement of performance in a cloud environment. The framework is divided into four stages Stage 1: Cloud service provider monitors the infrastructure level pattern of usage of resources and behavior of the cloud users. Stage 2: Report the monitoring activities about the usage to cloud service providers. Stage 3: Apply proposed Network Bandwidth Dependent DMMM algorithm .Stage 4: Allocate resources or provide services to cloud users, thereby leading to infrastructure level performance enhancement and efficient management of resources. Analysis of resource usage pattern is considered as an important factor for proper allocation of resources by the service providers, in this paper Google cluster trace has been used for accessing the resource usage pattern in cloud. Experiments have been conducted on cloudsim simulation framework and the results reveal that NBDMMM algorithm improvises allocation of resources in a virtualized cloud.