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A Self-Learning Scheduling in Cloud Software Defined Block Storage

https://doi.org/10.1109/cloud.2017.60

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1016/b978-1-55860-377-6.50032-3openalex-snapshot

Supervised and Unsupervised Discretization of Continuous Features

inferredcites →https://doi.org/10.1023/a:1024099825458openalex-snapshot

Tree Induction for Probability-Based Ranking

inferredcites →https://doi.org/10.1109/cloud.2016.0033openalex-snapshot

Vertigo: Programmable Micro-controllers for Software-Defined Object Storage

inferredcites →https://doi.org/10.1109/cloud.2016.0115openalex-snapshot

A Black-Box Self-Learning Scheduler for Cloud Block Storage Systems

inferredcites →https://doi.org/10.1109/cloudnet.2014.6968962openalex-snapshot

SLA-aware resource scheduling for cloud storage

inferredcites →https://doi.org/10.1109/icc.2015.7248353openalex-snapshot

Multi-dimensional scheduling in cloud storage systems

inferredcites →https://doi.org/10.1109/icc.2017.7996822openalex-snapshot

A trace-driven evaluation of cloud computing schedulers for IaaS

inferredcites →https://doi.org/10.1109/mascots.2006.12openalex-snapshot

An Empirical Exploration of Black-Box Performance Models for Storage Systems

inferredcites →https://doi.org/10.1109/tkde.2008.116openalex-snapshot

Self-Learning Disk Scheduling

inferredcites →https://doi.org/10.1145/2287076.2287087openalex-snapshot

QBox

inferredcites →https://doi.org/10.1145/3501714.3501727openalex-snapshot

Reverend Bayes on Inference Engines: A Distributed Hierarchical Approach

inferredcites →https://doi.org/10.48550/arxiv.1512.06432openalex-snapshot

Serifos: Workload Consolidation and Load Balancing for SSD Based Cloud Storage Systems

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Introduction to Fog Data Analytics for IoT Applications

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inferred← citeshttps://doi.org/10.1016/j.future.2018.06.045openalex-snapshot

A self-organized resource provisioning for cloud block storage

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Multi-Objective Scheduling using Logistic Regression for OpenStack-based Cloud

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Self-Managed Block Storage Scheduling for OpenStack-based Cloud

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All one needs to know about fog computing and related edge computing paradigms: A complete survey

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A review of fog computing and its simulators

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A Smart Background Scheduler for Storage Systems

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SLA-aware resource scheduling algorithm for cloud storage

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Fog Computing State of the Art: Concept and Classification of Platforms to Support Distributed Computing Systems

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Cloud-Edge Network Data Processing based on User Requirements using Modify MapReduce Algorithm and Machine Learning Techniques

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Active resource partitioning and planning for storage systems using time series forecasting and machine learning techniques

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Adaptive AI Model Selection for Cloud Infrastructure Optimization: A Framework for Intelligent, Self-Regulating Computing Environments

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АНАЛІЗ ШЛЯХІВ ЗМЕНШЕННЯ ЗАТРИМКИ МІКРОПОТОКІВ В КОМП’ЮТЕРНИХ СИСТЕМАХ ПІДТРИМКИ IOT НА ТУМАННІЙ ПЛАТФОРМІ

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Comprehensive Review of Edge Computing for Power Systems: State of the Art, Architecture, and Applications

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Deep Learning Framework for Liver Segmentation from T1-Weighted MRI Images

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Predictable Opportunities in Public and Private Sectors through Advanced Cloud Computing

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Fog computing state of the art: concept and classification of platforms to support distributed computing systems

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