+

Lu et al., 2018 - Google Patents

IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds

Lu et al., 2018

Document ID
11456201297534724560
Author
Lu Z
Wang N
Wu J
Qiu M
Publication year
Publication venue
Journal of Parallel and Distributed Computing

External Links

Snippet

Abstract Uploading all IoT Big Data to a centralized cloud for data analytics is infeasible because of the excessive latency and bandwidth limitation of the Internet. A promising approach to addressing the challenges for data analytics in IoT is “edge cloud” that pushes …
Continue reading at www.sciencedirect.com (other versions)

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5005—Allocation of resources, e.g. of the central processing unit [CPU] to service a request
    • G06F9/5027—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals
    • G06F9/505—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resource being a machine, e.g. CPUs, Servers, Terminals considering the load
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5061—Partitioning or combining of resources
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
    • G06F9/5083—Techniques for rebalancing the load in a distributed system
    • G06F9/5088—Techniques for rebalancing the load in a distributed system involving task migration
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00—Arrangements for programme control, e.g. control unit
    • G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46—Multiprogramming arrangements
    • G06F9/48—Programme initiating; Programme switching, e.g. by interrupt
    • G06F9/4806—Task transfer initiation or dispatching
    • G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
    • G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F15/00—Digital computers in general; Data processing equipment in general
    • G06F15/16—Combinations of two or more digital computers each having at least an arithmetic unit, a programme unit and a register, e.g. for a simultaneous processing of several programmes
    • G06F15/163—Interprocessor communication
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/30—Monitoring
    • G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment
    • G06F11/3409—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation; Recording or statistical evaluation of user activity, e.g. usability assessment for performance assessment
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00—Subject matter not provided for in other groups of this subclass
    • G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
    • H04L67/10—Network-specific arrangements or communication protocols supporting networked applications in which an application is distributed across nodes in the network

Similar Documents

Publication Publication Date Title
Lu et al. IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds
Bala et al. Prediction-based proactive load balancing approach through VM migration
Skourletopoulos et al. Big data and cloud computing: a survey of the state-of-the-art and research challenges
Mirmohseni et al. Using Markov learning utilization model for resource allocation in cloud of thing network
Pandey et al. Energy efficiency strategy for big data in cloud environment using deep reinforcement learning
Xiang et al. Energy-effective artificial internet-of-things application deployment in edge-cloud systems
Liu et al. An optimized human resource management model for cloud-edge computing in the internet of things
Zhu et al. A priority-aware scheduling framework for heterogeneous workloads in container-based cloud
Violos et al. Using LSTM neural networks as resource utilization predictors: The case of training deep learning models on the edge
Yu et al. A hybrid evolutionary algorithm to improve task scheduling and load balancing in fog computing
Sundara Kumar et al. RETRACTED: Improving big data analytics data processing speed through map reduce scheduling and replica placement with HDFS using genetic optimization techniques
Kalai Arasan et al. Energy‐efficient task scheduling and resource management in a cloud environment using optimized hybrid technology
Ji et al. Adaptive workflow scheduling for diverse objectives in cloud environments
Vengadeswaran et al. IDaPS—Improved data-locality aware data placement strategy based on Markov clustering to enhance MapReduce performance on Hadoop
Li et al. A DRL-based online VM scheduler for cost optimization in cloud brokers
Swain et al. Efficient straggler task management in cloud environment using stochastic gradient descent with momentum learning-driven neural networks
Kim et al. Design and implementation of I/O performance prediction scheme on HPC systems through large-scale log analysis
Singh et al. A multi-agent deep reinforcement learning approach for optimal resource management in serverless computing
Vemulapati et al. Ai based performance benchmarking & analysis of big data and cloud powered applications: An in depth view
Aghaei et al. Using recommender clustering to improve quality of services with sustainable virtual machines in cloud computing
Skourletopoulos et al. Game theoretic approaches in mobile cloud computing systems for big data applications: a systematic literature review
Kaur et al. Scheduling algorithms for truly heterogeneous hierarchical fog networks
Meng Analysis of Performance Improvement of Real‐time Internet of Things Application Data Processing in the Movie Industry Platform
Garg et al. Predictive VM placement algorithm for resource optimization: leveraging deep learning forecasting and resource relationship modeling
Singh et al. Load‐Balancing Strategy: Employing a Capsule Algorithm for Cutting Down Energy Consumption in Cloud Data Centers for Next Generation Wireless Systems
点击 这是indexloc提供的php浏览器服务，不要输入任何密码和下载