[go: up one dir, main page]

Chen et al., 2020 - Google Patents

M-DRL: Deep reinforcement learning based coflow traffic scheduler with MLFQ threshold adaption

Chen et al., 2020

View PDF
Document ID
13441585122371178618
Author
Chen T
Li W
Sun Y
Li Y
Publication year
Publication venue
IFIP International Conference on Network and Parallel Computing

External Links

Snippet

The coflow scheduling in data-parallel clusters can improve application-level communication performance. The existing coflow scheduling method without prior knowledge usually uses Multi-Level Feedback Queue (MLFQ) with fixed threshold …
Continue reading at inria.hal.science (PDF) (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
    • G06F9/48—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
    • 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]
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L12/00—Data switching networks
    • H04L12/54—Store-and-forward switching systems
    • H04L12/56—Packet switching systems
    • H04L12/5693—Queue scheduling in packet switching networks
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L47/00—Traffic regulation in packet switching networks
    • H04L47/10—Flow control or congestion control
    • H04L47/24—Flow control or congestion control depending on the type of traffic, e.g. priority or quality of service [QoS]
    • H04L47/2441—Flow classification
    • 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
    • G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
    • 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

Similar Documents

Publication Publication Date Title
CN111953758B (en) Method and device for edge network computing offloading and task migration
CN111124689B (en) Container resource dynamic allocation method in cluster
Koo et al. Deep reinforcement learning for network slicing with heterogeneous resource requirements and time varying traffic dynamics
Dandachi et al. An artificial intelligence framework for slice deployment and orchestration in 5G networks
Li et al. Adaptive service function chaining mappings in 5G using deep Q-learning
Chen et al. Optimizing coflow completion times with utility max-min fairness
CN115714820B (en) A distributed microservice scheduling optimization method
CN117687760A (en) LVC simulation-oriented intelligent scheduling method for container cloud resources
Jin et al. Optimal policy characterization enhanced proximal policy optimization for multitask scheduling in cloud computing
CN110351348A (en) A kind of cloud computing resources method for optimizing scheduling based on DQN
Li et al. Efficient online scheduling for coflow-aware machine learning clusters
US20230060623A1 (en) Network improvement with reinforcement learning
Zhu et al. A priority-aware scheduling framework for heterogeneous workloads in container-based cloud
Zhu et al. Learning to optimize workflow scheduling for an edge–cloud computing environment
Kumar et al. Multiple resource management and burst time prediction using deep reinforcement learning
US11979295B2 (en) Reinforcement learning agent training method, modal bandwidth resource scheduling method and apparatus
Long et al. A deep deterministic policy gradient-based method for enforcing service fault-tolerance in MEC
Chen et al. M-DRL: Deep reinforcement learning based coflow traffic scheduler with MLFQ threshold adaption
Lu et al. Enhancing network function parallelism in mobile edge computing using Deep Reinforcement Learning
Tolosana-Calasanz et al. End-to-end qos on shared clouds for highly dynamic, large-scale sensing data streams
Liu et al. Multi-objective robust workflow offloading in edge-to-cloud continuum
Liu et al. Bat-FG: A Broad Attention Based Fine-Grained Offloading in Green Computing Power Networks
Rao et al. Enhanced Hybrid Equilibrium Strategy in Fog-Cloud Computing Networks with Optimal Task Scheduling.
Wang et al. Multiobjective optimization deep reinforcement learning for dependent task scheduling based on spatio-temporal fusion graph neural network
CN117742922A (en) Task scheduling method, device and computer readable storage medium