[go: up one dir, main page]
More Web Proxy on the site http://driver.im/

Hu et al., 2022 - Google Patents

Improve the energy efficiency of datacenters with the awareness of workload variability

Hu et al., 2022

View PDF
Document ID
4456847152887173261
Author
Hu C
Guo Y
Deng Y
Lang L
Publication year
Publication venue
IEEE Transactions on Network and Service Management

External Links

Snippet

In modern datacenters, huge energy consumption is a significant problem that remains to be solved. Previous works reduce the system energy consumption by switching the idle servers to a low-power state. However, the workload demands on servers change dynamically and …
Continue reading at www.researchgate.net (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]
    • 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
    • G06F1/00—Details of data-processing equipment not covered by groups G06F3/00 - G06F13/00, e.g. cooling, packaging or power supply specially adapted for computer application
    • G06F1/26—Power supply means, e.g. regulation thereof
    • G06F1/32—Means for saving power
    • G06F1/3203—Power Management, i.e. event-based initiation of power-saving mode
    • G06F1/3234—Action, measure or step performed to reduce power consumption
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
    • Y02B60/10—Energy efficient computing
    • Y02B60/16—Reducing energy-consumption in distributed systems
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
    • Y02B60/10—Energy efficient computing
    • Y02B60/14—Reducing energy-consumption by means of multiprocessor or multiprocessing based techniques, other than acting upon the power supply
    • 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
    • G06F11/3442—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 planning or managing the needed capacity
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
    • Y02B60/10—Energy efficient computing
    • Y02B60/12—Reducing energy-consumption at the single machine level, e.g. processors, personal computers, peripherals, power supply
    • Y02B60/1207—Reducing energy-consumption at the single machine level, e.g. processors, personal computers, peripherals, power supply acting upon the main processing unit
    • Y02B60/1217—Frequency modification
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02B—INDEXING SCHEME RELATING TO CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO BUILDINGS, e.g. INCLUDING HOUSING AND APPLIANCES OR RELATED END-USER APPLICATIONS
    • Y02B60/00—Information and communication technologies [ICT] aiming at the reduction of own energy use
    • Y02B60/30—Techniques for reducing energy-consumption in wire-line communication networks

Similar Documents

Publication Publication Date Title
Li et al. SLA-aware and energy-efficient VM consolidation in cloud data centers using robust linear regression prediction model
Wu et al. Energy and migration cost-aware dynamic virtual machine consolidation in heterogeneous cloud datacenters
Yao et al. Power cost reduction in distributed data centers: A two-time-scale approach for delay tolerant workloads
Yang et al. Stackelberg game approach for energy-aware resource allocation in data centers
Hu et al. Improve the energy efficiency of datacenters with the awareness of workload variability
Ren et al. Exploiting processor heterogeneity in interactive services
Shen et al. Stochastic modeling of dynamic right-sizing for energy-efficiency in cloud data centers
Deng et al. Lifetime or energy: Consolidating servers with reliability control in virtualized cloud datacenters
Zhang et al. Energy-efficient tasks scheduling heuristics with multi-constraints in virtualized clouds
Li et al. Cost-aware automatic scaling and workload-aware replica management for edge-cloud environment
Song et al. Server consolidation energy-saving algorithm based on resource reservation and resource allocation strategy
Shao et al. A dynamic virtual machine resource consolidation strategy based on a gray model and improved discrete particle swarm optimization
Clay et al. Accelerating batch analytics with residual resources from interactive clouds
Zheng et al. FCTcon: Dynamic control of flow completion time in data center networks for power efficiency
Meng et al. Achieving energy efficiency through dynamic computing offloading in mobile edge-clouds
Li et al. SLA‐Aware and Energy‐Efficient VM Consolidation in Cloud Data Centers Using Host State 3rd‐Order Markov Chain Model
Patel et al. Efficient resource allocation strategy to improve energy consumption in cloud data centers
Xu et al. Trading cost and throughput in geo-distributed analytics with a two time scale approach
Wu et al. Precise power capping for latency-sensitive applications in datacenter
Uchechukwu et al. Scalable analytic models for performance efficiency in the cloud
Huixi et al. A combination of host overloading detection and virtual machine selection in cloud server consolidation based on learning method
Wang et al. Energy minimization for cloud services with stochastic requests
Guo et al. Heuristic algorithms for energy and performance dynamic optimization in cloud computing
Hu et al. Sampling workloads with dynamic time scale to promote the energy efficiency of datacenters
Liu et al. Hybrid Elastic Scaling Strategy for Container Cloud based on Load Prediction and Reinforcement Learning