Nothing Special   »   [go: up one dir, main page]

Liu et al., 2016 - Google Patents

CORP: Cooperative opportunistic resource provisioning for short-lived jobs in cloud systems

Liu et al., 2016

View PDF
Document ID
6777877642344453506
Author
Liu J
Shen H
Chen L
Publication year
Publication venue
2016 IEEE international conference on cluster computing (CLUSTER)

External Links

Snippet

In cloud systems, achieving high resource utilization and low Service Level Objective (SLO) violation rate are important to the cloud provider for high profit. For this purpose, recently, some methods have been proposed to predict allocated but unused resources and …
Continue reading at shenh.people.clemson.edu (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
    • G06F9/5011—Allocation of resources, e.g. of the central processing unit [CPU] to service a request the resources being hardware resources other than CPUs, Servers and Terminals
    • 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
    • G06F9/5072—Grid computing
    • 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
    • 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
    • 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
    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • 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
    • G06F17/50—Computer-aided design
    • G06F17/5009—Computer-aided design using simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computer systems based on biological models
    • G06N3/02—Computer systems based on biological models using neural network models
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2209/00—Indexing scheme relating to G06F9/00
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING; COUNTING
    • G06F—ELECTRICAL DIGITAL DATA PROCESSING
    • G06F2201/00—Indexing scheme relating to error detection, to error correction, and to monitoring
    • 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

Similar Documents

Publication Publication Date Title
US11392843B2 (en) Utilizing a machine learning model to predict a quantity of cloud resources to allocate to a customer
Jyoti et al. Dynamic provisioning of resources based on load balancing and service broker policy in cloud computing
Sreenu et al. W-Scheduler: whale optimization for task scheduling in cloud computing
Witt et al. Predictive performance modeling for distributed batch processing using black box monitoring and machine learning
Liu et al. CORP: Cooperative opportunistic resource provisioning for short-lived jobs in cloud systems
Ismaeel et al. Proactive dynamic virtual-machine consolidation for energy conservation in cloud data centres
Kim et al. A trust evaluation model for QoS guarantee in cloud systems
Zhang et al. An effective data locality aware task scheduling method for MapReduce framework in heterogeneous environments
Liu et al. CCRP: Customized cooperative resource provisioning for high resource utilization in clouds
Khan et al. HeporCloud: An energy and performance efficient resource orchestrator for hybrid heterogeneous cloud computing environments
Yadav et al. An enhanced ordinal optimization with lower scheduling overhead based novel approach for task scheduling in cloud computing environment
Siddesha et al. A novel deep reinforcement learning scheme for task scheduling in cloud computing
Tang et al. An effective reliability-driven technique of allocating tasks on heterogeneous cluster systems
Makrani et al. Adaptive performance modeling of data-intensive workloads for resource provisioning in virtualized environment
Neto et al. MULTS: A multi-cloud fault-tolerant architecture to manage transient servers in cloud computing
Yadav et al. Maintaining container sustainability through machine learning
Dinesh Kumar et al. An efficient proactive VM consolidation technique with improved LSTM network in a cloud environment
Deldari et al. A survey on preemptible IaaS cloud instances: challenges, issues, opportunities, and advantages
Muchori et al. Machine learning load balancing techniques in cloud computing: A review
Ghazali et al. A classification of Hadoop job schedulers based on performance optimization approaches
Karniavoura et al. Decision-making approaches for performance QoS in distributed storage systems: A survey
Mishra et al. Improving reliability and reducing cost of task execution on preemptible VM instances using machine learning approach
Ravandi et al. A black-box self-learning scheduler for cloud block storage systems
Shirali et al. Self-adaptive multi-population genetic algorithms for dynamic resource allocation in shared hosting platforms
Bawa et al. Migration of containers on the basis of load prediction with dynamic inertia weight based PSO algorithm