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

Wang et al., 2016 - Google Patents

Maximum likelihood estimation of closed queueing network demands from queue length data

Wang et al., 2016

View PDF
Document ID
6090336623483788053
Author
Wang W
Casale G
Kattepur A
Nambiar M
Publication year
Publication venue
Proceedings of the 7th ACM/SPEC on International Conference on Performance Engineering

External Links

Snippet

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent …
Continue reading at research.spec.org (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording 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
    • G06F11/3409Recording 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
    • G06F11/3414Workload generation, e.g. scripts, playback
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/48Programme initiating; Programme switching, e.g. by interrupt
    • G06F9/4806Task transfer initiation or dispatching
    • G06F9/4843Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
    • G06F9/4881Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/30Monitoring
    • G06F11/34Recording 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
    • G06F11/3466Performance evaluation by tracing or monitoring
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F9/00Arrangements for programme control, e.g. control unit
    • G06F9/06Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
    • G06F9/46Multiprogramming arrangements
    • G06F9/50Allocation of resources, e.g. of the central processing unit [CPU]
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q10/00Administration; Management
    • G06Q10/06Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
    • G06Q10/063Operations research or analysis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F2201/00Indexing scheme relating to error detection, to error correction, and to monitoring
    • G06F2201/875Monitoring of systems including the internet
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N99/00Subject matter not provided for in other groups of this subclass
    • G06N99/005Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA 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
    • G06Q30/00Commerce, e.g. shopping or e-commerce
    • G06Q30/02Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
    • G06Q30/0202Market predictions or demand forecasting
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions

Similar Documents

Publication Publication Date Title
US20200293835A1 (en) Method and apparatus for tuning adjustable parameters in computing environment
Nikravesh et al. An autonomic prediction suite for cloud resource provisioning
Yu et al. Microscaler: Cost-effective scaling for microservice applications in the cloud with an online learning approach
US8918496B2 (en) System and method for generating synthetic workload traces
Aiber et al. Autonomic self-optimization according to business objectives
Barna et al. Autonomic load-testing framework
JP5313990B2 (en) Estimating service resource consumption based on response time
Di et al. Google hostload prediction based on Bayesian model with optimized feature combination
Kuhlenkamp et al. Benchmarking elasticity of FaaS platforms as a foundation for objective-driven design of serverless applications
Wang et al. Maximum likelihood estimation of closed queueing network demands from queue length data
Pérez et al. Estimating computational requirements in multi-threaded applications
Stewart et al. Entomomodel: Understanding and avoiding performance anomaly manifestations
JP2010507146A (en) Method and apparatus for capacity planning and resource optimization of distributed systems
Pérez et al. An offline demand estimation method for multi-threaded applications
Kalbasi et al. MODE: Mix driven on-line resource demand estimation
Cremonesi et al. Indirect estimation of service demands in the presence of structural changes
Wu et al. Causal inference techniques for microservice performance diagnosis: Evaluation and guiding recommendations
Wang et al. A bayesian approach to parameter inference in queueing networks
Grohmann et al. SARDE: a framework for continuous and self-adaptive resource demand estimation
Rolia et al. Resource demand modeling for multi-tier services
Zheng et al. Integrated estimation and tracking of performance model parameters with autoregressive trends
US9188968B2 (en) Run-time characterization of on-demand analytical model accuracy
Amannejad et al. Predicting Web service response time percentiles
Awad et al. Deriving parameters for open and closed qn models of operational systems through black box optimization
Zhang et al. PaaS-oriented performance modeling for cloud computing