Imai et al., 2018 - Google Patents
Uncertainty-aware elastic virtual machine scheduling for stream processing systemsImai et al., 2018
View PDF- Document ID
- 2342309550672717326
- Author
- Imai S
- Patterson S
- Varela C
- Publication year
- Publication venue
- 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID)
External Links
Snippet
Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a …
- 238000000034 method 0 abstract description 25
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]
- G06F9/5083—Techniques for rebalancing the load in a distributed system
-
- 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
- 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
- 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
- G06Q10/063—Operations research or analysis
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/50—Network service management, i.e. ensuring proper service fulfillment according to an agreement or contract between two parties, e.g. between an IT-provider and a customer
- H04L41/5003—Managing service level agreement [SLA] or interaction between SLA and quality of service [QoS]
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L41/00—Arrangements for maintenance or administration or management of packet switching networks
- H04L41/14—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning
- H04L41/147—Arrangements for maintenance or administration or management of packet switching networks involving network analysis or design, e.g. simulation, network model or planning for prediction of network behaviour
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Toka et al. | Machine learning-based scaling management for kubernetes edge clusters | |
Imai et al. | Uncertainty-aware elastic virtual machine scheduling for stream processing systems | |
Qu et al. | Auto-scaling web applications in clouds: A taxonomy and survey | |
Daraghmeh et al. | Time series forecasting using facebook prophet for cloud resource management | |
Horovitz et al. | Efficient cloud auto-scaling with SLA objective using Q-learning | |
Wang et al. | Deepscaling: microservices autoscaling for stable cpu utilization in large scale cloud systems | |
Bi et al. | Dynamic provisioning modeling for virtualized multi-tier applications in cloud data center | |
Radhika et al. | A review on prediction based autoscaling techniques for heterogeneous applications in cloud environment | |
Jiang et al. | Optimal cloud resource auto-scaling for web applications | |
US9386086B2 (en) | Dynamic scaling for multi-tiered distributed systems using payoff optimization of application classes | |
Trihinas et al. | Low-cost adaptive monitoring techniques for the internet of things | |
Fernandez et al. | Autoscaling web applications in heterogeneous cloud infrastructures | |
Adnan et al. | Energy efficient geographical load balancing via dynamic deferral of workload | |
US9923785B1 (en) | Resource scaling in computing infrastructure | |
Morais et al. | Autoflex: Service agnostic auto-scaling framework for iaas deployment models | |
Arkian et al. | Model-based stream processing auto-scaling in geo-distributed environments | |
Krebs et al. | Resource usage control in multi-tenant applications | |
Salah et al. | Estimating service response time for elastic cloud applications | |
Ali-Eldin et al. | Workload classification for efficient auto-scaling of cloud resources | |
Shahin | Using multiple seasonal holt-winters exponential smoothing to predict cloud resource provisioning | |
Liu et al. | Prorenata: Proactive and reactive tuning to scale a distributed storage system | |
CN107566535A (en) | Adaptive load balancing strategy based on user concurrent access timing planning in a kind of web map service | |
Davis et al. | Storm prediction in a cloud | |
Oh et al. | A predictive-reactive method for improving the robustness of real-time data services | |
Adegboyega | Time-series models for cloud workload prediction: A comparison |