Benedetti et al., 2022 - Google Patents
Reinforcement learning applicability for resource-based auto-scaling in serverless edge applicationsBenedetti et al., 2022
- Document ID
- 7073612346761453222
- Author
- Benedetti P
- Femminella M
- Reali G
- Steenhaut K
- Publication year
- Publication venue
- 2022 IEEE international conference on pervasive computing and communications workshops and other affiliated events (PerCom Workshops)
External Links
Snippet
Serverless computing is an alternative deployment paradigm for cloud computing platforms, aimed to provide scalability and cost reduction without requiring any additional deployment overhead from developers. Generally, open-source serverless computing platforms rely on …
- 230000002787 reinforcement 0 title abstract description 10
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
- G06F9/50—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
- G06F9/48—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
- 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
- G06F11/3409—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
-
- 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
-
- 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
-
- 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
- H04L41/08—Configuration management of network or network elements
- H04L41/0803—Configuration setting of network or network elements
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W24/00—Supervisory, monitoring or testing arrangements
- H04W24/02—Arrangements for optimizing operational condition
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L43/00—Arrangements for monitoring or testing packet switching networks
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network-specific arrangements or communication protocols supporting networked applications
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
- H04W16/22—Traffic simulation tools or models
-
- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATIONS NETWORKS
- H04W72/00—Local resource management, e.g. wireless traffic scheduling or selection or allocation of wireless resources
- H04W72/04—Wireless resource allocation
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Benedetti et al. | Reinforcement learning applicability for resource-based auto-scaling in serverless edge applications | |
| Daraghmeh et al. | Time series forecasting using facebook prophet for cloud resource management | |
| US11405280B2 (en) | AI-driven capacity forecasting and planning for microservices apps | |
| JP6380110B2 (en) | Resource control system, control pattern generation device, control device, resource control method, and program | |
| EP3973397B1 (en) | Systems and methods for distribution of application logic in digital networks | |
| US20190324822A1 (en) | Deep Reinforcement Learning for Workflow Optimization Using Provenance-Based Simulation | |
| Renart et al. | Distributed operator placement for iot data analytics across edge and cloud resources | |
| Faraji Mehmandar et al. | A dynamic fog service provisioning approach for IoT applications | |
| Tam et al. | Multi-Agent Deep Q-Networks for Efficient Edge Federated Learning Communications in Software-Defined IoT. | |
| Mostafavi et al. | A stochastic approximation approach for foresighted task scheduling in cloud computing | |
| Makridis et al. | Robust dynamic CPU resource provisioning in virtualized servers | |
| Shifrin et al. | VM scaling and load balancing via cost optimal MDP solution | |
| da Silva Veith et al. | Multi-objective reinforcement learning for reconfiguring data stream analytics on edge computing | |
| US11310125B2 (en) | AI-enabled adaptive TCA thresholding for SLA assurance | |
| US20230086473A1 (en) | Smart retry policy for automated provisioning of online resources | |
| Zamani et al. | Edge-supported approximate analysis for long running computations | |
| Mehta et al. | Distributed cost-optimized placement for latency-critical applications in heterogeneous environments | |
| Nguyen et al. | Elasticity control for latency-intolerant mobile edge applications | |
| Patil et al. | Seamless service migration across multi-access edge computing (MEC) environments | |
| Laroui et al. | Scalable and cost efficient resource allocation algorithms using deep reinforcement learning | |
| Saqib et al. | Adaptive VNF placement considering overall latency and 5G wireless channel reliability in Industry 4.0: A reinforcement learning based approach | |
| Seracini et al. | A comprehensive resource management solution for web-based systems | |
| Silva et al. | A MAPE-K and queueing theory approach for VNF auto-scaling in edge computing | |
| Yousaf et al. | RAVA—Resource aware VNF agnostic NFV orchestration method for virtualized networks | |
| Wassie et al. | Deep reinforcement learning for context-aware online service function chain deployment and migration over 6g networks |