Schmidt, 2020 - Google Patents
Anomaly detection in cloud computing environmentsSchmidt, 2020
View PDF- Document ID
- 9991518590969481232
- Author
- Schmidt F
- Publication year
External Links
Snippet
Cloud computing is widely applied by modern software development companies. Providing digital services in a cloud environment offers both the possibility of cost-efficient usage of computation resources and the ability to dynamically scale applications on demand. Based …
Classifications
-
- 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
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
- 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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N7/00—Computer systems based on specific mathematical models
- G06N7/005—Probabilistic networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F21/00—Security arrangements for protecting computers, components thereof, programs or data against unauthorised activity
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| He et al. | A spatiotemporal deep learning approach for unsupervised anomaly detection in cloud systems | |
| CN112136143B (en) | Dynamic Discovery of Dependencies in Time Series Data Using Neural Networks | |
| CN114830091A (en) | Micro-service decomposition strategy for whole-block application | |
| Casimiro et al. | Self-Adaptation for Machine Learning Based Systems. | |
| Bendriss et al. | AI for SLA management in programmable networks | |
| Enokkaren et al. | A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management | |
| Elhabbash et al. | Self-awareness in software engineering: A systematic literature review | |
| Bendriss et al. | Forecasting and anticipating SLO breaches in programmable networks | |
| Chen et al. | Self‐learning and self‐adaptive resource allocation for cloud‐based software services | |
| Donta et al. | The promising role of representation learning for distributed computing continuum systems | |
| Sîrbu et al. | Towards operator-less data centers through data-driven, predictive, proactive autonomics | |
| Attipalli et al. | A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management | |
| Schmidt | Anomaly detection in cloud computing environments | |
| Velayutham et al. | Artificial Intelligence assisted Canary Testing of Cloud Native RAN in a mobile telecom system | |
| Bendriss | Cognitive management of SLA in software-based networks | |
| St-Onge et al. | Multivariate outlier filtering for A-NFVLearn: an advanced deep VNF resource usage forecasting technique: C. St-Onge et al. | |
| Franceschini et al. | Challenges for automation in adaptive abstraction | |
| Alkasem et al. | Utility cloud: a novel approach for diagnosis and self-healing based on the uncertainty in anomalous metrics | |
| Maddali et al. | Self-Adaptive Data Quality Frameworks with Continuous Learning Mechanisms | |
| Ghedass et al. | On the use of big data frameworks in big service management | |
| Vaidhyanathan | Data-driven self-adaptive architecting using machine learning | |
| Jehangiri et al. | Distributed predictive performance anomaly detection for virtualised platforms | |
| Hao et al. | Efficiently detecting anomalies in IoT: A novel multi-task federated learning method | |
| Kabanda | Performance of Machine Learning and Big Data Analytics Paradigms in Cyber Security | |
| Acker | Anomaly symptom recognition in distributed IT systems |