| 1. |
- Beldiceanu, Nicolas, et al.
(författare)
-
Assistant: Learning and robust decision support system for agile manufacturing environments
- 2021
-
Ingår i: IFAC-PapersOnLine. - : Elsevier. ; , s. 641-646
-
Konferensbidrag (refereegranskat)abstract
- The European project ASSISTANT will provide a set of AI-based digital twins that helps process engineers and production planners to operate collaborative mixed-model assembly lines based on the data collected from IoT devices and external data sources. Such a tool will help planners to design the assembly line, plan the production, operate the line, and improve process tuning. In addition, the system monitors the line in real-time, ensures that all required resources are available, and allows fast re-planning when necessary. ASSISTANT aims to make cost-effective decisions while ensuring product quality, safety and wellbeing of the workers, and managing the various sources of uncertainties. The resulting digital twin systems will be data-driven, agile, autonomous, collaborative and explainable, safe but reactive.
|
|
| 2. |
- Domaschka, Jörg, et al.
(författare)
-
Towards an Architecture for Reliable Capacity Provisioning for Distributed Clouds
- 2020
-
Ingår i: Managing Distributed Cloud Applications and Infrastructure. - Cham : Palgrave Macmillan. - 9783030398620 - 9783030398637 ; , s. 1-25
-
Bokkapitel (refereegranskat)abstract
- The complexity of computing along the cloud-to-edge continuum presents significant challenges to ICT operations and in particular reliable capacity planning and resource provisioning to meet unpredictable, fluctuating, and mobile demand. This chapter presents a high-level conceptual overview of RECAP—an architectural innovation to support reliable capacity provisioning for distributed clouds—and its operational modes and functional building blocks. In addition, the major design concepts informing its design—namely separation of concerns, model-centricism, modular design, and machine learning and artificial intelligence for IT operations—are also discussed.
|
|
| 3. |
|
|
| 4. |
- Krzywda, Jakub, 1989-, et al.
(författare)
-
Modeling and Simulation of QoS-Aware Power Budgeting in Cloud Data Centers
- 2020
-
Ingår i: 28th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP). - : IEEE conference proceedings. ; , s. 88-93
-
Konferensbidrag (refereegranskat)abstract
- Power budgeting is a commonly employed solution to reduce the negative consequences of high power consumption of large scale data centers. While various power budgeting techniques and algorithms have been proposed at different levels of data center infrastructures to optimize the power allocation toservers and hosted applications, testing them has been challengingwith no available simulation platform that enables such testingfor different scenarios and configurations. To facilitate evaluationand comparison of such techniques and algorithms, we introducea simulation model for Quality-of-Service aware power budgetingand its implementation in CloudSim. We validate the proposedsimulation model against a deployment on a real testbed, showcase simulator capabilities, and evaluate its scalability.
|
|
| 5. |
- Le Duc, Thang, 1980-, et al.
(författare)
-
Workload Diffusion Modeling for Distributed Applications in Fog/Edge Computing Environments
- 2020
-
Ingår i: ICPE '20. - New York : Association for Computing Machinery (ACM). - 9781450369916 ; , s. 218-229
-
Konferensbidrag (refereegranskat)abstract
- This paper addresses the problem of workload generation for distributed applications in fog/edge computing. Unlike most existing work that tends to generate workload data for individual network nodes using historical data from the targeted node, this work aims to extrapolate supplementary workloads for entire application / infrastructure graphs through diffusion of measurements from limited subsets of nodes. A framework for workload generation is proposed, which defines five diffusion algorithms that use different techniques for data extrapolation and generation. Each algorithm takes into account different constraints and assumptions when executing its diffusion task, and individual algorithms are applicable for modeling different types of applications and infrastructure networks. Experiments are performed to demonstrate the approach and evaluate the performance of the algorithms under realistic workload settings, and results are validated using statistical techniques.
|
|
| 6. |
- Leznik, Mark, et al.
(författare)
-
Multivariate Time Series Synthesis Using Generative Adversarial Networks
- 2021
-
Ingår i: ICPE 2021. - New York, NY, USA : Association for Computing Machinery, Inc. - 9781450381949 ; , s. 43-50
-
Konferensbidrag (refereegranskat)abstract
- Collection and analysis of distributed (cloud) computing workloads allows for a deeper understanding of user and system behavior and is necessary for efficient operation of infrastructures and applications. The availability of such workload data is however often limited as most cloud infrastructures are commercially operated and monitoring data is considered proprietary or falls under GPDR regulations. This work investigates the generation of synthetic workloads using Generative Adversarial Networks and addresses a current need for more data and better tools for workload generation. Resource utilization measurements such as the utilization rates of Content Delivery Network (CDN) caches are generated and a comparative evaluation pipeline using descriptive statistics and time-series analysis is developed to assess the statistical similarity of generated and measured workloads. We use CDN data open sourced by us in a data generation pipeline as well as back-end ISP workload data to demonstrate the multivariate synthesis capability of our approach. The work contributes a generation method for multivariate time series workload generation that can provide arbitrary amounts of statistically similar data sets based on small subsets of real data. The presented technique shows promising results, in particular for heterogeneous workloads not too irregular in temporal behavior.
|
|
| 7. |
- Östberg, Per-Olov, et al.
(författare)
-
Application Optimisation : Workload Prediction and Autonomous Autoscaling of Distributed Cloud Applications
- 2020
-
Ingår i: Managing Distributed Cloud Applications and Infrastructure. - Cham : Palgrave Macmillan. - 9783030398620 - 9783030398637 ; , s. 51-68
-
Bokkapitel (refereegranskat)abstract
- Optimisation of (the configuration and deployment of) distributed cloud applications is a complex problem that requires understanding factors such as infrastructure and application topologies, workload arrival and propagation patterns, and the predictability and variations of user behaviour. This chapter outlines the RECAP approach to application optimisation and presents its framework for joint modelling of applications, workloads, and the propagation of workloads in applications and networks. The interaction of the models and algorithms developed is described and presented along with the tools that build on them. Contributions in modelling, characterisation, and autoscaling of applications, as well as prediction and generation of workloads, are presented and discussed in the context of optimisation of distributed cloud applications operating in complex heterogeneous resource environments.
|
|
