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Search-based Methods for Multi-Cloud Configuration
[article]
2022
arXiv
pre-print
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Multi-cloud computing has become increasingly popular with enterprises looking to avoid vendor lock-in. ...
of the multi-cloud configuration domain, (b) hierarchical methods from AutoML can be used for the multi-cloud configuration task and can outperform state-of-the-art cloud configuration solutions and (c ...
Predictive Methods Given a target workload, predictive methods use a statistical model to predict how the workload will perform on a set of cloud configurations. ...
arXiv:2204.09437v1
fatcat:dxim7sw6ajccvew5uj46a2ndxe
HPCWMF: A Hybrid Predictive Cloud Workload Management Framework Using Improved LSTM Neural Network
2020
Cybernetics and Information Technologies
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A novel hybrid predictive approach is aiming at enhancing the prediction performance of the cloud workload. ...
Thus, in this paper, the authors propose a predictive cloud workload management framework to estimate the needed resources in advance based on a hybrid approach, which is a combination of an improved Long ...
To deal with this issue, an evolutionary algorithm along with opposition- based learning is adopted to select optimal parameters for the LSTM network to reduce the computational time and improve the prediction ...
doi:10.2478/cait-2020-0047
fatcat:z5zeo4cvcrhejh75wmqz3dkbd4
Edge-centric Optimization of Multi-modal ML-driven eHealth Applications
[article]
2022
arXiv
pre-print
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Run-time variations with continuous stream of noisy input data, unreliable network connection, computational requirements of ML algorithms, and choice of compute placement among sensor-edge-cloud layers ...
In this chapter, we present edge-centric techniques for optimized compute placement, exploration of accuracy-performance trade-offs, and cross-layered sense-compute co-optimization for ML-driven eHealth ...
The feasibility of such edge-layer based ML model tuning in collaboration with cloud-layer based model selection is another open research direction. ...
arXiv:2208.02597v1
fatcat:5gb4hnmmofakrad4hsdgv2nsye
Tr-Predictior: An Ensemble Transfer Learning Model for Small-Sample Cloud Workload Prediction
2022
Entropy
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The experimental results show that compared with the commonly used cloud workload prediction methods Tr-Predictor has higher prediction accuracy on the small-sample workload. ...
Specifically, a selection method of similar sequences combining time warp edit distance (TWED) and transfer entropy (TE) is proposed to select a source domain dataset with higher similarity for the target ...
Elastic and efficient resource management is the characteristic of cloud computing over other computing models [2, 3] . ...
doi:10.3390/e24121770
pmid:36554175
pmcid:PMC9778472
fatcat:rnnsohpqqbev7lh3zef44tuwqq
On the Potential of Execution Traces for Batch Processing Workload Optimization in Public Clouds
[article]
2021
arXiv
pre-print
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With the growing amount of data, data processing workloads and the management of their resource usage becomes increasingly important. ...
As the configuration of workloads and resources is often challenging, various methods have been proposed that either quickly profile towards a good configuration or determine one based on data from previous ...
Sharing of Confidential Execution Data Workload optimization through selection of more suitable cloud configurations is often realized with performance models. ...
arXiv:2111.08759v1
fatcat:velfbuafxrddxnkrgnafsed5ym
A Survey on Machine Learning for Geo-Distributed Cloud Data Center Management
[article]
2022
arXiv
pre-print
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The characterization, prediction, control, and optimization of complex, heterogeneous, and ever-changing distributed cloud resources and workloads employing ML methodologies have received much attention ...
Mathematical optimization techniques have historically been used to address cloud management issues. ...
The acquired features are then used to create an NN-based power consumption prediction model that predicts the power consumption pattern of each participating data center in a cloud. ...
arXiv:2205.08072v1
fatcat:nz3vvmdrard6hgydagnsadrnpm
Generic SDE and GA-based workload modeling for cloud systems
2021
Journal of Cloud Computing: Advances, Systems and Applications
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and type, (2) model sharp workload variations that are most likely to appear in cloud environments, and (3) with high degree of fidelity with respect to observed data, within a short execution time. ...
AbstractWorkload models are typically built based on user and application behavior in a system, limiting them to specific domains. ...
Claes Edstrom is Senior Specialist Cloud Computing and is based in Montreal. He is responsible for initiating and executing exploratory projects in the areas of NFV and Cloud technologies. ...
doi:10.1186/s13677-020-00223-5
pmid:33569265
pmcid:PMC7851955
fatcat:yh6ac2fatrgjpgfugtohi4a35q
Online Workload Forecasting
[chapter]
2017
Self-Aware Computing Systems
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It is the goal of this chapter to identify and present approaches for online workload forecasting that are required for a self-aware system to act proactively -in terms of problem prevention and optimization ...
We describe explicit limitations and advantages for each forecasting method. ...
Downsampling with feature selection A more automated method of feature generation for time series is to select vectors of past observations (and even components of these vectors). ...
doi:10.1007/978-3-319-47474-8_18
fatcat:rfkdlva3g5ge7mme6rva4ayeri
The CACTOS Vision of Context-Aware Cloud Topology Optimization and Simulation
2014
2014 IEEE 6th International Conference on Cloud Computing Technology and Science
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Laura Moore of SAP Global Research and Business Incubation Belfast, Belfast, Northern Ireland for work related to this research. ...
Technical Computing Technical or scientific computing is a term used for applications that based on a domain (e.g., physics or chemistry) model derive numerical models for simulation and prediction of ...
Key to the approach is predictive modeling and simulation of application resource requirements in conjunction with contextaware optimization methods. ...
doi:10.1109/cloudcom.2014.62
dblp:conf/cloudcom/OstbergGWBNSKATESKDHBSMPWRP14
fatcat:n3sv2ynetrbuzegh5dkbt72wne
Machine Learning Methods for Reliable Resource Provisioning in Edge-Cloud Computing
2019
ACM Computing Surveys
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A description of prior work based on pursued optimization objectives is also provided. ...
The survey is structured around a decomposition of the reliable resource provisioning problem into three categories of techniques: workload characterization and prediction, component placement and system ...
ACKNOWLEDGMENTS This work has received support from the EU Horizon 2020 programme grant no. 732667 (RECAP), and from the Spanish Ministry of Science, Innovation and Universities grant DisCoEdge (TIN2017 ...
doi:10.1145/3341145
fatcat:vkzofhgipfhqtm2z2w5swivksy
Power consumption prediction in cloud data center using machine learning
2020
International Journal of Electrical and Computer Engineering (IJECE)
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Power consumption by cloud data centers is one of the crucial issues for service providers in the domain of cloud computing. ...
methods. ...
The dataset can be handled with normalization, feature selection and find the relationship among features, through correlation method. ...
doi:10.11591/ijece.v10i2.pp1524-1532
fatcat:ye6osztehzcohkohfu4pqn5axm
Usage Patterns to Provision for Scientific Experimentation in Clouds
2010
2010 IEEE Second International Conference on Cloud Computing Technology and Science
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Using empirical analysis we establish the accuracy of our prediction approach for two different workloads and demonstrate how this knowledge can be used to improve job executions. ...
Job scheduling on cloud computing resources, unlike earlier platforms, is a balance between throughput and cost of executions. ...
The authors would like to thank Dennis Gannon (Microsoft Research), David Leake and Chathura Herath (Indiana University) for their invaluable inputs to this research and Dror Feitelson for archiving workload ...
doi:10.1109/cloudcom.2010.8
dblp:conf/cloudcom/WithanaP10
fatcat:b2zfw7p3urfk7ogilqsu2xmzfy
Fault detection for cloud computing systems with correlation analysis
2015
2015 IFIP/IEEE International Symposium on Integrated Network Management (IM)
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Our method detects anomalies by discovering the abrupt change of correlation coefficients with a EWMA control chart, and then locates suspicious metrics using a feature selection method combining ReliefF ...
Moreover, modeling the behaviors of complex applications always requires domain knowledge which is difficult to obtain. ...
Moreover, we models the behaviors of complex applications automatically without domain knowledge, which is suitable for managing large-scale cloud computing systems. ...
doi:10.1109/inm.2015.7140351
dblp:conf/im/WangZWZ15
fatcat:dtqzi36tq5bnzijmnyzvr4xmem
Optimizing the Performance of Fog Computing Environments Using AI and Co-Simulation
2022
Companion of the 2022 ACM/SPEC International Conference on Performance Engineering
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This tutorial presents a performance engineering approach for optimizing the Quality of Service (QoS) of Edge/Fog/Cloud Computing environments using AI and Coupled-Simulation being developed as part of ...
It also discusses how AI models, specifically, deep neural networks (DNNs), can be used in tandem with simulated estimates to take optimal resource management decisions. ...
The focus then shifts to the modeling of such distributed computing domains in the COSCO framework, with specific emphasis on the various inputs and outputs presented to an optimization engine, which in ...
doi:10.1145/3491204.3527490
fatcat:k74roo4ctrcctgfldhnzk3hyqe
A maturity model for AI-empowered cloud-native databases: from the perspective of resource management
2022
Journal of Cloud Computing: Advances, Systems and Applications
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Also, we develop an assessment tool based on the maturity model to help developers assess cloud-native databases. And we provide an assessment case to prove our maturity model. ...
Hence, we propose a maturity model for AI-empowered cloud-native databases from the perspective of resource management. ...
domains with 27 specific capabilities. ...
doi:10.1186/s13677-022-00318-1
fatcat:zhq2ju4ytrez7dug6xhqh5vcmq
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