| Nome |
# |
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Context-aware Data Quality Assessment for Big Data, file e0c31c0c-7173-4599-e053-1705fe0aef77
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1.573
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A Model-Driven DevOps Framework for QoS-Aware Cloud Applications, file e0c31c09-2439-4599-e053-1705fe0aef77
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910
|
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Machine Learning for Performance Prediction of Spark Cloud Applications, file e0c31c0d-f7b4-4599-e053-1705fe0aef77
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838
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Predicting the Performance of Big Data Applications on the Cloud, file e0c31c0f-e821-4599-e053-1705fe0aef77
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654
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BIGSEA: A Big Data analytics platform for public transportation information, file e0c31c0c-fba3-4599-e053-1705fe0aef77
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630
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Performance Prediction of Cloud-Based Big Data Applications, file e0c31c0b-f743-4599-e053-1705fe0aef77
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567
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Performance Prediction of Deep Learning Applications Training in GPU as a Service Systems, file e0c31c12-0554-4599-e053-1705fe0aef77
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462
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Modeling performance of Hadoop applications: A journey from queueing networks to stochastic well formed nets, file e0c31c0a-38c3-4599-e053-1705fe0aef77
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432
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Experiences and challenges in building a data intensive system for data migration, file e0c31c11-2149-4599-e053-1705fe0aef77
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417
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A Mixed Integer Linear Programming Optimization Approach for Multi-Cloud Capacity Allocation, file e0c31c09-e081-4599-e053-1705fe0aef77
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398
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Optimizing on-demand GPUs in the Cloud for Deep Learning Applications Training, file e0c31c0e-b9d0-4599-e053-1705fe0aef77
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397
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Generalized Nash Equilibria for the Service Provisioning Problem in Multi-Cloud Systems, file e0c31c08-84cb-4599-e053-1705fe0aef77
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392
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Analytical composite performance models for Big Data applications, file e0c31c0d-7412-4599-e053-1705fe0aef77
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385
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Performance Prediction of GPU-based Deep Learning Applications, file e0c31c0d-f631-4599-e053-1705fe0aef77
|
379
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Performance Degradation and Cost Impact Evaluation of Privacy Preserving Mechanisms in Big Data Systems, file e0c31c0b-a2e0-4599-e053-1705fe0aef77
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375
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Optimal Resource Allocation of Cloud-Based Spark Applications, file e0c31c0f-f7f8-4599-e053-1705fe0aef77
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356
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Architectural Design of Cloud Applications: a Performance-aware Cost Minimization Approach. IEEE Transactions on Cloud Computing, file e0c31c0f-e824-4599-e053-1705fe0aef77
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355
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Hierarchical Scheduling in on-demand GPU-as-a-Service Systems, file e0c31c10-6f90-4599-e053-1705fe0aef77
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326
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Optimal Map Reduce Job Capacity Allocation in Cloud Systems., file e0c31c07-be82-4599-e053-1705fe0aef77
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324
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A framework for joint resource allocation of MapReduce and web service applications in a shared cloud cluster, file e0c31c0c-7223-4599-e053-1705fe0aef77
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322
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Service provisioning problem in cloud and multi-cloud systems, file e0c31c0b-28d7-4599-e053-1705fe0aef77
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320
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A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications, file e0c31c0c-5db6-4599-e053-1705fe0aef77
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303
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SPACE4Cloud: a DevOps environment for multi-cloud applications, file e0c31c09-28b5-4599-e053-1705fe0aef77
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301
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Fluid Petri Nets for the Performance Evaluation of MapReduce Applications, file e0c31c0a-35de-4599-e053-1705fe0aef77
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281
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Pareto-Optimal Progressive Neural Architecture Search, file e0c31c11-0dbd-4599-e053-1705fe0aef77
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269
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A Combined Analytical Modeling Machine Learning Approach for Performance Prediction of MapReduce Jobs in Hadoop Clusters, file e0c31c0a-364a-4599-e053-1705fe0aef77
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226
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Gray-Box Models for Performance Assessment of Spark Applications, file e0c31c0d-851f-4599-e053-1705fe0aef77
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225
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The economics of the cloud: price competition and congestion, file e0c31c0e-ad30-4599-e053-1705fe0aef77
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207
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Energy-aware joint management of networks and Cloud infrastructures, file e0c31c0e-b7cf-4599-e053-1705fe0aef77
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195
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Palladio Optimization Suite: QoS Optimization for Component-based Cloud Applications, file e0c31c09-2e19-4599-e053-1705fe0aef77
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181
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D-SPACE4Cloud: A Design Tool for Big Data Applications, file e0c31c0a-3fb8-4599-e053-1705fe0aef77
|
174
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Enterprise applications cloud rightsizing through a joint benchmarking
and optimization approach, file e0c31c11-2588-4599-e053-1705fe0aef77
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142
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A Game-Theoretic Approach for Runtime Capacity Allocation in MapReduce, file e0c31c0b-26cb-4599-e053-1705fe0aef77
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140
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Advancing Design and Runtime Management of AI Applications with AI-SPRINT, file 0302a8fc-48b6-4dc4-a8a0-bea22b1dc72f
|
138
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D-SPACE4Cloud: Towards Quality-Aware Data Intensive Applications in the Cloud, file e0c31c0c-827f-4599-e053-1705fe0aef77
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132
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MALIBOO: When Machine Learning meets Bayesian Optimization, file 47031984-2cb4-4af6-af5a-65267611e791
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129
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A Randomized Greedy Method for AI Applications Component Placement and Resource Selection in Computing Continua, file e0c31c12-0eb1-4599-e053-1705fe0aef77
|
119
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Optimizing Quality-Aware Big Data Applications in the Cloud, file e0c31c10-4347-4599-e053-1705fe0aef77
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103
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A Joint Benchmark-Analytic Approach For Design-Time Assessment of Multi-Cloud Applications, file e0c31c0e-1f4f-4599-e053-1705fe0aef77
|
93
|
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The RISPOSTA procedure for the collection, storage and analysis of high quality, consistent and reliable damage data in the aftermath of floods, file e0c31c0e-a091-4599-e053-1705fe0aef77
|
82
|
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DICE: Quality-Driven Development of Data-Intensive Cloud Applications, file e0c31c0a-25de-4599-e053-1705fe0aef77
|
75
|
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A Stochastic Approach for Scheduling AI Training Jobs in GPU-based Systems, file eba694c7-7a96-4cf9-b6c4-ba90efd5d30d
|
55
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AMLLibrary: An AutoML Approach for Performance Prediction, file 5ea5dfbf-88c7-4006-8248-935a9a438243
|
53
|
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An optimization framework for the capacity allocation and admission control of MapReduce jobs in cloud systems, file e0c31c11-5612-4599-e053-1705fe0aef77
|
51
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ANDREAS: Artificial intelligence traiNing scheDuler foR accElerAted resource clusterS, file e0c31c11-c1c0-4599-e053-1705fe0aef77
|
50
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Bayesian optimization with machine learning for big data applications in the cloud, file fbd75bca-b682-44ed-9c16-31a769b7e08f
|
45
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Scheduling Deep Learning Jobs Training in the Cloud: Comparing Multiple Approaches, file a845ab18-fe26-4c0d-97f2-b6ffca71407c
|
43
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Runtime Management of Artificial Intelligence Applications for Smart Eyewears, file 7553cfb4-b593-461f-b4f6-f7d6d5a14a49
|
38
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Performance Models for Distributed Deep Learning Training Jobs on Ray, file a3826e8c-cafc-47e4-8478-d5f738540aad
|
37
|
|
SPACE4AI-R: a Runtime Management Tool for AI Applications Component Placement and Resource Scaling in Computing Continua, file beb4c010-630c-4f6e-b51e-90cb8f934aec
|
35
|
|
POPNASv2: An Efficient Multi-Objective Neural Architecture Search Technique, file e04544d4-c129-4350-9805-dde43ac483cd
|
32
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Tunable and Portable Extreme-Scale Drug Discovery Platform at Exascale: the LIGATE Approach, file f387b78b-0b0b-4e52-93d9-b9543d14997f
|
29
|
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An incentive mechanism based on a Stackelberg game for mobile crowdsensing systems with budget constraint, file db037c0d-e4ff-4e80-80c3-cacff15b5ae4
|
23
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A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems, file 43123258-0b8f-4dff-9424-2fd0315f347c
|
18
|
|
A Hybrid Machine Learning Approach for Performance Modeling of Cloud-Based
Big Data Applications, file c6ec77e7-d7d7-4b12-8f6f-56f852442014
|
16
|
|
Poli-RISPOSTA Mobile App, file e0c31c08-961b-4599-e053-1705fe0aef77
|
15
|
|
A Stackelberg Game approach for Managing AI Sensing Tasks in Mobile Crowdsensing, file 5a3cf01e-7f0a-48bc-90d4-edfc96b54b49
|
9
|
|
A Path Relinking Method for the Joint Online Scheduling and Capacity Allocation of DL Training Workloads in GPU as a Service Systems, file 19585f4a-12c1-4c68-b2f7-2bf9dc6122d7
|
6
|
|
A Random Greedy based Design Time Tool for AI Applications Component Placement and Resource Selection in Computing Continua, file 463dda8b-95fc-417c-8257-9628ef20e9b8
|
6
|
|
Fixed-Point Iteration Approach to Spark Scalable Performance Modeling
and Evaluation, file 8a5ee881-481f-4a0e-8fd3-2106b01b0eb0
|
5
|
|
OSCAR-P and aMLLibrary: Performance Profiling and Prediction of Computing Continua Applications, file 7684fc70-6fa7-4798-9281-c987e0373428
|
4
|
|
FIGARO: reinForcement learnInG mAnagement acRoss the computing cOntinuum, file 1336b833-3ec3-4526-a79c-05f3e0f8aae6
|
1
|
|
Implementing tools to meet the Floods Directive requirements: a “procedure” to collect, store and manage damage data in the aftermath of flood events, file e0c31c08-1c20-4599-e053-1705fe0aef77
|
1
|
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DICE: Quality-Driven Development of Data-Intensive Cloud Applications, file e0c31c09-6592-4599-e053-1705fe0aef77
|
1
|
|
The RISPOSTA procedure for the collection, storage and analysis of high quality, consistent and reliable damage data in the aftermath of floods, file e0c31c0a-7cb3-4599-e053-1705fe0aef77
|
1
|
| Totale |
14.901 |