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001448627 001__ 1448627
001448627 003__ OCoLC
001448627 005__ 20230310004248.0
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001448627 020__ $$a9783031145995$$q(electronic bk.)
001448627 020__ $$a3031145992$$q(electronic bk.)
001448627 020__ $$z3031145984
001448627 020__ $$z9783031145988
001448627 0247_ $$a10.1007/978-3-031-14599-5$$2doi
001448627 035__ $$aSP(OCoLC)1338980555
001448627 040__ $$aYDX$$beng$$cYDX$$dGW5XE$$dEBLCP$$dOCLCF$$dOCLCQ
001448627 049__ $$aISEA
001448627 050_4 $$aQA76.585
001448627 08204 $$a004.67/82$$223/eng/20220810
001448627 1112_ $$aInternational Conference on Cloud Computing, Big Data & Emerging Topics$$n(10th :$$d2022 :$$cLa Plata, Argentina ; Online)
001448627 24510 $$aCloud computing, big data & emerging topics :$$b10th conference, JCC-BD&ET 2022, La Plata, Argentina, June 28-30, 2022, proceedings /$$cEnzo Rucci, Marcelo Naiouf, Franco Chichizola, Laura De Giusti, Armando De Giusti (eds.).
001448627 2463_ $$aJCC-BD&ET 2022
001448627 260__ $$aCham, Switzerland :$$bSpringer,$$c2022.
001448627 300__ $$a1 online resource
001448627 4901_ $$aCommunications in computer and information science,$$x1865-0937 ;$$v1634
001448627 500__ $$aIncludes author index.
001448627 5050_ $$aIntro -- Preface -- Organization -- Contents -- Cloud and High-Performance Computing -- File Access Patterns of Distributed Deep Learning Applications -- 1 Introduction -- 2 Related Work -- 3 Characterizing the I/O Patterns Models of DDL Applications -- 3.1 Software Stack DL -- 3.2 File Access Pattern -- 4 Experimental Data-extraction for File Access Pattern Modelling Characterization -- 4.1 Experimental Environment -- 4.2 Mechanisms Used to Characterize File Access Patterns -- 4.3 Characterization of File Access Patterns to the CIFAR-10 Dataset
001448627 5058_ $$a4.4 Characterization of File Access Patterns to the MNIST Dataset -- 5 Conclusions -- References -- A Survey on Billing Models for Cloud-Native Applications -- 1 Introduction -- 2 Systematic Literature Review -- 3 Main Findings and Discussion -- 4 Conclusions and Research Opportunities -- References -- Performance Analysis of AES on CPU-GPU Heterogeneous Systems -- 1 Introduction -- 2 Background -- 2.1 AES Algorithm -- 2.2 Characterization of Heterogeneous Systems -- 2.3 Related Work -- 3 Previous Implementations of AES -- 3.1 AES for Multicore CPU -- 3.2 AES for Single-GPU and Multi-GPU
001448627 5058_ $$a4 AES for CPU-GPU Heterogeneous Systems -- 5 Experimental Results -- 6 Conclusions and Future Work -- References -- Network Traffic Monitor for IDS in IoT -- 1 Introduction -- 2 Network Traffic Monitor Architecture -- 3 Deployment and Testing -- 3.1 Creating Topology Elements. OpenFlow Switch -- 3.2 Creating Links Between Components -- 3.3 Connecting the Monitor -- 3.4 Creating Host 1 and Host 2 -- 3.5 Connecting Host 1 and Host 2 -- 4 Creating SDN Controller and Traffic Sniffer -- 5 Conclusions and Future Work -- References -- Crane: A Local Deployment Tool for Containerized Applications
001448627 5058_ $$a1 Introduction -- 2 Container Management Architecture Precedents -- 2.1 SWITCH -- 2.2 COCOS -- 2.3 Lightweight Kubernetes Distributions -- 3 Design Evolution of Crane -- 3.1 Instances Load Balancing -- 3.2 Container Automatic Scaling -- 3.3 Detected Implementation Problems -- 4 Conclusions and Future Work -- References -- Machine and Deep Learning -- Multi-class E-mail Classification with a Semi-Supervised Approach Based on Automatic Feature Selection and Information Retrieval -- 1 Introduction -- 2 Background -- 3 Research Methodology -- 3.1 Description of the Dataset
001448627 5058_ $$a3.2 Labeling of Documents -- 3.3 Email Indexing -- 3.4 Feature Selection Strategies -- 3.5 Retrieval of E-mails -- 3.6 Generation of the Classification Models -- 4 Experiments -- 5 Conclusions -- References -- Applying Game-Learning Environments to Power Capping Scenarios via Reinforcement Learning -- 1 Introduction -- 2 The RLlib and Gym Frameworks -- 2.1 RLlib -- 2.2 Gym -- 3 RL for Resource Management -- 4 Casting a Power Capping Scenario with Gym -- 4.1 Defining States -- 4.2 Defining Actions and Rewards -- 5 Experimental Results -- 5.1 Analysis Under Different Power Caps
001448627 506__ $$aAccess limited to authorized users.
001448627 520__ $$aThis book constitutes the revised selected papers of the 10th International Conference on Cloud Computing, Big Data & Emerging Topics, JCC-BD&ET 2022, held in La Plata, Argentina*, in June-July 2022. The 9 full papers were carefully reviewed and selected from a total of 23 submissions. The papers are organized in topical sections on: Parallel and Distributed Computing; Machine and Deep Learning; Cloud and High-Performance Computing, Machine and Deep Learning, and Virtual Reality.
001448627 588__ $$aOnline resource; title from PDF title page (SpringerLink, viewed August 10, 2022).
001448627 650_0 $$aCloud computing$$vCongresses.
001448627 650_0 $$aBig data$$vCongresses.
001448627 650_0 $$aComputer science$$vCongresses.
001448627 655_0 $$aElectronic books.
001448627 655_7 $$aConference papers and proceedings.$$2fast$$0(OCoLC)fst01423772
001448627 7001_ $$aRucci, Enzo,$$eeditor.
001448627 7001_ $$aNaiouf, Marcelo,$$eeditor.
001448627 7001_ $$aChichizola, Franco,$$eeditor.
001448627 7001_ $$aDe Giusti, Laura,$$eeditor.
001448627 7001_ $$aDe Giusti, Armando,$$eeditor.
001448627 77608 $$iPrint version: $$z3031145984$$z9783031145988$$w(OCoLC)1335115758
001448627 830_0 $$aCommunications in computer and information science ;$$v1634.$$x1865-0937
001448627 852__ $$bebk
001448627 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-14599-5$$zOnline Access$$91397441.1
001448627 909CO $$ooai:library.usi.edu:1448627$$pGLOBAL_SET
001448627 980__ $$aBIB
001448627 980__ $$aEBOOK
001448627 982__ $$aEbook
001448627 983__ $$aOnline
001448627 994__ $$a92$$bISE