Description

Book Synopsis

.- Cloud Computing.
.- Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning.
.- LARE-HPA: Co-optimizing Latency and Resource Efficiency for Horizontal Pod Autoscaling in Kubernetes.
.- STORELESS: Serverless workflow scheduling with federated storage in sky computing.
.- Not Best but Fair: Achieving a Fair Service Deployment through Sky Computing for Latency-Sensitive Applications.
.- QoS and SLA.
.- Integrated QoS- and Vulnerability-driven Self-Adaptation for Microservices Applications.
.- SLO-Aware Task Offloading within Collaborative Vehicle Platoons.
.- Client-Specific Homogeneous Service Composition at Runtime for QoS-Critical Tasks.
.- Network SLO-Aware Container Orchestration on Kubernetes Clusters.
.- Microservice.
.- CSMO: The Cross-Supervision Method for Microservice Optimization through Decentralized Data Management.
.- BOAM: a Business-Oriented identification Approach of Microservices within legacy systems.
.- Motif-based Linearizing Graph Transformer for Web API Recommendation.
.- Leveraging a Microservice Architecture, Access Control and Interoperability Patterns to Manage Privacy-related User Consents.
.- Service recommendation.
,- Deep Reinforcement Learning Approach for Explainable Location-based service Recommendation.
.- A Toolchain for Checking Domain- and Model-driven Properties of Jolie Microservices.
.- GSL-Mash: Enhancing Mashup Creation Service Recommendations through Graph Structure Learning.
.- Emerging technologies and approaches.
.- Circuit scheduling policies on current QPUs: QCRAFT Scheduler.
.- MuSS: Multimodal Satellite Service for Unsupervised Land-Cover Classification.
.- HSC: An Artificial Intelligence Service Composition Dataset from Hugging Face.
.- Service composition.
.- Choreography-Defined Network - A Case Study in DoS Mitigation.
.- Racing the Market: An Industry Support Analysis for Pricing-Driven DevOps in SaaS.
.- Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing.
.- Composing Smart Data Services in Shop Floors through Large Language Models.
.- Blockchain.
.- A query language to enhance security and privacy of Blockchain as a Service (BaaS).
.- Blockchain Based Efficient Pairing-Free Certificateless Authentication Scheme for Vehicular Ad-hoc Network.
.- A Blockchain-Enhanced Framework for Privacy and Data Integrity in Crowdsourced Drone Services.
.- Towards an Automated Verification Approach for ERC-based Smart Contracts.
.- Industry Papers.
.- Weather-Conditioned Multi-Graph Network for Ride-Hailing Demand Forecasting.
.- BIS: NL2SQL Service Evaluation Benchmark for BI Scenarios.
.- How Do Infrastructure-as-Code Practitioners Update their Provider Dependencies? An Empirical Study on the AWS Provider.

ServiceOriented Computing

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    Order before 4pm today for delivery by Mon 10 Aug 2026.

    A Paperback by Walid Gaaloul

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      Publisher: Springer
      Publication Date: Publication Date: 12/7/2024
      ISBN13: 9789819608072, 978-9819608072
      ISBN10: 9819608074
      Also in:
      Software Engineering

      Description

      Book Synopsis

      .- Cloud Computing.
      .- Cost-Aware Dynamic Cloud Workflow Scheduling using Self-Attention and Evolutionary Reinforcement Learning.
      .- LARE-HPA: Co-optimizing Latency and Resource Efficiency for Horizontal Pod Autoscaling in Kubernetes.
      .- STORELESS: Serverless workflow scheduling with federated storage in sky computing.
      .- Not Best but Fair: Achieving a Fair Service Deployment through Sky Computing for Latency-Sensitive Applications.
      .- QoS and SLA.
      .- Integrated QoS- and Vulnerability-driven Self-Adaptation for Microservices Applications.
      .- SLO-Aware Task Offloading within Collaborative Vehicle Platoons.
      .- Client-Specific Homogeneous Service Composition at Runtime for QoS-Critical Tasks.
      .- Network SLO-Aware Container Orchestration on Kubernetes Clusters.
      .- Microservice.
      .- CSMO: The Cross-Supervision Method for Microservice Optimization through Decentralized Data Management.
      .- BOAM: a Business-Oriented identification Approach of Microservices within legacy systems.
      .- Motif-based Linearizing Graph Transformer for Web API Recommendation.
      .- Leveraging a Microservice Architecture, Access Control and Interoperability Patterns to Manage Privacy-related User Consents.
      .- Service recommendation.
      ,- Deep Reinforcement Learning Approach for Explainable Location-based service Recommendation.
      .- A Toolchain for Checking Domain- and Model-driven Properties of Jolie Microservices.
      .- GSL-Mash: Enhancing Mashup Creation Service Recommendations through Graph Structure Learning.
      .- Emerging technologies and approaches.
      .- Circuit scheduling policies on current QPUs: QCRAFT Scheduler.
      .- MuSS: Multimodal Satellite Service for Unsupervised Land-Cover Classification.
      .- HSC: An Artificial Intelligence Service Composition Dataset from Hugging Face.
      .- Service composition.
      .- Choreography-Defined Network - A Case Study in DoS Mitigation.
      .- Racing the Market: An Industry Support Analysis for Pricing-Driven DevOps in SaaS.
      .- Compositio Prompto: An Architecture to Employ Large Language Models in Automated Service Computing.
      .- Composing Smart Data Services in Shop Floors through Large Language Models.
      .- Blockchain.
      .- A query language to enhance security and privacy of Blockchain as a Service (BaaS).
      .- Blockchain Based Efficient Pairing-Free Certificateless Authentication Scheme for Vehicular Ad-hoc Network.
      .- A Blockchain-Enhanced Framework for Privacy and Data Integrity in Crowdsourced Drone Services.
      .- Towards an Automated Verification Approach for ERC-based Smart Contracts.
      .- Industry Papers.
      .- Weather-Conditioned Multi-Graph Network for Ride-Hailing Demand Forecasting.
      .- BIS: NL2SQL Service Evaluation Benchmark for BI Scenarios.
      .- How Do Infrastructure-as-Code Practitioners Update their Provider Dependencies? An Empirical Study on the AWS Provider.

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