Edge Cloud Computing Research Topics

Edge Cloud computing Research Topics is one of the significant topics that are proposed in this research and it overcomes some of the issues in the existing research. Here we provide some important topics that are utilized to go through the edge cloud environment related information:

  1. Define Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches

In the first stage first we understand the definition of Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches, it denotes the capacity of intelligent methods to enhance the latency, efficiency and whole quality of cloud services by manipulating edge computing resources and intelligent methods.

  1. What is Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches?

Succeeding the definition we notice the detailed understandings of Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches, it determines to how great the cloud service performs when supported by edge computing technology and executing intelligent methodologies to optimize achievements.

  1. Where Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches used?

Next to the detailed understanding we converse where to use Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches, it is widely utilized in different applications or industries like retail, healthcare, smart cities and transportations to enhance reliability, agility and latency of cloud services by manipulating edge computing resources and intelligent methodologies.

  1. Why Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches technology proposed? , previous technology issues

In this research the technology is proposed to enhance the Cloud Service Performance in Edge Assisted Cloud Environment by utilizing intelligent methods. Some of the existing technology issues are high network traffic and latency. These issues are tackled by using some novel methods in this research are “Dynamic Policy base Access control method, Chaotic map based camellia encryption (CMCE) Algorithm, Multi behaviour Analysis based Nomadic People Optimizer Algorithm and Johnson’s rule based Stochastic Gradient Descent Algorithm” are the methods to be used to overcome the previous issues.

  1. Algorithms / protocols

Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches is utilized to address several previous technology issues. The methods to be used are “Multi behaviour Analysis based Nomadic People Optimizer Algorithm, Johnson’s rule based Stochastic Gradient Descent Algorithm, Chaotic map based camellia encryption (CMCE) Algorithm and Dynamic Policy base Access control method”.

  1. Comparative study / Analysis

The Proposed Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches is compared with various methods to obtain the correct outcomes for this research. The methods that we compared are

  • The integration of biometric characteristics and DU’s apparatus on PUF can be utilized to customize cloud safety and offer cloud services to justify DUs.
  • Handle various sorts of service requests (non-difficult and difficult) in a surroundings of cloud that overview of a problem.
  • Utilization of block chain technology and trust management will assists to interpret several safety problems.
  • The use of block chain technology is to gather service history details, strategy and belief from Edge and Cloud Service Providers in order to attain high performance when maintaining high security levels.
  • A safe and intelligent cloud service discovery system considers SLA as well as QoS needs.
  1. Simulation results / Parameters

The Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches is proposed in this research moreover some of them that we utilized to compare are Response time and Latency with the Number of CSPs, and the Latency and Response Time with the Number of Requests are some of the performance metrics to be compared for our research to get the possible outcome.

  1. Dataset LINKS / Important URL

We proposed Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches; the below we offered are the links that are used to access the explanations on the basis of cloud edge based environments:

  1. Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches Applications

Numerous applications to be used for the achievement of Cloud Service in Edge Assisted Cloud Environment will be optimized by employing the intelligent methods and applications. These method manipulate enhanced methods like AI and Machine learning to offer Workload balancing, latency reduction and effective resource allocation, this gives an enhanced achievement and user experience.

  1. Topology for Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches

Now the topology to be concentrated for this proposed research is achieved through the intelligent techniques. To improve the network topology to perform effective data processing and delivery, ultimately increasing the resource utilization and user experience.

  1. Environment in Cloud Service Performance in Edge Assisted Cloud Environment using Intelligent Approaches

In this research we create an optimized environment for improving cloud service performance among edge-assisted cloud arrangements. Manipulating intelligent methods, that intends to decrease latency, enhance resource allocation and enhance overall service quality in this complicated computing environment.

  1. Simulation tools

The edge assisted cloud environment is proposed in this research; here we provide the software requirements to be needed for this research. The tool that employed for implementing the work is Cloud Sim, Net-beans 12.3 and the programming language that require for this research is JAVA. The Operating system that uses this research is Windows 10-(64-bit).

  1. Results

Edge cloud environment is proposed in this research and is overcomes numerous existing technology issues. The proposed method is here compared with different performance metrics to attain the best results. The tool to be utilized for this research is Cloud Sim and Net-beans 12.3.

Edge Cloud Computing Research Ideas:

Below we given are some of the research topics on the basis of edge cloud environment and these topics are helpful to interpret the ideas about our proposed research.

  1. Hybrid Cloud Energy Management for Edge Computing
  2. Edge Cloud Manufacturing Service Platform and the Resource Allocation Optimization Method
  3. DRL-Based Service Function Chain Edge-to-Edge and Edge-to-Cloud Joint Offloading in Edge-Cloud Network
  4. Software-Defined Multi-Access Edge/Cloud Computing for 5G/6G Time-Critical Services
  5. Construction of Edge Computing Cloud Service Platform Based on BP Neural Network
  6. Federated Learning Model Training Mechanism with Edge Cloud Collaboration for Services in Smart Cities
  7. Cost-Minimized Computation Offloading of Online Multifunction Services in Collaborative Edge-Cloud Networks
  8. Near-Optimal and Collaborative Service Caching in Mobile Edge Clouds
  9. QoS-Aware Deployment of Service Compositions in 5G-Empowered Edge-Cloud Continuum
  10. Personalized Secure Demand-Oriented Data Service Toward Edge-Cloud Collaborative IoT
  11. Security-Aware and Time-Guaranteed Service Placement in Edge Clouds
  12. SDN-based Internet of Video Things Platform Enabling Real-Time Edge/Cloud Video Analytics
  13. Mobility-Aware Service Function Chain Deployment with Migration in NFV-Based Edge-Cloud
  14. SAP: Subchain-Aware NFV Service Placement in Mobile Edge Cloud
  15. Efficient Caching in Vehicular Edge Computing Based on Edge-Cloud Collaboration
  16. Multitenant Containers as a Service (CaaS) for Clouds and Edge Clouds
  17. Intelligent video surveillance with edge-cloud collaboration based on scheduling policy
  18. Analysis of Dynamic Scheduling for Edge Cloud Computing
  19. A Novel Data Placement and Retrieval Service for Cooperative Edge Clouds
  20. Edge-Cloud Collaboration Architecture for Efficient Web-Based Cognitive Services
  21. Collaborative Edge-Cloud AI for IoT Driven Secure Healthcare System
  22. Enabling Intelligence Inclusiveness in Edge to Cloud Continuum: Challenges and Opportunities
  23. Intelligent Resource Allocation for Edge-Cloud Collaborative Networks: A Hybrid DDPG-D3QN Approach
  24. Blockchain-Based Privacy-Aware Contextual Online Learning for Collaborative Edge-Cloud-Enabled Nursing System in Internet of Things
  25. A Novel Cost-Aware Data Placement Strategy for Edge-Cloud Collaborative Smart Systems
  26. Poster: Accessible, Distributed Hydro-Surveillance Through Integrated End-Edge-Cloud Architecture
  27. An Edge-Cloud Collaboration Framework for Graph Processing in Smart Society
  28. Design and Implementation of a Slice as a Service Architecture on the Edge Cloud with Resource Constraints
  29. Research on Edge Cloud Storage Identity Authentication Mechanism Based on Multi-Layer Integration
  30. Infrastructure-efficient Virtual-Machine Placement and Workload Assignment in Cooperative Edge-Cloud Computing Over Backhaul Networks
  31. Priority-Aware Task Offloading and Resource Allocation in Satellite and HAP Assisted Edge-Cloud Collaborative Networks
  32. Task Offloading for Deep Learning Empowered Automatic Speech Analysis in Mobile Edge-Cloud Computing Networks
  33. Security Aware Resource Management Framework (SARMF) for Edge-Cloud Computing
  34. Towards an Edge Cloud Based Coordination Platform for Multi-User AR Applications Built on Open-Source SLAMs
  35. Towards a Reference Component Model of Edge-Cloud Continuum
  36. Edge-Cloud Computing Systems for Unmanned Aerial Vehicles Capable of Optimal Work Offloading with Delay
  37. Object Detection for Video Surveillance Using Edge-Cloud Collaboration
  38. MPDM: A Multi-Paradigm Deployment Model for Large-Scale Edge-Cloud Intelligence
  39. Resource Allocation With Edge-Cloud Collaborative Traffic Prediction in Integrated Radio and Optical Networks
  40. Edge-Cloud Architecture for Precision Aquaculture
  41. pDPoSt+sPBFT: A High Performance Blockchain-Assisted Parallel Reinforcement Learning in Industrial Edge-Cloud Collaborative Network
  42. SatEdge: Platform of Edge Cloud at Satellite and Scheduling Mechanism for Microservice Modules
  43. Towards an Edge Cloud Based Coordination Platform for Multi-User AR Applications Built on Open-Source SLAMs
  44. Admission and Placement Policies for Latency-Compliant Secure Services in 5G Edge–Cloud System
  45. Safety Virtual-Reality Evaluation as a Service for Intelligent Electronic AVs: An Edge-Cloud Consumer-Customized Approach
  46. Joint Optimization Across Timescales: Resource Placement and Task Dispatching in Edge Clouds
  47. ECCVideo: A Scalable Edge Cloud Collaborative Video Analysis System
  48. A Holistic QoS View of Crowdsourced Edge Cloud Platform
  49. A NoSQL DBMS Transparent Data Encryption Approach for Cloud/Edge Continuum
  50. On the Game-Theoretic Analysis of Dynamic VNF Service Chaining in Edge-Cloud EONs