Edge Computing Research Trends Newsletter -April 2023

I’m thrilled to share briefly about the articles that you may use to advance your research in the exciting field of fog computing or edge computing, where you have the unique opportunity to address real-world challenges faced by users.

This research domain is still in its infancy, with vast potential waiting to be discovered.

As more internet-connected devices and applications grow in the next 5 to 10 years, researchers like you will have many opportunities to solve new challenges.

In this newsletter, I’ve put together some great papers that I think may help you learn more and move forward in your research field of edge computing and offloading management.

So, here they are:

  •  Task offloading paradigm in mobile edge computing-current issues adopted approaches, and future directions :
    • This study explores the challenges and opportunities in Mobile Edge Computing (MEC) arising from emerging technologies like IoT, Autonomous Vehicles, 5G, and Augmented Reality.
    • By conducting a comprehensive survey using a mixed-method systematic literature review, the researchers analyze task offloading approaches in areas such as Vehicular Edge Computing, IoT, Radio Access Networks, and 5G.
    • They provide a taxonomy of journal papers based on adopted techniques and highlight major offloading-related issues in MEC.
    • The review also suggests potential research areas, algorithm contributions, and future research directions, serving as a valuable resource for scholars in the field of edge and fog computing.
  •  Computing Offloading Decision Based on Multi-objective Immune Algorithm in Mobile Edge Computing Scenario :
    • This research focuses on mobile edge computing (MEC), which allows mobile devices to offload tasks to edge servers, resulting in reduced response time and energy use.
    • The challenge lies in making task-offloading decisions when the number of devices increases.
    • The researchers designed a new algorithm to address this issue by modeling it as a multi-objective optimization problem.
    • Their solution provides better performance compared to existing methods, with lower energy consumption and similar response times.
    • The study currently addresses single-user and single-server setups, but future work will explore multi-user and multi-server scenarios.
    • This future scope of multi-user and multi-server approach is an area that you can explore because not many researchers are working in this field of study and this requires you to do some testing of use cases to produce the results.
  •  Relating Edge Computing and Microservices by means of Architecture Approaches and Features, Orchestration, Choreography, and Offloading: A Systematic Literature Review :
    • This study investigates the usefulness of applying microservice architecture to edge computing and its really comprehensive.
    • The researchers identified crucial elements of microservice architectures, such as architecture techniques and characteristics, composition, and offloading, through a comprehensive assessment of the literature that included 111 pertinent publications.
    • They discovered that while choreography and particular microservice orchestrators for edge computing are research gaps, orchestration, and design patterns are significant trends.
    • Trends were also found in auxiliary systems and offloading techniques, with Raspberry Pi devices being the most common edge devices.
    • Overall, the best part of this paper is its literature review, which offers a thorough review of edge computing’s use of microservices, which helps in comprehending the area.
  •  AI-Based Approaches for Task Offloading, Resource Allocation and Service Placement of IoT Applications: State of the Art :
    • The difficulties and solutions in Mobile Edge Computing (MEC) for IoT applications are covered in this study. MEC moves processing and decision-making closer to edge devices, but it can be hard to optimize energy use and performance due to competing objectives.
    • To solve this, they have investigated work offloading, resource allocation, and service placement multi-objective optimization (MOO) strategies, particularly employing artificial intelligence (AI).
    • This paper evaluates current MOO techniques for edge computing and emphasizes the significance of AI for addressing the needs of IoT applications.
    • Again as usual future research will combine MOO strategies to maximize performance while minimizing energy use.
  •  Wireless Powered Mobile Edge Computing Networks- A Survey :
    • This paper is an interesting read because it really discusses the practical and powerful aspects of edge computing.
    • Prominently it discusses
      • Wireless Powered Mobile Edge Computing (WPMEC) highlights areas for improvement and challenges, such as combining optimization methods with reinforcement learning,
      • using UAVs for wireless charging and computation offloading,
      • designing time allocation schemes for dynamic wireless channels,
      • integrating renewable energy sources with wireless power transfer, and
      • addressing privacy and radiation security concerns in WPMEC systems.
    • A must-read if you really want to expand your domain of research.
  •  Management and Orchestration of Edge Computing for IoT: A Comprehensive Survey :
    • Again this paper was a great read, while reviewing the literature on service orchestration and resource management in edge computing, this paper discusses
      • architectures,
      • advantages,
      • enabling technologies,
      • standards, and
      • cutting-edge management and orchestration approaches.
    • This study proposes a very broad sub-domain based on future research directions in edge computing that cover a range of topics, such as
      • improving the architecture,
      • integrating heterogeneous networks,
      • expanding scalability, and
      • guaranteeing interoperability through standardization.
    • This paper also emphasized that in order to preserve service quality and user experience, it is also essential to use edge AI for real-time decision-making, implement network slicing and virtualization, and improve resilience and fault tolerance.
    • The paper also discussed the importance of concentrating on environment-friendly and energy-efficient solutions which would lessen the influence on the environment and increase the long-term viability of edge computing.

uff, after so many days I have written this long!

So this is it for now.

I hope this newsletter will really help you in accelerating your research work and help you in deciding your future topics.

Also, I would really like to hear from you! so make sure you write a comment below and share it with your friends, this will boost my morale.



Cloud Computing Research Trends- May 2022

The latest trending research work in the field of Cloud computing includes the implementation of Artificial Intelligence, Deep Learning, and Reinforcement learning along with Metaheuristic algorithms, opening up new dimensions to improve performance, specifically in resource scheduling, task scheduling, energy efficiency, etc.

Here is a brief review of a few articles related to Deep learning implementation in cloud computing published in January 2022, and here they are:

​​Reinforcement Learning Applications for Performance Improvement in Cloud Computing

A Systematic Review – This paper will provide an overview of various reinforcement learning-based published works and their advancement during the last 10 years. This paper emphasizes the study of resource allotment problems and Virtual Machine problems.

​​A Systematic Review of Deep Learning Approaches for Computer Network and Information Security

This research survey consolidates the details of 32 articles that published their work on deep learning implementation for improving network anomaly detection, intrusion detection, network traffic analysis, and classification. Also, this paper discussed some open issues and future recommendations for further improvement. If you are struggling to find your research topic, you may follow the recommendation leads from this paper.

Comprehensive Study on Machine Learning-Based Container Scheduling in Cloud

This paper referred to different approaches for container scheduling, like heuristic, metaheuristic, mathematical modeling, and machine learning. And summaries of the published research work on container orchestration and container scheduling. It is a good read for those interested in scheduling problems as this paper refers to the main features, advantages, and disadvantages of some existing algorithms from the past four years.

​​Resource allocation optimization using artificial intelligence methods in various computing paradigms: A Review

This paper reviewed a broader aspect of artificial intelligent methods for optimizing and increasing the efficiency of network node communications(dataflow) and resource allocation. Also, this paper summarizes various methods used to solve the resource allocation problem in different computing environments. It analyses their performance on response time, energy efficiency, throughput, cost, service-consuming delay, convergence time, and latency. This is a long article, but I believe it is worth reading to have a baseline understanding of the broader range of resource allocation problems in a cloud computing environment.

​​Towards Metaheuristic Scheduling Techniques in Cloud and Fog: An Extensive Taxonomic Review

This paper presented a comprehensive taxonomic review and analysis of recent metaheuristic scheduling techniques using exhaustive evaluation criteria in the cloud and fog environment. This is an open-source article and a good read for those just starting their research work based on metaheuristic approaches.

​So this is it; I hope you found this helpful.

It’s just another try to make my contribution to the Cloudsim research community.

​For any other updates, you may register to join our daily webinar broadcast on https://social.anupinder.com/cloudsimkickstart​


Anupinder Singh