Hybrid Method for Minimizing Service Delay in Edge Cloud Computing Through VM Migration and Transmission Power Control

By Tiago Gama Rodrigues, Katsuya Suto, Hiroki Nishiyama, Nei Kato. IEEE transactions on computers, 2017

337 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Information Systems

View Paper: https://www.semanticscholar.org/paper/ef07edffe4b6c245aa1337d56d3bf0bb5cb7f742

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jiajia Liu
2. Sabita Maharjan
3. Nei Kato
4. Yuichi Kawamoto
5. Yan Zhang
6. Kaoru Ota
7. Mianxiong Dong
8. Zubair Muhammad Fadlullah

In Information Systems

1. Yan Zhang
2. Sabita Maharjan
3. Wei Yu
4. Kaoru Ota
5. Mukesh Prasad
6. Nei Kato
7. Xuemin Shen
8. Mianxiong Dong

Citations in the corpus

• Task Offloading in Vehicular Edge Computing Networks: A Load-Balancing Solution (2020, background): “bottleneck in the development of vehicular networks [3].”
• Edge of Things: The Big Picture on the Integration of Edge, IoT and the Cloud in a Distributed Computing Environment (2018, background): “The experimental results show that the proposed method has lesser processing delays when compared with other conventional methods [30].”
• Collaborative Computation Offloading for Multiaccess Edge Computing Over Fiber–Wireless Networks (2018, background): “Compared to traditional MCO schemes, MECO can achieve lower latency and higher reliability, and a number of research work on MECO has been proposed over the past several years [16], [17].”
• A Survey on Network Methodologies for Real-Time Analytics of Massive IoT Data and Open Research Issues (2017, background): “ducted in [185] and [186], for reducing the service delay and improving the transmission time in the edge analytics network through the virtual machine migration and consid-”
• Blockchain Empowered Asynchronous Federated Learning for Secure Data Sharing in Internet of Vehicles (2020, background): “In [24], [25], the authors lowered the delay and raised the scalability of MEC by using the proposed intelligent resource optimization scheme which simultaneously considers communication, computation,”
• Serving at the Edge: A Scalable IoT Architecture Based on Transparent Computing (2017, background): “As a growing number of IoT applications are deployed for realtime data processing and context-aware missions [3, 4], moving service provisioning back to the vicinity of IoT devices becomes a potential way to address the challenges of cloud computing and promotes the emergence and development of edge”
• A Survey on the Edge Computing for the Internet of Things (2018, background): “in [67] proposed an analytic model for minimizing service delay in an edge cloud computing environment.”
• Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing (2018, background): “Edge computing is another important technology for IoT services [5] [6] [7].”
• A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “For this scenario, in Reference [127], the authors propose an approach for minimizing offloading delay with two cloudlet servers.”
• Mobility-Aware Edge Caching and Computing in Vehicle Networks: A Deep Reinforcement Learning (2018, background): “2867191 and management from the centralized cloud to the edge of the network [8]–[10].”
• Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “The works [17]–[19] study radio resource allocation for computation offloading in edge computing.”