Energy-Efficient Mobile Edge Computing: Three-Tier Computing under Heterogeneous Networks

Pei, Yongsheng; Peng, Zhangyou; Wang, Zhenling; Wang, Haojia

doi:10.1155/2020/6098786

Cited by 16 publications

(9 citation statements)

References 35 publications

(91 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Authors in [29] provided a joint offloading and resource allocation framework for a hierarchical cooperative fog computing nodes to optimise energy consumption through intensive simulation experiments. Pei et al [30] studied energy-efficient resource allocation through latencysensitive tasks offloading in the hierarchical Mobile Edge Computing (MEC) architecture in heterogeneous networks. Their numerical simulation-based experiments exhibited energy efficiency improvements of the proposed solution.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

Mansouri¹,

Prokhorenko²,

Ullah³

et al. 2021

Preprint

View full text Add to dashboard Cite

A benchmark study of modern distributed databases is an important source of information to select the right technology for managing data in the cloud-edge paradigms. To make the right decision, it is required to conduct an extensive experimental study on a variety of hardware infrastructures. While most of the state-of-the-art studies have investigated only response time and scalability of distributed databases, focusing on other various metrics (e.g., energy, bandwidth, and storage consumption) is essential to fully understand the resources consumption of the distributed databases. Also, existing studies have explored the response time and scalability of these databases either in private or public cloud. Hence, there is a paucity of investigation into the evaluation of these databases deployed in a hybrid cloud, which is the seamless integration of public and private cloud. To address these research gaps, in this paper, we investigate energy, bandwidth and storage consumption of the most used and common distributed databases. For this purpose, we have evaluated four open-source databases (Cassandra, Mongo, Redis and MySQL) on the hybrid cloud spanning over local OpenStack and Microsoft Azure, and a variety of edge computing nodes including Raspberry Pi, a cluster of Raspberry Pi, and low and high power servers. Our extensive experimental results reveal several helpful insights for the deployment selection of modern distributed databases in edge-cloud environments.

show abstract

Section: Related Workmentioning

confidence: 99%

Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

Mansouri¹,

Prokhorenko²,

Ullah³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Task offloading mechanisms for hierarchical edge computing networks have been analyzed in [12], and [13]. A hierarchical edge computing network is composed of edge servers placed on both small base stations (SBSs) and macro base stations (MBSs).…”

Section: Energy Saving In Edge Computingmentioning

confidence: 99%

“…The former are placed near the terminal nodes and have limited computing capabilities, while the latter can execute compute-intensive tasks. In [12] the terminal node offloads parts of its task to both SBS and MBSs. The model computes the optimal workload placement strategy, minimizing the energy consumption under latency constraints.…”

Section: Energy Saving In Edge Computingmentioning

confidence: 99%

Edge Computing vs Centralized Cloud: Impact of Communication Latency on the Energy Consumption of LTE Terminal Nodes

Caiazza¹,

Giordano²,

Luconi³

et al. 2021

Preprint

View full text Add to dashboard Cite

Edge computing brings several advantages, such as reduced latency, increased bandwidth, and improved locality of traffic. One aspect that is not sufficiently understood is the impact of the different communication latency experienced in the edge-cloud continuum on the energy consumption of clients. We studied how a request-response communication scheme is influenced by different placements of the server, when communication is based on LTE. Results show that by accurately selecting the operational parameters a significant amount of energy can be saved.

show abstract

“…Pie et al [86] proposed an energy-efficient resource allocation scheme (EE-RAS) for MEC. A heterogeneous based three-tier computing model is established.…”

Section: ) Ee-rasmentioning

confidence: 99%

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

et al. 2020

View full text Add to dashboard Cite

The realm of cloud computing has revolutionized access to cloud resources and their utilization and applications over the Internet. However, deploying cloud computing for delay critical applications and reducing the delay in access to the resources are challenging. The Mobile Edge Computing (MEC) paradigm is one of the effective solutions, which brings the cloud computing services to the proximity of the edge network and leverages the available resources. This paper presents a survey of the latest and state-of-the-art algorithms, techniques, and concepts of MEC. The proposed work is unique in that the most novel algorithms are considered, which are not considered by the existing surveys. Moreover, the chosen novel literature of the existing researchers is classified in terms of performance metrics by describing the realms of promising performance and the regions where the margin of improvement exists for future investigation for the future researchers. This also eases the choice of a particular algorithm for a particular application. As compared to the existing surveys, the bibliometric overview is provided, which is further helpful for the researchers, engineers, and scientists for a thorough insight, application selection, and future consideration for improvement. In addition, applications related to the MEC platform are presented. Open research challenges, future directions, and lessons learned in area of the MEC are provided for further future investigation.

show abstract

Energy-Efficient Mobile Edge Computing: Three-Tier Computing under Heterogeneous Networks

Cited by 16 publications

References 35 publications

Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

Evaluation of Distributed Databases in Hybrid Clouds and Edge Computing: Energy, Bandwidth, and Storage Consumption

Edge Computing vs Centralized Cloud: Impact of Communication Latency on the Energy Consumption of LTE Terminal Nodes

Towards Mobile Edge Computing: Taxonomy, Challenges, Applications and Future Realms

Contact Info

Product

Resources

About