Revisiting the Arguments for Edge Computing Research

Varghese, Blesson; Lara, Eyal de; Ding, Aaron Yi; Hong, Cheol-Ho; Bonomi, Flavio; Dustdar, Schahram; Harvey, Paul; Hewkin, Peter; Shi, Wei; Thiele, Mark; Willis, Peter

doi:10.1109/mic.2021.3093924

Cited by 32 publications

(5 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Results are averages of 10 times measurements We assumed Mobile Networks like 4G and 5G as a network between a server and a client. This network is a low-bandwidth network with 50Mbps [11] bandwidth and 40ms [34] delay (RTT). Fig.…”

Section: Time Required For Updatingmentioning

confidence: 99%

Efficient Container Image Updating in Low-bandwidth Networks with Delta Encoding

Matsumoto,

Kotani,

Okabe

2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

View full text Add to dashboard Cite

Containers are the technology for Linux to isolate execution environments. By distributing a container image, which is a collection of files contained in the container, users can use an execution environment that includes the necessary files and libraries. However, container images are tens to hundreds of megabytes in size and require many network resources to be transferred. Especially in low-bandwidth network environments like edge computing, frequent image updating can be difficult and affect other services' communication. In this paper, we propose a method to reduce the data size required for image updates using delta encoding. We use delta encoding to reduce data size and finish updating quickly, but generating and applying deltas is a time-consuming operation. Our method proposes DeltaMerging which enables faster delta generation by merging existing deltas, and Di3FS which applies deltas lazily. The proposed method reduces the data size required to update container images from 5 to 40% of that of existing methods. Also, the time required to generate and apply deltas is greatly reduced with DeltaMerging and Di3FS. Furthermore, the performance degradation of the application in the container was almost negligible.

show abstract

Section: Time Required For Updatingmentioning

confidence: 99%

Efficient Container Image Updating in Low-bandwidth Networks with Delta Encoding

Matsumoto,

Kotani,

Okabe

2023

2023 IEEE International Conference on Cloud Engineering (IC2E)

View full text Add to dashboard Cite

show abstract

“…Today, a wide range of services can be accessed with just an Internet connection and a browser. However, cloud services tend to be provided centrally from servers located anywhere in the world, therefore suffering from several shortcomings [29]:…”

Section: B Multi-access Edge Computingmentioning

confidence: 99%

Live Free-Viewpoint Video in Immersive Media Production Over 5G Networks

Pérez

Corregidor²,

Garrido

et al. 2022

IEEE Trans. on Broadcast.

View full text Add to dashboard Cite

5G is a key tool for the cloud-based wireless production of audio-visual contents. By providing higher throughput and lower latency than previous mobile network technologies, as well as flexible allocation of network resources, it enables that some content production pipelines, which are currently implemented using wired connectivity, are provided on top of a 5G network. This brings down production costs, reduces environmental impact and increases operational efficiency. In particular, live immersive production services, such as free-viewpoint video (FVV), can be provided in a much more efficient way. In this paper, we address the challenge of producing and streaming FVV services in real time through 5G networks. We have adapted a state-of-the-art FVV system to integrate it within a 5G architecture, using three key technological enablers: mmWave radio access network to support the uplink traffic requirements from FVV cameras, multi-access edge computing to run video processing algorithms with minimum latency, and end-to-end slicing to guarantee a sufficient quality of service (QoS) within the production pipeline. We have built a field trial over the production network of a telecommunication operator, including a mmWave pilot deployment, edge cloud processing, and remote content production, involving three different locations across Spain. We have measured the key performance indicators at the relevant parts of our trial deployment, showing that, with existing 5G technology, it is possible to achieve live FVV production, although with some limitations. We have also analyzed such limitations, obtaining some insights on how the next generation of 5G networks can overcome them to achieve higher quality of experience (QoE).

show abstract

“…almost globally [31,32,36]. As a result, we see edge-cloud interplay as key to extend cloud computing reach outside of datacenters, and enhance its services by leveraging an infrastructure closer to the end-users [66,87]. We believe that effective application offloading is a crucial problem for edgecloud computing that must be addressed when thinking at scale.…”

Section: Introductionmentioning

confidence: 99%

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Cozzolino

Tonetto

Mohan

et al. 2023

IEEE Trans. Cloud Comput.

Self Cite

View full text Add to dashboard Cite

Widespread adoption of mobile augmented reality (AR) and virtual reality (VR) applications depends on their smoothness and immersiveness. Modern AR applications applying computationally intensive computer vision algorithms can burden today's mobile devices, and cause high energy consumption and/or poor performance. To tackle this challenge, it is possible to offload part of the computation to nearby devices at the edge. However, this calls for smart task placement strategies in order to efficiently use the resources of the edge infrastructure. In this paper, we introduce Nimbus -a task placement and offloading solution for a multi-tier, edge-cloud infrastructure where deep learning tasks are extracted from the AR application pipeline and offloaded to nearby GPU-powered edge devices. Our aim is to minimize the latency experienced by end-users and the energy costs on mobile devices. Our multifaceted evaluation, based on benchmarked performance of AR tasks, shows the efficacy of our solution. Overall, Nimbus reduces the task latency by ∼4× and the energy consumption by ∼77% for real-time object detection in AR applications. We also benchmark three variants of our offloading algorithm, disclosing the trade-off of centralized versus distributed execution.

show abstract

Revisiting the Arguments for Edge Computing Research

Cited by 32 publications

References 16 publications

Efficient Container Image Updating in Low-bandwidth Networks with Delta Encoding

Efficient Container Image Updating in Low-bandwidth Networks with Delta Encoding

Live Free-Viewpoint Video in Immersive Media Production Over 5G Networks

Nimbus: Towards Latency-Energy Efficient Task Offloading for AR Services

Contact Info

Product

Resources

About