ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

Xu, Chen; Shi, Qian; Yang, Lei; Xu, Jie

doi:10.1109/mnet.2018.1700145

Cited by 187 publications

(77 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional mobile DNN computation is either solely performed on mobile devices or wholly offloaded to cloud/edge servers. Unfortunately, both approaches may lead to poor performance (i.e., high end-to-end latency), making it difficult to meet real-time applications latency requirements [10]. For illustration, we employ a Raspberry Pi and a desktop PC to emulate the mobile device and edge server respectively, and perform image recognition task over cifar-10 dataset with the classical AlexNet model [42].…”

Section: B Insufficiency Of Device-or Edge-only Dnn Inferencementioning

confidence: 99%

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Zeng

Zhou

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

565

218

View full text Add to dashboard Cite

As a key technology of enabling Artificial Intelligence (AI) applications in 5G era, Deep Neural Networks (DNNs) have quickly attracted widespread attention. However, it is challenging to run computation-intensive DNN-based tasks on mobile devices due to the limited computation resources. What's worse, traditional cloud-assisted DNN inference is heavily hindered by the significant wide-area network latency, leading to poor real-time performance as well as low quality of user experience. To address these challenges, in this paper, we propose Edgent, a framework that leverages edge computing for DNN collaborative inference through device-edge synergy. Edgent exploits two design knobs: (1) DNN partitioning that adaptively partitions computation between device and edge for purpose of coordinating the powerful cloud resource and the proximal edge resource for real-time DNN inference; (2) DNN right-sizing that further reduces computing latency via early exiting inference at an appropriate intermediate DNN layer. In addition, considering the potential network fluctuation in realworld deployment, Edgent is properly design to specialize for both static and dynamic network environment. Specifically, in a static environment where the bandwidth changes slowly, Edgent derives the best configurations with the assist of regressionbased prediction models, while in a dynamic environment where the bandwidth varies dramatically, Edgent generates the best execution plan through the online change point detection algorithm that maps the current bandwidth state to the optimal configuration. We implement Edgent prototype based on the Raspberry Pi and the desktop PC and the extensive experimental evaluations demonstrate Edgent's effectiveness in enabling ondemand low-latency edge intelligence.

show abstract

Section: B Insufficiency Of Device-or Edge-only Dnn Inferencementioning

confidence: 99%

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Zeng

Zhou

et al. 2020

IEEE Trans. Wireless Commun.

Self Cite

565

218

View full text Add to dashboard Cite

show abstract

“…Chen et al [21] devised a resource-efficient edge computing scheme in which an intelligent IoT device user can support the device's computation-intensive task through proper task offloading across local and nearby devices and the edge cloud in proximity. They explored the perspective of resource efficiency and proposed a delay-aware task graph partition algorithm and an optimal virtual machine selection method to minimize the edge resource occupancy of an intelligent IoT device by satisfying its quality of service (QoS) requirement.…”

Section: A Related Work and Contributionsmentioning

confidence: 99%

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Yousafzai

Yaqoob

Imran

et al. 2020

IEEE Internet Things J.

View full text Add to dashboard Cite

Mobile devices have become an indispensable component of Internet of Things (IoT). However, these devices have resource constraints in processing capabilities, battery power, and storage space, thus hindering the execution of computationintensive applications that often require broad bandwidth, stringent response time, long battery life, and heavy computing power. Mobile cloud computing and mobile edge computing (MEC) are emerging technologies that can meet the aforementioned requirements using offloading algorithms. In this paper, we analyze the effect of platform-dependent native applications on computational offloading in edge networks and propose a lightweight process migration-based computational offloading framework. The proposed framework does not require application binaries at edge servers and thus seamlessly migrates native applications. The proposed framework is evaluated using an experimental testbed. Numerical results reveal that the proposed framework saves almost 44% of the execution time and 84% of the energy consumption. Hence, the proposed framework shows profound potential for resource-intensive IoT application processing in MEC.

show abstract

“…To satisfy these mission-critical mobile applications that require ultra-low latency, mobile edge computing (MEC) [3] [4] has been proposed as an extension of centralized cloud computing, which deploys a cloud computing platform at the edge of radio access network (RAN) in close proximity to mobile devices and users. Here an edge is typically a microdata center or cluster of servers that can host cloud applications [5], attached to a base station (BS) or an access point, and available for use by nearby devices. In the paradigm of MEC, as user workload is served by a nearby edge node rather than the remote cloud, the end-to-end latency is significantly reduced [6].…”

Section: Base Stationmentioning

confidence: 99%

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

Ouyang

Zhou

Chen

2018

2018 IEEE/ACM 26th International Symposium on Quality of Service (IWQoS)

Self Cite

View full text Add to dashboard Cite

Mobile edge computing is a new computing paradigm, which pushes cloud computing capabilities away from the centralized cloud to the network edge. However, with the sinking of computing capabilities, the new challenge incurred by user mobility arises: since end-users typically move erratically, the services should be dynamically migrated among multiple edges to maintain the service performance, i.e., user-perceived latency. Tackling this problem is non-trivial since frequent service migration would greatly increase the operational cost. To address this challenge in terms of the performance-cost trade-off, in this paper we study the mobile edge service performance optimization problem under long-term cost budget constraint. To address user mobility which is typically unpredictable, we apply Lyapunov optimization to decompose the long-term optimization problem into a series of real-time optimization problems which do not require a priori knowledge such as user mobility. As the decomposed problem is NP-hard, we first design an approximation algorithm based on Markov approximation to seek a nearoptimal solution. To make our solution scalable and amenable to future 5G application scenario with large-scale user devices, we further propose a distributed approximation scheme with greatly reduced time complexity, based on the technique of best response update. Rigorous theoretical analysis and extensive evaluations demonstrate the efficacy of the proposed centralized and distributed schemes.

show abstract

ThriftyEdge: Resource-Efficient Edge Computing for Intelligent IoT Applications

Cited by 187 publications

References 14 publications

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Process Migration-Based Computational Offloading Framework for IoT-Supported Mobile Edge/Cloud Computing

Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing

Contact Info

Product

Resources

About