The Service Computational Resource Management Strategy Based On Edge-Cloud Collaboration

Li, You; Xu, Liutong

doi:10.1109/icsess47205.2019.9040830

Cited by 13 publications

(5 citation statements)

References 13 publications

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“…As the cloud servers and edge servers are both of significance and have their advantages respectively, their collaboration has become a trend in the related works of edge computing [38]. In recent years, more and more research has revolved around improving the overall performance of the system through the collaboration of them [39,40].…”

Section: Near-endmentioning

confidence: 99%

Edge Intelligence with Distributed Processing of DNNs: A Survey

Tang¹,

Cui²,

Qi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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With the rapid development of deep learning, the size of data sets and deep neural networks (DNNs) models are also booming. As a result, the intolerable long time for models' training or inference with conventional strategies can not meet the satisfaction of modern tasks gradually. Moreover, devices stay idle in the scenario of edge computing (EC), which presents a waste of resources since they can share the pressure of the busy devices but they do not. To address the problem, the strategy leveraging distributed processing has been applied to load computation tasks from a single processor to a group of devices, which results in the acceleration of training or inference of DNN models and promotes the high utilization of devices in edge computing. Compared with existing papers, this paper presents an enlightening and novel review of applying distributed processing with data and model parallelism to improve deep learning tasks in edge computing. Considering the practicalities, commonly used lightweight models in a distributed system are introduced as well. As the key technique, the parallel strategy will be described in detail. Then some typical applications of distributed processing will be analyzed. Finally, the challenges of distributed processing with edge computing will be described.

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Section: Near-endmentioning

confidence: 99%

Edge Intelligence with Distributed Processing of DNNs: A Survey

Tang¹,

Cui²,

Qi³

et al. 2023

Computer Modeling in Engineering &Amp; Sciences

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“…Existing studies on the allocation of computing and communication resources to different network slices in 5G networks can be broadly classified based on their architectures as either one-tier schemes [ 15 , 16 , 17 , 18 , 19 ], two-tier schemes [ 20 , 21 , 22 , 23 , 24 ], as well as Graph-based deep learning and reinforcement learning schemes [ 25 , 26 , 27 , 28 , 29 ]. The related studies are reviewed in the following sub-sections.…”

Section: Related Workmentioning

confidence: 99%

“…Li et al. [ 18 ] presented a resource management and control strategy for edge and cloud collaborative computing, but provided only a brief discussion of the considered factors and conditions. Wang et al.…”

Section: Related Workmentioning

confidence: 99%

“…In general, the studies in [ 15 , 16 , 17 , 18 , 19 ] limit the network slicing problem to a one-tier architecture with only a single computing resource and ignore the architectures of the cloud and edge networks. Furthermore, even though the study in [ 19 ] presents an approach for dynamically adjusting the weights of the edge resource, the dynamic adjustment process takes account of only the edge computing resources and ignores the communication resources.…”

Section: Related Workmentioning

confidence: 99%

“…However, the authors did not consider the allocation of the RAN resources across multiple users or the strict latency requirements of certain 5G services, such as URLLC. Li et al [18] presented a resource management and control strategy for edge and cloud collaborative computing, but provided only a brief discussion of the considered factors and conditions. Wang et al [19] developed an approach for the effective allocation and adjustment of edge resources by dynamically adjusting the weights of each edge cloud using four different weights.…”

Section: One-tier Architecturementioning

confidence: 99%

See 2 more Smart Citations

Dynamic Resource Allocation for Network Slicing with Multi-Tenants in 5G Two-Tier Networks

Lin

Chou

Hwang

2023

Sensors

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Virtualization is a core 5G network technology which helps telecom companies significantly reduce capital expenditure and operating expenses by deploying multiple services on the same hardware infrastructure. However, providing QoS-guaranteed services for multi-tenants poses a significant challenge due to multi-tenant service diversity. Network slicing has been proposed as a means of addressing this problem by isolating computing and communication resources for the different tenants of different services. However, optimizing the allocation of the network and computation resources across multiple network slices is a critical but extremely difficult problem. Accordingly, this study proposes two heuristic algorithms, namely Minimum Cost Resource Allocation (MCRA) and Fast Latency Decrease Resource Allocation (FLDRA), to perform dynamic path routing and resource allocation for multi-tenant network slices in a two-tier architecture. The simulation results show that both algorithms significantly outperform the Upper-tier First with Latency-bounded Overprovisioning Prevention (UFLOP) algorithm proposed in previous work. Furthermore, the MCRA algorithm achieves a higher resource utilization than the FLDRA algorithm.

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