Cost Efficient Scheduling for Delay-Sensitive Tasks in Edge Computing System

2018 ◽  
Author(s):  
Yongchao Zhang ◽  
Xin Chen ◽  
Ying Chen ◽  
Zhuo Li ◽  
Jiwei Huang
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Delay Sensitive ◽  
Cost Efficient

Two-tier end-edge collaborative computation offloading for edge computing

2021 ◽  
pp. 1-12
Author(s):  
Yong Xiao ◽  
Ling Wei ◽  
Junhao Feng ◽  
Wang En
Keyword(s):  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Computation Offloading ◽  
Iot Applications ◽  
Delay Sensitive ◽  
Task Processing ◽  
Queue Lengths ◽  
The Stability ◽  
The Cost

Edge computing has emerged for meeting the ever-increasing computation demands from delay-sensitive Internet of Things (IoT) applications. However, the computing capability of an edge device, including a computing-enabled end user and an edge server, is insufficient to support massive amounts of tasks generated from IoT applications. In this paper, we aim to propose a two-tier end-edge collaborative computation offloading policy to support as much as possible computation-intensive tasks while making the edge computing system strongly stable. We formulate the two-tier end-edge collaborative offloading problem with the objective of minimizing the task processing and offloading cost constrained to the stability of queue lengths of end users and edge servers. We perform analysis of the Lyapunov drift-plus-penalty properties of the problem. Then, a cost-aware computation offloading (CACO) algorithm is proposed to find out optimal two-tier offloading decisions so as to minimize the cost while making the edge computing system stable. Our simulation results show that the proposed CACO outperforms the benchmarked algorithms, especially under various number of end users and edge servers.

scholarly journals Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

2021 ◽  
Author(s):  
vikas kumar ◽  
Mithun Mukherjee
Keyword(s):  
Bandwidth Allocation ◽  
Transmission Rate ◽  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Worst Case ◽  
Delay Sensitive ◽  
Maximum Delay ◽  
Computational Resources ◽  
Quadratically Constrained

The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

scholarly journals Reconfigurable Intelligent Surface-assisted Edge Computing to Minimize Delay in Task Offloading

2021 ◽  
Author(s):  
vikas kumar ◽  
Mithun Mukherjee
Keyword(s):  
Bandwidth Allocation ◽  
Transmission Rate ◽  
Computing System ◽  
End Users ◽  
Edge Computing ◽  
Worst Case ◽  
Delay Sensitive ◽  
Maximum Delay ◽  
Computational Resources ◽  
Quadratically Constrained

The advantage of computational resources in edge computing near the data source has kindled growing interest in delay-sensitive Internet of Things (IoT) applications. However, the benefit of the edge server is limited by the uploading and downloading links between end-users and edge servers when these end-users seek computational resources from edge servers. The scenario becomes more severe when the user-end's devices are in the shaded region resulting in low uplink/downlink quality. In this paper, we consider a reconfigurable intelligent surface (RIS)-assisted edge computing system, where the benefits of RIS are exploited to improve the uploading transmission rate. We further aim to minimize the delay of worst-case in the network when the end-users either compute task data in their local CPU or offload task data to the edge server. Next, we optimize the uploading bandwidth allocation for every end-user's task data to minimize the maximum delay in the network. The above optimization problem is formulated as quadratically constrained quadratic programming. Afterward, we solve this problem by semidefinite relaxation. Finally, the simulation results demonstrate that the proposed strategy is scalable under various network settings.

Blockchain-based mobile edge computing system

2021 ◽  
Vol 561 ◽  
pp. 70-80
Author(s):  
Guangshun Li ◽  
Xinrong Ren ◽  
Junhua Wu ◽  
Wanting Ji ◽  
Haili Yu ◽  
...  
Keyword(s):  
Computing System ◽  
Edge Computing ◽  
Mobile Edge Computing

An Edge Computing System for Estimating Road Surface Condition on Winter Expressway

2021 ◽  
Author(s):  
Masahiro Yagi ◽  
Tomoyuki Takase ◽  
Sho Takahashi ◽  
Toru Hagiwara ◽  
Tomonori Ohiro ◽  
...  
Keyword(s):  
Surface Condition ◽  
Computing System ◽  
Road Surface ◽  
Edge Computing
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