Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS
Abstract
:1. Introduction
- The minimization problem of multimedia application execution latency and energy consumption of IoT devices is studied by the allocation of computing resources in the edge servers while adopting the DVS technology.
- The studied problem of latency and energy consumption is formulated. Due to the formulated problem being an MINP problem, an efficient multimedia applications offloading scheme is proposed, and the solution of it is obtained.
- Simulation results are performed to evaluate the efficacy of the proposed multimedia applications offloading scheme by comparing with the two baseline schemes. The theoretical analysis and simulation results indicate that the multimedia applications’ offloading scheme proposed in this paper can perform better than the baseline methods, which can integrate the dependability aspects into the design of SIoT systems.
2. Related Work
3. System Architecture
3.1. Local Computation Model
3.2. Edge Cloud Model
3.3. Problem Formulation
4. Solution Method
4.1. Solution Method
4.2. Local Computation Problem
4.3. Edge Cloud Computation Problem
4.4. Application Offloading Decision
Algorithm 1 Computational Resource Allocation and Applications Offloading Algorithm. |
Input: |
1: N applications; |
Output: |
2: The application offloading decision made by each user of IoT device and the system computation overhead; |
3: Obtain the optimal resource allocation of each IoT device by solving problem P1; |
4: Based on Equation (16), IoT devices make adjustments to their voltage and clock frequency to obtain the adaptive CPU frequency according to the weight coefficient values by applying the DVS technology; |
5: Obtain the optimized local computation cost of each IoT device based on Equation (17); |
6: Calculate the allocated computational resource of each IoT device by solving problem P2; |
7: Obtain the optimal computation cost of the edge cloud computation model from Equation (11); |
8: if then |
9: ; |
10: else |
11: ; |
12: end if |
5. Simulation Results
5.1. Parameter Setting
5.2. Effect of Weight Coefficient Values
5.3. Effect of the Edge Cloud Capacity
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
Abbreviations
MEC | Mobile Edge Computing |
MCC | Mobile Cloud Computing |
SIoT | Social Internet of Things |
DVS | Dynamic voltage scaling |
MINP | Mixed-integer nonlinear programming |
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Reference | DVS | Application Execution Time | Energy Consumption | Computation Resources |
---|---|---|---|---|
Chen et al. [16] | No | No | Yes | No |
Wang et al. [21] | Yes | Yes | Yes | No |
Wang et al. [30] | No | No | No | No |
Zhong et al. [35] | No | Yes | Yes | No |
Li et al. [22] | No | Yes | Yes | No |
Wang et al. [23] | No | No | Yes | Yes |
Liu et al. [33] | No | No | Yes | No |
Li et al. [14] | No | No | Yes | No |
Li et al. [38] | Yes | Yes | Yes | No |
Yang et al. [36] | No | Yes | Yes | No |
Liu et al. [24] | No | Yes | Yes | Yes |
Lyu et al. [26] | No | Yes | Yes | Yes |
You et al. [25] | No | Yes | Yes | Yes |
Zhao et al. [27] | No | Yes | Yes | Yes |
Kabir et al. [39] | No | Yes | Yes | No |
Zhang et al. [37] | Yes | Yes | Yes | No |
Sun et al. [31] | No | Yes | Yes | No |
Meskar et al. [29] | No | Yes | Yes | No |
Zeng et al. [28] | No | Yes | Yes | No |
Zhu et al. [40] | No | Yes | Yes | Yes |
This Study | Yes | Yes | Yes | Yes |
Notation | Description |
---|---|
The processing rate of the IoT device i | |
The local processing time of the IoT device i | |
The local energy consumption of the IoT device i | |
The transmission time of the IoT device i | |
The transmission energy consumption of the IoT device i | |
The application i’s data size | |
The needed CPU cycles to process application of IoT device i | |
The weighting parameter of application processing time | |
The weighting parameter of energy consumption | |
The channel bandwidth | |
The transmit power of the IoT device i | |
The channel gain | |
The background noise power | |
The uplink transmission rate for the IoT device i | |
The application offloading decision that make by the IoT device i | |
The allocated computational resources in the edge cloud to device i | |
The maximum processing rate of the IoT device i | |
F | The total computational resources in the edge servers |
Parameters | Values |
---|---|
The number of IoT devices N | 5 |
The bandwidth allocation | 1.6 MHz |
The data sizes of multimedia applications | [0.42, 4.2] Mb |
The transmission power | 1 W |
The needed computation resources | [0, 1] cycles |
The channel gain | |
The maximum processing rate | 1 GHz |
The background noise power | W |
The computation capacity of the edge cloud F | 20 GHz |
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Li, X.; Chen, G.; Zhao, L.; Wei, B. Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS. Mathematics 2022, 10, 1593. https://doi.org/10.3390/math10091593
Li X, Chen G, Zhao L, Wei B. Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS. Mathematics. 2022; 10(9):1593. https://doi.org/10.3390/math10091593
Chicago/Turabian StyleLi, Xianwei, Guolong Chen, Liang Zhao, and Bo Wei. 2022. "Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS" Mathematics 10, no. 9: 1593. https://doi.org/10.3390/math10091593
APA StyleLi, X., Chen, G., Zhao, L., & Wei, B. (2022). Multimedia Applications Processing and Computation Resource Allocation in MEC-Assisted SIoT Systems with DVS. Mathematics, 10(9), 1593. https://doi.org/10.3390/math10091593