Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm
Abstract
:1. Introduction
- (1)
- Propose a local, MEC server, and edge cloud server collaborative processing task system, reasonably allocate energy and computing resources, and improve system performance, e.g., task processing capacity;
- (2)
- Use Lyapunov stability theory for local mobile device power stability and task processing delay as optimization goals, and jointly consider the local mobile device’s energy collection, computing power allocation, wireless link transmit power allocation, and MEC–edge cloud server resource allocation problem. In order to solve this problem, a Lyapunov algorithm based on task priority is first proposed to decompose this NP-hard problem into an integer programming problem. The task priority attribute can improve the performance of the system, and then an integer programming algorithm based on CPU utilization optimization is proposed to obtain the optimal resource allocation strategy. By comparing and analyzing with the integer linear programming (ILP) algorithm, the optimal integer linear programming (OILP) algorithm proposed has the lower latency and higher resource utilization;
- (3)
- Considering that MEC server resources are scarce, especially during the peak traffic, the CPU computing resources provided by the server through virtualization technology may not be utilized effectively. When the CPU resources are insufficient to handle the entire unload task, the proposed algorithm will split the task into two parts, one of which will be processed on the local device, and the other on the MEC server. This not only increases server resource utilization, but also reduces the number of tasks that cannot be processed. The simulation results show that the number of unprocessed tasks can be optimized by more than 10%.
2. Related Work
3. System Model and Problem Formulation
3.1. Task Offloading Model
3.2. Computing Model
3.2.1. Local Computing
3.2.2. MEC Server Computing
3.2.3. Edge Cloud Server Computing
3.3. Energy Harvesting Model
3.4. Objective Function Based on Lyapunov Optimization
4. Proposed Algorithm
4.1. Lyapunov Optimization Based on Task Priority
4.2. Integer Programming Algorithm Based on CPU Utilization Optimization Strategy
Algorithm 1. Optimal integer linear programming (OILP) algorithm. |
Input: () Output: (,) 1. For t = 1, 2, … T do 2. For i =1, 2, … N do 3. Obtain the values of , , and through the Lyapunov optimization, based on the task priority 4. End for 5. Solve P5 by integer programming algorithm 6. Find the tasks that cannot be processed 7. If the task was not processed due to the insufficient MEC server CPU resources and the edge cloud link resources then 8. Split the task, offload one part to MEC server, process the other locally, and keep the energy consumption as the energy consumed if the total task is offloaded to the MEC server for processing 9. Solve algebraically to obtain , and 10. If then 11. Implement the optimization strategy and update the MEC server CPU usage 12. else 13. Continue to optimize the next unhandled task 14. endif 15. endif 16. 17. End for |
5. Simulation Results
5.1. System Setting
5.2. Results Analysis
6. Conclusions
Author Contributions
Funding
Conflicts of Interest
Abbreviations
CPU | Central Processing Unit |
QoS | Quality of Service |
NP | Non-deterministic Polynomial |
UAV | Unmanned Aerial Vehicle |
QoE | Quality of Experience |
DVFS | Dynamic Voltage and Frequency Scaling |
s.t. | subject to |
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Notation | Description | Notation | Description |
---|---|---|---|
index set of local devices | frequency of | ||
index set of MEC server | maximum value of | ||
index set of time slot | maximum value of transmit power | ||
time slot interval | channel transmission rate | ||
the distance between and | the energy consumption of the task processing locally | ||
task size (bits) generated by local device | the time delay of the task processing locally | ||
number of CPU cycles required to process one bit of data | the energy consumption of task processing by the MEC server | ||
task priority | the time delay of task processing by the MEC server | ||
probability of high priority tasks | the energy consumption of task processing by the edge cloud server | ||
the size (bits) of task that be processed by MEC server | the time delay of task processing by the edge cloud server | ||
the size (bits) of task that be processed locally | number of divided wired links | ||
battery level | penalty value if the task cannot be processed | ||
maximum value of | weight of battery energy stability and time delay | ||
the value of energy collected locally | CPU usage of MEC server | ||
maximum value of | best value of | ||
the mode of task processing |
Parameter Attributes | Value |
---|---|
Local device number () | 5–30 |
MEC server number ( | 2–3 |
Task size (bits) generated by local device (D) | 4 Kb |
Time slot interval | 2 ms |
Safe CPU utilization () | 0.7 |
Local CPU maximum () | 1.5 GHz |
MEC CPU capability () | 4 Ghz |
Edge cloud CPU capability () | 4 Ghz |
Wired link upload rate () | 100 M/bps |
Number of wired speed limit links | 3 |
Probability of generating a priority task () | 0.2 |
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Li, D.; Jin, Y.; Liu, H. Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm. Symmetry 2020, 12, 972. https://doi.org/10.3390/sym12060972
Li D, Jin Y, Liu H. Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm. Symmetry. 2020; 12(6):972. https://doi.org/10.3390/sym12060972
Chicago/Turabian StyleLi, Daoquan, Yingnan Jin, and Haoxin Liu. 2020. "Resource Allocation Strategy of Edge Systems Based on Task Priority and an Optimal Integer Linear Programming Algorithm" Symmetry 12, no. 6: 972. https://doi.org/10.3390/sym12060972