Abstract
To leverage the synergy between cloud computing (CC) and edge computing (EC) to support various services while reducing the CC/EC switching overhead, the two-timescale utility maximization problem for an end-to-end network slicing-aided cloud-edge collaborative network (E2E-CECN) is formulated covering both the high throughput and low delay service requirements. To solve the utility maximization problem while dynamically adjusting the weights of the E2E-CECN utility to accommodate the variation of users' service requests, we proposed a reward comparison double deep Q network algorithm to optimize the large timescale joint virtual base station activation and CC-EC scheduling, and a reward comparison deep deterministic policy gradient algorithm to optimize the small timescale allocation of backhaul link capacity (BLC), CC/EC capability and transmission power. Numerical results show that both the high-throughput and low-delay service requirements can be satisfied simultaneously under the reasonable BLC usage and power consumption.
| Original language | English |
|---|---|
| Pages (from-to) | 9777-9789 |
| Number of pages | 13 |
| Journal | IEEE Transactions on Vehicular Technology |
| Volume | 74 |
| Issue number | 6 |
| DOIs | |
| Publication status | Accepted/In press - 2025 |
Keywords
- cloud computing
- Cloud-edge collaborative network
- edge computing
- end-to-end network slicing
- two-timescale