Efficient Virtual Machine Selection for Improved Performance in Mobile Edge Computing Environments
Author:
Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-48465-0_55
Reference17 articles.
1. Cao, X., Wang, F., Xu, J., Zhang, R., Cui, S.: Joint computation and communication cooperation for mobile edge computing. In: 16th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks pp. 1–6. IEEE (2018)
2. Wang, L., Jiao, L., He, T., Li, J., Mühlhäuser, M.: Service entity placement for social virtual reality applications in edge computing. In: IEEE INFOCOM Conference on Computer Communications, pp. 468–476. IEEE (2018)
3. Ren, J., He, Y., Huang, G., Yu, G., Cai, Y., Zhang, Z.: An edge-computing based architecture for mobile augmented reality. IEEE Netw. 33(4), 162–169 (2019)
4. Tan, T., Cao, G.: FastVA: deep learning video analytics through edge processing and NPU in mobile. In: IEEE INFOCOM Conference on Computer Communications, pp. 1947–1956 (2020)
5. Liu, H., Cao, G.: Deep reinforcement learning-based server selection for mobile edge computing. IEEE Trans. Veh. Technol. 70(12), 13351–13363 (2021)
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