Abstract
Mobile Edge Cloud endures limited computational resources as compared to back-end cloud. Semi-Markov Decision Process (SMDP) based Multi-Resource Allocation (MRA) work [6] introduces optimal resource allocation for mobile requests in the resource constrained edge cloud environments. In this study, we scale existing SMDP MRA work for real-world scenarios. First, we structure the policy tables in a two dimensional matrix such that columns represent states of the system and rows for the actions. Second, we propose an index based search technique over structured policy tables. Simulation results demonstrate that our approach outperforms the legacy method and retrieves an optimal action from the policy tables in the order of microseconds, which meets the delay criteria of real-time applications in edge cloud based systems.
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- 1.
The wireless bandwidth and VM units are defined in [6]. Where “bandwidth refers to the wireless connections between the end devices and the EC, and one wireless bandwidth unit refers to the minimum bandwidth required to support mobile computing offloading, for example, 50, 100 Kbps, etc. Similarly, VM refers to the minimum computational resource required to execute a service request, e.g. 1 core of CPU with 1 GB memory. Then, the total bandwidth/VM available can be expressed as the integer multiple of the bandwidth and VM unit. For the simplicity of computation, we assume a single service request requires at least one basic unit of wireless bandwidth and VM units, and only the integral numbers of basic bandwidth units and VM units are allocated".
References
Taleb, T., Samdanis, K., Mada, B., Flinck, H., Dutta, S., Sabella, D.: On multi-access edge computing: a survey of the emerging 5G network edge cloud architecture and orchestration. IEEE Commun. Surv. Tutor. 19(3), 1657–1681 (2017)
Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018)
Borgia, E., Bruno, R., Conti, M., Mascitti, D., Passarella, A.: Mobile edge clouds for information-centric IoT services. In: Proceedings of IEEE Symposium Computers and Communications (ISCC), Messina, Italy, June 2016, pp. 422–428 (2016)
Jararweh, Y., et al.: The future of mobile cloud computing: integrating cloudlets and mobile edge computing. In: Proceedings of 23rd International Conference on Telecommunications (ICT), Thessaloniki, Greece, May 2016, pp. 1–5 (2016)
Yang, L., Liu, B., Cao, J., Sahni, Y., Wang, Z.: Joint computation partitioning and resource allocation for latency sensitive applications in mobile edge clouds. In: 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, CA, 2017, pp. 246–253 (2017)
Liu, Y., Lee, M.J., Zheng, Y.: Adaptive multi-resource allocation for cloudlet-based mobile cloud computing system. IEEE Trans. Mobile Comput. 15(10), 2398–2410 (2016)
Liu, Y., Lee, M.J.: An adaptive resource allocation algorithm for partitioned services in mobile cloud computing. In: IEEE Symposium on Service-Oriented System Engineering, San Francisco Bay, CA, pp. 209–215 (2015)
Piri, E., et al.: 5GTN: a test network for 5G application development and testing. In: 2016 European Conference on Networks and Communications (EuCNC), Athens, 2016, pp. 313–318 (2016)
NSF, PAWR: COSMOS: Cloud Enhanced Open Software Defined Mobile Wireless Testbed for City-Scale Deployment. https://cosmos-lab.org
Osseiran, A., Monserrat, J.F.: 5G Mobile and Wireless Communications Technology, pp. 240–270. Cambridge University Press, Cambridge (2016)
Shah, S., Shaikh, A.: Hash based optimization for faster access to inverted index. In: 2016 International Conference on Inventive Computation Technologies (ICICT), Coimbatore, 2016, pp. 1–5 (2016)
Lin, C., Hsu, C., Hsieh, S.: A multi-index hybrid trie for lookup and updates. IEEE Trans. Parallel Distrib. Syst. 25(10), 2486–2498 (2014)
Bulysheva, L., Bulyshev, A., Kataev, M.: Visual database design: indexing methods. In: 2018 Sixth International Conference on Enterprise Systems (ES), Limassol, 2018, pp. 25–29 (2018)
Database Normalization. https://support.microsoft.com/en-us/help/283878/description-of-the-database-normalization-basics
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This work is partially supported by USA Grant number NSF Award 181884.
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Qadeer, A., Lee, M.J., Tsukamoto, K. (2020). Real-Time Multi-resource Allocation via a Structured Policy Table. In: Barolli, L., Nishino, H., Miwa, H. (eds) Advances in Intelligent Networking and Collaborative Systems. INCoS 2019. Advances in Intelligent Systems and Computing, vol 1035. Springer, Cham. https://doi.org/10.1007/978-3-030-29035-1_36
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