Abstract
Mobile Edge Computing (MEC) is a paradigm that aims to bring cloud services closer to mobile clients, effectively reducing latency and saving backbone bandwidth. As in cloud environments, many applications make use of replication to enhance their quality of service. However, here, data generated by the mobile devices is usually kept near its source, and can have multiple replicas scattered through the network (e.g., on the mobile devices or on edge servers). When requesting data, replica selection can have a significant impact in multiple aspects of a system, e.g., load balancing, throughput, or energy efficiency. Thus, the possible herd behavior combined with the unreliable wireless communication channels can cause systems to under-perform. In this paper, we propose Mecerra, a replica ranking algorithm tailored for the characteristics of MEC environments. Additionally, we detail Wasabi, a flexible replica ranking framework that also handles the management of system metrics. We implement Mecerra in Wasabi, and integrate it into a data storage system for edge networks, building an adaptive replica selection scheme. We use the resulting system to evaluate our proposal and compare it against related work. Results show that Mecerra is able to greatly increase the probability of finding the best replica, and Wasabi provides low overhead.
Keywords
This work was partially supported by Fundação para a Ciência e a Tecnologia through project DeDuCe (PTDC/CCI-COM/32166/2017) and the NOVA LINCS research center (UIDB/04516/2020).
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Abbas, N., Zhang, Y., Taherkordi, A., Skeie, T.: Mobile edge computing: a survey. IEEE Internet Things J. 5(1), 450–465 (2018). https://doi.org/10.1109/JIOT.2017.2750180
Beck, M.T., Werner, M., Feld, S., Schimper, T.: Mobile edge computing: a taxonomy. In: AFIN 2014: The Sixth International Conference on Advances in Future Internet, pp. 48–54. IARIA (2014)
Jiang, W., Xie, H., Zhou, X., Fang, L., Wang, J.: Performance analysis and improvement of replica selection algorithms for key-value stores. In: Fox, G.C. (ed.) 2017 IEEE 10th International Conference on Cloud Computing (CLOUD), Honolulu, HI, USA, 25–30 June 2017, pp. 786–789. IEEE Computer Society (2017). https://doi.org/10.1109/CLOUD.2017.115
Jiang, W., Xie, H., Zhou, X., Fang, L., Wang, J.: Haste makes waste: the on-off algorithm for replica selection in key-value stores. J. Parallel Distrib. Comput. 130, 80–90 (2019). https://doi.org/10.1016/j.jpdc.2019.03.017
Li, C., Tang, J., Luo, Y.: Scalable replica selection based on node service capability for improving data access performance in edge computing environment. J. Supercomput. 75(11), 7209–7243 (2019). https://doi.org/10.1007/s11227-019-02930-6
Li, C., Song, M., Zhang, M., Luo, Y.: Effective replica management for improving reliability and availability in edge-cloud computing environment. J. Parallel Distrib. Comput. 143, 107–128 (2020). https://doi.org/10.1016/j.jpdc.2020.04.012
Li, C., Wang, Y., Tang, H., Luo, Y.: Dynamic multi-objective optimized replica placement and migration strategies for SaaS applications in edge cloud. Future Gener. Comput. Syst. 100, 921–937 (2019). https://doi.org/10.1016/j.future.2019.05.003
Mamei, M., Zambonelli, F.: Programming pervasive and mobile computing applications with the TOTA middleware. In: Proceedings of the Second IEEE International Conference on Pervasive Computing and Communications (PerCom 2004), 14–17 March 2004, Orlando, FL, USA, pp. 263–276. IEEE Computer Society (2004). https://doi.org/10.1109/PERCOM.2004.1276864
Metri, G., Agrawal, A., Peri, R., Shi, W.: What is eating up battery life on my smartphone: a case study. In: International Conference on Energy Aware Computing, ICEAC 2012, Guzelyurt, Cyprus, 3–5 December 2012, pp. 1–6. IEEE (2012). https://doi.org/10.1109/ICEAC.2012.6471003
Ratnasamy, S., et al.: GHT: a geographic hash table for data-centric storage. In: Raghavendra, C.S., Sivalingam, K.M. (eds.) Proceedings of the First ACM International Workshop on Wireless Sensor Networks and Applications, WSNA 2002, Atlanta, Georgia, USA, 28 September 2002, pp. 78–87. ACM (2002). https://doi.org/10.1145/570738.570750
Shao, Z., Huang, C., Li, H.: Replica selection and placement techniques on the IoT and edge computing: a deep study. Wirel. Networks 27(7), 5039–5055 (2021). https://doi.org/10.1007/s11276-021-02793-x
Silva, J.A., Cerqueira, F., Paulino, H., Lourenço, J.M., Leitão, J., Preguiça, N.M.: It’s about thyme: on the design and implementation of a time-aware reactive storage system for pervasive edge computing environments. Future Gener. Comput. Syst. 118, 14–36 (2021). https://doi.org/10.1016/j.future.2020.12.008
Silva, J.A., Vieira, P., Paulino, H.: Data storage and sharing for mobile devices in multi-region edge networks. In: 21st IEEE International Symposium on “A World of Wireless, Mobile and Multimedia Networks”, WoWMoM 2020, Cork, Ireland, 31 August–3 September 2020, pp. 40–49. IEEE (2020). https://doi.org/10.1109/WoWMoM49955.2020.00021
Silva, J.A., Monteiro, R., Paulino, H., Lourenço, J.M.: Ephemeral data storage for networks of hand-held devices. In: IEEE Trustcom/BigDataSE/ISPA, pp. 1106–1113. IEEE (2016). https://doi.org/10.1109/TrustCom.2016.0182
Su, Y., Feng, D., Hua, Y., Shi, Z., Zhu, T.: NetRS: cutting response latency in distributed key-value stores with in-network replica selection. In: 38th IEEE International Conference on Distributed Computing Systems, ICDCS 2018, Vienna, Austria, 2–6 July 2018, pp. 143–153. IEEE Computer Society (2018). https://doi.org/10.1109/ICDCS.2018.00024
Suresh, P.L., Canini, M., Schmid, S., Feldmann, A.: C3: cutting tail latency in cloud data stores via adaptive replica selection. In: 12th USENIX Symposium on Networked Systems Design and Implementation, NSDI 2015, Oakland, CA, USA, 4–6 May 2015, pp. 513–527. USENIX Association (2015). https://www.usenix.org/conference/nsdi15/technical-sessions/presentation/suresh
Thilakarathna, K., Petander, H., Mestre, J., Seneviratne, A.: MobiTribe: cost efficient distributed user generated content sharing on smartphones. IEEE Trans. Mob. Comput. 13(9), 2058–2070 (2014). https://doi.org/10.1109/TMC.2013.89
Vallina-Rodriguez, N., Hui, P., Crowcroft, J., Rice, A.C.: Exhausting battery statistics: understanding the energy demands on mobile handsets. In: Cox, L.P., Wolman, A. (eds.) Proceedings of the 2ndt ACM SIGCOMM Workshop on Networking, Systems, and Applications for Mobile Handhelds, MobiHeld 2010, New Delhi, India, 30 August 2010, pp. 9–14. ACM (2010). https://doi.org/10.1145/1851322.1851327
Zhou, X., Fang, L., Xie, H., Jiang, W.: TAP: timeliness-aware predication-based replica selection algorithm for key-value stores. Concurr. Comput. Pract. Exp. 31(17) (2019). https://doi.org/10.1002/cpe.5171
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Dias, J., Silva, J.A., Paulino, H. (2022). Adaptive Replica Selection in Mobile Edge Environments. In: Hara, T., Yamaguchi, H. (eds) Mobile and Ubiquitous Systems: Computing, Networking and Services. MobiQuitous 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 419. Springer, Cham. https://doi.org/10.1007/978-3-030-94822-1_14
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