This alert has been successfully added and will be sent to:
You will be notified whenever a record that you have chosen has been cited.
To manage your alert preferences, click on the button below.
Mobile edge computing provides the opportunity for wireless users to exploit the power of cloud computing without a large communication delay. To serve data-intensive applications (e.g., video analytics, machine learning tasks) from the edge, we need, in addition to computation resources, storage resources for storing server code and data as well as network bandwidth for receiving user-provided data. Moreover, due to time-varying demands, the code and data placement needs to be adjusted over time, which raises concerns of system stability and operation cost. In this paper, we address these issues by proposing a two-time-scale framework that jointly optimizes service (code and data) placement and request scheduling, while considering storage, communication, computation, and budget constraints. First, by analyzing the hardness of various cases, we completely characterize the complexity of our problem. Next, we develop a polynomial-time service placement algorithm by formulating our problem as a set function optimization, which attains a constant-factor approximation under certain conditions. Furthermore, we develop a polynomial-time request scheduling algorithm by computing the maximum flow in a carefully constructed auxiliary graph, which satisfies hard resource constraints and is provably optimal in the special case where requests have homogeneous resource demands. Extensive synthetic and trace-driven simulations show that the proposed algorithms achieve 90% of the optimal performance.
V. Farhadiet al., “Service placement and request scheduling for data-intensive applications in edge clouds,” in Proc. IEEE Conf. Comput. Commun., Apr. 2019, pp. 1279–1287.
T. He, H. Khamfroush, S. Wang, T. La Porta, and S. Stein, “It’s hard to share: Joint service placement and request scheduling in edge clouds with sharable and non-sharable resources,” in Proc. IEEE 38th Int. Conf. Distrib. Comput. Syst. (ICDCS), Jul. 2018, pp. 365–375.
P. Mach and Z. Becvar, “Mobile edge computing: A survey on architecture and computation offloading,” IEEE Commun. Surveys Tuts., vol. 19, no. 3, pp. 1628–1656, 3rd Quart., 2017.
S. Wang, R. Urgaonkar, M. Zafer, T. He, K. Chan, and K. K. Leung, “Dynamic service migration in mobile edge-clouds,” in Proc. IFIP Netw. Conf., May 2015, pp. 1–9.
M. Satyanarayanan, G. Lewis, E. Morris, S. Simanta, J. Boleng, and K. Ha, “The role of cloudlets in hostile environments,” IEEE Pervas. Comput., vol. 12, no. 4, pp. 40–49, Oct. 2013.
F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, “Fog computing and its role in the Internet of Things,” in Proc. 1st MCC workshop Mobile Cloud Comput., 2012, pp. 13–16.
T. Taleb and A. Ksentini, “Follow me cloud: Interworking federated clouds and distributed mobile networks,” IEEE Netw., vol. 27, no. 5, pp. 12–19, Sep. 2013.
S. Wang, R. Urgaonkar, T. He, K. Chan, M. Zafer, and K. K. Leung, “Dynamic service placement for mobile micro-clouds with predicted future costs,” IEEE Trans. Parallel Distrib. Syst., vol. 28, no. 4, pp. 1002–1016, Apr. 2017.
K. Haet al., “Adaptive VM handoff across cloudlets,” School Comput. Sci., Carnegie Mellon Univ., Pittsburgh, PA, USA, Tech. Rep. CMU-CS-15-113, Jun. 2015. [Online]. Available: https://www.cs.cmu.edu/~satya/docdir/CMU-CS-15-113.pdf
K. Haet al., “You can teach elephants to dance: Agile VM handoff for edge computing,” in Proc. 2nd ACM/IEEE Symp. Edge Comput., Oct. 2017, pp. 1–14.
A. Ksentini, T. Taleb, and M. Chen, “A Markov decision process-based service migration procedure for follow me cloud,” in Proc. IEEE Int. Conf. Commun. (ICC), Jun. 2014, pp. 1350–1354.
S. Wang, R. Urgaonkar, T. He, M. Zafer, K. Chan, and K. K. Leung, “Mobility-induced service migration in mobile micro-clouds,” in Proc. IEEE Mil. Commun. Conf., Oct. 2014, pp. 835–840.
T. Taleb, A. Ksentini, and P. A. Frangoudis, “Follow-me cloud: When cloud services follow mobile users,” IEEE Trans. Cloud Comput., vol. 7, no. 2, pp. 369–382, Apr. 2019.
M. Jia, J. Cao, and W. Liang, “Optimal cloudlet placement and user to cloudlet allocation in wireless metropolitan area networks,” IEEE Trans. Cloud Comput., vol. 5, no. 4, pp. 725–737, Oct. 2017.
Z. Xu, W. Liang, W. Xu, M. Jia, and S. Guo, “Efficient algorithms for capacitated cloudlet placements,” IEEE Trans. Parallel Distrib. Syst., vol. 27, no. 10, pp. 2866–2880, Oct. 2016.
A. Ceselli, M. Premoli, and S. Secci, “Mobile edge cloud network design optimization,” IEEE/ACM Trans. Netw., vol. 25, no. 3, pp. 1818–1831, Jun. 2017.