Security and performance-aware resource allocation for enterprise multimedia in mobile edge computing

  • Zhongjin Li
  • Haiyang HuEmail author
  • Binbin Huang
  • Jie Chen
  • Chuanyi Li
  • Hua Hu
  • Liguo Huang


Mobile edge computing (MEC) is a promising computing model and has gained remarkable popularity, as it deploys the resources (e.g., computation, network, and storage) to the evolved NodeB (eNB) to provide enormous benefits such as low delay and energy consumption. More and more enterprises construct their edge computing platforms to store multimedia contents (i.e., video, audio, photos, and text data) for the user equipment (UE). However, both the eNB and UEs will experience serious security attacks when transmitting or receiving multimedia data via the wireless network. Existing MEC studies mainly focus on task offloading and performance improvement without considering the enterprise multimedia security problem. This paper proposes a security and performance-aware resource allocation (Spara) algorithm for enterprise multimedia in MEC environment. More specifically, we first build the architecture of enterprise multimedia security for sending the data requests to UEs, which mainly consists of computing and bandwidth resource allocation. Then, we formulate the stochastic data transmission problem to minimize the delay and energy consumption of UEs subject to the security guarantee. To achieve this goal, two queues, namely front-end queue and back-end queue, are used for each UE, and the Lyapunov optimization technique is applied to determine how to allocate the computing and bandwidth resources. Rigorous theoretical analysis shows that Spara algorithm meets the [O(1/V), O(V)] energy-delay tradeoff. Extensive simulation experiments validate this analysis result and the effectiveness of Spara algorithm.


Enterprise multimedia security Resource allocation Lyapunov optimization Mobile edge computing 



This work was supported by the National Natural Science Foundation of China (No. 61802095, 61802167, 61572162, 61702144), the Zhejiang Provincial Key Science and Technology Project Foundation (No. 2018C01012), the Zhejiang Provincial National Science Foundation of China (No. LQ19F020011, LQ17F020003), the Open Foundation of State Key Laboratory of Networking and Switching Technology (Beijing University of Posts and Telecommunications) (No. SKLNST-2019-2-15).


  1. 1.
    Abbas N, Zhang Y, Taherkordi A, Skeie T (2018) Mobile edge computing: a survey. IEEE Internet Things J 5(1):450–465CrossRefGoogle Scholar
  2. 2.
    Mao Y, You C, Zhang J, Huang K, Letaief KB (2017) A survey on mobile edge computing: the communication perspective. IEEE Commun Surveys Tuts 19(4):2322–2358CrossRefGoogle Scholar
  3. 3.
    Hu H, Zhang H, Yang Y (2018) Security risk situation quantification method based on threat prediction for multimedia communication network. Multimed Tools Appl 77(16):21693–21723CrossRefGoogle Scholar
  4. 4.
    Liu Y, Li C, Luo Y, Shao Y, Zhang J (2018) Scheduling multimedia services in cloud computing environment. Enterprise IS 12(2):218–235CrossRefGoogle Scholar
  5. 5.
    Duncan B, Whittington M, Chang V (2017) Enterprise security and privacy: why adding IoT and big data makes it so much more difficult. In: International conference on engineering and technology (ICET), IEEE, pp 1-7Google Scholar
  6. 6.
    Roman R, López J, Mambo M (2018) Mobile edge computing, fog et al.: a survey and analysis of security threats and challenges. Futur Gener Comput Syst 78:680–698CrossRefGoogle Scholar
  7. 7.
    Shirazi SN, Gouglidis A, Farshad A, Hutchison D (2017) The extended cloud: review and analysis of mobile edge computing and fog from a security and resilience perspective. IEEE J Sel Areas Commun 35(11):2586–2595CrossRefGoogle Scholar
  8. 8.
    Hsu RH, Lee J (2015) Group anonymous D2D communication with end-to-end security in LTE-A. In: IEEE conference on communications and network security (CNS), IEEE, pp 451-459Google Scholar
  9. 9.
    Gupta BB, Yamaguchi S, Agrawal DP (2018) Advances in security and privacy of multimedia big data in mobile and cloud computing. Multimed Tools Appl 77:9203–9208CrossRefGoogle Scholar
  10. 10.
    Sun G, Liao D, Li H, Yu H, Chang V (2017) L2P2: a location-label based approach for privacy preserving in LBS. Futur Gener Comput Syst 74:375–384CrossRefGoogle Scholar
  11. 11.
    Haus M, Waqas M, Ding AY, Li Y, Tarkoma S, Ott J (2017) Security and privacy in device-to-device (D2D) communication: a review. IEEE Commun Surveys Tuts 19(2):1054–1079CrossRefGoogle Scholar
  12. 12.
    Zeng LF, Veeravalli B, Li XR (2015) SABA: a security-aware and budget-aware workflow scheduling strategy in clouds. J Parallel Distrib Comput 75:141–151CrossRefGoogle Scholar
  13. 13.
    Li Z, Ge J, Yang H, Huang L, Hu HY, Hu H, Luo B (2016) A security and cost aware scheduling algorithm for heterogeneous tasks of scientific workflow in clouds. Futur Gener Comput Syst 65:140–152CrossRefGoogle Scholar
  14. 14.
    Salodkar N, Karandikar N, Borkar VS (2010) A stable online algorithm for energy-efficient multiuser scheduling. IEEE Trans Mob Comput 9(10):1391–1406CrossRefGoogle Scholar
  15. 15.
    Lin X, Wang Y, Xie Q, Pedram M (2015) Task scheduling with dynamic voltage and frequency scaling for energy minimization in the mobile cloud computing environment. IEEE Trans Serv Comput 8(2):175–186CrossRefGoogle Scholar
  16. 16.
    Sun Y, Zhou S, Xu J (2017) EMM: energy-aware mobility management for mobile edge computing in ultra dense networks. IEEE J Sel Areas Commun 35(11):2637–2646CrossRefGoogle Scholar
  17. 17.
    Sun G, Chang V, Ramachandran M, Sun Z, Li G, Yu H, Liao D (2017) Efficient location privacy algorithm for internet of things (IoT) services and applications. J Netw Comput Appl 89:3–13CrossRefGoogle Scholar
  18. 18.
    Mao Y, Zhang J, Song SH, Letaief KB (2017) Stochastic joint radio and computational resource management for multi-user mobile-edge computing systems. IEEE Trans Wirel Commun 16(9):5994–6009CrossRefGoogle Scholar
  19. 19.
    Tran TX, Pompili D (2019) Joint task offloading and resource allocation for multi-server mobile-edge computing networks. IEEE Trans Veh Technol 68(1):856–868CrossRefGoogle Scholar
  20. 20.
    Liu CF, Bennis M, Vincent Poor H (2017) Latency and reliability-aware task offloading and resource allocation for mobile edge computing. In: IEEE global communications conference (GLOBECOM workshops), IEEE, pp 1-7Google Scholar
  21. 21.
    Hu H, Cheng K, Li Z, Chen J, Hu H (2019) Workflow recognition with structured two-stream convolutional networks. Pattern Recogn Lett.
  22. 22.
    Yu CH, Doppler K, Ribeiro CB, Tirkkonen O (2011) Resource sharing optimization for device-to-device communication underlaying cellular networks. IEEE Trans Wirel Commun 10(8):2752–2763CrossRefGoogle Scholar
  23. 23.
    Lee C (2017) A collaborative power control and resources allocation for D2D (device-to-device) communication underlaying LTE cellular networks. Clust Comput 20(1):559–567CrossRefGoogle Scholar
  24. 24.
    Xu J, Guo C, Zhang H (2018) Joint channel allocation and power control based on PSO for cellular networks with D2D communications. Comput Netw 133:104–119CrossRefGoogle Scholar
  25. 25.
    Abdallah A, Mansour MM, Chehab A (2018) Power control and channel allocation for D2D underlaid cellular networks. IEEE Trans Commun 66(7):3217–3234CrossRefGoogle Scholar
  26. 26.
    Beraldi R, Mtibaa A, Alnuweiri HM (2017) Cooperative load balancing scheme for edge computing resources. In: International conference on fog and Mobile edge computing (FMEC), IEEE, pp 94-100Google Scholar
  27. 27.
    Zhao T, Zhou S, Guo S, Niu Z (2017) Tasks scheduling and resource allocation in heterogeneous cloud for delay-bounded mobile edge computing. In: IEEE international conference on communications (ICC), IEEE pp 1-7Google Scholar
  28. 28.
    Wang CM, Yu FR, Chen Q, Tang L (2017) Joint computation and radio resource management for cellular networks with mobile edge computing. In: IEEE International conference on communications (ICC), IEEE, pp 1-6Google Scholar
  29. 29.
    Li J, Wen M, Zhang T (2016) Group-based authentication and key agreement with dynamic policy updating for MTC in LTE-A networks. IEEE Internet Things J 3(3):408–417CrossRefGoogle Scholar
  30. 30.
    Zhang A, Chen J, Hu RQ, Qian Y (2016) SeDS: secure data sharing strategy for D2D communication in LTE-advanced networks. IEEE Trans Veh Technol 65(4):2659–2672CrossRefGoogle Scholar
  31. 31.
    Hong H, Sun Z (2018) Achieving secure data access control and efficient key updating in mobile multimedia sensor networks. Multimed Tools Appl 77:4477–4490CrossRefGoogle Scholar
  32. 32.
    Santhi S, Sheba Kezia Malarchelvi PD (2018) Self-similar key generation for secure communication in multimedia applications. Multimed Tools Appl 77:10329–10346CrossRefGoogle Scholar
  33. 33.
    Hussain M, Du Q, Sun L, Ren P (2016) Security enhancement for video transmission via noise aggregation in immersive systems. Multimed Tools Appl 75:5345–5357CrossRefGoogle Scholar
  34. 34.
    Xie T, Qin X (2006) Scheduling security-critical real-time applications on clusters. IEEE Trans Comput 55(7):864–879CrossRefGoogle Scholar
  35. 35.
    Song S, Hwang K, Kwok YK (2006) Risk-resilient heuristics and genetic algorithms for security-assured grid job scheduling. IEEE Trans Comput 55(6):703–719CrossRefGoogle Scholar
  36. 36.
    Tang X, Li K, Zeng Z, Veeravalli B (2011) A novel security-driven scheduling algorithm for precedence-constrained tasks in heterogeneous distributed systems. IEEE Trans Comput 60(7):1017–1029MathSciNetzbMATHCrossRefGoogle Scholar
  37. 37.
    Li Z, Ge J, Li C, Yang H, Hu H, Luo B, Chang V (2017) Energy cost minimization with job security guarantee in internet data center. Futur Gener Comput Syst 73:63–78CrossRefGoogle Scholar
  38. 38.
    Chen H, Zhu X, Qiu D, Liu L, Du Z (2017) Scheduling for workflows with security-sensitive intermediate data by selective tasks duplication in clouds. IEEE Trans Parallel Distrib Syst 28(9):2674–2688CrossRefGoogle Scholar
  39. 39.
    Jiang W, Jiang K, Zhang X, Ma Y (2015) Energy optimization of security-critical real-time applications with guaranteed security protection. J Syst Archit 61:282–292CrossRefGoogle Scholar
  40. 40.
    Huang B, Li Z, Tang P, Wang S, Zhao J, Hu H, Li W, Chang V (2019) Security modeling and efficient computation offloading for service workflow in mobile edge computing. Futur Gener Comput Syst 97:755–774CrossRefGoogle Scholar
  41. 41.
    Song W, Jacobsen HA (2018) Static and dynamic process change. IEEE Trans Serv Comput 11(1):215–231CrossRefGoogle Scholar
  42. 42.
    Song W, Chen F, Jacobsen HA, Xia X, Ye C, Ma X (2017) Scientific workflow mining in clouds. IEEE Trans Parallel Distrib Syst 28(10):2979–2992CrossRefGoogle Scholar
  43. 43.
    Yao Y, Huang L, Sharma AB, Golubchik L, Neely MJ (2014) Power cost reduction in distributed data centers: a two-time-scale approach for delay tolerant workloads. IEEE Trans Parallel Distrib Syst 25(1):200–211CrossRefGoogle Scholar
  44. 44.
    Xie T, Qin X (2008) Security-aware resource allocation for real-time parallel jobs on homogeneous and heterogeneous clusters. IEEE Trans Parallel Distrib Syst 19(5):682–697CrossRefGoogle Scholar
  45. 45.
    Wang F, Xu J, Wang X, Cui S (2018) Joint offloading and computing optimization in wireless powered mobile-edge computing systems. IEEE Trans Wirel Commun 17(3):1784–1797CrossRefGoogle Scholar
  46. 46.
    Georgiadis L, Neely MJ, Tassiulas L (2006) Resource allocation and cross-layer control in wireless networks. Foundations and Trends in Networking 1(1):1–149zbMATHCrossRefGoogle Scholar
  47. 47.
    Neely MJ (2010) Stochastic network optimization with application to communication and queueing systems. Synthesis Lectures on Communication Networks, Morgan & Claypool PublishersGoogle Scholar
  48. 48.
    Lyu X, Ni W, Tian H, Liu RP, Wang X, Giannakis GB, Paulraj A (2017) Optimal schedule of mobile edge computing for internet of things using partial information. IEEE J Sel Areas Commun 35(11):2606–2615CrossRefGoogle Scholar
  49. 49.
    Jiang W, Feng G, Qin S, Yum TSP (2018) Efficient D2D content caching using multi-agent reinforcement learning. In: IEEE conference on computer communications workshops (INFOCOM WKSHPS), IEEE, pp 511-516Google Scholar
  50. 50.
    Zhang W, Wen Y, Wu DO (2015) Collaborative task execution in mobile cloud computing under a stochastic wireless channel. IEEE Trans Wirel Commun 14(1):81–93CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2020

Authors and Affiliations

  1. 1.School of Computer Science and TechnologyHangzhou Dianzi UniversityHangzhouChina
  2. 2.State Key Laboratory of Networking and Switching TechnologyBeijing University of Posts and TelecommunicationsBeijingChina
  3. 3.Software InstituteNanjing UniversityNanjingChina
  4. 4.Institute of Service EngineeringHangzhou Normal UniversityHangzhouChina
  5. 5.Department of Computer Science and EngineeringSouthern Methodist UniversityDallasUSA

Personalised recommendations