Abstract
Vehicular Cloud Computing (VCC) facilitates real-time execution of many emerging user and intelligent transportation system (ITS) applications by exploiting under-utilized on-board computing resources available in nearby vehicles. These applications have heterogeneous time criticality, i.e., they demand different Quality-of-Service levels. In addition to that, mobility of the vehicles makes the problem of scheduling different application tasks on the vehicular computing resources a challenging one. In this article, we have formulated the task scheduling problem as a mixed integer linear program (MILP) optimization that increases the computation reliability even as reducing the job execution delay. Vehicular on-board units (OBUs), manufactured by different vendors, have different architecture and computing capabilities. We have exploited MapReduce computation model to address the problem of resource heterogeneity and to support computation parallelization. Performance of the proposed solution is evaluated in network simulator version 3 (ns-3) by running MapReduce applications in urban road environment and the results are compared with the state-of-the-art works. The results show that significant performance improvements in terms of reliability and job execution time can be achieved by the proposed task scheduling model.
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Notes
Because in the case, the Map and Reduce slots reside in the same worker with higher probability.
References
Hafeez KA, Zhao L, Ma B, Mark JW (2013) Performance analysis and enhancement of the DSRC for VANET’s safety applications. IEEE Trans Veh Technol 62(7):3069–3083
Gramaglia M, Calderon M, Bernardos CJ (2014) ABEONA monitored traffic: VANET-assisted cooperative traffic congestion forecasting. IEEE Veh Technol Mag 9(2):50–57
Gerla M (2012) Vehicular cloud computing. In: Ad Hoc networking workshop (Med-Hoc-Net), 11th annual mediterranean, pp 152–155
Eltoweissy M, Olariu S, Younis M (2010) Towards autonomous vehicular clouds, Ad Hoc networks. Berlin Heidelberg 49:1–16
Olariu S, Khalil I, Abuelela M (2011) Taking VANET to the clouds. Int’l J Pervasive Comput Comm 7(1):7–21
He W, Yan G, Xu LD (2014) Developing vehicular data cloud services in the IoT environment. IEEE Trans Indus Inf 10(2):1587–1595
Lee E, Lee E-K, Gerla M, Oh SY (2014) Vehicular cloud networking: architecture and design principles. IEEE Comm Mag 52(2):148–155
Son J, Eun H, Oh H, Kim S, Hussain R (2012) Rethinking vehicular communications: merging VANET with cloud computing. In: IEEE Int’l conf. on cloud computing (CLOUDCOM), pp 606–609
Bitam S, Mellouk A (2012) ITS-Cloud: cloud computing for intelligent transportation system. In: Global comm conf. (GLOBECOM). IEEE, pp 2054–2059
Aminizadeh L, Yousefi S (2014) Cost minimization scheduling for deadline constrained applications on vehicular cloud infrastructure. In: IEEE Int’l conf. on computer and knowledge engineering (ICCKE), pp 358–363
Wang H, Liu RP, Ni W, Chen W, Collings IB (2015) VANET modeling and clustering design under practical traffic, channel and mobility conditions. IEEE Trans Comm 63(3):870–881
Das AK, Adhikary T, Razzaque MA, Hong CS (2013) An intelligent approach for virtual machine and QoS provisioning in cloud computing. In: IEEE Int’l conf. on information networking (ICOIN), pp 462–467
Adhikary T, Das AK, Razzaque MA, Sarkar AMJ (2013) Energy-efficient scheduling algorithms for data center resources in cloud computing. In: IEEE int’l conf on high performance computing and communications (HPCC), pp 1715–1720. Zhangjiajie
Qin Y, Huang D, Zhang X (2012) VehiCloud: cloud computing facilitating routing in vehicular networks, trust. In: IEEE Int’l conf. on security and privacy in computing and communications (TrustCom), pp 1438–1445
Abid H, Phuong LTT, Wang J, Lee S, Qaisar S (2011) V-Cloud: vehicular cyber-physical systems and cloud computing. In: Proc of Int’l symposium on applied sciences in biomedical and commun technologies. ACM
Yu R, Zhang Y, Gjessing S, Xia W, Yang K (2013) Toward cloud-based vehicular networks with efficient resource management. IEEE Netw 27(5):48–55
Basagni S, Conti M, Giordano S, Stojmenovic I (2013) The next paradign shift: from vehicular networks to vehicular clouds, mobile Ad Hoc networking: the cutting edge directions, 1st edn, Wiley-IEEE Press, pp 645–700
Gerla M, Weng J-T, Pau G (2013) Pics-on-wheels: photo surveillance in vehicular cloud. In: Int’l conf on computing, netw. and comm (ICNC), pp 1123–1127
Elespuru PR, Shakya S, Mishra S (2009) MapReduce system over heterogeneous mobile devices. In: Int’l workshop on software technologies for embedded and ubiq systems. Springer-Verlag, Heidelberg, pp 168–179
Network simulator version 3 (ns-3), https://www.nsnam.org/. Accessed 25 Mar 2015
MRPerf simulator, http://research.cs.vt.edu/dssl/mrperf/. Accessed 20 Mar 2015
Simulation of urban mobility (SUMO), http://sumo.sourceforge.net. Accessed 18 Mar 2015
Lai Y-C, Lin P, Liao W, Chen C-M (2011) A region-based clustering mechanism for channel access in vehicular Ad Hoc networks. IEEE J Selected Areas Comm 29(1):83–93
Chai R, Yang B, Li L, Sun X, Chen Q (2013) Clustering-based data transmission algorithms for VANET. In: Int’l conf on wireless comm & sig. proc. (WCSP), pp 1–6
Shih HY, Leu JS (2012) Improving resource utilization in a heterogeneous cloud environment. In: Asia-Pacific conference on comm (APCC), pp 185–189
NEOS optimization server, http://www.neos-server.org/neos/. Accessed 10 Sep 2015
Acknowledgments
This work was supported by the Deanship of Scientific Research at King Saud University, Riyadh, Saudi Arabia through the International Research Group Project IRG-14-17.
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Adhikary, T., Das, A.K., Razzaque, M.A. et al. Quality of Service Aware Reliable Task Scheduling in Vehicular Cloud Computing. Mobile Netw Appl 21, 482–493 (2016). https://doi.org/10.1007/s11036-015-0657-5
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DOI: https://doi.org/10.1007/s11036-015-0657-5