, MigCostZero is equivalent to the initial placement approach proposed in Part I, which is able to deal with large-scale problems and to find solutions with low WAL values. However, NB mig is not taken into account in this approach, and thus can be relatively high. MigCostAware appears as the best compromise taking all the criteria into account. It gets execution times similar to (or even lower than) MigCostZero. Compared with MigCostZero, MigCostAware highly lowers NB mig while introducing an insignificant increase of WAL. The disadvantage of MigCostAware is that, given a placement problem, MigCostAware can find only a single solution, WAL / NB mig value is too high), it must leverage on other algorithms

D. Evans, The internet of things: How the next evolution of the internet is changing everything, vol.1, pp.1-11, 2011.

P. Mell and T. Grance, The nist definition of cloud computing, 2011.

F. Bonomi, R. Milito, J. Zhu, and S. Addepalli, Fog computing and its role in the internet of things, Proceedings of the first edition of the MCC workshop on Mobile cloud computing, pp.13-16, 2012.

M. Luis, L. Vaquero, and . Rodero-merino, Finding your way in the fog: Towards a comprehensive definition of fog computing, ACM SIGCOMM Computer Communication Review, vol.44, issue.5, pp.27-32, 2014.

V. Cardellini, V. Grassi, F. L. Presti, and M. Nardelli, Optimal operator placement for distributed stream processing applications, Proceedings of the 10th ACM International Conference on Distributed and Event-based Systems, pp.69-80, 2016.

A. Brogi, S. Forti, and A. Ibrahim, How to best deploy your fog applications, probably, International Conference on Edge and Fog Computing, 2017.

Y. Xia, X. Etchevers, L. Letondeur, L. Adrien, T. Coupaye et al., Combining Heuristics to Optimize and Scale the Placement of IoT Applications in the Fog, IEEE/ACM International Conference on Utility and Cloud Computing, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01942097

Y. Xia, X. Etchevers, L. Letondeur, T. Coupaye, and F. Desprez, Combining Hardware Nodes and Software Components Ordering-based Heuristics for Optimizing the Placement of Distributed IoT Applications in the Fog, The 33rd ACM/SIGAPP Symposium On Applied Computing, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01908928

N. Gershenfeld, R. Krikorian, and D. Cohen, The internet of things, Scientific American, vol.291, issue.4, pp.76-81, 2004.

L. Atzori, A. Iera, and G. Morabito, The internet of things: A survey, Computer networks, vol.54, issue.15, pp.2787-2805, 2010.

A. Botta, W. D. Donato, V. Persico, and A. Pescapé, Integration of cloud computing and internet of things: a survey, Future Generation Computer Systems, vol.56, pp.684-700, 2016.

L. Lo¨?clo¨?c, O. François-gaël, and C. Thierry, A demo of application lifecycle management for IoT collaborative neighborhood in the fog -Practical experiments and lessons learned around docker, Fog World Congress, 2017.

F. Bonomi, R. Milito, P. Natarajan, and J. Zhu, Fog computing: A platform for internet of things and analytics, Big data and internet of things: A roadmap for smart environments, pp.169-186, 2014.

. Evangelos-a-kosmatos, D. Nikolaos, A. C. Tselikas, and . Boucouvalas, Integrating rfids and smart objects into a unifiedinternet of things architecture, Advances in Internet of Things, vol.1, issue.01, 2011.

J. Sahoo, S. Mohapatra, and R. Lath, Virtualization: A survey on concepts, taxonomy and associated security issues, Computer and Network Technology (ICCNT), 2010 Second International Conference on, pp.222-226, 2010.

C. Clark, K. Fraser, S. Hand, J. G. Hansen, E. Jul et al., Live migration of virtual machines, Proceedings of the 2nd Conference on Symposium on Networked Systems Design & Implementation, vol.2, pp.273-286, 2005.

K. Toczé and S. Nadjm-tehrani, A taxonomy for management and optimization of multiple resources in edge computing, Wireless Communications and Mobile Computing, 2018.

J. Zare, S. Abolfazli, M. Shojafar, and A. Kamsin, Resource scheduling in mobile cloud computing: Taxonomy and open challenges, 2015 IEEE International Conference on Data Science and Data Intensive Systems (DSDIS), pp.594-603, 2015.

. Zoltánzoltán´-zoltán´adám-mann, Allocation of virtual machines in cloud data centersa survey of problem models and optimization algorithms, Acm Computing Surveys (CSUR), vol.48, issue.1, p.11, 2015.

M. Guzek, P. Bouvry, and E. Talbi, A survey of evolutionary computation for resource management of processing in cloud computing, IEEE Computational Intelligence Magazine, vol.10, issue.2, pp.53-67, 2015.

M. Rupali and . Pandharpatte, A review: Resource allocation problem in cloud environment, International Journal of Engineering and Technology, pp.1695-1700, 2017.

M. Masdari, V. Sayyid-shahab-nabavi, and . Ahmadi, An overview of virtual machine placement schemes in cloud computing, Journal of Network and Computer Applications, vol.66, pp.106-127, 2016.

Z. Zhang, C. Hsu, and M. Chang, Cool cloud: A practical dynamic virtual machine placement framework for energy aware data centers, 2015 IEEE 8th International Conference on Cloud Computing (CLOUD), pp.758-765, 2015.

W. Tärneberg, A. Mehta, E. Wadbro, J. Tordsson, J. Eker et al., Dynamic application placement in the mobile cloud network, Future Generation Computer Systems, vol.70, pp.163-177, 2017.

O. Skarlat, M. Nardelli, S. Schulte, and S. Dustdar, Towards qos-aware fog service placement, 2017 IEEE 1st International Conference on, pp.89-96, 2017.
DOI : 10.1109/icfec.2017.12

O. Skarlat, M. Nardelli, S. Schulte, M. Borkowski, and P. Leitner, Optimized IoT service placement in the fog, Service Oriented Computing and Applications, pp.1-17, 2017.

A. Brogi and S. Forti, Qos-aware deployment of iot applications through the fog, IEEE Internet of Things Journal, 2017.

S. Rizou, F. Diirr, and K. Rothermel, Fulfilling end-toend latency constraints in large-scale streaming environments, Performance Computing and Communications Conference (IPCCC), pp.1-8, 2011.

X. Zhi-hui-zhan, Y. Liu, J. Gong, H. S. Zhang, Y. Chung et al., Cloud computing resource scheduling and a survey of its evolutionary approaches, ACM Computing Surveys (CSUR), vol.47, issue.4, p.63, 2015.

M. Kalra and S. Singh, A review of metaheuristic scheduling techniques in cloud computing, Egyptian informatics journal, vol.16, issue.3, pp.275-295, 2015.

A. Ioannis, H. D. Moschakis, and . Karatza, A meta-heuristic optimization approach to the scheduling of bag-of-tasks applications on heterogeneous clouds with multi-level arrivals and critical jobs, Simulation Modelling Practice and Theory, vol.57, pp.1-25, 2015.

F. Xhafa, J. Carretero, B. Dorronsoro, and E. Alba, A tabu search algorithm for scheduling independent jobs in computational grids, Computing and informatics, vol.28, issue.2, pp.237-250, 2012.

M. Md-hasanul-ferdaus, R. N. Murshed, R. Calheiros, and . Buyya, Virtual machine consolidation in cloud data centers using aco metaheuristic, European Conference on Parallel Processing, pp.306-317, 2014.

J. Li, D. Li, J. Zheng, and Y. Quan, Location-aware multi-user resource allocation in distributed clouds, Advanced Computer Architecture, pp.152-162, 2014.

S. Agarwal, J. Dunagan, N. Jain, S. Saroiu, A. Wolman et al., Volley: Automated data placement for geo-distributed cloud services, NSDI, vol.10, pp.28-28, 2010.

X. Liu and R. Buyya, Performance-oriented deployment of streaming applications on cloud, 2017.

F. Dabek, R. Cox, F. Kaashoek, and R. Morris, Vivaldi: A decentralized network coordinate system, ACM SIGCOMM Computer Communication Review, vol.34, pp.15-26, 2004.

M. Barshan, H. Moens, S. Latre, B. Volckaert, and F. D. Turck, Algorithms for network-aware application component placement for cloud resource allocation, Journal of Communications and Networks, vol.19, issue.5, pp.493-508, 2017.

H. Casanova, A. Giersch, A. Legrand, M. Quinson, and F. Suter, Versatile, scalable, and accurate simulation of distributed applications and platforms, Journal of Parallel and Distributed Computing, vol.74, issue.10, pp.2899-2917, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01017319

M. Dias-de-assuncao, A. Da, S. Veith, and R. Buyya, Resource elasticity for distributed data stream processing: A survey and future directions, 2017.

S. Wang, K. Chan, R. Urgaonkar, T. He, and K. K. Leung, Emulation-based study of dynamic service placement in mobile micro-clouds, Military Communications Conference, MILCOM 2015-2015 IEEE, pp.1046-1051, 2015.

F. Luiz, J. Bittencourt, R. Diaz-montes, . Buyya, F. Omer et al., Mobility-aware application scheduling in fog computing, vol.4, pp.26-35, 2017.

J. Jiang, T. Lan, S. Ha, M. Chen, and M. Chiang, Joint vm placement and routing for data center traffic engineering, INFOCOM, vol.12, pp.2876-2880, 2012.

R. Yuval, L. Hanoch, and B. Eli, On dynamic placement of resources in cloud computing, pp.2018-2020

A. Yousefpour, A. Patil, G. Ishigaki, I. Kim, X. Wang et al., Qos-aware dynamic fog service provisioning, 2018.
DOI : 10.1109/jiot.2019.2896311

V. Cardellini, V. Grassi, F. L. Presti, and M. Nardelli, Distributed qos-aware scheduling in storm, Proceedings of the 9th ACM International Conference on Distributed Event-Based Systems, pp.344-347, 2015.

S. Wang, R. Urgaonkar, T. He, K. Chan, M. Zafer et al., Dynamic service placement for mobile microclouds with predicted future costs, IEEE Transactions on Parallel and Distributed Systems, vol.28, issue.4, pp.1002-1016, 2017.
DOI : 10.1109/icc.2015.7249199
URL : http://arxiv.org/pdf/1503.02735

B. Ottenwälder, B. Koldehofe, K. Rothermel, and U. Ramachandran, Migcep: operator migration for mobility driven distributed complex event processing, Proceedings of the 7th ACM international conference on Distributed event-based systems, pp.183-194, 2013.

E. David and . Goldberg, Genetic algorithms in search, optimization, and machine learning, 1989.