Estimation of fog utility pricing: a bio-inspired optimisation techniques' perspective

References

• Bonomi F, Milito R, Zhu J, et al. Fog computing and its role in the internet of things. In: Proceedings of the first edition of the MCC workshop on mobile cloud computing. ACM; 2012. p. 13–16.
   Google Scholar
• Rottondi C, Barbato A, Chen L, et al. Enabling privacy in a distributed game-theoretical scheduling system for domestic appliances. IEEE Trans Smart Grid. 2017;8(3):1220–1230. doi: 10.1109/TSG.2015.2511038
   Web of Science ®Google Scholar
• Alam MR, Ibne Reaz MB, Mohd Ali MA. A review of smart homes-past, present, and future. IEEE Trans Syst Man Cybern Part C (Appl Rev). 2012;42(6):1190–1203. doi: 10.1109/TSMCC.2012.2189204
   Web of Science ®Google Scholar
• Hafsa SB, Amjad Z, Ali M, et al. Pigeon inspired optimization and enhanced differential evolution using time of use tariff in smart grid. In: International Conference on Intelligent Networking and Collaborative Systems. Cham: Springer; 2017. p. 563–575.
   Google Scholar
• Amjad Z, Batool S, Arshad H, et al. Pigeon inspired optimization and enhanced differential evolution in smart grid using critical peak pricing. In: International Conference on Intelligent Networking and Collaborative Systems. Cham: Springer; 2017. p. 505–514.
   Google Scholar
• Duan H, Qiao P. Pigeon-inspired optimization: a new swarm intelligence optimizer for air robot path planning. Int J Intell Comput Cybern. 2014;7(1):24–37. doi: 10.1108/IJICC-02-2014-0005
   Google Scholar
• Yang X-S. A new metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Berlin: Springer; 2010. p. 6574.
   Google Scholar
• Mirjalili S, Mirjalili SM, Yang XS. Binary bat algorithm. Neural Comput Appl. 2014;25(3–4):663–681. doi: 10.1007/s00521-013-1525-5
   Web of Science ®Google Scholar
• Yang X-S, He X. Bat algorithm: literature review and applications. Int J Bio-Inspired Comput. 2013;5(3):141–149. doi: 10.1504/IJBIC.2013.055093
   Web of Science ®Google Scholar
• Wang L, Ni H, Yang R, et al. A simple human learning optimization algorithm. In: International Conference on Life System Modeling and Simulation and International Conference on Intelligent Computing for Sustainable Energy and Environment. Berlin: Springer; 2014. p. 56–65.
   Google Scholar
• Arshad H, Shah MA, Khattak HA, et al. Evaluating bio-inspired optimization techniques for utility price estimation in fog computing. In: 2018 IEEE International Conference on Smart Cloud (SmartCloud). IEEE; 2018. p. 84–89.
   Google Scholar
• Pham XQ, Man ND, Tri NDT, et al. A cost-and performance-effective approach for task scheduling based on collaboration between cloud and fog computing. Int J Distrib Sens Netw. 2017;13(11):1550147717742073. doi: 10.1177/1550147717742073
   Web of Science ®Google Scholar
• Yang L, Cao J, Liang G, et al. Cost aware service placement and load dispatching in mobile cloud systems. IEEE Trans Comput. 2016;65(5):1440–1452. doi: 10.1109/TC.2015.2435781
   Web of Science ®Google Scholar
• Sidhu HS. Cost-deadline based task scheduling in cloud computing. In: Advances in Computing and Communication Engineering (ICACCE), 2015 Second International Conference on. IEEE; 2015. p. 273–279.
   Google Scholar
• Kang DK, Kim SH, Youn CH, et al. Cost adaptive workflow scheduling in cloud computing. In: Proceedings of the 8th International Conference on Ubiquitous Information Management and Communication. ACM; 2014. p. 65.
   Google Scholar
• Aadkane T, Monga S. An energy efficient cost aware virtual machine migration approach for the cloud environment.
   Google Scholar
• Aslanpour MS, Arani MG, Toosi AN. Auto-scaling web applications in clouds: a cost-aware approach. J Netw Comput Appl. 2017;95:26–41. doi: 10.1016/j.jnca.2017.07.012
   Web of Science ®Google Scholar
• Aazam M, Huh EN. Broker as a service (baas) pricing and resource estimation model. In: Cloud Computing Technology and Science (CloudCom), 2014 IEEE 6th International Conference on. IEEE; 2014. p. 463–468.
   Google Scholar
• Kim SH, Kang DK, Kim WJ, et al. A science gateway cloud with cost-adaptive VM management for computational science and applications. IEEE Syst J. 2017;11(1):173–185. doi: 10.1109/JSYST.2015.2501750
   Web of Science ®Google Scholar
• Kim Y, Kwak J, Chong S. Dual-side optimization for cost-delay tradeoff in mobile edge computing. IEEE Trans Veh Technol. 2018;67(2):1765–1781. doi: 10.1109/TVT.2017.2762423
   Web of Science ®Google Scholar
• Kim Y, Kwak J, Chong S. Dual-side dynamic controls for cost minimization in mobile cloud computing systems. In: Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2015 13th International Symposium on. IEEE; 2015. p. 443–450.
   Google Scholar
• Cheng M, Li J, Nazarian S. DRL-cloud: deep reinforcement learning-based resource provisioning and task scheduling for cloud service providers. In: Design Automation Conference (ASP-DAC), 2018 23rd Asia and South Pacific. IEEE; 2018. p. 129–134.
   Google Scholar
• Kansal S, Kumar H, Kaushal S, et al. Genetic algorithm-based cost minimization pricing model for on-demand IaaS cloud service. J Supercomput. 2018;3:1–26.
   Google Scholar
• Shahapure NH, Jayarekha P. Load balancing with optimal cost scheduling algorithm. In: Computation of Power, Energy, Information and Communication (ICCPEIC), 2014 International Conference on. IEEE; 2014. p. 24–31.
   Google Scholar
• Li J, Wang Y, Cui T, et al. Negotiation-based task scheduling to minimize user's electricity bills under dynamic energy prices. In: Green Communications (OnlineGreencomm), 2014 IEEE Online Conference on. IEEE; 2014. p. 1–6.
   Google Scholar
• Li J, Wang Y, Cui T, et al. Negotiation-based task scheduling and storage control algorithm to minimize user's electric bills under dynamic prices. In: Design Automation Conference (ASP-DAC), 2015 20th Asia and South Pacific. IEEE; 2015. p. 261–266.
   Google Scholar
• Patel S, Bhujade RK, Sinhal A, et al. Resource optimization and cost reduction by dynamic virtual machine provisioning in cloud. In: Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on, pp. 857–861. IEEE, 2013
   Google Scholar
• Xia Q, Liang W, Xu W. Throughput maximization for online request admissions in mobile cloudlets. In: Local Computer Networks (LCN), 2013 IEEE 38th Conference on. IEEE; 2013. p. 589–596.
   Google Scholar
• Sturm T, Jrad F, Streit A. Storage CloudSim. In: Proceedings of the 4th International Conference on Cloud Computing and Services Science. Lda: SCITEPRESS-Science and Technology Publications; 2014. p. 186–192.
   Google Scholar
• Youn CH, Chen M, Dazzi P. VM placement via resource brokers in a cloud datacenter. In: Cloud Broker and Cloudlet for Workflow Scheduling. Singapore: Springer; 2017. p. 47–73.
   Google Scholar
• Cao Z, Lin J, Wan C, et al. Optimal cloud computing resource allocation for demand side management in smart grid. IEEE Trans Smart Grid. 2017;8(4):1943–1955.
   Web of Science ®Google Scholar
• Singh S, Chana I. Q-aware: quality of service-based cloud resource provisioning. Comput Electr Eng. 2015;47:138–160. doi: 10.1016/j.compeleceng.2015.02.003
   Web of Science ®Google Scholar
• Zhao J, Li H, Wu C, et al. Dynamic pricing and profit maximization for the cloud with geo-distributed data centers. In: INFOCOM, 2014 Proceedings IEEE. IEEE; 2014. p. 118–126.
   Google Scholar
• Sheeja YS, Jayalekshmi S. Cost effective load balancing based on honey bee behaviour in cloud environment. In: Computational Systems and Communications (ICCSC), 2014 First International Conference on. IEEE; 2014. p. 214–219.
   Google Scholar
• Ren S, Schaar MVD. Dynamic scheduling and pricing in wireless cloud computing. IEEE Trans Mobile Comput. 2014;13(10):2283–2292. doi: 10.1109/TMC.2013.57
   Web of Science ®Google Scholar
• Yaghmaee MH, Moghaddassian M, Garcia AL. Power consumption scheduling for future connected smart homes using bi-level cost-wise optimization approach. In: Smart City 360∘. Cham: Springer; 2016. p. 326–338.
   Google Scholar
• Chopra N, Singh S. Deadline and cost based workflow scheduling in hybrid cloud. In: Advances in Computing, Communications and Informatics (ICACCI), 2013 International Conference on. IEEE; 2013. p. 840–846.
   Google Scholar
• Pandey M, Verma SK. Cost based resource allocation strategy for the cloud computing environment. In: Computing, Communication and Networking Technologies (ICCCNT), 2017 8th International Conference on. IEEE; 2017. p. 1–7.
   Google Scholar
• Yang B, Chai WK, Xu Z, et al. Cost-efficient NFV-enabled mobile edge-cloud for low latency mobile applications. IEEE Trans Netw Serv Managet. 2018;15(1):478–488.
   Web of Science ®Google Scholar
• Ibrahim RW, Gani A. A mathematical model of cloud computing in the economic fractional dynamic system. Iran J Sci Technol Trans A: Sci. 2018;42(1):65–72. doi: 10.1007/s40995-018-0494-z
   Google Scholar
• Mehmi S, Verma HK, Sangal AL. Simulation modeling of cloud computing for smart grid using CloudSim. J Electr Syst Inf Technol. 2017;4(1):159–172.
   Google Scholar
• Khan N, Javaid N, Khan M, et al. Harmony pigeon inspired optimization for appliance scheduling in smart grid.
   Google Scholar
• Guo Z, Huang H, Deng C, et al. An enhanced differential evolution with elite chaotic local search. Intelligence and Neuroscience</DIFdel>Comput Intell Neurosci. 2015;2015:6.
   Google Scholar