ABSTRACT
Cloud computing have established the utility computing paradigm as a standard for application development and execution. As heterogeneity in applications requirements become a norm, fog computing has emerged recently to introduce computing capacity layers between the edge and the cloud, creating a hierarchy of computing power that can be used as a utility to run highly heterogeneous applications. However, in order to make this layered infrastructure a reality, new resource management mechanisms are necessary. In this paper we propose a component-based scheduler that considers application requirements heterogeneity as well as the fog-cloud computing hierarchy to improve applications execution in a cloud-fog computing infrastructure. The proposed algorithm takes into account the delay-priority of applications when taking scheduling decision on the fog-cloud infrastructure. We evaluate the proposal in a simulator, and preliminary results suggest the component-based scheduling algorithm is able to reduce average delays for applications with stricter requirements.
- Luiz Bittencourt, Javier Diaz-Montes, Rajkumar Buyya, Omer Rana, and Manish Parashar. 2017. Mobility-Aware Application Scheduling in Fog Computing. IEEE Cloud Computing , Vol. 4, 2 (March 2017), 26--35.Google ScholarCross Ref
- Luiz Bittencourt, Roger Immich, Rizos Sakellariou, Nelson Fonseca, Edmundo Madeira, Marilia Curado, Leandro Villas, Luiz DaSilva, Craig Lee, and Omer Rana. 2018. The Internet of Things, Fog and Cloud Continuum: Integration and challenges. Internet of Things , Vol. 3--4 (2018), 134 -- 155.Google Scholar
- Flavio Bonomi, Rodolfo Milito, Jiang Zhu, and Sateesh Addepalli. 2012. 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, 13--16.Google ScholarDigital Library
- Rodrigo N Calheiros, Rajiv Ranjan, Anton Beloglazov, César AF De Rose, and Rajkumar Buyya. 2011. CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Software: Practice and experience , Vol. 41, 1 (2011), 23--50.Google Scholar
- Harshit Gupta, Amir Vahid Dastjerdi, Soumya K Ghosh, and Rajkumar Buyya. 2017. iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments. Software: Practice and Experience , Vol. 47, 9 (2017), 1275--1296.Google ScholarCross Ref
- S. Kosta, A. Aucinas , Pan Hui, R. Mortier, and Xinwen Zhang. 2012. ThinkAir: Dynamic resource allocation and parallel execution in the cloud for mobile code offloading. In 2012 Proceedings IEEE INFOCOM . 945--953.Google ScholarCross Ref
- Karthik Kumar and Yung-Hsiang Lu. 2010. Cloud computing for mobile users: Can offloading computation save energy? Computer 4 (2010), 51--56.Google ScholarDigital Library
- Y. Lin and H. Shen. 2015. Cloud Fog: Towards High Quality of Experience in Cloud Gaming. In 2015 44th International Conference on Parallel Processing. 500--509.Google Scholar
- Md Redowan Mahmud, Mahbuba Afrin, Md Abdur Razzaque, Mohammad Mehedi Hassan, Abdulhameed Alelaiwi, and Majed Alrubaian. 2016. Maximizing quality of experience through context-aware mobile application scheduling in Cloudlet infrastructure. Software: Practice and Experience , Vol. 46, 11 (2016), 1525--1545.Google ScholarDigital Library
- Redowan Mahmud, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2018. Latency-Aware Application Module Management for Fog Computing Environments. ACM Trans. Internet Technol. , Vol. 19, 1, Article 9 (Nov. 2018), bibinfonumpages21 pages.Google ScholarDigital Library
- Redowan Mahmud, Satish Narayana Srirama, Kotagiri Ramamohanarao, and Rajkumar Buyya. 2019. Quality of Experience (QoE)-aware placement of applications in Fog computing environments. J. Parallel and Distrib. Comput. , Vol. 132 (2019), 190 -- 203.Google ScholarDigital Library
- O. Skarlat, M. Nardelli, S. Schulte, and S. Dustdar. 2017. Towards QoS-Aware Fog Service Placement. In 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC). 89--96.Google Scholar
- M. Taneja and A. Davy. 2017. Resource aware placement of IoT application modules in Fog-Cloud Computing Paradigm. In 2017 IFIP/IEEE Symposium on Integrated Network and Service Management (IM) . 1222--1228.Google Scholar
Index Terms
- Component-based Scheduling for Fog Computing
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