Towards energy-aware fog-enabled cloud of things for healthcare

Abstract The Internet-of-Things (IoT) represents the next groundbreaking change in information and communication technology (ICT) after the Internet. IoT is concerned with making everything connected and accessible through the Internet. However, IoT objects (things) are characterized by constrained computing and storage resources. Therefore, the Cloud of Things (CoT) paradigm that integrates the Cloud with IoT is proposed to meet the IoT requirements. In CoT, the IoT capabilities (e.g., sensing) are provisioned as services. Unfortunately, the two-tier CoT model is not efficient in the use cases sensitive to delays and energy consumption (e.g., in healthcare). Consequently, Fog Computing is proposed to support such IoT services and applications. This paper reviews the most relevant Fog-enabled CoT system models and proposes an energy-aware allocation strategy for placing application modules (tasks) on Fog devices. Finally, the performance of the proposed strategy is evaluated in comparison with the default allocation and Cloud-only policies, using the iFogSim simulator. The proposed solution was observed to be more energy-efficient, saving approximately 2.72% of the energy compared to Cloud-only and approximately 1.6% of the energy compared to the Fog-default.

[1]  Hao Liang,et al.  Optimal Workload Allocation in Fog-Cloud Computing Toward Balanced Delay and Power Consumption , 2016, IEEE Internet of Things Journal.

[2]  Sergio Barbarossa,et al.  The Fog Balancing: Load Distribution for Small Cell Cloud Computing , 2015, 2015 IEEE 81st Vehicular Technology Conference (VTC Spring).

[3]  Zhenyu Wen,et al.  Fog Orchestration for Internet of Things Services , 2017, IEEE Internet Computing.

[4]  Paulo F. Pires,et al.  On the interplay of Internet of Things and Cloud Computing: A systematic mapping study , 2016, Comput. Commun..

[5]  Qun Li,et al.  A Survey of Fog Computing: Concepts, Applications and Issues , 2015, Mobidata@MobiHoc.

[6]  Rajkumar Buyya,et al.  Energy-aware simulation with DVFS , 2013, Simul. Model. Pract. Theory.

[7]  Rajkumar Buyya,et al.  Mobility-Aware Application Scheduling in Fog Computing , 2017, IEEE Cloud Computing.

[8]  Hui Wang,et al.  The fog computing service for healthcare , 2015, 2015 2nd International Symposium on Future Information and Communication Technologies for Ubiquitous HealthCare (Ubi-HealthTech).

[9]  Eui-Nam Huh,et al.  Fog Computing: The Cloud-IoT\/IoE Middleware Paradigm , 2016, IEEE Potentials.

[10]  Rong Yu,et al.  CachinMobile: An energy-efficient users caching scheme for fog computing , 2016, 2016 IEEE/CIC International Conference on Communications in China (ICCC).

[11]  Rajkumar Buyya,et al.  iFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments , 2016, Softw. Pract. Exp..

[12]  R. K. Pateriya,et al.  Cloud Server Optimization with Load Balancing and Green Computing Techniques Using Dynamic Compare and Balance Algorithm , 2013, 2013 5th International Conference on Computational Intelligence and Communication Networks.

[13]  Xavier Masip-Bruin,et al.  Handling service allocation in combined Fog-cloud scenarios , 2016, 2016 IEEE International Conference on Communications (ICC).

[14]  Michelle M. Zhu,et al.  Enhanced Weighted Round Robin (EWRR) with DVFS Technology in Cloud Energy-Aware , 2014, 2014 International Conference on Computational Science and Computational Intelligence.

[15]  Mohammad Aazam,et al.  Using DEVS for modeling and simulating a Fog Computing environment , 2017, 2017 International Conference on Computing, Networking and Communications (ICNC).

[16]  Paulo F. Pires,et al.  System modelling and performance evaluation of a three-tier Cloud of Things , 2017, Future Gener. Comput. Syst..

[17]  Fei Wang,et al.  A Task Scheduling Algorithm Based on Load Balancing in Cloud Computing , 2010, WISM.

[18]  Gonzalo Mateos,et al.  Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing: Opportunities and Challenges , 2015, 2015 IEEE International Conference on Services Computing.

[19]  Antonio Pescapè,et al.  Integration of Cloud computing and Internet of Things: A survey , 2016, Future Gener. Comput. Syst..

[20]  Abhishek Swaroop,et al.  A survey on techniques to achive energy efficiency in cloud computing , 2016, 2016 International Conference on Computing, Communication and Automation (ICCCA).

[21]  Xavier Masip-Bruin,et al.  Towards Distributed Service Allocation in Fog-to-Cloud (F2C) Scenarios , 2016, 2016 IEEE Global Communications Conference (GLOBECOM).

[22]  B. Thirumala Rao,et al.  A study on cloud based Internet of Things: CloudIoT , 2015, 2015 Global Conference on Communication Technologies (GCCT).

[23]  Harsh Kumar Singh,et al.  An efficient data replication and load balancing technique for fog computing environment , 2016, 2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom).

[24]  Julian de Hoog,et al.  Interconnecting Fog computing and microgrids for greening IoT , 2016, 2016 IEEE Innovative Smart Grid Technologies - Asia (ISGT-Asia).

[25]  Ajay Gupta,et al.  A remote patient monitoring system in an ad hoc sensor network environment , 2011, ICIMU 2011 : Proceedings of the 5th international Conference on Information Technology & Multimedia.

[26]  Eui-nam Huh,et al.  Towards task scheduling in a cloud-fog computing system , 2016, 2016 18th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[27]  Theofanis Orphanoudakis,et al.  Integrating IoT and Fog Computing for Healthcare Service Delivery , 2017 .

[28]  Tao Zhang,et al.  Fog and IoT: An Overview of Research Opportunities , 2016, IEEE Internet of Things Journal.

[29]  Manuel Díaz,et al.  State-of-the-art, challenges, and open issues in the integration of Internet of things and cloud computing , 2016, J. Netw. Comput. Appl..

[30]  Rajkumar Buyya,et al.  Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in Cloud data centers , 2012, Concurr. Comput. Pract. Exp..