Energy Consumption Optimization With a Delay Threshold in Cloud-Fog Cooperation Computing

With the rapid development of the Internet of Things (IoT), the number of mobile terminal devices is increasing. Massive data are generated by mobile terminal devices, resulting in high delay and high energy consumption. In most cases, however, a low delay means high energy consumption. To balance energy consumption and delay, we adopt a tradeoff strategy that can realize optimal energy consumption with a delay threshold in this paper. First, we introduce the role of the delay threshold in reducing delay. Then, we describe the delay and energy consumption of the mobile terminal layer, fog node layer and cloud server layer with queue theory. Nonlinear programming is used to solve the energy optimization problem by calculating the optimal workload of each layer. We design a cloud-fog cooperation scheduling algorithm to reduce energy consumption. A task offloading algorithm is also designed to complete tasks when their nodes leave. The experimental results show that the energy consumption is reduced by approximately 22%, while the delay is 12.5% less than the first come first served (FCFS) approach.

[1]  Miaowen Wen,et al.  MBID: Micro-Blockchain-Based Geographical Dynamic Intrusion Detection for V2X , 2019, IEEE Communications Magazine.

[2]  Christian Bonnet,et al.  Fog Computing architecture to enable consumer centric Internet of Things services , 2015, 2015 International Symposium on Consumer Electronics (ISCE).

[3]  Jiguo Yu,et al.  An Invocation Cost Optimization Method for Web Services in Cloud Environment , 2017, Sci. Program..

[4]  Zhipeng Cai,et al.  A Private and Efficient Mechanism for Data Uploading in Smart Cyber-Physical Systems , 2020, IEEE Transactions on Network Science and Engineering.

[5]  Junhua Wu,et al.  Methods of Resource Scheduling Based on Optimized Fuzzy Clustering in Fog Computing , 2019, Sensors.

[6]  Jun Wu,et al.  Making Knowledge Tradable in Edge-AI Enabled IoT: A Consortium Blockchain-Based Efficient and Incentive Approach , 2019, IEEE Transactions on Industrial Informatics.

[7]  Doan B. Hoang,et al.  FBRC: Optimization of task Scheduling in Fog-Based Region and Cloud , 2017, 2017 IEEE Trustcom/BigDataSE/ICESS.

[8]  Kim-Kwang Raymond Choo,et al.  Multi-dimensional data indexing and range query processing via Voronoi diagram for internet of things , 2019, Future Gener. Comput. Syst..

[9]  Mung Chiang,et al.  Leveraging fog and cloud computing for efficient computational offloading , 2017, 2017 IEEE MIT Undergraduate Research Technology Conference (URTC).

[10]  Xuyun Zhang,et al.  An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles , 2019, Future Gener. Comput. Syst..

[11]  John A. Stankovic,et al.  Research Directions for the Internet of Things , 2014, IEEE Internet of Things Journal.

[12]  Ciprian Dobre,et al.  Big Data and Internet of Things: A Roadmap for Smart Environments , 2014, Big Data and Internet of Things.

[13]  Wei Wang,et al.  Delay-Constrained Hybrid Computation Offloading With Cloud and Fog Computing , 2017, IEEE Access.

[14]  Raja Lavanya,et al.  Fog Computing and Its Role in the Internet of Things , 2019, Advances in Computer and Electrical Engineering.

[15]  Junhua Wu,et al.  Data Processing Delay Optimization in Mobile Edge Computing , 2018, Wirel. Commun. Mob. Comput..

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

[17]  Jiguo Yu,et al.  A Differential-Private Framework for Urban Traffic Flows Estimation via Taxi Companies , 2019, IEEE Transactions on Industrial Informatics.

[18]  Sneha A. Dalvi,et al.  Internet of Things for Smart Cities , 2017 .

[19]  Lida Xu,et al.  The internet of things: a survey , 2014, Information Systems Frontiers.

[20]  Ivan Stojmenovic,et al.  The Fog computing paradigm: Scenarios and security issues , 2014, 2014 Federated Conference on Computer Science and Information Systems.

[21]  Rongxing Lu,et al.  Towards power consumption-delay tradeoff by workload allocation in cloud-fog computing , 2015, 2015 IEEE International Conference on Communications (ICC).

[22]  Xu Chen,et al.  When D2D meets cloud: Hybrid mobile task offloadings in fog computing , 2017, 2017 IEEE International Conference on Communications (ICC).

[23]  Takashi Okuda,et al.  Queueing theoretic approach to job assignment strategy considering various inter-arrival of job in fog computing , 2017, 2017 19th Asia-Pacific Network Operations and Management Symposium (APNOMS).

[24]  Tapani Ristaniemi,et al.  Multi-objective optimization for computation offloading in mobile-edge computing , 2017, 2017 IEEE Symposium on Computers and Communications (ISCC).

[25]  Shuzhen Xu,et al.  Resource Scheduling Based on Improved Spectral Clustering Algorithm in Edge Computing , 2018, Sci. Program..

[26]  Longfei Wu,et al.  EFFECT: an efficient flexible privacy-preserving data aggregation scheme with authentication in smart grid , 2019, Science China Information Sciences.

[27]  Tapani Ristaniemi,et al.  Multiobjective Optimization for Computation Offloading in Fog Computing , 2018, IEEE Internet of Things Journal.

[28]  Jiang Zhu,et al.  Fog Computing: A Platform for Internet of Things and Analytics , 2014, Big Data and Internet of Things.