Reliable scheduling and load balancing for requests in cloud-fog computing

Fog computing broadens the computing services to serve requests of Internet of Things (IoT) by resources at the edge of Cloud-Fog environments instead of serving these requests by resources at the environment’s core. The aim of fog computing is to reduce load of computing in data centers and reduce latency of requests, especially real-time ones. Load balancing and scheduling play essential roles and represent main key challenges to guarantee high throughput and reliability of services in Cloud-Fog environments. Therefore, this paper introduces a reliable scheduling approach for allocating customers’ requests to the resources of Cloud-Fog environments. The approach is called Load Balanced Service Scheduling Approach (LBSSA) and it considers load balancing among resources when assigning requests to them by classifying requests to real-time, important and time-tolerant. In addition, scheduling of requests in the proposed approach considers the failure rate of resources in order to provide high reliability for requested services. The approach has a set of algorithms for handling different types of requests. Simulation experiments using CloudSim are conducted to assess the LBSSA approach in terms of number of computing resources, utilization of resources, load balance variance and running time.

[1]  Mohammed Amoon,et al.  A Fault Tolerant Scheduling System Based on Checkpointing for Computational Grids , 2012 .

[2]  Thar Baker,et al.  The Security of Big Data in Fog-Enabled IoT Applications Including Blockchain: A Survey , 2019, Sensors.

[3]  Roberto Beraldi,et al.  Distributed load balancing for heterogeneous fog computing infrastructures in smart cities , 2020, Pervasive Mob. Comput..

[4]  Juan Luo,et al.  Tasks Scheduling and Resource Allocation in Fog Computing Based on Containers for Smart Manufacturing , 2018, IEEE Transactions on Industrial Informatics.

[5]  Melody Moh,et al.  Prioritized task scheduling in fog computing , 2018, ACM Southeast Regional Conference.

[6]  Heiko Ludwig,et al.  Zenith: Utility-Aware Resource Allocation for Edge Computing , 2017, 2017 IEEE International Conference on Edge Computing (EDGE).

[7]  Ramani Kannan,et al.  Resource scheduling algorithm with load balancing for cloud service provisioning , 2019, Appl. Soft Comput..

[8]  Chungang Yan,et al.  Resource Allocation Strategy in Fog Computing Based on Priced Timed Petri Nets , 2017, IEEE Internet of Things Journal.

[9]  Xin Fan,et al.  Container-based fog computing architecture and energy-balancing scheduling algorithm for energy IoT , 2019, Future Gener. Comput. Syst..

[10]  M. M. Sufyan Beg,et al.  Fog Computing for Internet of Things (IoT)-Aided Smart Grid Architectures , 2019, Big Data Cogn. Comput..

[11]  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).

[12]  Xingming Sun,et al.  Dynamic Resource Allocation for Load Balancing in Fog Environment , 2018, Wirel. Commun. Mob. Comput..

[13]  Nima Jafari Navimipour,et al.  Nature inspired meta-heuristic algorithms for solving the load-balancing problem in cloud environments , 2019, Comput. Oper. Res..

[14]  Rajkumar Buyya,et al.  CloudSim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms , 2011, Softw. Pract. Exp..

[15]  Mohammad Patwary,et al.  Cognitive Edge Computing based resource allocation framework for Internet of Things , 2017, 2017 Second International Conference on Fog and Mobile Edge Computing (FMEC).

[16]  Hesham A. Ali,et al.  A load balancing and optimization strategy (LBOS) using reinforcement learning in fog computing environment , 2020, Journal of Ambient Intelligence and Humanized Computing.

[17]  Hemraj Saini,et al.  A novel four-tier architecture for delay aware scheduling and load balancing in fog environment , 2019, Sustain. Comput. Informatics Syst..

[18]  Abdulaziz Alarifi,et al.  A fault-tolerant aware scheduling method for fog-cloud environments , 2019, PloS one.

[19]  Nadeem Javaid,et al.  Efficient Resource Provisioning for Smart Buildings Utilizing Fog and Cloud Based Environment , 2018, 2018 14th International Wireless Communications & Mobile Computing Conference (IWCMC).

[20]  Hesham A. Ali,et al.  Effective Load Balancing Strategy (ELBS) for Real-Time Fog Computing Environment Using Fuzzy and Probabilistic Neural Networks , 2019, Journal of Network and Systems Management.

[21]  Ishfaq Ahmad,et al.  Energy- and performance-aware load-balancing in vehicular fog computing , 2021, Sustain. Comput. Informatics Syst..

[22]  Jaafar M. H. Elmirghani,et al.  Energy Efficient Service Distribution in Internet of Things , 2018, 2018 20th International Conference on Transparent Optical Networks (ICTON).

[23]  Taha Landolsi,et al.  Scheduling Internet of Things requests to minimize latency in hybrid Fog-Cloud​ computing , 2020, Future Gener. Comput. Syst..

[24]  Shideh Saraeian,et al.  A hybrid of firefly and improved particle swarm optimization algorithms for load balancing in cloud environments: Performance evaluation , 2019, Comput. Networks.

[25]  Mainak Adhikari,et al.  Meta heuristic-based task deployment mechanism for load balancing in IaaS cloud , 2019, J. Netw. Comput. Appl..

[26]  Jason P. Jue,et al.  All One Needs to Know about Fog Computing and Related Edge Computing Paradigms , 2019 .

[27]  Jiafu Wan,et al.  Fog Computing for Energy-Aware Load Balancing and Scheduling in Smart Factory , 2018, IEEE Transactions on Industrial Informatics.

[28]  Yan Lindsay Sun,et al.  Multi-objective Optimization of Resource Scheduling in Fog Computing Using an Improved NSGA-II , 2018, Wirel. Pers. Commun..

[29]  Mohsen Nickray,et al.  Fault-tolerant with load balancing scheduling in a fog-based IoT application , 2020, IET Commun..