An Efficient Architecture and Algorithm for Resource Provisioning in Fog Computing

Cloud computing is a model of sharing computing resources over any communication network by using virtualization. Virtualization allows a server to be sliced in virtual machines. Each virtual machine has its own operating system/applications that rapidly adjust resource allocation. Cloud computing offers many benefits, one of them is elastic resource allocation. To fulfill the requirements of clients, cloud environment should be flexible in nature and can be achieve by efficient resource allocation. Resource allocation is the process of assigning available resources to clients over the internet and plays vital role in Infrastructure-as-aService (IaaS) model of cloud computing. Elastic resource allocation is required to optimize the allocation of resources, minimizing the response time and maximizing the throughput to improve the performance of cloud computing. Sufficient solutions have been proposed for cloud computing to improve the performance but for fog computing still efficient solution have to be found. Fog computing is the virtualized intermediate layer between clients and cloud. It is a highly virtualized technology which is similar to cloud and provide data, computation, storage, and networking services between end users and cloud servers. This paper presents an efficient architecture and algorithm for resources provisioning in fog computing environment by using virtualization technique.

[1]  Arunima Jaiswal,et al.  Virtualization in Cloud Computing , 2014 .

[2]  Zhen Xiao,et al.  Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment , 2013, IEEE Transactions on Parallel and Distributed Systems.

[3]  R. B. Wagh,et al.  Priority based dynamic resource allocation in Cloud computing with modified waiting queue , 2013, 2013 International Conference on Intelligent Systems and Signal Processing (ISSP).

[4]  Maninder Singh,et al.  A Task Scheduling and Resource Allocation Algorithm for Cloud using Live Migration and Priorities , 2013 .

[5]  Ritesh Patel,et al.  Cloud Analyst: An Insight of Service Broker Policy , 2015 .

[6]  Lakshmi Kurup,et al.  Optimization of FCFS Based Resource Provisioning Algorithm for Cloud Computing , 2013 .

[7]  N. R. R. Mohan,et al.  Resource Allocation Techniques in Cloud Computing -- Research Challenges for Applications , 2012, 2012 Fourth International Conference on Computational Intelligence and Communication Networks.

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

[9]  Rahul Malhotra,et al.  Study and Comparison of Various Cloud Simulators Available in the Cloud Computing , 2013 .

[10]  Judith Kelner,et al.  Resource allocation for distributed cloud: concepts and research challenges , 2011, IEEE Network.

[11]  Chen Liu,et al.  Study on cloud resource allocation strategy based on particle swarm ant colony optimization algorithm , 2012, 2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems.

[12]  S. Sukumaran,et al.  Different scheduling algorithms in various cloud environment , 2016 .

[13]  Wentong Cai,et al.  Dynamic Bin Packing for On-Demand Cloud Resource Allocation , 2016, IEEE Transactions on Parallel and Distributed Systems.

[14]  Vivek Thapar,et al.  An Efficient Service Broker Policy for Cloud Computing Environment , 2014 .

[15]  Hussein M. Alnuweiri,et al.  Resource allocation and scheduling in cloud computing , 2012, 2012 International Conference on Computing, Networking and Communications (ICNC).

[16]  Rajkumar Buyya,et al.  Modeling and simulation of scalable Cloud computing environments and the CloudSim toolkit: Challenges and opportunities , 2009, 2009 International Conference on High Performance Computing & Simulation.

[17]  Shashank Yadav,et al.  An architecture for elastic resource allocation in Fog Computing , 2015 .

[18]  Fei Zhang,et al.  A resource scheduling algorithm of cloud computing based on energy efficient optimization methods , 2012, 2012 International Green Computing Conference (IGCC).