Energy-aware hybrid fruitfly optimization for load balancing in cloud environments for EHR applications

Abstract Cloud computing has gained precise attention from the research community and management of IT, due to its scalable and dynamic capabilities. It is evolving as a vibrant technology to modernize and restructure healthcare organization to provide best services to the consumers. The rising demand for healthcare services and applications in cloud computing leads to the imbalance in resource usage and drastically increases the power consumption resulting in high operating cost. To achieve fast execution time and optimum utilization of the virtual machines, we propose a multi-objective hybrid fruitfly optimization technique based on simulated annealing to improve the convergence rate and optimization accuracy. The proposed approach is used to achieve the optimal resource utilization and reduces the energy consumption and cost in cloud computing environment. The result attained in our proposed technique provides an improved solution. The experimental results show that the proposed algorithm efficiently outperforms compared to the existing load balancing algorithms.

[1]  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..

[2]  Nabil Ahmed Sultan,et al.  Making use of cloud computing for healthcare provision: Opportunities and challenges , 2014, Int. J. Inf. Manag..

[3]  Mahdi Fahmideh,et al.  Cloud migration process - A survey, evaluation framework, and open challenges , 2016, J. Syst. Softw..

[4]  Shengyao Wang,et al.  A novel binary fruit fly optimization algorithm for solving the multidimensional knapsack problem , 2013, Knowl. Based Syst..

[5]  Rajkumar Buyya,et al.  Managing Overloaded Hosts for Dynamic Consolidation of Virtual Machines in Cloud Data Centers under Quality of Service Constraints , 2013, IEEE Transactions on Parallel and Distributed Systems.

[6]  Wen-Tsao Pan,et al.  A new Fruit Fly Optimization Algorithm: Taking the financial distress model as an example , 2012, Knowl. Based Syst..

[7]  Mohammed Abdullahi,et al.  Hybrid Symbiotic Organisms Search Optimization Algorithm for Scheduling of Tasks on Cloud Computing Environment , 2016, PloS one.

[8]  Rajkumar Buyya,et al.  Energy-aware resource allocation heuristics for efficient management of data centers for Cloud computing , 2012, Future Gener. Comput. Syst..

[9]  Yao Zheng,et al.  Scalable and Secure Sharing of Personal Health Records in Cloud Computing Using Attribute-Based Encryption , 2019, IEEE Transactions on Parallel and Distributed Systems.

[10]  Thomas A. Runkler,et al.  Rescheduling and optimization of logistic processes using GA and ACO , 2008, Eng. Appl. Artif. Intell..

[11]  Jian Xie,et al.  Independent Tasks Scheduling Based on Genetic Algorithm in Cloud Computing , 2009, 2009 5th International Conference on Wireless Communications, Networking and Mobile Computing.

[12]  Xiaoying Wang,et al.  An adaptive model-free resource and power management approach for multi-tier cloud environments , 2012, J. Syst. Softw..

[13]  Jordi Guitart,et al.  A service framework for energy-aware monitoring and VM management in Clouds , 2013, Future Gener. Comput. Syst..

[14]  Xin Yang,et al.  Tuning of PID controller based on Fruit Fly Optimization Algorithm , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[15]  P. Venkata Krishna,et al.  Bio-inspired algorithms for cloud computing: a review , 2015 .

[16]  R. Srikant,et al.  Stochastic models of load balancing and scheduling in cloud computing clusters , 2012, 2012 Proceedings IEEE INFOCOM.

[17]  Chun Lu,et al.  An improved GA and a novel PSO-GA-based hybrid algorithm , 2005, Inf. Process. Lett..

[18]  Xiaomin Zhu,et al.  Real-Time Tasks Oriented Energy-Aware Scheduling in Virtualized Clouds , 2014, IEEE Transactions on Cloud Computing.

[19]  Quan-Ke Pan,et al.  Solving the steelmaking casting problem using an effective fruit fly optimisation algorithm , 2014, Knowl. Based Syst..

[20]  Manpreet Singh,et al.  Adaptive and Dynamic Load Balancing in Grid Using Ant Colony Optimization , 2012 .

[21]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

[22]  Subhajyoti Bandyopadhyay,et al.  Cloud computing - The business perspective , 2011, Decis. Support Syst..

[23]  Yan Wang,et al.  An optimization algorithm for service composition based on an improved FOA , 2015 .

[24]  Nitin S. Choubey,et al.  Fruit Fly Optimization Algorithm for Travelling Salesperson Problem , 2014 .

[25]  Su-Mei Lin,et al.  Analysis of service satisfaction in web auction logistics service using a combination of Fruit fly optimization algorithm and general regression neural network , 2011, Neural Computing and Applications.

[26]  Ardeshir Bahreininejad,et al.  Optimizing a location allocation-inventory problem in a two-echelon supply chain network: A modified fruit fly optimization algorithm , 2015, Comput. Ind. Eng..

[27]  Shafii Muhammad Abdulhamid,et al.  Symbiotic Organism Search optimization based task scheduling in cloud computing environment , 2016, Future Gener. Comput. Syst..

[28]  Albert Y. Zomaya,et al.  Energy efficient utilization of resources in cloud computing systems , 2010, The Journal of Supercomputing.

[29]  Kousik Dasgupta,et al.  Load Balancing in Cloud Computing using Stochastic Hill Climbing-A Soft Computing Approach , 2012 .

[30]  Albert Y. Zomaya,et al.  Author manuscript, published in "Journal of Parallel and Distributed Computing (2011)" A Parallel Bi-objective Hybrid Metaheuristic for Energy-aware Scheduling for Cloud Computing Systems , 2011 .

[31]  M. Ajit,et al.  VM level load balancing in cloud environment , 2013, 2013 Fourth International Conference on Computing, Communications and Networking Technologies (ICCCNT).

[32]  Sen Guo,et al.  A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm , 2013, Knowl. Based Syst..

[33]  Matej Crepinsek,et al.  A note on teaching-learning-based optimization algorithm , 2012, Inf. Sci..

[34]  Farookh Khadeer Hussain,et al.  Task-Based System Load Balancing in Cloud Computing Using Particle Swarm Optimization , 2013, International Journal of Parallel Programming.

[35]  Gaochao Xu,et al.  A Load Balancing Model Based on Cloud Partitioning for the Public Cloud , 2013 .

[36]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[37]  Peng Zhang,et al.  Grouped Fruit-Fly Optimization Algorithm for the No-Wait Lot Streaming Flow Shop Scheduling , 2014, ICIC.

[38]  A. Paulin Florence,et al.  A Load Balancing Model using Firefly Algorithm in Cloud Computing , 2014, J. Comput. Sci..

[39]  Balamurugan Balusamy,et al.  Energy-Aware Fruitfly Optimisation Algorithm for Load balancing in Cloud Computing Environments , 2017 .

[40]  Rajkumar Buyya,et al.  Energy Efficient Resource Management in Virtualized Cloud Data Centers , 2010, 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing.

[41]  Inderveer Chana,et al.  Energy aware scheduling of deadline-constrained tasks in cloud computing , 2016, Cluster Computing.

[42]  Saudi Arabia,et al.  A Guide to Dynamic Load Balancing in Distributed Computer Systems , 2010 .

[43]  Mohsen Moradi,et al.  A new time optimizing probabilistic load balancing algorithm in grid computing , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[44]  L. D. Dhinesh Babu,et al.  Honey bee behavior inspired load balancing of tasks in cloud computing environments , 2013, Appl. Soft Comput..