Dynamic Tree Growth Algorithm for Load Scheduling in Cloud Environments

The cloud computing is an emerging paradigm that enables dynamic provision of elastic, scalable and distributed computer resources to the end-users. One of the most important tasks of the cloud service provider is to deliver services to the end-users in an efficient manner from a finite pool of available physical and virtual resources. The efficiency in terms of both, cost-efficiency and computational-efficiency, can be accomplished through the load scheduling that has significant impact on the overall cloud system performance and represents one of the most important challenges in this domain. In this paper, we introduce two implementations of the original and improved versions of the tree growth algorithm for load scheduling in cloud computing environments. Tree growth algorithm is classified as swarm intelligence metaheuristic, that are able to successfully tackle NP hard problems such as cloud load scheduling. Both algorithms are implemented in the CloudSim environment and comparative analysis with other techniques and metaheuristics for this problem was performed. According to the obtained results, the improved version of the tree growth algorithm outperformed all other techniques and can be successfully applied to load scheduling in cloud systems.

[1]  Milan Tuba,et al.  Mobile Robot Path Planning by Improved Brain Storm Optimization Algorithm , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[2]  Marko Beko,et al.  Modified Monarch Butterfly Optimization Algorithm for RFID Network Planning , 2018, 2018 6th International Conference on Multimedia Computing and Systems (ICMCS).

[3]  Marko Beko,et al.  Bare Bones Fireworks Algorithm for the RFID Network Planning Problem , 2018, 2018 IEEE Congress on Evolutionary Computation (CEC).

[4]  Mohammad Mahdi Paydar,et al.  Tree Growth Algorithm (TGA): A novel approach for solving optimization problems , 2018, Eng. Appl. Artif. Intell..

[5]  Sarbjeet Singh,et al.  A review of metaheuristic scheduling techniques in cloud computing , 2015 .

[6]  Milan Tuba,et al.  RFID Network Planning by ABC Algorithm Hybridized with Heuristic for Initial Number and Locations of Readers , 2015, 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim).

[7]  Milan Tuba,et al.  Enhanced firefly algorithm for constrained numerical optimization , 2017, 2017 IEEE Congress on Evolutionary Computation (CEC).

[8]  Milan Tuba,et al.  Hybridized bat algorithm for multi-objective radio frequency identification (RFID) network planning , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[9]  Bibhudatta Sahoo,et al.  Load balancing in cloud computing: A big picture , 2018, J. King Saud Univ. Comput. Inf. Sci..

[10]  C. Rama Krishna,et al.  Critical Path-Based Ant Colony Optimization for Scientific Workflow Scheduling in Cloud Computing Under Deadline Constraint , 2018 .

[11]  Milan Tuba,et al.  Hybridized Elephant Herding Optimization Algorithm for Constrained Optimization , 2017, HIS.

[12]  M. Tuba,et al.  Static drone placement by elephant herding optimization algorithm , 2017, 2017 25th Telecommunication Forum (TELFOR).

[13]  Kalka Dubey,et al.  Elastic and flexible deadline constraint load Balancing algorithm for Cloud Computing , 2018 .

[14]  Milan Tuba,et al.  Multilevel image thresholding by fireworks algorithm , 2015, 2015 25th International Conference Radioelektronika (RADIOELEKTRONIKA).

[15]  Milan Tuba,et al.  Cooperative clustering algorithm based on brain storm optimization and K-means , 2018, 2018 28th International Conference Radioelektronika (RADIOELEKTRONIKA).

[16]  Milan Tuba,et al.  Adjusted Fireworks Algorithm Applied to Retinal Image Registration , 2017 .

[17]  Xin-She Yang,et al.  Swarm intelligence based algorithms: a critical analysis , 2013, Evolutionary Intelligence.

[18]  Milan Tuba,et al.  Artificial Bee Colony (ABC) Algorithm for Constrained Optimization Improved with Genetic Operators , 2012 .

[19]  Bhabani Shankar Prasad Mishra,et al.  Workflow Scheduling in Cloud Computing Environment Using Bat Algorithm , 2018 .

[20]  Milan Tuba,et al.  Firefly Algorithm for Cardinality Constrained Mean-Variance Portfolio Optimization Problem with Entropy Diversity Constraint , 2014, TheScientificWorldJournal.

[21]  Mohit Kumar,et al.  PSO-COGENT: Cost and energy efficient scheduling in cloud environment with deadline constraint , 2018, Sustain. Comput. Informatics Syst..

[22]  Marko Beko,et al.  Hybridized moth search algorithm for constrained optimization problems , 2018, 2018 International Young Engineers Forum (YEF-ECE).

[23]  Tingting Wang,et al.  Load Balancing Task Scheduling Based on Genetic Algorithm in Cloud Computing , 2014, 2014 IEEE 12th International Conference on Dependable, Autonomic and Secure Computing.

[24]  Zong Woo Geem,et al.  Foreword: New theoretical insights and practical applications of bio-inspired computation approaches , 2019, Swarm Evol. Comput..

[25]  Divya Chaudhary,et al.  Cloudy GSA for load scheduling in cloud computing , 2018, Appl. Soft Comput..

[26]  Rajkumar Buyya,et al.  Cloudbus Toolkit for Market-Oriented Cloud Computing , 2009, CloudCom.

[27]  Milan Tuba,et al.  Ant colony optimization algorithm with pheromone correction strategy for the minimum connected dominating set problem , 2013, Comput. Sci. Inf. Syst..

[28]  Milan Tuba,et al.  Improved seeker optimization algorithm hybridized with firefly algorithm for constrained optimization problems , 2014, Neurocomputing.

[29]  Milan Tuba,et al.  Unmanned Combat Aerial Vehicle Path Planning by Brain Storm Optimization Algorithm , 2018 .

[30]  Jun Zhang,et al.  Load Balance Aware Genetic Algorithm for Task Scheduling in Cloud Computing , 2014, SEAL.