META-HEURISTICS TECHNIQUES IN CLOUD COMPUTING: APPLICATIONS AND CHALLENGES

Cloud computing has emerged as highly demanding technology in recent years. The transformation of data and services towards cloud has reduced the increased expenditure on hardware and software in the market. The integration of cloud with many technologies like mobile, Internet of Things, etc. has brought new challenges in cloud computing. Researchers have applied various swarm intelligence, nature inspired, and hybrid algorithms to find a pare-to optimal solution for them. State of art optimization algorithms that are applied in solving these problems is presented in this paper. The applications of these algorithms in load balancing, scheduling, resource allocation, virtual machine allocation, and placement have been discussed and analyzed in cloud computing. The impact of these algorithms on quality of service is also analyzed to present some valuable suggestion.

[1]  T. Prem Jacob,et al.  A Hybrid Approach for Task Scheduling Using the Cuckoo and Harmony Search in Cloud Computing Environment , 2018, Wireless Personal Communications.

[2]  B. Saravana Balaji,et al.  Epsilon-fuzzy dominance sort-based composite discrete artificial bee colony optimisation for multi-objective cloud task scheduling problem , 2017, Int. J. Bus. Intell. Data Min..

[3]  Saeed Sharifian,et al.  A water cycle optimized wavelet neural network algorithm for demand prediction in cloud computing , 2019, Cluster Computing.

[4]  Anita Singhrova PRIORITIZED GA-PSO ALGORITHM FOR EFFICIENT RESOURCE ALLOCATION IN FOG COMPUTING , 2020, Indian Journal of Computer Science and Engineering.

[5]  Lin Li,et al.  Task scheduling in cloud computing based on hybrid moth search algorithm and differential evolution , 2019, Knowl. Based Syst..

[6]  Mohamed Elhoseny,et al.  Energy consumption analysis of Virtual Machine migration in cloud using hybrid swarm optimization (ABC–BA) , 2018, The Journal of Supercomputing.

[7]  Shafii Muhammad Abdulhamid,et al.  Hybrid gradient descent cuckoo search (HGDCS) algorithm for resource scheduling in IaaS cloud computing environment , 2018, Cluster Computing.

[8]  Hefeng Chen,et al.  Task scheduling in cloud computing using particle swarm optimization with time varying inertia weight strategies , 2019, Cluster Computing.

[9]  C. Arun,et al.  A New Multi-Objective Optimal Programming Model for Task Scheduling using Genetic Gray Wolf Optimization in Cloud Computing , 2018, Comput. J..

[10]  Songfeng Lu,et al.  Improved multiobjective salp swarm optimization for virtual machine placement in cloud computing , 2019, Human-centric Computing and Information Sciences.

[11]  Jun Zhang,et al.  An Energy Efficient Ant Colony System for Virtual Machine Placement in Cloud Computing , 2018, IEEE Transactions on Evolutionary Computation.

[12]  Lesley Fitzer Recent Trends in Data Science and Soft Computing , 2019, Advances in Intelligent Systems and Computing.

[13]  Archana N. Jethava,et al.  Optimizing Multi Objective Based Dynamic Workflow Using ACO and Black Hole Algorithm in Cloud Computing , 2019, 2019 3rd International Conference on Computing Methodologies and Communication (ICCMC).

[14]  Minoo Soltanshahi,et al.  Energy-aware virtual machines allocation by krill herd algorithm in cloud data centers , 2019, Heliyon.

[15]  Ugo Fiore,et al.  Optimal fitness aware cloud service composition using modified invasive weed optimization , 2019, Swarm Evol. Comput..

[16]  Omer K. Jasim Mohammad,et al.  GALO:A New Intelligent Task Scheduling Algorithm in Cloud Computing Environment , 2018, International Journal of Engineering & Technology.

[17]  D. Kesavaraja,et al.  QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization , 2017, J. Parallel Distributed Comput..

[18]  Ahmed Aliyu,et al.  Energy-efficient Virtual Machine Allocation Technique Using Flower Pollination Algorithm in Cloud Datacenter: A Panacea to Green Computing , 2019, Journal of Bionic Engineering.

[19]  Syed Hamid Hussain Madni,et al.  Multi-objective-Oriented Cuckoo Search Optimization-Based Resource Scheduling Algorithm for Clouds , 2018, Arabian Journal for Science and Engineering.

[20]  Mohammed F. AlRahmawy,et al.  An extended Intelligent Water Drops algorithm for workflow scheduling in cloud computing environment , 2017 .

[21]  Zalmiyah Zakaria,et al.  Orthogonal Taguchi-based cat algorithm for solving task scheduling problem in cloud computing , 2016, Neural Computing and Applications.

[22]  Radha Senthilkumar,et al.  Optimal Scheduling of Tasks in Cloud Computing Using Hybrid Firefly-Genetic Algorithm , 2019, Learning and Analytics in Intelligent Systems.

[23]  Gobalakrishnan Natesan,et al.  Optimal Task Scheduling in the Cloud Environment using a Mean Grey Wolf Optimization Algorithm , 2019, International Journal of Technology.

[24]  Lin Li,et al.  Multi-objective Energy Efficient Resource Allocation Using Virus Colony Search (VCS) Algorithm , 2018, 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS).

[25]  Ahmad M. Manasrah,et al.  Workflow Scheduling Using Hybrid GA-PSO Algorithm in Cloud Computing , 2018, Wirel. Commun. Mob. Comput..

[26]  Sanjay Kadam,et al.  A novel multi-objective bacteria foraging optimization algorithm (MOBFOA) for multi-objective scheduling , 2018, Appl. Soft Comput..

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

[28]  Divya Chaudhary,et al.  A New Balanced Particle Swarm Optimisation for Load Scheduling in Cloud Computing , 2018, J. Inf. Knowl. Manag..

[29]  Karnam Sreenu,et al.  W-Scheduler: whale optimization for task scheduling in cloud computing , 2017, Cluster Computing.

[30]  Arun Kumar Sangaiah,et al.  An improved Lévy based whale optimization algorithm for bandwidth-efficient virtual machine placement in cloud computing environment , 2018, Cluster Computing.

[31]  Himansu Das,et al.  Nature Inspired Optimizations in Cloud Computing: Applications and Challenges , 2018 .

[32]  Gang Li,et al.  Ant Colony Optimization Task Scheduling Algorithm for SWIM Based on Load Balancing , 2019, Future Internet.