Adaptive League Championship Algorithm (ALCA) for Independent Task Scheduling in Cloud Computing Anup Gade 1* , Mundukur Nirupama Bhat 1 , Nita Thakare 2 1 VFSTR Deemed to be University, Vadalamudi 522213, AP, India 2 Priyadarshani College of Engineering, Nagpur 440019, MS, India Corresponding Author Email: gadeanup@gmail.com https://doi.org/10.18280/isi.240316 ABSTRACT Received: 12 March 2019 Accepted: 28 May 2019 Scheduling is a heart of cloud computing as without appropriate scheduling it is impossible to get the desired results. Primary focus of this article is to focus on minimization of makespan, minimum utilization of resources and make the cloud services economic for an independant task. Out of the various task scheduling strategies, in last few years meta-heuristic algorithms have gained recognition in successful operation of task scheduling algorithms. League Championship based Algorithm (LCA) is fascinated from sports leagues through which best team/task in this case can be find out for scheduling. Task scheduling using Adaptive League Championship Algorithm (ALCA) is employed in this article and thereby it shrinks makespan, cloud utilization and cost. ALCA is implemented with cloudsim simulator using java as a programming tool and scheduling has followed the non-premptive approach. Implementation of ALCA results reducation in makespan by 32.95 %, 20.99 % and 7.29 % against customary Ant Colony Optimization (ACO), Genetic Algorithm (GA) and Global League Championship Algorithm (GBLCA) respectively. ALCA also reduces significantly cloud utilization value and improves economy of scale. ALCA may serve as preferred choice for cloud broker as it proved to be multipurpose in the area of makespan, resource utilization and economy. Keywords: meta-heuristic algorithms, LCA, makespan, cloud utilization, job scheduling, economy of scale, resource utilization 1. INTRODUCTION Cloud computing has evolved as a technological model in which user need not have to own any kind of resources, users will have to pay for only those resources which users will utilize. This paradigm of rented services like rented cab, electricity, aeroplane services, etc. has attracted cloud computing, commercial as well as small users. Cloud computing services are available with huge infrastructure which includes servers, infinite storage capacity, large scale of CPU’s, memory, etc. Whenever it has been stated that it has infinite resources it actually doesn’t mean infinite it has some limitations and from the perspectives of cloud service providers efforts are usually made to minimize resource utilization, particularly in case of peak time. Multi-tenancy, on-demand services and any service-any time are the features which makes cloud computing even more happening [1]. Maintaining these huge amount resources while providing guarantee of services is a tedious task. Due to popularity of cloud services multiple issues need to take care of, issues like resource management, load balancing, task scheduling, energy efficiency, economy and security requires critical attention to satisfy customer demands. One of the most crucial and vital responsibility in cloud computing is supposed to be task scheduling. As task scheduling is NP-hard type of problem for which providing best solution is not possible hence sub-optimal solution is taken into consideration [2]. It is possible to provide sub-optimal solution only within polynomial time in case of NP-hard problem. Task scheduling can be broadly divided into three categories as heuristic algorithms, meta-heuristic algorithms and hybrid [3]. Heuristic algorithms can be static or dynamic whereas meta-heuristic algorithms are broadly classified into nature inspired and swarm intelligence. Recently meta-heuristic algorithms has gained fair popularity few of them are Genetic Algorithm (GA) based on Darwin’s theory of fittest of the survival, Ant Colony Optimization (ACO) giving optimized path to the ants searching for the food, Particle Swarm Optimization (PSO) motivated by communal behaviour of flock, BAT, Lion optimization algorithm, Cuckoo Search algorithm are also used popularly, League Championship Algorithm (LCA) analogous to the sports league played to find out the best/fittest team of the season. In case of task scheduling it gives best task to schedule which has smaller makespan. Makespan can be roughly defined as finishing point (time) of last task in a group which need to be optimized, LCA has gained fair amount of results in terms of minimization of makespan time. LCA is an optimization algorithms based on sports league first proposed by Kashan [4]. Author has tailored it to the optimization of numerical function by proposing some idealized rules. This algorithm is applicable on sports league following the round robin time table. Applicability of LCA on task scheduling in cloud computing is depicted by Abdulhamid [5] but adaptivity in algorithm can make LCA even better as only minimization of makespan will not solve the purpose. This article provide scheduling algorithm which is adaptive in nature and along with the adaptivity it reduces cloud utilization for deriving it to be economic in nature. An outstanding results given by this scheme when applied to the search space, motivates for further research in the vicinity of task scheduling in cloud computing. This article Ingénierie des Systèmes d’Information Vol. 24, No. 3, June, 2019, pp. 353-359 Journal homepage: http://iieta.org/journals/isi 353