job scheduling in apollo cloud computing

A detailed survey of the current areas of LCA application is presented by Abdulhamid et al. To form a synthetic championship setting, the author states some idealized rules to follow, then introduces the promising computational intelligence algorithm that is modeled based on a number of fascinating results relative to sports championship round robin timetable. League Championship Algorithm (LCA) is a population based metaheuristic optimization algorithm that is first proposed by Kashan. Cloud scheduling is meant to solve the problem of task scheduling within diverse environments.

The SA cannot control the distributed systems components directly and are therefore related to broker or agents. These results are generated by scheduling algorithms (SAs), more often than not as an ingredient of a scheduler.

ƝƤ-complete problems are often addressed by using heuristic or meta-heuristic methods and approximation algorithms. The most notable characteristic of this type of problem is that no fast solution to them is known and also no exact solution is known. Task scheduling in IaaS Cloud is the ƝƤ-complete problem. This situation is nonetheless ƝƤ-complete and consequently sub-optimal results are search as an alternative. When dealing with huge amount of tasks that compete to acquire resources in order to get executed, it becomes clear that an optimal mapping between tasks and resources is considered necessary. Consecutively, to properly manage and supervise such complex infrastructures efficient and secures scientific application scheduling is required. The capacity of Cloud application and infrastructure services is continuously rising, and hence worse the intricacy of the infrastructures behind the cloud services. In recent times, Cloud Computing paradigm is generally employed to convey cloud services over the cyberspace for both scientific and cost-effective resource usage. The makespan time is directly responsible for the tasks execution cost in this environment. Nevertheless, the autonomous attribute and the resource heterogeneity within the Clouds and the VM execution necessitate different schemes for task scheduling in the IaaS Cloud computing to be used and tested in order to minimize the makespan time. Tasks scheduling optimization had been an area of research in IaaS Cloud because it is an NP-hard problem. The Cloud Computing provides computational resources such as the Virtual Machines (VM) to Cloud users on-demand basis.

In view of the experimental results, the proposed technique provides better-quality scheduling solution that is suitable for scientific applications task execution in the Cloud Computing environment than the MinMin, MaxMin, Genetic Algorithm (GA) and Ant Colony Optimization (ACO) scheduling techniques. It also shows significant reduction in the time taken to securely schedule applications as parametrically measured in terms of the response time. The experimental results show that, the proposed GBLCA technique produced remarkable performance improvement rate on the makespan that ranges between 14.44% to 46.41%. The experiment is carried out using CloudSim simulator. In this paper, scientific applications scheduling techniques using the Global League Championship Algorithm (GBLCA) optimization technique is first presented for global task scheduling in the cloud environment. Recently, a number of metaheuristics optimization schemes have been applied to address the challenges of applications scheduling in the cloud system, without much emphasis on the issue of secure global scheduling. Scientific applications scheduling in the cloud computing environment is identified as NP-hard problem due to the dynamic nature of heterogeneous resources. Cloud computing system is a huge cluster of interconnected servers residing in a datacenter and dynamically provisioned to clients on-demand via a front-end interface.

Job scheduling in apollo cloud computing