Algorithm For Task Scheduling In Cloud Research Articles

DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

DRLBTSA: Deep reinforcement learning based task-scheduling algorithm in cloud computing

Classification and Performance Study of Task Scheduling Algorithms in Cloud Computing Environment

Cloud computing is becoming very common in recent years and is growing rapidly due to its attractive benefits and features such as resource pooling, accessibility, availability, scalability, reliability, cost saving, security, flexibility, on-demand services, pay-per-use services, use from anywhere, quality of service, resilience, etc. With this rapid growth of cloud computing, there may exist too many users that require services or need to execute their tasks simultaneously by resources provided by service providers. To get these services with the best performance, and minimum cost, response time, makespan, effective use of resources, etc. an intelligent and efficient task scheduling technique is required and considered as one of the main and essential issues in the cloud computing environment. It is necessary for allocating tasks to the proper cloud resources and optimizing the overall system performance. To this end, researchers put huge efforts to develop several classes of scheduling algorithms to be suitable for the various computing environments and to satisfy the needs of the various types of individuals and organizations. This research article provides a classification of proposed scheduling strategies and developed algorithms in cloud computing environment along with the evaluation of their performance. A comparison of the performance of these algorithms with existing ones is also given. Additionally, the future research work in the reviewed articles (if available) is also pointed out. This research work includes a review of 88 task scheduling algorithms in cloud computing environment distributed over the seven scheduling classes suggested in this study. Each article deals with a novel scheduling technique and the performance improvement it introduces compared with previously existing task scheduling algorithms. Keywords: Cloud computing, Task scheduling, Load balancing, Makespan, Energy-aware, Turnaround time, Response time, Cost of task, QoS, Multi-objective. DOI: 10.7176/IKM/12-5-03 Publication date: September 30 th 2022

Overview of task scheduling algorithms in cloud computing

Overview of task scheduling algorithms in cloud computing

A Survey on Heuristic Approach for Task Scheduling in Cloud Computing

In cloud computing, data or resources are provided to the user by paying for the share of resources used by them. For efficient allocation of resources to the appropriate user, an efficient task scheduling algorithm is required, which can achieve minimum response time, high throughput and proper resource utilization, etc. Heuristic based algorithm is one such way to achieve the optimal or near optimal solution of task scheduling in the cloud environment. Heuristic methods are the subset of meta-heuristic approach. In this paper, various type of task scheduling algorithms in cloud computing have been surveyed. Keywords: Cloud computing, heuristic algorithm, optimization, task scheduling.

Intuitionistic Fuzzy Set Based Task Scheduling with QoS Preference Awareness

Existing task scheduling algorithms in cloud computing lack the ability to be aware of users' quality of service(QoS) preference. In order to address this problem, intuitionistic fuzzy analysis is introduced to determine users' QoS preference. In addition, the optimal sequence decision helps experts use their professional knowledge to decide the weights of QoS classes. Using these methods, we propose the Intuitionistic Fuzzy Set Based Task Scheduling with QoS Preference Awareness: IFS-QoS PA algorithm. By considering both user and expert experience, the method can determine users' QoS preference and reflect the characteristic of cloud storage system. The simulation results show that this method offers acceptable user satisfaction rate, has low computation complexity and more suitable for large scale task scheduling as compare to particle swarm optimization based ones.

QoS Driven Task Scheduling in Cloud Computing

Cloud computing systems promise to offer pay per use, on demand computing services to users worldwide. Recently, there has been a dramatic increase in the demand for delivering services to a large number of users, so they need to offer differentiated services to users and meet their expected quality requirements. Most of scheduling schemes proceeding nowadays have no QoS (Quality of Service) differentiation, which is necessary for Cloud Computing service operation. As a cloud must provide services to many users at the same time and different users have different QoS requirements, the scheduling schemes should be developed having different QoS requirements. So, this paper explores various methods of task scheduling done in cloud computing. Real-time applications play a significant role in cloud environment. We have examine the particular scheduling algorithms for real-time tasks, that is, priority-based strategies.The purpose of this paper is to discuss the fixed priority preemptive task scheduling algorithms in cloud computing for improving the QoS parameters.