Efficient Task Scheduling Technique under Batch Mode Heuristic for Cloud Environment

Job scheduling process under the roof of Cloud is consisting of three phases: Resource Discovery, Resource Selection and Task Scheduling (Yousif, A., et al., 2011. A taxonomy of grid resource selection mechanisms. International Journal of Grid and Distributed Computing, 4(3), pp.107.117). Among them, task scheduling is always treated a cumbersome activity because it mapped task(s) to their assigned resource(s) based on various constraints and impositions according to requirements. Task scheduling in Cloud environment is broadly categorized into two streams that are: Heuristic and Meta-Heuristic (sometimes combination of both). Heuristic approach is further categorized into two streams: Immediate mode or online approach and Batch mode or offline scheduling technique. MaxStd, heuristic mapping, is one of the efficient batch modes scheduling technique for independent task(s) due to its inherent efficiency and performance. In this paper, we have proposed an improved version of MaxStd (I-MaxStd) that refines the mapping process of conventional MaxStd to yields an efficient output in the form of reduced makespan and better resource average utilization rate without compromising its legacy. The validation of proposed work has been done for heterogeneous types of ETC matrices being used as dataset.

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Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

International Journal of Scientific Research in Science Engineering and Technology ◽

10.32628/ijsrset21841125 ◽

2018 ◽

pp. 96-101

Author(s):

Shailendra Raghuvanshi ◽

Priyanka Dubey

Keyword(s):

Load Balancing ◽

Task Scheduling ◽

Waiting Time ◽

Virtual Machines ◽

Scheduling Algorithm ◽

Cloud Environment ◽

Task Scheduling Algorithm ◽

Machine Utilization ◽

Independent Tasks ◽

Optimal Machine

Load balancing of non-preemptive independent tasks on virtual machines (VMs) is an important aspect of task scheduling in clouds. Whenever certain VMs are overloaded and remaining VMs are under loaded with tasks for processing, the load has to be balanced to achieve optimal machine utilization. In this paper, we propose an algorithm named honey bee behavior inspired load balancing, which aims to achieve well balanced load across virtual machines for maximizing the throughput. The proposed algorithm also balances the priorities of tasks on the machines in such a way that the amount of waiting time of the tasks in the queue is minimal. We have compared the proposed algorithm with existing load balancing and scheduling algorithms. The experimental results show that the algorithm is effective when compared with existing algorithms. Our approach illustrates that there is a significant improvement in average execution time and reduction in waiting time of tasks on queue using workflowsim simulator in JAVA.

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Modified Artificial Fish Swarm Algorithm for Efficient Task Scheduling in Cloud Environment

International Journal of Computer Sciences and Engineering ◽

10.26438/ijcse/v7i5.13631371 ◽

2019 ◽

Vol 7 (5) ◽

pp. 1363-1371

Author(s):

H. Krishnaveni ◽

V. Sinthu Janita

Keyword(s):

Task Scheduling ◽

Cloud Environment ◽

Artificial Fish Swarm Algorithm ◽

Artificial Fish Swarm ◽

Swarm Algorithm

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Task Scheduling in cloud using ACO

Recent Advances in Computer Science and Communications ◽

10.2174/2666255813999200831112705 ◽

2020 ◽

Vol 13 ◽

Author(s):

Yuvaraj Natarajan ◽

Srihari Kannan ◽

Gaurav Dhiman

Keyword(s):

Load Balancing ◽

Task Scheduling ◽

Limited Resource ◽

Cloud Environment ◽

Clear Cut ◽

Strategic Approach ◽

User Demand ◽

Scheduling Strategies ◽

Cloud Users ◽

And Task

Background: Cloud computing is a multi-tenant model for computation that offers various features for computing and storage based on user demand. With increasing cloud users, the usage increases that highlights the problem of load balancing with limited resource availability based on dynamic cloud environment. In such cases, task scheduling creates fundamental issue in cloud environment. Introduction: Certain problems such as, inefficiencies in load balancing latency, throughput ratio, proper utilization of the cloud resources, better energy consumption and response time have been observed. These drawbacks can be efficiently resolved through the incorporation of efficient load balancing and task scheduling strategies. Method: In this paper, we develop an efficient co-operative method to solve the most recent approaches against load balancing and task scheduling have been proposed using Ant Colony Optimization (ACO). These approaches enables in the clear cut identification of the problems associated with the load balancing and task scheduling strategies in the cloud environment. Results: The simulation is conducted to find the efficacy of the improved ACO system for load balancing in cloud than the other methods. The result shows that the proposed method obtains reduced execution time, reduced cost and delay. Conclusion: A unique strategic approach is developed in this paper, Load Balancing, which works with the ACO in relation to the cloud workload balancing task through the incorporation of the ACO technique. The strategy for determining the applicant nodes is based on which the load balancing approach would essentially depend. By incorporating two different approaches: the maximum minute rules and the forward-backward ant, this reliability task can be established. This method is intended to articulate the initialization of the pheromone and thus upgrade the relevant cloud-based physical properties.

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