A new flower pollination based task scheduling algorithm in cloud environment

Kaur, Jaspinder; Sidhu, Brahmaleen Kaur

doi:10.1109/ispcc.2017.8269722

Cited by 8 publications

(2 citation statements)

References 5 publications

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“…Furthermore, it addresses the limitations of previous works such as the HABCCS algorithm, the GTS task algorithm, and the krill herd algorithm in terms of high execution time, throughput, and delay, which can result in a significant degradation in overall system performance. Kaur and Sidhu [17] have developed TSFPA, an independent Task Scheduling method based on the Flower Pollination Algorithm. The authors' goal is to solve the above problem by assigning tasks to the virtual instance types in a heterogeneous cloud environment so that makespan could be minimized.…”

Section: Related Workmentioning

confidence: 99%

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

Malti

Benmammar

Hakem

2022

Modelling and Implementation of Complex Systems

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Cloud computing is a standard way of hosting software applications and services that is booming day by day thanks to the different utilities offered to users according to their needs and contracts. Most of these services are in the form of tasks and their execution in such environments requires efficient scheduling strategies that take into account both algorithmic and architectural features. The objective is to orchestrate the suitable assignment of the submitted tasks to the available resources on the basis of various functional requirements of end users. To overcome the scheduling issue, which is an NP-hard problem, various metaheuristic algorithms are used in literature to achieve near optimal solution. For this purpose, this paper aims to perform a comparative investigation of three common metaheuristic algorithms in the optimization process such as Shuffled Frog Leaping Algorithm (SFLA), Flower Pollination Algorithm (FPA) and Gray Wolf Optimization (GWO). Both standard and synthetic workloads are employed to analyze the performance of these algorithms by evaluating its objective function in term of two metrics which are makespan and resource utilization rate. The simulation results obtained using the CloudSim framework are very satisfactory and clearly show the value of our study.

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Section: Related Workmentioning

confidence: 99%

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

Malti

Benmammar

Hakem

2022

Modelling and Implementation of Complex Systems

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“…d) A transition rule of the heuristic desirability and of pheromone trail. [12]: For scheduling tasks in cloud environment here uses nature-inspired algorithm called Flower pollination Algorithm. This task scheduling approach is proposed in order to map tasks and resources in best optimized way, thus minimizing make-span as a consequence.…”

Section: Aco Can Be Applied To Any Problem If It Definesmentioning

confidence: 99%

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

Venu¹

2021

IJRASET

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The on-demand availability of computer system resources such as data storage and computing power is cloud computing. Scheduling is the method of allocating jobs onto resources in time. Scheduling increases the efficiency and performance of cloud environment by maximizing the resource utilization. This scheduling process has to respect constraints given by the jobs and the cloud providers. Ordering the tasks by scheduler along with maintaining the balance between Quality of Service (QoS), fairness and efficiency of jobs is difficult. Scheduling algorithms are designed and implemented considering some parameters like latency, cost, priority, etc. This paper proposes a method for task scheduling in cloud using a three-stage method. The first stage makes use of historical scheduling data to classify tasks. Based on this VMs are created. In the second stage newly arrived tasks are considered. Based on Bayes classifier principle a matching degree is calculated to mark a task with a VM Type. In the third stage, tasks are matched with corresponding VMs. Then the tasks are scheduled using a scheduling algorithm and are executed in the host. Keywords: Task scheduling, Cloud computing. I. INTRODUCTION Cloud is a trendy term for a network or remote servers that can be accessed via an Internet connection store and manage information. Two types of models present such as deployment models and service models. The deployment model includes private, public and hybrid cloud. Private Clouds are Data center architecture owned by a single company. E.g., IBM's BlueCloud, SunCloud, WindowAzure. Public Clouds are basically the Internet. To make resource available to general people, service provider use internet. E.g., Gmail, Office 365, Dropbox. In case of hybrid clouds, for instance during peak periods individual applications or portion of applications can be migrate to the public cloud. The service model includes Software as a Service (SaaS) such as Dropbox, Google Apps, Platform as a Service (PaaS) such as Google Compute Engine, Apache Stratos and Infrastructure as a Service (IaaS) such as Virtual Machine, Storage, Servers. Cloud provides on demand computational resources in the form of virtual machines. These Virtual Machines are deployed in a cloud provider's data center. The computational resources are shared among different cloud consumers who pay for the service accessed as per the usage. Allocation of resources and proper scheduling has a considerable impact on the performance and efficiency of the system. The main goal of cloud computing is to provide efficient access to remote and geographically distributed resources. An efficient scheduling is a key to manage the access to different resources, load balancing as well as resource allocation. Different types of resource scheduling algorithms are available in cloud computing based on certain parameters like time, cost, and performance, utilization of resources, throughput, bandwidth, resource availability, physical distances and priority. A. Scheduling In Cloud Computing Scheduli...

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A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing

Kumar

Bhagwan

2021

Artificial Intelligence and Sustainable Computing

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A new flower pollination based task scheduling algorithm in cloud environment

Cited by 8 publications

References 5 publications

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

A Comparative Study of Metaheuristics Based Task Scheduling in Cloud Computing

Task Scheduling in Cloud Based on Three Stage Method using Historical Data

A Comparative Study of Meta-Heuristic-Based Task Scheduling in Cloud Computing

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