scholarly journals Hybrid RSO Algorithm with SFLA for Scientific Workflow Scheduling in Cloud Using Clustering Techniques

2021 ◽  
Author(s):  
KARPAGAM M
Keyword(s):  
Cloud Computing ◽  
Information Exchange ◽  
Virtual Machines ◽  
Optimal Schedule ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Optimization Heuristics ◽  
Shuffled Frog Leaping

Abstract An inevitable part of the cloud computing environment is virtualization, as it can multiplex or combine many virtual machines in a single physical machine, and simultaneously an isolated environment is provided to every virtual machine. An important issue in cloud computing is workflow scheduling, which maps tasks of workflow to VMs based on various functional and non-functional requisites. Workflow scheduling is an NP-hard optimization problem and it is quite hard to achieve an optimal schedule. Metaheuristic algorithms helped in solving the problem of cloud task scheduling and this was compared to other heuristics. Reactive Search (RSO) and its structure will consist of a local heuristic based on a certain neighborhood complemented by making use of a memory-based mechanism. The Shuffled Frog Leaping Algorithm (SFLA) is based on swarm evolution that imitates information exchange divided into memeplexes when searching for food. This paper proposes a new set of optimization heuristics along with hybrid optimizations (RSO - SFLA) to solve problems in combinatorial optimization.

SWSA: A Hybrid Scientific Workflow Scheduling Algorithm based on Metaheuristic Approach in Cloud Computing Environment

2021 ◽  
pp. 2150035
Author(s):  
Leyli Abbasi ◽  
Hossien Momeni ◽  
Mehdi Yaghoubi
Keyword(s):  
Cloud Computing ◽  
Large Scale ◽  
Scheduling Algorithm ◽  
Scientific Workflow ◽  
Workflow Scheduling ◽  
Initial Population ◽  
Large Set ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Ranking Tasks

The cloud computing environment with a set of distributed computing resources is a suitable platform for the execution of large-scale applications. One of these applications is scientific workflow applications in which a large set of interrelated tasks are executed for a certain purpose. Scientific workflow scheduling is one of the main challenges in this area, which aims at the optimal assignment of tasks to computational resources. Given the heterogeneity of cloud computing resources, the scientific workflow scheduling is an NP-Complete problem that can be solved by heuristic methods. In this paper, an improved evolutionary algorithm called Scientific Workflow Scheduling Algorithm (SWSA) for scheduling scientific workflows in the cloud will be provided by ranking tasks and improving the initial population of tasks. The objective of this algorithm is to create a balance and an improvement in the parameters of the execution cost and workflow execution completion time. In this proposed approach, a heuristic algorithm is used to rank and generate the initial population, which increases the convergence rate. The experimental results show that SWSA is more efficient in terms of cost and execution time compared with other approaches.

Workflow Scheduling for Scientific Application in Homogeneous Cloud Environment

2017 ◽  
Vol 7 (7) ◽  
pp. 137
Author(s):  
. Monika ◽  
Pardeep Kumar ◽  
Sanjay Tyagi
Keyword(s):  
Cloud Computing ◽  
Big Data ◽  
Large Data ◽  
Workflow Scheduling ◽  
Cloud Environment ◽  
Computing Environment ◽  
Scientific Application ◽  
Cloud Computing Environment ◽  
Flow Of Information

In Cloud computing environment QoS i.e. Quality-of-Service and cost is the key element that to be take care of. As, today in the era of big data, the data must be handled properly while satisfying the request. In such case, while handling request of large data or for scientific applications request, flow of information must be sustained. In this paper, a brief introduction of workflow scheduling is given and also a detailed survey of various scheduling algorithms is performed using various parameter.

Multidimensional Resource Scheduling in Cloud Data Centers

2014 ◽  
Vol 1008-1009 ◽  
pp. 1513-1516
Author(s):  
Hai Na Song ◽  
Xiao Qing Zhang ◽  
Zhong Tang He
Keyword(s):  
Cloud Computing ◽  
Data Centers ◽  
Bin Packing ◽  
Virtual Machines ◽  
Three Dimensional ◽  
Resource Scheduling ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Cloud Data ◽  
Cloud Data Centers

Cloud computing environment is regarded as a kind of multi-tenant computing mode. With virtulization as a support technology, cloud computing realizes the integration of multiple workloads in one server through the package and seperation of virtual machines. Aiming at the contradiction between the heterogeneous applications and uniform shared resource pool, using the idea of bin packing, the multidimensional resource scheduling problem is analyzed in this paper. We carry out some example analysis in one-dimensional resource scheduling, two-dimensional resource schduling and three-dimensional resource scheduling. The results shows that the resource utilization of cloud data centers will be improved greatly when the resource sheduling is conducted after reorganizing rationally the heterogeneous demands.

Provisioning of Virtual Machines in the Context of an Auto-Scaling Cloud Computing Environment

2020 ◽  
Vol 17 (6) ◽  
pp. 2430-2434
Author(s):  
R. S. Rajput ◽  
Dinesh Goyal ◽  
Rashid Hussain ◽  
Pratham Singh
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Resource Utilization ◽  
Virtual Machines ◽  
Cloud Environment ◽  
Computing Environment ◽  
Cloud Computing Environment ◽  
Auto Scaling ◽  
Adequate Resource ◽  
Cloud Resources

The cloud computing environment is accomplishing cloud workload by distributing between several nodes or shift to the higher resource so that no computing resource will be overloaded. However, several techniques are used for the management of computing workload in the cloud environment, but still, it is an exciting domain of investigation and research. Control of the workload and scaling of cloud resources are some essential aspects of the cloud computing environment. A well-organized load balancing plan ensures adequate resource utilization. The auto-scaling is a technique to include or terminate additional computing resources based on the scaling policies without involving humans efforts. In the present paper, we developed a method for optimal use of cloud resources by the implementation of a modified auto-scaling feature. We also incorporated an auto-scaling controller for the optimal use of cloud resources.

scholarly journals A Hybrid Technique for Server Consolidation in Cloud Computing Environment

2020 ◽  
Vol 20 (1) ◽  
pp. 36-52
Author(s):  
C. Vijaya ◽  
P. Srinivasan
Keyword(s):  
Cloud Computing ◽  
Virtual Machine ◽  
Virtual Machines ◽  
Virtual Machine Placement ◽  
Hybrid Technique ◽  
Computing Environment ◽  
Placement Problem ◽  
Cloud Computing Environment ◽  
Server Consolidation ◽  
Memory Utilization

AbstractThe goal of data centers in the cloud computing environment is to provision the workloads and the computing resources as demanded by the users without the intervention of the providers. To achieve this, virtualization based server consolidation acts as a vital part in virtual machine placement process. Consolidating the Virtual Machines (VMs) on the Physical Machines (PMs) cuts down the unused physical servers, decreasing the energy consumption, while keeping the constraints for CPU and memory utilization. This technique also reduces the resource wastage and optimizes the available resources efficiently. Ant Colony Optimization (ACO) that is a well-known multi objective heuristic algorithm and Grey Wolf Algorithm (GWO) has been used to consolidate the servers used in the virtual machine placement problem. The proposed Fuzzy HAGA algorithm outperforms the other algorithms MMAS, ACS, FFD and Fuzzy ACS compared against it as the number of processors and memory utilization are lesser than these algorithms.

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