Performance Improvisation through Resource Usage Optimization in Cloud Environment

2014 ◽  
Author(s):  
Hardik A. Chaudhari ◽  
Chirag S. Thaker ◽  
Jay Patel
Keyword(s):  
Resource Usage ◽  
Cloud Environment

Analysis and Development of Load Balancing Algorithms in Cloud Computing

2021 ◽  
pp. 1177-1197
Author(s):  
Deepa Bura ◽  
Meeta Singh ◽  
Poonam Nandal
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
Data Centers ◽  
Emerging Technology ◽  
The Internet ◽  
Resource Usage ◽  
Cloud Environment ◽  
End User ◽  
Web Engineering ◽  
Load Balancing Algorithm

This article describes how cloud computing utilizes the benefits of web engineering and its applications by improving the performance and reducing the load on cloud providers. As the cloud is one of the emerging technology in the field of computing, it is used to provide various services to the user through the internet. One of the major concerns in cloud computing is accessibility of cloud. For estimating the availability of cloud, various load balancing algorithms are deployed in data centers of the cloud environment. Load balancing is a technique that distributes a signal load across various computers for optimizing resource usage, reducing response time, etc. There are different load balancing algorithms, for performing the load distribution across various centers. This article analyses different load balancing algorithms and develop a new algorithm for efficient load balancing. The proposed load balancing algorithm utilizes the concepts of web engineering to prioritize the request of end user using parsing technique, which will assign the resources to the end users based on the priority set by the data centers.

scholarly journals Managing the CERN Batch System with Kubernetes

2020 ◽  
Vol 245 ◽  
pp. 07048
Author(s):  
Luis Fernandez Alvarez ◽  
Olga Datskova ◽  
Ben Jones ◽  
Gavin McCance
Keyword(s):  
Open Source ◽  
Batch Service ◽  
Resource Usage ◽  
Cloud Environment ◽  
Operational Model ◽  
Batch System ◽  
Wide Range ◽  
Cloud Providers ◽  
The Impact ◽  
Usage Efficiency

The CERN Batch Service faces many challenges in order to get ready for the computing demands of future LHC runs. These challenges require that we look at all potential resources, assessing how efficiently we use them and that we explore different alternatives to exploit opportunistic resources in our infrastructure as well as outside of the CERN computing centre. Several projects, like BEER, Helix Nebula Science Cloud and the new OCRE project, have proven our ability to run batch workloads on a wide range of non-traditional resources. However, the challenge is not only to obtain the raw compute resources needed but how to define an operational model that is cost and time efficient, scalable and flexible enough to adapt to a heterogeneous infrastructure. In order to tackle both the provisioning and operational challenges it was decided to use Kubernetes. By using Kubernetes we benefit from a de-facto standard in containerised environments, available in nearly all cloud providers and surrounded by a vibrant ecosystem of open-source projects. Leveraging Kubernetes’ built-in functionality, and other open-source tools such as Helm, Terraform and GitLab CI, we have deployed a first cluster prototype which we discuss in detail. The effort has simplified many of the existing operational procedures we currently have, but has also made us rethink established procedures and assumptions that were only valid in a VM-based cloud environment. This contribution presents how we have adopted Kubernetes into the CERN Batch Service, the impact its adoption has in daily operations, a comparison on resource usage efficiency and the experience so far evolving our infrastructure towards this model.

scholarly journals Dynamic Load Aware Scheduler of Map Reduce Tasks for Cloud Environments

2019 ◽  
Vol 8 (2S11) ◽  
pp. 510-517
Keyword(s):  
Resource Availability ◽  
Map Reduce ◽  
Resource Usage ◽  
Cloud Environment ◽  
Big Data Applications ◽  
Cloud Environments ◽  
Cloud Infrastructures ◽  
Dynamic Scheduler ◽  
Virtual Resources ◽  
Hadoop Framework

Most of the current day applications are data and compute intensive which led to invention of technologies like Hadoop. Hadoop uses Map Reduce framework for parallel processing of big data applications using the computing resources of multiple nodes. Hadoop is designed for cluster environments and has few limitations when executed in cloud environments. Hadoop on cloud has become a common choice due to its easy establishment of infrastructure and pay as you use model. Hadoop performance on cloud infrastructures is affected by the virtualization overhead of cloud environment. The execution times of Hadoop on cloud can be improved if the virtual resources are effectively used to schedule the tasks by studying the resource usage characteristics of the tasks and resource availability of the nodes. The proposed work is to build a dynamic scheduler for Hadoop framework which can make scheduling decision dynamically based on job resource usage and node load. The results of the proposed work indicate an improvement of up to 23% in execution time of the Hadoop Map Reduce applications.

Task Scheduling Algorithm based on Resources Segregation in Cloud Environment

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.

A Review on Load Balancing Model Using Best Partition Technique

2017 ◽  
Vol 7 (8) ◽  
pp. 284
Author(s):  
M. Chaitanya ◽  
K. Durga Charan
Keyword(s):  
Cloud Computing ◽  
Fault Tolerance ◽  
Load Balancing ◽  
Load Balance ◽  
Large Impact ◽  
Cloud Environment ◽  
Public Cloud ◽  
The Public ◽  
Partition Technique ◽  
Textual Content

Load balancing makes cloud computing greater knowledgeable and could increase client pleasure. At reward cloud computing is among the all most systems which offer garage of expertise in very lowers charge and available all the time over the net. However, it has extra vital hassle like security, load administration and fault tolerance. Load balancing inside the cloud computing surroundings has a large impact at the presentation. The set of regulations relates the sport idea to the load balancing manner to amplify the abilties in the public cloud environment. This textual content pronounces an extended load balance mannequin for the majority cloud concentrated on the cloud segregating proposal with a swap mechanism to select specific strategies for great occasions.

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.

Enhanced Data Security from Single Clouds to Multiclouds

2018 ◽  
Vol 6 (9) ◽  
pp. 66
Author(s):  
Umesh Banodha ◽  
Praveen Kumar Kataria
Keyword(s):  
Data Storage ◽  
Network Connectivity ◽  
Data Replication ◽  
Recovery Process ◽  
Cloud Environment ◽  
Storage Security ◽  
Cloud Data ◽  
Replication Strategy ◽  
Cloud Data Storage ◽  
Data Files

Cloud is an emerging technology that stores the necessary data and electronic form of data is produced in gigantic quantity. It is vital to maintain the efficacy of this data the need of data recovery services is highly essential. Cloud computing is anticipated as the vital foundation for the creation of IT enterprise and it is an impeccable solution to move databases and application software to big data centers where managing data and services is not completely reliable. Our focus will be on the cloud data storage security which is a vital feature when it comes to giving quality service. It should also be noted that cloud environment comprises of extremely dynamic and heterogeneous environment and because of high scale physical data and resources, the failure of data centre nodes is completely normal.Therefore, cloud environment needs effective adaptive management of data replication to handle the indispensable characteristic of the cloud environment. Disaster recovery using cloud resources is an attractive approach and data replication strategy which attentively helps to choose the data files for replication and the strategy proposed tells dynamically about the number of replicas and effective data nodes for replication. Thus, the objective of future algorithm is useful to help users together the information from a remote location where network connectivity is absent and secondly to recover files in case it gets deleted or wrecked because of any reason. Even, time oriented problems are getting resolved so in less time recovery process is executed.

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