scholarly journals Experimental Setup for Investigating the Efficient Load Balancing Algorithms on Virtual Cloud

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7342
Author(s):  
Bhavya Alankar ◽  
Gaurav Sharma ◽  
Harleen Kaur ◽  
Raul Valverde ◽  
Victor Chang
Keyword(s):  
Load Balancing ◽  
High Performance ◽  
Round Robin ◽  
Experimental Setup ◽  
Cloud Infrastructure ◽  
Use Of Resources ◽  
Virtualization Technology ◽  
Distribution Of Resources ◽  
Load Balancer ◽  
Performance Computing

Cloud computing has emerged as the primary choice for developers in developing applications that require high-performance computing. Virtualization technology has helped in the distribution of resources to multiple users. Increased use of cloud infrastructure has led to the challenge of developing a load balancing mechanism to provide optimized use of resources and better performance. Round robin and least connections load balancing algorithms have been developed to allocate user requests across a cluster of servers in the cloud in a time-bound manner. In this paper, we have applied the round robin and least connections approach of load balancing to HAProxy, virtual machine clusters and web servers. The experimental results are visualized and summarized using Apache Jmeter and a further comparative study of round robin and least connections is also depicted. Experimental setup and results show that the round robin algorithm performs better as compared to the least connections algorithm in all measuring parameters of load balancer in this paper.

A Conceptual Framework Towards Implementing a Cloud-Based Dynamic Load Balancer Using a Weighted Round-Robin Algorithm

2020 ◽  
Vol 10 (2) ◽  
pp. 22-35 ◽  
Author(s):  
Sudipta Sahana ◽  
Tanmoy Mukherjee ◽  
Debabrata Sarddar
Keyword(s):  
Load Balancing ◽  
Response Time ◽  
Dynamic Load ◽  
Processing Time ◽  
Round Robin ◽  
Cloud Infrastructure ◽  
Previous Survey ◽  
Survey Papers ◽  
Multiple Servers ◽  
Load Balancer

Cloud load balancing has become one of the most vital aspects of Cloud computing that has captured the attention of IT organizations and business firms in recent years. Among the issues related to this particular aspect, one such issue which needs to be addressed is the issue of effectively serving the clients' requests among multiple servers using an appropriate load balancer. Previous survey papers discussed various issues of cloud load balancing and accordingly devised various methods and techniques to address those issues with the objectives of reduction of processing time and response time along with optimization of costs. In this article, we have discussed an effective load balancing technique using the weighted Round-Robin algorithm which can process the client requests among multiple servers with minimal response time. Considering all these aspects, a cloud-based dynamic load balancer is being used to solve the problem of load balancing in the cloud infrastructure.

scholarly journals Developing Load Balancing for IoT - Cloud Computing Based on Advanced Firefly and Weighted Round Robin Algorithms

2019 ◽  
Vol 16 (1) ◽  
pp. 0130 ◽  
Author(s):  
Abed Et al.
Keyword(s):  
Cloud Computing ◽  
Load Balancing ◽  
High Performance ◽  
Human Life ◽  
Round Robin ◽  
Use Of Resources ◽  
Huge Data ◽  
Hypertext Transfer Protocol ◽  
Iot Devices

The evolution of the Internet of things (IoT) led to connect billions of heterogeneous physical devices together to improve the quality of human life by collecting data from their environment. However, there is a need to store huge data in big storage and high computational capabilities.   Cloud computing can be used to store big data.  The data of IoT devices is transferred using two types of protocols: Message Queuing Telemetry Transport (MQTT) and Hypertext Transfer Protocol (HTTP). This paper aims to make a high performance and more reliable system through efficient use of resources. Thus, load balancing in cloud computing is used to dynamically distribute the workload across nodes to avoid overloading any individual resource, by combining two types of algorithms: dynamic algorithm (adaptive firefly) and static algorithm (weighted round robin). The results show improvement in resource utilization, increased productivity, and reduced response time.

Analisis Mirror Server menggunakan Load Balancing pada Jaringan Area Lokal

2020 ◽  
Vol 10 (4) ◽  
pp. 173-178
Author(s):  
Alfian Nurdiansyah ◽  
Nugroho Suharto ◽  
Hudiono Hudiono
Keyword(s):  
Load Balancing ◽  
Load Balance ◽  
Round Robin ◽  
Data Set ◽  
Load Balancer

Server merupakan serbuah sistem yang memberikan layanan tertentu pada suatu jaringan komputer. Server mempunyai sistem operasi sendiri yang disebut sistem operasi jaringan. Server juga mengontrol semua akses terhadap jaringan yang ada didalamnya.  Agar membantu tugas server, dibuatlah sistem mirroring server dimana server tersebut menduplikasi sebuah data set atau tiruan persis dari sebuah server yang menyediakan berbagai informasi. Mirror server atau disebut juga sinkronisasi server merupakan duplikat dari suatu server. Untuk menambah kinerja dari server maka dibutuhkan load balancer. Load balancing adalah teknik untuk mendistribusikan internet dua jalur koneksi secara seimbang. Dengan penerapan load balancing trafik akan berjalan lebih optimal, memaksimalkan throughput dan menghindari overload pada jalur koneksi. Iptables digunakan untuk memfilter IP sehigga client mengakses server sesuai dengan zona server yang paling dekat. Sehingga load balance yang dipadukan dengan iptables dapat membuat kinerja server menjadi lebih ringan. Masalah yang sering terjadi adalah ketika banyaknya client yang mengakses sebuah server maka server akan overload dan mengakibatkan kinerja server menjadi berat karena padatnya trafik. Client yang mengakses juga mendapatkan efek dari hal tersebut yaitu akses yang lama. Dari hasil penelitian tentang perpaduan antara load balance dan iptables didapati bahwa load balance dengan algoritma round robin rata-rata delay yang didapatkan untuk server1 yaitu 0,149 detik dan 0,19122. Server2 rata-rata delay yang didapatkan 0,161 detik dan 0,012 detik.

Cloud Computing for Scientific Simulation and High Performance Computing

2013 ◽  
pp. 51-70
Author(s):  
Adrian Jackson ◽  
Michèle Weiland
Keyword(s):  
Cloud Computing ◽  
High Performance Computing ◽  
High Performance ◽  
Large Scale ◽  
Parallel Programs ◽  
Small Scale ◽  
Cloud Infrastructure ◽  
Scientific Simulations ◽  
Cloud Infrastructures ◽  
Performance Computing

This chapter describes experiences using Cloud infrastructures for scientific computing, both for serial and parallel computing. Amazon’s High Performance Computing (HPC) Cloud computing resources were compared to traditional HPC resources to quantify performance as well as assessing the complexity and cost of using the Cloud. Furthermore, a shared Cloud infrastructure is compared to standard desktop resources for scientific simulations. Whilst this is only a small scale evaluation these Cloud offerings, it does allow some conclusions to be drawn, particularly that the Cloud can currently not match the parallel performance of dedicated HPC machines for large scale parallel programs but can match the serial performance of standard computing resources for serial and small scale parallel programs. Also, the shared Cloud infrastructure cannot match dedicated computing resources for low level benchmarks, although for an actual scientific code, performance is comparable.

Hierarchical Load Balancing Model by Optimal Resource Utilization

2021 ◽  
pp. 150-164
Author(s):  
Jagdish Chandra Patni
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
Resource Utilization ◽  
Resource Availability ◽  
High Performance ◽  
Low Cost ◽  
Original Form ◽  
Computing Paradigm ◽  
Optimal Resource ◽  
Performance Computing

Powerful computational capabilities and resource availability at a low cost is the utmost demand for high performance computing. The resources for computing can viewed as the edges of an interconnected grid. It can attain the capabilities of grid computing by balancing the load at various levels. Since the nature of resources are heterogeneous and distributed geographically, the grid computing paradigm in its original form cannot be used to meet the requirements, so it can use the capabilities of the cloud and other technologies to achieve the goal. Resource heterogeneity makes grid computing more dynamic and challenging. Therefore, in this article the problem of scalability, heterogeneity and adaptability of grid computing is discussed with a perspective of providing high computing, load balancing and availability of resources.

scholarly journals Service for parallel applications based on JINR cloud and HybriLIT resources

2019 ◽  
Vol 214 ◽  
pp. 07012 ◽  
Author(s):  
Nikita Balashov ◽  
Maxim Bashashin ◽  
Pavel Goncharov ◽  
Ruslan Kuchumov ◽  
Nikolay Kutovskiy ◽  
...  
Keyword(s):  
High Performance ◽  
Cloud Service ◽  
Parallel Applications ◽  
Cloud Infrastructure ◽  
Modular Architecture ◽  
Practical Applications ◽  
Speed Up ◽  
Scientific Results ◽  
Computational Resources ◽  
Performance Computing

Cloud computing has become a routine tool for scientists in many fields. The JINR cloud infrastructure provides JINR users with computational resources to perform various scientific calculations. In order to speed up achievements of scientific results the JINR cloud service for parallel applications has been developed. It consists of several components and implements a flexible and modular architecture which allows to utilize both more applications and various types of resources as computational backends. An example of using the Cloud&HybriLIT resources in scientific computing is the study of superconducting processes in the stacked long Josephson junctions (LJJ). The LJJ systems have undergone intensive research because of the perspective of practical applications in nano-electronics and quantum computing. In this contribution we generalize the experience in application of the Cloud&HybriLIT resources for high performance computing of physical characteristics in the LJJ system.

scholarly journals Host Utilization Algorithm to Reduce Energy Consumption in Cloud Computing

2020 ◽  
Vol 9 (5) ◽  
pp. 352-359
Keyword(s):  
High Performance ◽  
Detection Algorithm ◽  
Balance Model ◽  
Public Cloud ◽  
Ip Address ◽  
Switch Mechanism ◽  
Large Increment ◽  
Reduce Energy Consumption ◽  
Load Balancer ◽  
Performance Computing

The utilization of distributed computing server farm is developing quickly to fulfill the large increment required for systems administration, High-Performance Computing(HPC) as well as stockpiling assets for executing business and logical applications. The process of Virtual Machine (VM) solidification is inclusive of VMs getting relocated in order to make use of less physical servers. As a result, it enables the shut down or lowpower mode of more number of servers which enhances the vitality utilization effectiveness, working expense and CO2 discharge. An urgent advance in VM union is have over-burden discovery, which endeavors to foresee whether a physical server is going to be oversubscribed with VMs. On the contrary to usual studies which performed utilization of CPU being the standalone indicator for host overload, a multiple correlation host overload detection algorithm was proposed in the recent study by considering a lot of factors in this regard. A higher load balance model was introduced in this text for the general public cloud, supported by the concept of cloud partitioning, in addition to a switch mechanism used to strategize differently under different scenarios. The IP address is generally shared by a true server and carbo balance. In this regard, the load balancer considers the interface developed with IP address which accepts request packets and the packets are directed to the selected servers. With an aim to improve the efficiency in public cloud environment, the algorithm employed the sport theory in the load balancing strategy.

scholarly journals Analisis Perbandingan Algoritma Static Round-Robin dengan Least-Connection Terhadap Efisiensi Load Balancing pada Load Balancer Haproxy

2019 ◽  
Vol 4 (1) ◽  
pp. 70-75
Author(s):  
Hasta Triangga ◽  
Ilham Faisal ◽  
Imran Lubis
Keyword(s):  
Load Balancing ◽  
Operating Systems ◽  
Web Server ◽  
Scheduling Algorithms ◽  
Round Robin ◽  
Average Throughput ◽  
Total Delay ◽  
Average Percentage ◽  
Cpu Usage ◽  
Load Balancer

In IT networking, load balancing used to share the traffic between backend servers. The idea is to make effective and efficient load sharing. Load balancing uses scheduling algorithms in the process includes Static round-robin and Least-connection algorithm. Haproxy is a load balancer that can be used to perform the load balancing technique and run by Linux operating systems. In this research, Haproxy uses 4 Nginx web server as backend servers. Haproxy act as a reverse proxy which accessed by the client while the backend servers handle HTTP requests. The experiment involves 20 Client PCs that are used to perform HTTP requests simultaneously, using the Static round-robin algorithm and Least-connection on the haproxy load balancer alternately. When using Static round-robin algorithm, the results obtained average percentages of CPU usage successively for 1 minute; 5 minutes; and 15 minutes are; 0.1%; 0.25%; and 1.15% with average throughput produced is 14.74 kbps. Average total delay produced 64.3 kbps. The average total delay and jitter is 181.3 ms and 11.1 ms, respectively. As for the Least-connection algorithm average percentage obtained successively for 1 minute; 5 minutes; and 15 minutes are 0.1%; 0.3%; and 1.25% with the average throughput produced is 14.66 kbps. The average total delay and jitter is 350.3 ms and 24.5 ms, respectively. It means Static round-robin algorithm is more efficient than the algorithms Least-connection because it can produce a greater throughput with less CPU load and less total delay.

Applications of Virtualization Technology in Grid Systems and Cloud Servers

2018 ◽  
pp. 1-28
Author(s):  
Mohammad Samadi Gharajeh
Keyword(s):  
High Performance Computing ◽  
High Performance ◽  
Global Network ◽  
Grid Systems ◽  
Virtual Clusters ◽  
Virtualization Technology ◽  
Computing Environments ◽  
Cloud Servers ◽  
Performance Rate ◽  
Performance Computing

Grid systems and cloud servers are two distributed networks that deliver computing resources (e.g., file storages) to users' services via a large and often global network of computers. Virtualization technology can enhance the efficiency of these networks by dedicating the available resources to multiple execution environments. This chapter describes applications of virtualization technology in grid systems and cloud servers. It presents different aspects of virtualized networks in systematic and teaching issues. Virtual machine abstraction virtualizes high-performance computing environments to increase the service quality. Besides, grid virtualization engine and virtual clusters are used in grid systems to accomplish users' services in virtualized environments, efficiently. The chapter, also, explains various virtualization technologies in cloud severs. The evaluation results analyze performance rate of the high-performance computing and virtualized grid systems in terms of bandwidth, latency, number of nodes, and throughput.

scholarly journals Service Oriented Load Balancing Framework in Computational Grid Environment

2013 ◽  
Vol 9 (3) ◽  
pp. 1091-1098 ◽  
Author(s):  
Sukalyan Goswami ◽  
Ajanta De Sarkar
Keyword(s):  
Load Balancing ◽  
Grid Computing ◽  
High Performance Computing ◽  
Resource Sharing ◽  
High Performance ◽  
Computational Grid ◽  
Computing Systems ◽  
Grid Systems ◽  
Service Oriented ◽  
Performance Computing

Grid computing or computational grid has become a vast research field in academics. It is a promising platform that provides resource sharing through multi-institutional virtual organizations for dynamic problem solving. Such platforms are much more cost-effective than traditional high performance computing systems. Due to the provision of scalability of resources, these days grid computing has become popular in industry as well. However, computational grid has different constraints and requirements to those of traditional high performance computing systems. In order to fully exploit such grid systems, resource management and scheduling are key challenges, where issues of task allocation and load balancing represent a common problem for most grid systems as because the load scenarios of individual grid resources are dynamic in nature. The objective of this paper is to review different existing load balancing algorithms or techniques applicable in grid computing and propose a layered service oriented framework for computational grid to solve the prevailing problem of dynamic load balancing.

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