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International Journal of Trend in Scientific Research and Development (IJTSRD) 
Volume 5 Issue 4, May-June 2021 Available Online: www. ijtsrd.com e-ISSN: 2456 - 6470 


A Study on Replication and Failover 
Cluster to Maximize System Uptime 
Miss Pratiksha Bhagawati}, Mrs. Priya N2 


1MCA Scholar, Assistant Professor, 
12School of CS & IT, Department of MCA, Jain (Deemed-to-be) University, Bangalore, Karnataka, India 


ABSTRACT 


Different types of clients over the globe uses Cloud services because cloud 
computing involves various features and advantages such as building cost- 
effectives solutions for business, scale resources up and down according to the 
current demand and many more. But from the cloud-provider point of view, 
there are many challenges that need to be faced in order to ensure a hassle 
free service delivery to the clients. One such problem is to maintain high 
availability of services. This project aims at presenting a high available (HA) Research and 
solution for business continuity and disaster recovery through configuration 
of various other services such as load balancing, elasticity and replication. 


How to cite this paper: Miss Pratiksha 
Bhagawati | Mrs. Priya N "A Study on 
Replication and Failover Cluster to 
Maximize System | pyaar 
Uptime" Published in 
International Journal 
of Trend in Scientific 








Development 








KEYWORDS: Cloud services, cloud-provider, high availability, business continuity, 


disaster recovery, load balancing, elasticity, replication 


I. INTRODUCTION 

The major concerns of cloud computing involves Reliability 
and High availability of resources. From the cloud provider 
point of view, it has been always an essential job to provide 
the customers with on-demand services ensuring they are 
reliable, secured and available on time. Without these, the 
customers or the clients are tend to face revenue losses in 
the business end hampering their continuity of business 
plans. Service downtime not only effects user experience ina 
bad way but also directly translates into money loss. To 
eliminate these kind of outages, cloud providers have been 
focusing on finding ways to enhance their infrastructure and 
management strategies to achieve high available services. 
For something like this, it’s not just enough to have a failover 
cluster but also we need multiple redundant energy sources 
and even to have replication between multiple locations in 
case of disasters. It is mainly seen that only multinational 
companies could afford such a setup. But with the help of 
IaaS and PaaS, however, the cost of building such a service 
have decreased dramatically. 


This project aims on building a High Availability (HA) 
architecture to host websites in a reliable manner. The 
websites should be scalable, fault tolerant, have a disaster 
recovery plan and at any point of time the customer should 
not be facing a problem or a connectivity issue. 


Il. Literature Review 

1. Inthis paper, the author uses Digital Media Distribution 
platform to deliver multimedia content. The author here 
presents a modern solution for server less platform of 

















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Copyright © 2021 by author(s) and 
International Journal of Trend in Scientific 
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digital media for distribution of media content on 
Amzon Web Services (AWS). This platform uses Amazon 
AWS services for storage, content delivery optimization, 
lambda execution, media transcoding, authentication 
and logging. 


This paper states that an user experience and the costs 
of providing the same video streaming service can vary 
when using different cloud CDNs. There are users over 
the internet who would be using video streaming and 
those users are then emulated to find the best Content 
Delivery Network among many such as AWS Cloud 
Front, Microsft Azure Verizon CDN and Google Cloud 
CDN over a platform known as PlanetLab. Quality of 
Experience (QoE) is evalutated. 


In this paper the author demonstrates an approach on 
how to expedite the auto-scaling strategy for their use 
case: public transportation web sites using the AWS 
application suite. If Social network monitoring and auto 
scaling frameworks are combined, this approach can be 
greatly used to reduce OpEx impact of over-provisiong 
and under-provisioning as well can reduce the business 
cost later. 


In this paper, the author has introduced a measurement- 
driven methodology for evaluating the impact of 
replication on the QoS of relational DBaaS offerings. The 
methodology builds upon an analytical model 
representing the database cluster configurations 





@IJTSRD | Unique Paper ID-IJTSRD41249 | Volume-5|Issue-4 | May-June 2021 Page 377 


International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 


combined with an environment model to represent the 
transient replication stages. This model thus represents 
how a cloud database can achieve High Availability (HA) 
when data is automatically replicated on multiples 
nodes. 


5. Inthis paper, the author proposes a scheme called PDFE 
where they use Parallel State Machine Replication which 
allows execution of ordered commands in a flexible 
manner. There are two kind of threads namely the work 
thread and ordered threads. The binding between these 
threads can be dynamic and hence any thread that faces 
high work load can be executed first. Such flexibility can 
help achieve load balancing. 


6. Inthis paper, the author proposes a way to minimize the 
power consumption while transfer of data called SWIN 
(Sliding Window replica strategy) which is data aware. 
It minimizes the amount of data transferred and the 
storage needed also cutting cost to some extent. This can 
be applied not only to data grids but also cloud 
computing systems. 


7. Over the years, distributed storage clusters are being 
widely used. But replication in such cluster have been a 
concern since the internal bandwidth of the cluster is 
sometimes low. If any replication is misplaced then it 
might effect the overall performance of the cluster. In 
order to reduce the internal network traffic and to 
improve the load balancing, the author has proposed a 
centralized replication management scheme. It captures 
replica location and network traffic. It uses 0-1 
programming scheme to locate replicas. 


8. Inthis paper, the author discusses the usage of artificial 
intelligence for high availability of resources. After the 
training of artificial neural networks , it can choose the 
best node possible for resource group fall over. The 
above scheme helps us to choose the best possible 
failover node in the cluster through ANN. 


9. The author says that traditionally used hardware 
firewalls had many disadvantages due to its limitations 
in physical deployment. These problems thus can be 
mitigated through Network Function Virtualizations 
NVF by implementing various network functions in 
software. It provides various synchronization strategies 
that allows sharing of connection states among the 
cluster to maintain high availability and scalability. 


10. Inthis paper the author proposes an architecture which 
is helpful for intensive trace analysis. This architecture 
contains essential techniques that amalgamate 
SolrCloud, Apache Spark, and SMW. The architecture 
provides a way to develop cloud monitoring applications 
with advance algorithms for forecasting data and 
identifying workload patterns. 


Ill. Methodologies/ Algorithms 

Cloud CDN: Amazon Cloud Front is a web service that gives 
business and web application developers an easy and cost 
effective way to distribute content with low latency and high 
data transfer speed. It also helps to protect websites against 
some common malicious attacks such as Distributed Denial 
of Service (DDoS) attacks. Cloud Front comes with two types 
namely Web and RTMP. RTMP is mainly used for streaming 
media files using Adobe Flash Media whereas Web is used 
for normal contents example -html, .css, .php and graphic 


files which used HTTP and HTTPs for distribution. The one 
that we use in this architecture is Web. 


State Machine Replication: It isa method/approach used in 
distributed computing for building fault tolerant systems. 
State machine at any point stores a state of the system. It 
receives a set of commands or inputs and it applies these 
commands in a sequential order using a transition function 
to generate an output. An example of State Machine 
Replication is the Bitcoin ledger. In a fault tolerant state 
machine replication, instead of maintaining a single server, 
this system uses multiple server replicas some of which can 
be faulty. The consolidation of several servers are 
represented as the same interface as that ofa single server to 
the client. However one main disadvantage of this algorithm 
is that it doesn’t necessarily guarantee the increase of 
service throughput. 


Back Propagation Neural Network: BPNN algorithm is a 
multi layer network and is one of the widely applied neural 
network models. It can be used to store mapping relations of 
input-output models. This algorithm works by computing 
the gradient of the loss function with respect to each weight. 
The central idea is to get the smallest error though adjusting 
the weight of network. That is, using gradient search 
technology to make the square error values minimum 
between the actual output of network and expectation. 


Markov Decision Process: MDP is a discrete time control 
process. It provides a mathematical framework for 
versioning decision making situations where some outcomes 
are partly random and while other are under the control ofa 
decision maker. This algorithm can be used to determine 
whether to migrate a service or not in case of failover cluster 
during any disaster or when needed. 


Sliding Window Protocol: The Sliding Window Protocolisa 
well known method which can be used for reliable and 
effective transfer of data over various undependable 
channels that can lose, re-assemble and duplicate messages. 
There are mainly two components: the sender and the 
receiver. They are mostly used in case that needs high 
reliability of data transmission. 


IV. Scope 

The objectives of the project is as follow: 

To build a scalable environment. 

To have a disaster recovery plan. 

To have an environment which is highly available. 

To enhance the trust and satisfaction of customers. 

To ensure business continuity. 

To have a backup plan available. 

To configure various replications in different regions 
which will ensure fault tolerance. 


VVVVVVV 


The scope of the project is limited to the following points: 

> Incase of failure, if all the regions fails, data would be 
lost. So it is always better to ensure that one of the both 
(or both) sites is working properly. 

> As the traffic grows, the cost of using Cloud Front can 
increase very rapidly. 


V. Conclusions 

As far as the proceedings have been done, it is clear that a 
proper planned architecture for data replication and failover 
cluster is a necessary thing to do since it helps us to plan and 
control the flow of data maintaining a backup system for 
data safety in case of disaster recovery. This project on 





@IJTSRD | Unique Paper ID-IJTSRD41249 | 


Volume-5|Issue-4 | 


May-June 2021 Page 378 


International Journal of Trend in Scientific Research and Development (IJTSRD) @ www.ijtsrd.com eISSN: 2456-6470 


building a High Availability (HA) architecture to host 
websites in a reliable manner has thus become a perfect 
solution in order to keep customer’s trust. The websites is 
thus scalable, fault tolerant, have a disaster recovery plan 
and at any point of time the customer shall not face a 
problem or a connectivity issue. 


VI. 
[1] 


[2] 


[3] 


[4] 


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Artificial Intelligence. 


Wang, Chen., Jayaseelan, Andal., Kim, Hyong., (2018). 
“Comparing Cloud Content Delivery Networks for 
Adaptive Video Streaming”, 2018 IEEE 11th 
International Conference on Cloud Computing. 


Smith,Peter., Gonzalez—Velez, Horacio., Caton, Simon. 
(2018). “Social Auto-Scaling”, 26th Euromicro 
International Conference on Parallel, Distributed, and 
Network-Based Processing. 


Osman, Rasha., F. Perez, Juan., Casale, Giuliano., 
(2016). “Quantifying the Impact of Replication on the 
Quality-of-Service in Cloud Databases”, 2016 IEEE 
International Conference on Software Quality, 
Reliability and Security. 


[5] 


[6] 


[7] 


[8] 


[9] 


[10] 


Wu, Lihui., Wu*, Weigang., Huang, Ning., Chen, 
Zhiguang. (2018). “PDFE: Flexible Parallel State 
Machine Replication for Cloud Computing”, 2018 IEEE 
International Conference on Cluster Computing. 


V. Vrbsky, Susan., Lei, Ming., Smith, Karl., Byrd, Jeff. 
(2010). “Data Replication and Power Consumption in 
Data Grids”, 2010 2nd IEEE International Conference 
on Cloud Computing Technology and Science. 


Huang, Kangxian., Li, Dagang., Sun, Yongyue. (2014). 
“CRMS: a Centralized Replication Management 
Scheme for Cloud Storage System”, IEEE/CIC ICCC 
2014 Symposium on Social Networks and Big Data. 


R Yerravalli, Venkateswar., Tharigonda, Aditya. 
(2015). “High Availability Cluster Failover Mechanism 
Using Artificial Neural Networks”, 2015 IEEE 
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Gray*, Nicholas., Lorenz}, Claas., Mussig ~ +, 
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International Conference on Networked Systems 
(NetSys). 


Singh, Samneet., Liu, Yan. (2016). “A Cloud Service 
Architecture for Analyzing Big Monitoring Data”, 
Tsinghua science and technology. 





@IJTSRD | 


Unique Paper ID - IJTSRD41249 | 


Volume-5|Issue-4 | 


May-June 2021 Page 379