Comparison of Cloud Load Balancers

This paper compares different cloud load balancers and their specifications, including AWS ELB, Google Cloud Load Balancing, Node Balancers, Rackspace Cloud Load Balancers, Azure Load Balancer, DigitalOcean Load Balancer, and Incapsula Load Balancer.

Running head: Comparison of Cloud load Balancers.
Comparison
Of
Cloud load Balancers
Name of the Student
Name of the University
Author Note:

1Cloud Load Balancer
The aim of this discussion to elaborate the services and specifications of cloud load
balancers. Cloud load balancers are introduced in order to effectively balance the loads in the
computing operations in which it distributes the operations and processes among the
computing resources (Anderson et al., 2015). There are several load balancers present in the
computing field which includes the AWS ELB, Google Cloud Load Balancing, Node
Balancers, Rackspace Cloud Load Balancers, Azure Load Balancer, DigitalOcean Load
Balancer as well as the incapsula Load Balancer (Ferris et al., 2014). This paper will further
proceed with the comparison of Alibaba Cloud Server with the above mentioned load
balancers which will consist the discussion of the specification on the listed cloud load
Balancer as well as it will explain how the load balancers helps the online businesses to
effectively balance the customer load. Alibaba Cloud Server Load Balancer offers the service
of effectively distribute the workload as well as the traffics among the multiple resources in
order to enhance the services of the respective business platform (Patel et al., 2013).
Followed by this aspect in has been noticed that this is one of the high performance cloud
load balancers in the computing field. While comparing the AWS ELB with the Alibaba
Cloud Server load Balancer it has been noticed that both the cloud load balancer offers their
services in entirely different regions as the Alibaba follows the city centric regions where as
the AWS ELB works by incorporating global region followed by this the data availability
zones are also different between both the cloud load balancers (Lu et al., 2017). Along with
the difference between the AWS ELB with the Alibaba Cloud Server Load Balancer there is
a significant difference of the operations of Google cloud load balancer while implementing
it on an online business platform. The fundamental operation of Google cloud in to
implement a software in a regional phase from where the by using the TCP/UDP in order to
balance the work load and the internet traffic. Followed by this the application of Node
Balancer also offers several benefits by managing the incoming the request in order to

2Cloud Load Balancer
equally distribute or organize the process present in the online business. Along with this the
Rackspace Service Network works in two cloud network in which it effectively manages the
traffics present in the internal as well as in the external network. Apart from the application
of the above cloud balancers Azure load balancer works by utilizing the TCP/UDP layer 4 in
order to distribute the traffics among the VMs for the better availability of the information.
Another effective cloud load balancer in the computing field is the Incapsula load balancer
which works by distributing the business data and traffics in order to reduce the response
time as well as to enhance the service quality of the business. Lastly, the DigitalOcean load
balancer works by bringing high scalability as well as high availability to the business
infrastructure in order to effectively distribute the traffics as the pool of droplets across the
servers.

3Cloud Load Balancer
Reference:
Anderson, E.K., Google Inc, 2015. Transparent load-balancing for cloud computing services.
U.S. Patent 8,958,293.
Ferris, J.M., Red Hat Inc, 2014. Load balancing in cloud-based networks. U.S. Patent
8,849,971.
Lu, C., Ye, K., Xu, G., Xu, C.Z. and Bai, T., 2017, December. Imbalance in the cloud: An
analysis on alibaba cluster trace. In 2017 IEEE International Conference on Big Data (Big
Data)(pp. 2884-2892). IEEE.
Patel, P., Bansal, D., Yuan, L., Murthy, A., Greenberg, A., Maltz, D.A., Kern, R., Kumar, H.,
Zikos, M., Wu, H. and Kim, C., 2013, August. Ananta: Cloud scale load balancing. In ACM
SIGCOMM Computer Communication Review (Vol. 43, No. 4, pp. 207-218). ACM.