Analysis of Load Balancing Techniques for Cloud Services

This report provides a detailed analysis of load balancing for cloud services. It begins by discussing the two main approaches: static and dynamic load balancing, explaining their differences and applicability. The report then delves into the various levels of cloud operations where load balancing is implemented, including application and network load balancers. Finally, it examines how load balancing is performed in non-AWS cloud systems, highlighting the importance of data distribution and the consequences of improper load balancing. The report also includes references to relevant research papers.

Running head: LOAD BALANCING FOR CLOUD SERVICES
Load balancing for cloud services
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Discussion on load balancing for cloud services:
Load balancing permits enterprises in managing workload demands and applications. This is
done through allocating the resources among various servers, networks and computers. Here, load
balancing in the cloud is analyzed through various aspects.
1. Discussing the main approaches towards the load balancing in the cloud:
There are primarily two kinds of approaches to load balancing that are used in cloud
computing. The first one is a static approach. This is primarily defined under implementation and
design system. The algorithm of static load balancing distinguishes the various traffic in an
equivalent way between every user. Here, the information is used regarding the average behaviour of
the system (Chen, Chen and Kuo 2017). These are highly simple and found to be ignoring the
present state and the load of the nod within a system. Then, there is the dynamic approach. Here, the
present state of the system is considered as the load balancing decision is considered. This has been
more appropriate for the broadly distributed framework like cloud computing. The dynamic
approach comprises of a couple of sections. The first one is a centralized approach. This the node
that is liable for distributing and managing under the overall system. Then, there is a distributed
approach. Here, all the nodes are built independently over the individual load. Here, every decision
is undertaken through a load vector locally (Milani and Navimipour 2016).
2. Providing indications of various levels of cloud operations:
The different levels of cloud operations involve load balancing are discussed hereafter. The
application load balancer has been functioning at the application layer. As the load balancer gains
any request, this analyzes the rules of the listener to find the rule to be applied. Here, the configured
listener can be configured to different route requests. This is intended for various groups of targets

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based on the content of application traffic. The network load balancer works at the network
infrastructure and can control millions of requests every second. As it gains the connection request,
this chooses the target from the targeted group regarding default rule (Mesbahi and Rahmani 2016).
Moreover, it attempts to open the TCP connection to the target chosen on the specified port in the
configuration of the listener. Lastly, the load balancer can direct the traffic originating from
particular URLs. This is done towards the backed service or backend bucket (Ghomi, Rahmani and
Qader 2017). Thus, for instance, the load balancer has been able to send the requests for various
static content to the bucket of cloud storages and various requests regarding dynamic content.
3. Ways in which load balancing is done on the non-AWS cloud system:
Figure 1: “An illustrative load balancing methods that are not from Amazon AWS”

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(Source: Created by Author)
In the above non-AWS cloud system, the load balancer helps in distributing the data. This is
dependent on how every node or server is busy. As the load balance is absent, the client has been
waiting as the process is processed. Here, different data such as tasks to waiting in a queue, the
processing rate of CPU, rate of job arrival and so on are exchanges between the processors. This
happens as the load balancing process take place. However, the failure in the proper application of
the load balances has been leading to significant outcomes. Here, the data getting lost is one of the
criteria.
From the above study, the primary approaches towards the load balancing in the cloud are
understood. Then, indications are provided at various cloud operation levels. Next, the ways in
which the load balancing is evaluated in over non-AWS cloud system is appropriately discussed.

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References:
Chen, S.L., Chen, Y.Y. and Kuo, S.H., 2017. CLB: A novel load balancing architecture and
algorithm for cloud services. Computers & Electrical Engineering, 58, pp.154-160.
Ghomi, E.J., Rahmani, A.M. and Qader, N.N., 2017. Load-balancing algorithms in cloud computing:
A survey. Journal of Network and Computer Applications, 88, pp.50-71.
Mesbahi, M. and Rahmani, A.M., 2016. Load balancing in cloud computing: a state of the art
survey. Int. J. Mod. Educ. Comput. Sci, 8(3), p.64.
Milani, A.S. and Navimipour, N.J., 2016. Load balancing mechanisms and techniques in the cloud
environments: Systematic literature review and future trends. Journal of Network and Computer
Applications, 71, pp.86-98.