Load balancing in cloud computing a big picture

Cloud Computing Assignments

Literature Review (7 pages)
You will submit a draft of the literature review portion of your research paper. The literature review will form the main body of your final research paper. This will be where you provide a synthesis of the articles you have found related to your topic. When writing a literature review, you should include or consider the following:

An introduction and a conclusion
Avoid direct quotes.
Organize by topic or theme rather than by author
Use headings
Show relationships and consider the flow of ideas

Load Balancing in Cloud Computing

Extended Annotated Bibliography

Name: Kavati Anil Kumar

Institution: University of Cumberland’s

Professor: Steven Case

Date: 09-26-2020 Load Balancing in Cloud Computing

Overview of Load Balancing in Cloud

1)

Mishra, K. S., Sahoo, B., & Parida, P. (2018). Load balancing in cloud computing: A big picture. Journal of King Saud University –Computer and Information Sciences, 32(2), p.149-158. https://www.sciencedirect.com/science/article/pii/S1319157817303361

Mishra group and group researched load balancing in the cloud. Load balancing involves detecting overloaded and underloaded nodes and balancing among them. Mishra and the group proposed various load balancing approaches in the cloud. The researchers performed simulations in CloudSim. I think the author's method was sound; Mishra and group analyzed balancing algorithms using Max-Min, MET, MCT through simulations that were generated in the databases. The authors analyzed various balancing strategies in different forms, that is homogenous and heterogeneous cloud computing environments. What is missing is that the authors did not include how the proposed algorithms can work in the real-world cloud.

The article fills a literature gap in that the study provides more insights on how to solve cloud problems. These problems include overloaded and underloaded systems that result in machine failure. The authors provide strategies that can be conducted to ensure load balancing. The author would provide load balancing strategies for my study.

2)

Load Balancing Algorithms in Cloud

Kumar, S., & Singh, D. (2015). Various dynamic load balancing algorithms in the cloud environment: A survey. International Journal of Computer Applications, 129(6), 14-19. https://doi.org/10.5120/ijca2015906927

Kumar &Rana conducted a study to evaluate various dynamics of load balancing techniques in cloud computing. According to the authors, load balancing is a technique that distributes workload to all nodes. Static and dynamic load balancing are examples of load balancing techniques. Kumar &Rana performed a survey on different dynamics of the cloud environment while comparing based on different load balancing metrics. The authors compared algorithms of different metrics to find the scope of improving fault tolerance, resource utilization, minimizing response time, and migration time.

The article fills a gap in the literature as the authors compared the algorithms based on defined metrics. These are overhead, throughput, fault tolerance, response time, scalability, and resource allocation. The authors did not provide ways to design a new dynamic balancing using fault tolerance for better resource utilization. The article is useful for my study as it provides information on some of the load balancing techniques in cloud computing.

3)

Sahu, Y., & Pateriya, R. K. (2015). Cloud Computing Overview with Load Balancing Techniques. International Journal of Computer Applications (0975 – 8887), 65(24), p.40-44.

Sahu &Pateriya provides analyzed the current cloud computing techniques used in cloud computing. The authors used existing load balancing methods that manage the load when nodes are overloaded while others are underloaded. According to the authors, load balancing is where overloaded nodes are searched, and information is transferred to under loads. The author’s method was sound as they used existing methods of balancing. Information was also missing in the study since the authors did not provide new insights on achieving load balancing in the cloud. Information was missing in the study as the authors failed to provide insights on how to enhance low system efficiency. The article is a scholarly source.

The article fills a literature gap since it provides some of the strategies that can counter the problem of overloaded nodes. The study is crucial for my study since it would provide information on the existing load balancing strategies.

4)

Mukati, L., & Upadhyay, A. (2019). A survey on static and dynamic load balancing algorithms in cloud computing. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3365568

According to the authors, cloud computing provides the most reliable, proficient, and useful way of solving technological problems. The authors performed a survey on different load balancing algorithms and categorized them based on third dynamicity. Besides, the authors provide challenges faced by algorithms. The researchers used the Hidden Markov Model techniques, that classifies load balancing lancing scenarios. The method is appropriate since it can predict the next workload coming to the server.

The article fills a literature gap since it provides insights into how researchers can predict the next workload coming to the server. The article is essential to my study because it provides insights into using the Hidden Markov Model.

5)

Challenges of Current Load Balancing Techniques

Afzal, S., Kavitha, G. (2019) Load balancing in cloud computing – A hierarchical taxonomical classification. J Cloud Comp 8, 22. https://doi.org/10.1186/s13677-019-0146-7

The authors provide detailed information on load balancing techniques, disadvantages of the existing techniques and insights, and strategies of developing efficient load balancing algorithms. The paper also analyzes the causes of load unbalancing problems in the cloud. The author's method was sound since the paper analyzes a systematic overview of the research. Through the systematic review, the authors found that majority of research conducted did not provide algorithmic complexity. The authors suggest that algorithmic complexity should be used as a benchmark to develop new balancing approaches. The authors fail to provide strategies to counter real-time load balancing problems.

The articles fill a gap in the literature as it provides information on the challenges that current research on load balancing has. The authors argue that algorithm complexity has not been given much attention. The article is crucial for my study as it will provide knowledge on how I can approach the future algorithmic load balancing techniques.

6)

Kumar, P., & Kumar, R. (2019). Issues and challenges of load balancing techniques in cloud computing. ACM Computing Surveys, 51(6), 1-35. https://doi.org/10.1145/3281010

Kumar and Kumar discuss different load balancing techniques that solve issues in the cloud computing environment. The authors argue that load balancing aims to ensure customer satisfaction through efficient distribution of nodes.

The authors examine some of the challenges faced by the current algorithms. The authors propose future algorithms should contain a high throughput for efficient performance. Besides, for efficient work balancing, the techniques should propose a minimum associated overhead. Besides, the proposed algorithms should be fault-tolerant, in that it should contain an excellent fault-tolerant approach.

This article's information adds to gaps in the current literature on the challenge faced by load balancing techniques. The authors provide relevant information for my research as it provides information on issues that the current algorithms of load balancing faces.

7)

Future of Load Balancing Techniques

Kumar, M., Dubey, K., & Sharma, S. (2018). Elastic and flexible deadline constraint load balancing algorithm for cloud computing. Procedia Computer Science, 125, 717-724. https://doi.org/10.1016/j.procs.2017.12.092

According to the authors, research has proposed various load balancing techniques, but none provides elasticity. The authors propose a technique where a user submits task request T1,…T2…T3….Tn in the form of software or hardware. Simultaneously, considering the (QoS) parameters such as availability, elasticity, deadline, and priority using Turing tests. Then the upcoming request is checked if it is from a legitimate user. A controller node is also used where a node provides interaction between IaaS and Saas.

The authors propose a load balancing algorithm that will objectively reduce make span time and task rejection ration in the cloud environment. The proposed algorithm has a scheduler, workload analyzer, ELB, cloud deprovisioning, and resource provisioning. The authors found that their proposed algorithm reduces the make span time and task rejection ration. Besides, the algorithm has a proposed scale compared to Min-Min, FCFS, and SJF.

The authors have added more knowledge to the existing literature gap by suggesting a more elastic and flexible cloud algorithm. The article provides information on the role of load balancing in future cloud computing. It also provides insights on elastic and flexible algorithms that will be used in the future.

8)

Rhaghava, N. S., & Sigh, D. (2016). Comparative Study on Load Balancing Techniques in Cloud Computing. Open Journal Of Mobile Computing And Cloud Computing, 1,p1825. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.934.5444&rep=rep1&type=pdf

The authors analyzed various proposed algorithms by researchers and compared them to propose a gist of the current load balancing approaches in cloud computing. The article compares various algorithms based on resource utilization, algorithm complexity, network overhead, scalability, response time, and fault tolerance.

The authors propose that future algorithm load balances should have high throughput for efficient performance. The overhead should be as low as possible. Fault tolerance should be high. The authors propose algorithms that minimize the migration time. Besides, the response time should be minimized to boost the overall performance. Researchers should also propose highly scalable algorithms.

The article provides information on how future balancing techniques should look like. It is a crucial aspect of my study as I will understand the challenges of the current algorithms. Cloud computing is currently characterized by users demanding more services and better results, hence increasing load balances to enhance performance.

9)

Dobale, R. G., Sonar R.P., (2015). Load Balancing in Cloud. International Journal of Engineering Research and General Science, 3(3), p159-167. https://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.736.9013&rep=rep1&type=pdf

Dobale and Sonar present a Load Balance Search Algorithm (LBSA), an algorithm that solves cloud computing problems. Instead of migrating files using different chunks to varied servers, the LBSA algorithm will deal with the imbalance problem. In this algorithm, the load migrates one user to one whole file to the nearest node. According to the authors, the proposed algorithm will eliminate previous time-consuming procedures. The authors suggest that this approach will reduce demand movement costs, maximize the throughput, and minimize the response time.

Dobale and Sonar add more information on existing literature Gap by suggesting the benefits of using the Load Balance Search Algorithm. The article is crucial for my study as it adds more insights on proposed strategies for future load balancing techniques.

10)

Liang, P., & Yang, J. (2015). Evaluation of two-level global load balancing framework in the cloud environment. International Journal of Computer Science and Information Technology, 7(2), 1-11. https://doi.org/10.5121/ijcsit.2015.7201

Liang and Yang proposed a global server framework of Web sites in the cloud using two load balancing models. The framework is designed to adapt to an open-source load balancing system. The framework also enables a network provider to deploy a balancer to various data centers in the cloud. The users should have more load balancers to increase their availability. The authors suggest that the proposed framework could save IT costs and is easily deployed.

The proposed framework has a Load Balancer Selector (LBS) and a software-based load balancer. The software-based load balancer is adapted to a Linux Virtual Server (LVS). LVS provides services such as IP encapsulation, address translation, and direct rooting. LVS also plays as the virtual machine in the cloud for specific Web cluster. The authors did not provide sufficient information on how to improve performances for large users. Besides, they did not perform the suggested framework in a hybrid environment.

The authors address a literature gap by proposing how two load balancing techniques can work in the cloud. The article is crucial to my study as it will provide more insights into load balancing in the future cloud.