Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

Jyoti, Amrita; Shrimali, Manish Dev; Tiwari, Shailesh; Singh, Harshinder

doi:10.1007/s12652-020-01747-z

Cited by 52 publications

(24 citation statements)

References 101 publications

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“…Amrita Jyoti et al [37] reviewed different papers related to cloud computing from 2015 to 2018. They compared and contrasted the various load balancing algorithms used in load balancers, as well as the brokering policies utilized for each service and its scheduling types.…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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Cloud computing offers flexible, interactive, and observable access to shared resources on the Internet. It frees users from the requirements of managing computing on their hardware. It enables users to not only store their data and computing over the internet but also can access it whenever and wherever it is required. The frequent use of smart devices has helped cloud computing to realize the need for its rapid growth. As more users are adapting to the cloud environment, the focus has been placed on load balancing. Load balancing allocates tasks or resources to different devices. In cloud computing, and load balancing has played a major role in the efficient usage of resources for the highest performance. This requirement results in the development of algorithms that can optimally assign resources while managing load and improving quality of service (QoS). This paper provides a survey of load balancing algorithms inspired by swarm intelligence (SI). The algorithms considered in the discussion are Genetic Algorithm, BAT Algorithm, Ant Colony, Grey Wolf, Artificial Bee Colony, Particle Swarm, Whale, Social Spider, Dragonfly, and Raven roosting Optimization. An analysis of the main objectives, area of applications, and targeted issues of each algorithm (with advancements) is presented. In addition, performance analysis has been performed based on average response time, data center processing time, and other quality parameters.

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Section: Literature Reviewmentioning

confidence: 99%

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Elmagzoub¹,

Syed²,

Shaikh³

et al. 2021

Electronics

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“…In the third model which is IaaS, computer resources, data storing and networking and all other infrastructure related computing resources are provided as services. The infrastructure is seen as a virtual infrastructure and clients can use the resources whenever and wherever they require them 17,18 …”

Section: Cloud Computingmentioning

confidence: 99%

Multicellular 4G and load balancing over cloud computing

Hamd

Yahiya

Kirci

2021

Concurrency and Computation

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Summary LTE‐based Multicellular 4G is nowadays gaining an enormous success thanks to its flexible core network features and its large data rate. As the number of users increases, along with the different services provided by the telcos, it is becoming a challenging issue to cope with the contextual increase in the backhaul traffic size: a congestion problem. In this perspective, when combining multicellular LTE with Cloud Computing (CC), this would increase the core network load and the subsequent load on the servers located in the CC that delivers services to the LTE users. To address this issue, we introduce an architecture combining both multicellular LTE and CC, and we investigate the performance of different load balancer mechanisms, namely static and semi‐dynamic. Extensive simulation scenarios using different algorithms were set up. The obtained results were analyzed considering the Quality of Service (QoS) and computational resources for a deployed Web application.

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“…When these applications are utilised more and more, the number of concurrent requests generated and sent to the cloud systems exponentially (Jyoti, A., 2020). This creates huge load on cloud system which is incurred due to number of users, data size per requests, types of requests (Jyoti, A., 2020). Hence to manage and balance this load for consistent and reliable performance, there is a need to have a sustainable method.…”

Section: Introductionmentioning

confidence: 99%

“…Hence to manage and balance this load for consistent and reliable performance, there is a need to have a sustainable method. This job is done using load balancing algorithms (Jyoti, A., 2020). This is known as Load Balancing Problem in Cloud Computing.…”

Section: Introductionmentioning

confidence: 99%

“…This is known as Load Balancing Problem in Cloud Computing. In case of business applications and social networking applications, the performance is measured in the form of time (Jyoti, A., 2020).to service the request and it is a key differentiator between cloud based multi scale applications even to the extent of its wide spread acceptance and success. Many multi scale applications have lost the race towards success due to the lagging of speeds in terms of time taken to process the requests.…”

Section: Introductionmentioning

confidence: 99%

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Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

Kumbhar¹

2021

TURCOMAT

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Cloud computing is new image of Information Technology and the way applications are available to end-users. Also, the applications available are gaining popularity as being able to demonstrate the flexibility of use of resources as in Multi-Scale Cloud-Based Applications. These applications on the cloud are hosted on remote virtual machines with multi-tenancy architecture. Multi-tenant cloud computing architecture helps cloud users and customers to share computing resources and still the individual tenant's data is stored separately so as to make it is invisible to other tenants. With this multi-tenancy architecture, the application is being used by multiple people at the same time. Also, the application resides in various copies across various servers situated at different geographical locations. Virtual Machines that are housed in multiple Data Centers are coupled by way of load balancers. These load balancers are responsible for the optimal distribution of load/demand/tenant requests and the size of the data held within the request. So in short, two areas need to be focused on when architecting such systems. Firstly, the load must be balanced optimally across data centers and virtual machines for seamless operation at the back end. Secondly, at the front end, it is imperative that every tenant enjoys optimal service speeds without any degradation of service request speed. In case the back-end virtual machines are unbalanced in their load then there is bound to be degradation of services. For this very purpose, various researchers/academicians, and practitioners in the cloud space have been working tirelessly towards developing optimal algorithms to get the best load balancing architecture to ensure that the objective of seamless back end and front end quality of service in terms of average response times. Multi-Scale applications consist of variable payload for time and often unpredictable i.e. load can be increased/decreased suddenly or gradually. Hence to balance these loads equally at any time, a new algorithm is proposed. This algorithm considers the characteristics of multiscale applications in terms of data size and runtime characteristics of VM in terms of capacity and availability. Finally, the performance of this method is analyzed qualitatively as well as quantitatively by performing simulation experiments of multiscale applications.

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Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey

Cited by 52 publications

References 101 publications

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

A Survey of Swarm Intelligence Based Load Balancing Techniques in Cloud Computing Environment

Multicellular 4G and load balancing over cloud computing

Least Afflicted Load Balancing Algorithm (LALBA) for Performance Improvement of Multi-Scale Applications in Cloud Environment

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