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

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Abstract

Cloud computing is the big boom technology in IT industry infrastructure. Many people are moving to cloud computing because of dynamic allocation of resources and reduction in cost. Cloud computing delivers infrastructure, software, and platforms as a service to all consumers. But still, it has numerous issues related to performance unpredictability, resource sharing, security, storage capacity, availability of resources on each requirement, data confidentiality and many more. Load balancing and service brokering are the two main key areas, which ensures reliability, scalability, minimize response time, maximize throughput and cost in the cloud environment. These are the main things we have to focus to improve the performance of the computation. This survey paper presents a comparative and comprehensive study of various load balancing algorithm used in the load balancer and brokering policy used for each service and their scheduling types. The objectives of this survey is to (1) Determine, illustrate, compare and analyze newer methods developed for load balancing and service brokering (the most notable problem) by systematically reviewing papers from the year 2015 to 2018; (2) Classify and analyze techniques based on the key parameters in cloud computing techniques; (3) Ultimately set an updated, thorough and rigorous discussion on load balancing and service broker techniques so as to motivate and direct with valuable references for future research development and direction.

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  1. Department of Computer Science and Engineering, ABES Engineering College, Ghaziabad, Uttar Pradesh, India

    Amrita Jyoti, Shailesh Tiwari & Harivans Pratap Singh

  2. JRN Rajasthan Vidyapeeth University, Udaipur, Rajasthan, India

    Manish Shrimali

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  1. Amrita Jyoti
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Jyoti, A., Shrimali, M., Tiwari, S. et al. Cloud computing using load balancing and service broker policy for IT service: a taxonomy and survey. J Ambient Intell Human Comput 11, 4785–4814 (2020). https://doi.org/10.1007/s12652-020-01747-z

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