Abstract
Recently, abundant domains such as education, healthcare, science, finance and many other applications have been popular to establish the recent contemporary content-based applications. These applications need large gathering, generation and processing of data and have all of them in a big heterogeneous system consisting of a diverse range of public/private cloud systems that are dispersed geographically. With this context, provisioning and allocation of resources become a great challenge to distribute modern systems because of unpredictable workload that change or fluctuate during their execution. To cope with this, an adaptive algorithm, namely enhanced honey bee (EHB) load balancing algorithm, is proposed. The main aim of this algorithm is to distribute the workloads of numerous network links to avoid overutilization and underutilization of resources through self-aggregation and the concept of foraging nature of honeybees. This is achieved by assigning the ready tasks to virtual machine (VM) by satisfying two yardsticks. One is that the tasks count of the current available VM should be lesser than other VMs and second is that the deviation/variation of the processing time of current VM from the meantime of processing in all VMs should not exceed the predetermined threshold. The evaluated empirical results show efficiency of the proposed EHB model through the response and processing time, deviation of load and the extent/degree of the imbalance.
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Nithya, B., Mogalapalli, H., Khanna, K., Moharana, S. (2020). Enhanced Honey Bee Load Balancing in Large Heterogeneous Cloud Environments. In: M. Thampi, S., et al. Applied Soft Computing and Communication Networks. ACN 2019. Lecture Notes in Networks and Systems, vol 125. Springer, Singapore. https://doi.org/10.1007/978-981-15-3852-0_12
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