BeeM-NN: An efficient workload optimization using Bee Mutation Neural Network in federated cloud environment
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ORIGINAL RESEARCH
BeeM‑NN: An efficient workload optimization using Bee Mutation Neural Network in federated cloud environment S. R. Shishira1 · A. Kandasamy1 Received: 3 March 2020 / Accepted: 14 August 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Cloud computing is an extensively implemented technique to handle enormous amount of data as it provides flexibility and scalability features. In an established cloud environment, users process their request to share the data that are stored in it. Under the dynamic cloud environment, multiple requests are processed in a short time, which leads to the problem of resource allocation. Virtual Machines or servers aid the cloud in maintaining the workflow active through proper distribution of resources. However, the accurate workload prediction model is necessary for effective resource management. In the present paper, a novel BeeM-NN framework is proposed through the integration of Workload Neural Network Algorithm (WNNA) and Novel Bee Mutation Optimization Algorithm (NBMOA) for optimized workload prediction in a cloud environment. The proposed model encloses the Fitness Feature Extraction Algorithm initially to extract the feature dataset from Azure public dataset and is provided to train the WNNA. The predicted workloads are optimized with the NBMOA in the cloud. The generated model is tested using the workload data traces from the federated cloud service provider and is evaluated and compared with the existing models. The outcome showed the prediction model achieved an accuracy of 99.98% better than the current models with optimum performance in the consumption of resources and cost. The future work is to use the predicted workloads for scheduling in the cloud. Keywords Cloud · Workload · Prediction · Optimization · BeeM-NN
1 Introduction As an alternative to traditional information technology, cloud computing is considered because of its vital features like adequate resource sharing with minimal maintenance and operational issues (Armbrust et al. 2010). Cloud computing allocates remote facilities with software, user data, and its computation. It is a prototype that offers access to shared computing resources on demand through storage, servers, applications, and network services (Saha and Dasgupta 2018). Virtualization enables cloud service providers to run many virtual machines and servers for dynamic resource scaling to maintain the Quality of Service (Beloglazov and * S. R. Shishira [email protected] A. Kandasamy [email protected] 1
Department of Mathematical and Computational Sciences, National Institute of Technology Karnataka Surathkal, Mangalore, India
Buyya 2010). Due to the extreme dynamicity among cloud workloads, characterizing becomes a tedious process. Most of the workloads arrive very frequently and exist over a short period (Lu et al. 2016). For retaining better performance, the workload prediction and scheduling over three significant components is essential viz, utilization of CPU, availability of space in memor
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