Abstract
Cloud Computing has changed the way we are thinking about computer security and the way how corporations organize their internal processes. Therefore the Cloud computing is a new paradigm to convey computing architecture and assistance in acquiring the chances and difficulties in the region of distributed resources management. Resource scalability and security are the two major issues under Infrastructure as a Service (IaaS) of resource allocation. In this manner, the Entropy-based Adaptive Krill herd optimization for auto-scaling in the cloud is proposed. Here, auto-scaling is a significant cloud computing feature under IaaS, which is utilized to dynamically assign computational resources to applications to coordinate their present loads absolutely, in this way removing resources that would diversely stay idle and waste power. In the first stage, the task is monitored by determining the trust-based anomaly detection objectives such as Frequency Value, Trust Hypothesis Statistics, trust factor value, and trust policy. At that point, the given task is scheduled to find the task status. Then it is scaled using the execution time and workload calculation. After that, the scaled data is optimized utilizing the entropy-based krill herd algorithm. At long last, the comparisons of the proposed and existing methods are evaluated.
Similar content being viewed by others
References
Davidovic, V., Ilijevic, D., Luk, V., & Pogarcic, I. (2015). Private cloud computing and delegation of control. Procedia Engineering, 100, 196–205.
Toosi, A. N., Calheiros, R. N., & Buyya, R. (2014). Interconnected cloud computing environments: Challenges, taxonomy, and survey. ACM Computing Surveys (CSUR), 47(1), 7.
Al-Dulaimy, A., Taheri, J., Kassler, A., Farahabady, M. R. H., Deng, S., & Zomaya, A. (2020). MULTISCALER: A Multi-Loop Auto-Scaling Approach for Cloud-Based Applications. IEEE Transactions on Cloud Computing, (01), 1–1.
Liang, H., Du, Y., & Li, F. (2018). Business value-aware task scheduling for hybrid IaaS cloud. Decision Support Systems, 112, 1–14.
Manvi, S. S., & Shyam, G. K. (2014). Resource management for infrastructure as a service (IaaS) in cloud computing: A survey. Journal of Network and Computer Applications, 41, 424–440.
Laili, Y., Tao, F., Wang, F., Zhang, L., & Lin, T. (2018). An iterative budget algorithm for dynamic virtual machine consolidation under cloud computing environment (revised December 2017). IEEE Transactions on Services Computing, 14(1), 30–43.
Podolskiy, V., Jindal, A., & Gerndt, M. (2019). Multilayered autoscaling performance evaluation: Can virtual machines and containers co-scale? International Journal of Applied Mathematics and Computer Science, 29(2), 227–244.
Guo, Y., Stolyar, A. L., & Walid, A. (2018). Online VM auto-scaling algorithms for application hosting in a cloud. IEEE Transactions on Cloud Computing, 8(3), 889–898.
Toosi, A. N., Son, J., Chi, Q., & Buyya, R. (2019). ElasticSFC: Auto-scaling techniques for elastic service function chaining in network functions virtualization-based clouds. Journal of Systems and Software, 152, 108–119.
Srirama, S. N., Adhikari, M., & Paul, S. (2020). Application deployment using containers with auto-scaling for microservices in cloud environment. Journal of Network and Computer Applications, 160, 102629.
Aslanpour, M. S., Ghobaei-Arani, M., & Toosi, A. N. (2017). Auto-scaling web applications in clouds: A cost-aware approach. Journal of Network and Computer Applications, 95, 26–41.
Kim, H.-W., & Young-Sik, J. (2016). Efficient auto-scaling scheme for rapid storage service using many-core of desktop storage virtualization based on IoT. Neurocomputing, 209, 67–74.
Al-Dhuraibi, Y., Paraiso, F., Djarallah, N., & Merle, P. (2017). Elasticity in cloud computing: State of the art and research challenges. IEEE Transactions on Services Computing, 11(2), 430–447.
Hummaida, A. R., Paton, N. W., & Sakellariou, R. (2016). Adaptation in cloud resource configuration: A survey. Journal of Cloud Computing, 5(7), 1–16.
Muñoz-Escoí, F. D., & Bernabéu-Aubán, J. M. (2017). A survey on elasticity management in PaaS systems. Computing, 99(7), 617–656.
Pereira, P., Araujo, J., & Maciel, P. (2019). A hybrid mechanism of horizontal auto-scaling based on thresholds and time series. In 2019 IEEE international conference on systems, man and cybernetics (SMC) (IEEE), pp. 2065–2070.
Jazayeri, F., Shahidinejad, A., & Ghobaei-Arani, M. (2020). Autonomous computation offloading and auto-scaling the in the mobile fog computing: A deep reinforcement learning-based approach. Journal of Ambient Intelligence and Humanized Computing, 1–20.
Guo, M., Guan, Q., Chen, W., Ji, F., & Peng, Z. (2019). Delay-optimal scheduling of VMs in a queueing cloud computing system with heterogeneous workloads. IEEE Transactions on Services Computing, (01), 1–1.
Lorido-Botran, T., Miguel-Alonso, J., & Lozano, J. A. (2014). A review of auto-scaling techniques for elastic applications in cloud environments. Journal of Grid Computing, 12(4), 559–592.
Gambi, A., Hummer, W., Truong, H.-L., & Dustdar, S. (2013). Testing elastic computing systems. IEEE Internet Computing, 17(6), 76–82.
Babu, K. R. R., & Samuel, P. (2018). Interference aware prediction mechanism for auto scaling in cloud. Computers & Electrical Engineering, 69, 351–363.
Li, H.-W., Wu, Y.-S., Chen, Y.-Y., Wang, C.-M., & Huang, Y.-N. (2017). Application execution time prediction for effective CPU provisioning in virtualization environment. IEEE Transactions on Parallel and Distributed Systems, 28(11), 3074–3088.
Kirthica, S., & Sridhar, R. (2018). A residue-based approach for resource provisioning by horizontal scaling across heterogeneous clouds. International Journal of Approximate Reasoning, 101, 88–106.
Atrey, A., Van Seghbroeck, G., Volckaert, B., & De Turck, F. (2018). BRAHMA+: A framework for resource scaling of streaming and ASAP time-varying workflows. IEEE Transactions on Network and Service Management, 15(3), 894–908.
Moghaddam, S. K., Buyya, R., & Ramamohanarao, K. (2019). ACAS: An anomaly-based cause aware auto-scaling framework for clouds. Journal of Parallel and Distributed Computing, 126, 107–120.
Du, M., & Li, F. (2017). ATOM: Efficient tracking, monitoring, and orchestration of cloud resources. IEEE Transactions on Parallel & Distributed Systems, 8, 2172–2189.
Park, J., Choi, D. H., Jeon, Y.-B., Nam, Y., Hong, M., & Park, D.-S. (2018). Network anomaly detection based on probabilistic analysis. Soft Computing, 22(20), 6621–6627.
Farshchi, M., Schneider, J.-G., Weber, I., & Grundy, J. (2018). Metric selection and anomaly detection for cloud operations using log and metric correlation analysis. Journal of Systems and Software, 137, 531–549.
Xoxa, N., Zotaj, M., Tafa, I., & Fejzaj, J. (2014). Simulation of first come first served (FCFS) and shortest job first (SJF) algorithms. Tirana, Albania: IJCSN-International Journal of Computer Science and Network, 3(6), 444–449.
Wang, G., Guo, L., Wang, H., Duan, H., Liu, L., & Li, J. (2014). Incorporating mutation scheme into krill herd algorithm for global numerical optimization. Neural Computing and Applications, 24(3–4), 853–871.
Messias, V. R., Estrella, J. C., Ehlers, R., Santana, M. J., Santana, R. C., & Reiff-Marganiec, S. (2016). Combining time series prediction models using genetic algorithm to autoscaling web applications hosted in the cloud infrastructure. Neural Computing and Applications, 27(8), 2383–2406.
Ghobaei-Arani, M., Rahmanian, A. A., Aslanpour, M. S., & Dashti, S. E. (2018). CSA-WSC: Cuckoo search algorithm for web service composition in cloud environments. Soft Computing, 22(24), 8353–8378.
Biswas, T., Kuila, P., Ray, A. K. (2019). A novel scheduling with multi-criteria for high-performance computing systems: An improved genetic algorithm-based approach. Engineering with Computers, 35(4), 1475–1490.
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Rahumath, A.S., Natarajan, M. & Malangai, A.R. Resource Scalability and Security Using Entropy Based Adaptive Krill Herd Optimization for Auto Scaling in Cloud. Wireless Pers Commun 119, 791–813 (2021). https://doi.org/10.1007/s11277-021-08238-0
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11277-021-08238-0