Abstract
In the cloud environment, the number of requests for user tasks may be large. It will inevitably cause server overload if the system is only deployed on a single server. Therefore, based on the user’s demand for different computing capabilities, the solution of elastic computing is presented in this system. Elastic computing is mainly divided into client and server, where the server is deployed in the same operating system environment as the system, and the client is deployed on any terminal. The server side function includes monitoring the load rate of the current system and the size of the current running files, intelligently analyzing the current number of servers owned, packaging the files to be calculated, sending and receiving files. The client side function includes receiving the file sent by the server, calling the local resource for calculation, and returning the result file after the calculation is completed. At the same time, if only single-thread is called on the server side to calculate, it will inevitably cause waste of server resources. The most effective method is to enable multithreading invocation at the same time under the load balance state, so as to maximize the utilization of server hardware resources. The application of elastic computing provides a cheap and effective way to expand the bandwidth of network devices and servers, increase the throughput, and strengthen the network data processing ability, which can meet the computing requirements of different users. At the same time of minimizing the cost increase, it can better play the role of cloud computing, and raise the flexibility and availability of network.
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References
Singh, S.K., Kumar, P.: A load balancing virtual level routing (LBVLR) using mobile mule for large sensor networks. J. Supercomput. 75(11), 7426–7459 (2019)
Camacho-Vallejo, J.-F., Nucamendi-Guillén, S., González-Ramírez, R.G.: An optimization framework for the distribution process of a manufacturing company balancing deliverymen workload and customer’s waiting times. Comput. Ind. Eng. 137, 106080 (2019)
Vijayakumar, V., Suresh Joseph, K.: Adaptive load balancing schema for efficient data dissemination in vehicular ad-hoc network VANET. Alex. Eng. J. 58, 1157–1166 (2019)
Medhat, D., Yousef, A.H., Salama, C.: Cost-aware load balancing for multilingual record linkage using MapReduce. Ain Shams Eng. J. 11, 419–433 (2019)
Ling-Hong, H., Wes, L., Radhika, A.S., Saranya, D.A.R., Yuguang, X., Eric, S., Yee, Y.K.: Holistic optimization of an RNA-seq workflow for multi-threaded environments. Bioinformatics 35, 4173–4175 (2019)
Asyabi, E., Sharafzadeh, E., SanaeeKohroudi, S., Sharifi, M.: CTS: an operating system CPU scheduler to mitigate tail latency for latency-sensitive multi-threaded applications. J. Parallel Distrib. Comput. 133, 232-243 (2019)
Peña-Fernández, M., Serrano-Cases, A., Lindoso, A., García-Valderas, M., Entrena, L., Martínez-Álvarez, A., Cuenca-Asensi, S.: Dual-core lockstep enhanced with redundant multithread support and control-flow error detection. Microelectron. Reliab. 100, 113447 (2019)
Paola, B., Vedova Gianluca, D., Yuri, P., Marco, P., Raffaella, R.: Multithread multistring Burrows-Wheeler transform and longest common prefix Array. J. Comput. Biol.: J. Comput. Mol. Cell Biol. 26(9), 948–961 (2019)
Kim, T.H., Schaarschmidt, T., Yang, H.J., Kim, Y.K., Chun, K.J., Choi, Y., Chung, H.-T.: Development of an IAEA phase-space dataset for the Leksell Gamma Knife ® Perfexion™ using multi-threaded Geant4 simulations. Phys. Med. 64, 222–229 (2019)
Nada Radwan, M.B., Abdelhalim, Ashraf AbdelRaouf.: Implement 3D video call using cloud computing infrastructure. Ain Shams Eng. J. (2019)
Wu, X., Wang, H., Wei, D., Shi, M.: ANFIS with natural language processing and grey relational analysis based cloud computing framework for real time energy efficient resource allocation. Comput. Commun. (2019)
Acknowledgements
This work is supported in part by the PhD startup Foundation Project of JiLin Agricultural Science and Technology University on 2018 and the Digital Agriculture key discipline of JiLin province Foundation.
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Li, Z., Ma, L., Tang, Y. (2021). Assumption of Load Balancing and Multithreading Algorithm in Cloud Environment. In: Sugumaran, V., Xu, Z., Zhou, H. (eds) Application of Intelligent Systems in Multi-modal Information Analytics. MMIA 2020. Advances in Intelligent Systems and Computing, vol 1233. Springer, Cham. https://doi.org/10.1007/978-3-030-51431-0_42
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DOI: https://doi.org/10.1007/978-3-030-51431-0_42
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