With the development of the Internet and the advent of the mobile Internet era, to meet the business needs of big data, the scale of clusters is also increasing, and there are also problems in distributed automated development and automated management. The system is becoming more and more obvious. The purpose of this paper is to study the design and implementation of a resource management and distributed system development platform that considers big data. The platform is built on the OCF framework, and based on the existing OCF components, services are developed and deployed as components. The platform is based on the resource management of the underlying system, adopts an extensible node allocation strategy for node allocation, deploys and uninstalls services in the form of components, and closely integrates the development and deployment of services, thereby reducing costs and difficulty in operation and maintenance. Performance analysis test results show that when the data grows significantly, the time taken by the system also increases, especially when the data goes from 500 MB to 1 GB. The test results of service offloading latency show that data size has little effect on offloading service latency, but the overall efficiency of resource management and distributed system development platform is higher than that of Ansible.

Amaren Charun. Distributed System Resource Management and Deployment Platform Considering Big Data. Distributed Processing System (2021), Vol. 2, Issue 2: 42-50. https://doi.org/10.38007/DPS.2021.020206.

[1] Papageorgiou A, Fernandez-Fernandez A, Siddiqui S, et al. On 5G network slice modelling: Service-, resource-, or deployment-driven?. Computer Communications, 2020, 149(Jan.):232-240. https://doi.org/10.1016/j.comcom.2019.10.024

[2] Ray L. Enterprise Resource Planning Deployment and Business Process Reengineering at a Major University: A Case Study. International journal of strategic information technology and applications, 2018, 9(1):98-109. https://doi.org/10.4018/IJSITA.2018010105

[3] Woiceshyn K, Kashino Z, Nejat G, et al. Vehicle Routing for Resource Management in Time-Phased Deployment of Sensor Networks. IEEE Transactions on Automation Science and Engineering, 2018, PP(2):1-13. https://doi.org/10.1109/TASE.2018.2857630

[4] Awaysheh F M, Alazab M, Garg S, et al. Big Data Resource Management & Networks: Taxonomy, Survey, and Future Directions. IEEE Communications Surveys & Tutorials, 2021, PP(99):1-1.

[5] Yoon S, Cho J H, Dan D K, et al. DESOLATER: Deep Reinforcement Learning-based Resource Allocation and Moving Target Defense Deployment Framework. IEEE Access, 2021, PP(99):1-1.

[6] Elsahn Z, Siedlok F. "Can we build it? Yes, we can!" complexities of resource re-deployment to fight pandemic. Industrial Marketing Management, 2021, 93(3):191-207.

[7] Busolo G, Nderu L, Ogada K, et al. Application of a Multilevel Technology Acceptance Management Model for Effective Technology Deployment. International Journal of Computer Science and Information Technology, 2021, 13(1):57-74. https://doi.org/10.5121/ijcsit.2021.13105

[8] Thurasamy R, Alfarraj O, Alzahrani A, et al. End users' resistance behaviour paradigm in pre-deployment stage of ERP systems: evidence from Bangladeshi manufacturing industry. Business Process Management Journal, 2021, 27(5):1496-1521. https://doi.org/10.1108/BPMJ-08-2019-0350

[9] Rahman A, Islam M J, Montieri A, et al. SmartBlock-SDN: An Optimized Blockchain-SDN Framework for Resource Management in IoT. IEEE Access, 2021, PP(99):1-1.

[10] Ahmad T, Singh S P, Johri P, et al. Cloud computing for mobile applications: outsourcing the deployment of virtual machines. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 2021, 12(14):2120-2132.

[11] Avasalcai C, Tsigkanos C, Dustdar S. Resource Management for Latency-Sensitive IoT Applications with Satisfiability. IEEE Transactions on Services Computing, 2021, PP(99):1-1. https://doi.org/10.1109/TSC.2021.3074188

[12] Keshavamurthy P, Pateromichelakis E, Dahlhaus D, et al. Edge Cloud-Enabled Radio Resource Management for Co-Operative Automated Driving. IEEE Journal on Selected Areas in Communications, 2020, PP(99):1-1.

[13] Kazakova N, Mel'Nik M, Dudorova E. Prospects for Implementing Big Data Analytics into the Auditing Profession. Auditor, 2021, 7(3):40-47.

[14] Zacharias J. Addressing Global Climate Change With Big Data-Driven Urban Planning Policy. International Journal of E-Planning Research, 2021, 10(It 4):1-16. https://doi.org/10.4018/IJEPR.20211001.oa1

[15] Lorenc A, Burinskiene A. Improve the orders picking in e-commerce by using WMS data and BigData analysis. FME Transactions, 2021, 49(1):233-243. https://doi.org/10.5937/fme2101233L

[16] Laterza V. Could Cambridge Analytica Have Delivered Donald Trump's 2016 Presidential Victory? An Anthropologist's Look at Big Data and Political Campaigning. Public Anthropologist, 2021, 3(1):119-147.

[17] Mohamed M A, Abdel-Fattah M A, Khedr A E. Challenges and Recommendations in Big Data Indexing Strategies. International Journal of e-Collaboration, 2021, 14(2):22-39. https://doi.org/10.4018/IJeC.2021040102

[18] Awan M J, Shafry M, Rahim M, et al. Social Media and Stock Market Prediction: A Big Data Approach. Computers, Materials and Continua, 2021, 67(2):2569–2583. https://doi.org/10.32604/cmc.2021.014253