CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services. Cloud computing focuses on delivery of reliable, secure, fault-tolerant, sustainable, and scalable infrastructures for hosting Internet-based application services. These applications have different composition, configuration, and deployment requirements. Quantifying the performance of scheduling and allocation policy on a Cloud infrastructure (hardware, software, services) for different application and service models under varying load, energy performance (power consumption, heat dissipation), and system size is an extremely challenging problem to tackle. To simplify this process, in this paper we propose CloudSim: a new generalized and extensible simulation framework that enables seamless modelling, simulation, and experimentation of emerging Cloud computing infrastructures and management services. The simulation framework has the following novel features: (i) support for modelling and instantiation of large scale Cloud computing infrastructure, including data centers on a single physical computing node and java virtual machine; (ii) a self-contained platform for modelling data centers, service brokers, scheduling, and allocations policies; (iii) availability of virtualization engine, which aids in creation and management of multiple, independent, and co-hosted virtualized services on a data center node; and (iv) flexibility to switch between space-shared and time-shared allocation of processing cores to virtualized services

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  1. Shalu; Singh, Dinesh: Artificial neural network-based virtual machine allocation in cloud computing (2021)
  2. Tchórzewski, Jacek; Jakóbik, Agnieszka; Iacono, Mauro: An ANN-based scalable hashing algorithm for computational clouds with schedulers (2021)
  3. Wan, Shuzhen; Qi, Lixin: An improved coral reef optimization-based scheduling algorithm for cloud computing (2021)
  4. Ghose, Manojit; Kaur, Sawinder; Sahu, Aryabartta: Scheduling real time tasks in an energy-efficient way using VMs with discrete compute capacities (2020)
  5. Martinovic, John; Hähnel, Markus; Scheithauer, Guntram; Dargie, Waltenegus; Fischer, Andreas: Cutting stock problems with nondeterministic item lengths: a new approach to server consolidation (2019)
  6. Idrissi, Hind; Ennahbaoui, Mohammed; El Hajji, Said; Souidi, El Mamoun: A secure cloud-based IDPS using cryptographic traces and revocation protocol (2017)
  7. Harshit Gupta, Amir Vahid Dastjerdi, Soumya K. Ghosh, Rajkumar Buyya: iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments (2016) arXiv
  8. Li, Hongjian; Zhu, Guofeng; Cui, Chengyuan; Tang, Hong; Dou, Yusheng; He, Chen: Energy-efficient migration and consolidation algorithm of virtual machines in data centers for cloud computing (2016)
  9. Pacini, Elina; Mateos, Cristian; García Garino, Carlos: Multi-objective swarm intelligence schedulers for online scientific clouds (2016) ioport
  10. Salimian, Leili; Safi Esfahani, Faramarz; Nadimi-Shahraki, Mohammad-Hossein: An adaptive fuzzy threshold-based approach for energy and performance efficient consolidation of virtual machines (2016) ioport
  11. Samimi, Parnia; Teimouri, Youness; Mukhtar, Muriati: A combinatorial double auction resource allocation model in cloud computing (2016)
  12. Song, Biao; Hassan, Mohammad Mehedi; Alamri, Atif; Alelaiwi, Abdulhameed; Tian, Yuan; Pathan, Mukaddim; Almogren, Ahmad: A two-stage approach for task and resource management in multimedia cloud environment (2016)
  13. Velayudhan Kumar, Mohan Raj; Raghunathan, Shriram: Heterogeneity and thermal aware adaptive heuristics for energy efficient consolidation of virtual machines in infrastructure clouds (2016) ioport
  14. Chen, Zhijia; Zhu, Yuanchang; Di, Yanqiang; Feng, Shaochong: A dynamic resource scheduling method based on fuzzy control theory in cloud environment (2015)
  15. Fu, Xiong; Cang, Yeliang; Zhu, Xinxin; Deng, Song: Scheduling method of data-intensive applications in cloud computing environments (2015)
  16. Johnsen, Einar Broch; Schlatte, Rudolf; Tapia Tarifa, S. Lizeth: Integrating deployment architectures and resource consumption in timed object-oriented models (2015)
  17. Lloret, Jaime; Garcia, Miguel; Tomas, Jesus; Rodrigues, Joel J. P. C.: Architecture and protocol for intercloud communication (2014) ioport
  18. Rodriguez, Juan Manuel; Mateos, Cristian; Zunino, Alejandro: Energy-efficient job stealing for CPU-intensive processing in mobile devices (2014)
  19. Zhao, Jiaqi; Mhedheb, Yousri; Tao, Jie; Jrad, Foued; Liu, Qinghuai; Streit, Achim: Using a vision cognitive algorithm to schedule virtual machines (2014)
  20. Xiao, Peng; Hu, Zhi-Gang; Zhang, Yan-Ping: An energy-aware heuristic scheduling for data-intensive workflows in virtualized datacenters (2013) ioport

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