[AISWorld] Free simulators from our group.

[Devki Jha (PGR)]

[Apologies if you receive multiple copies of this message]

We have released the code for multiple simulators.

1. BigDataSimSDN:
BigDataSDNSim differs from other simulation tools in supporting a holistic simulation framework that simulates MapReduce applications, BDMS, and SDN-related networks in cloud-based environments. In particular, BigDataSDNSim is capable of modeling and simulating:

- A generic big data approach for executing different big data programming models (e.g. MapReduce, Stream) simultaneously;
- MapReduce applications within big data cluster management (BDMS), which is one of the key prominent framework for running different big data models;
- The behaviors and features of SDN dynamic networks coupled with the coordination and interaction with MapReduce applications within cloud environments;
- Dynamic routing mechanisms based on graph theory to enable any type of network topology to be seamlessly simulated;
- Modeling several policies for SDN, MapReduce, and VM within cloud data centers for multilevel optimization;
- Variety of cloud data center nodes combined with SDN controllers to satisfy the requirements of big data applications

Find this link to download the simulator and user manual.
LINK: https://rajivranjan.net/iotsim/iotsim-release/. For any query please contact Khaled Alwasel (kalwasel at gmail.com).

Also check our publication
K. Alwasel, R. N. Calheiros, S. Garg, R. Buyya, and R. Ranjan, "BigDataSDNSim: A Simulator for Analyzing Big Data Applications in Software-Defined Cloud Data Centers." (https://arxiv.org/pdf/1910.04517.pdf).


2. IoTSim-Stream:
IoTSim-Stream is an IoT Simulator for Stream processing on the big data that offers an environment to model complex stream graph applications in Multicloud environment, where the large-scale simulation-based studies can be conducted to evaluate and analyze these applications. It leverages the features of CloudSim and integrating real-time processing model with workflow scheduling and execution to execute the modeled stream graph application in Multicloud environment. In particular, IoTSim-Stream is capable of modeling and simulating:


- Support modeling data incentive IoT-based applications using stream processing model (aka stream graph applications).
- Support modeling Multicloud environment as an execution environment for stream graph application.
- Support user-defined resource provisioning and scheduling policies.

Find this link to download the simulator and user manual.
LINK: https://rajivranjan.net/iotsim/iotsim-release/. For any query please contact Mutaz Barkia (mutaz.barika at utas.edu.au)

Also check our publication
M. Barika, S. Garg, A. Chan, R. N. Calheiros and R. Ranjan, "IoTSim-Stream: Modelling stream graph application in cloud simulation." Future Generation Computer Systems 99 (2019): 86-105.


3. IoTSim-Edge:
Tha main properties of the simulator are listed as follows.
- New IoT application graph modeling abstraction that allows practitioners to define the data analytic operations and their mapping to different parts of the infrastructure (e.g. IoT and edge).
- The abstraction that supports modeling of heterogeneous IoT protocols along with their energy consumption profile.
- It allows practitioners to define the configuration of edge and IoT devices along with the specific protocols they support for networking.
- The abstraction that supports modeling of mobile IoT devices.
- It also captures the effect of handoff caused by the movement of IoT devices.
- This supports a cooperative edge-to-edge communication that allows the transfer of the processed data of the respective IoT device by one edge via another edge.

Find this link to download the simulator and user manual.
LINK: https://rajivranjan.net/iotsim/iotsim-release/. For any query please contact Devki Jha(i.dnjha at gmail.com / dnjha.github.io).

Also check our publication for more details. D.N. Jha, K. Alwasel, A. Alshoshan, et al., "IoTSim-Edge: A simulation framework for modeling the behavior of Internet of Things and edge computing environments." Software Practice and Experience 50 (2020): 844– 867. https://doi.org/10.1002/spe.2787)