Simulation Tools for Fog Computing: A Comparative Analysis
Abstract
:1. Introduction
- We analyze eight recent and most popular fog simulators and compare them from a technical and non-technical point of view (e.g., their current support for different features along with their future goals).
- The performance of three selected simulators is further compared in a practical way by simulating three different applications along with variations in the complexity of scenarios (number of fog and edge devices) and provides discussion on the suitability of simulators for these applications.
- We provide future guidelines for researchers that need attention and need to be addressed in the future.
2. Related Work
3. Cloud-Fog Simulators
3.1. Simulators Overview
3.1.1. iFogSim and iFogSim2
3.1.2. FogNetSim++
3.1.3. EdgeCloudSim
3.1.4. FogComputingSim
3.1.5. PureEdgeSim
3.1.6. YAFS
3.1.7. LEAF
3.2. Non-Technical Comparison
3.3. Technical Comparisons
- Documentation: Represents whether the simulator is accompanied by documentation, wiki, etc. It is important to note that the quality and completeness of the documentation may vary. Documentation plays an important role in maintaining the simulator by the community.
- Graphical support: Represents if the simulator is accompanied by a Graphical User Interface (GUI). In other words, shows if you can build the Fog network architecture using an interface.
- Migration support: Depicts whether the simulator has mechanisms for migrating applications from one node to another.
- Mobility/Location-aware: Shows whether the simulator supports the motion of IoT devices. This feature is essential for representing real scenarios with moving users.
- Energy-aware: Depicts whether the simulator has knowledge of the energy consumption of the architecture and application they are simulating. We decided to separate it into sub-categories: infrastructure, application, network, technology, and carbon emission. These features are crucial to designing less energy-intensive systems.
- Cost-aware: Represents whether the simulator has knowledge of the monetary costs involved in FC. This feature is crucial in seeking to optimize deployment, operational, and other costs.
- Microservices: Depicts if the simulator supports an orchestration model for microservices deployed across the multi-tier infrastructure.
- CPU consumption gives the CPU usage of the machine running the simulation.
- Memory consumption gives the memory usage of the machine running the simulation.
- Bandwidth consumption provides the usage of bandwidth during the simulation.
- Energy consumption is the energy consumption of each node during the simulation following an energy model. We have decided to distinguish consumption into 5 parts: infrastructure, network (e.g., WAN, LAN), application, technology (e.g., Wifi, Cellular), and carbon emission.
- Deployment cost gives the total cost of the simulation following a cost model.
- Latency giving the total latency of the simulation.
- Execution time is the total execution time of the simulation.
- CPU time gives the total CPU time of the machine running the simulation.
- Network time is the total time of network usage.
- Migration time is the total time passed on migration during the simulation.
- Failed tasks is the number of failed tasks during the simulation.
- Waiting time is the total time waiting during the simulation.
- Link availability provides the total availability of each link.
- Node availability provides the total availability of each node.
3.4. Summary and Discussion
4. Practical Comparisons
4.1. Simulation Framework
4.2. Applications
4.3. Infrastructure
4.4. Results
4.5. Summary and Discussion
5. Open Issues and Challenges
5.1. Lack of Documentation
5.2. Version Support
5.3. Support for Telco-Cloud Experiments
“a horizontal, physical or virtual resource paradigm that resides between smart end-devices and traditional cloud or data centers. This paradigm supports vertically-isolated, latency-sensitive applications by providing ubiquitous, scalable, layered, federated, and distributed computing, storage, and network connectivity.”
5.4. Selection between Emulators, Simulators and Industry solutions
5.5. Graphical Support
5.6. Support for Features and Consistency in Terminologies
5.7. Serverless Fog Computing
5.8. Green Fog Computing
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
CLOUDS | Cloud computing and Distributed Systems |
FC | Fog Computing |
EC | Edge Computing |
IoT | Internet of Things |
MEC | Multi-access Edge Computing |
GUI | Graphical User Interface |
RANs | Radio Access Networks |
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Ref. | Year | Citations | Environment | Comparison Criteria | Future Guidelines | Issues | ||||
---|---|---|---|---|---|---|---|---|---|---|
Cloud | Fog | Edge | Technical | Non-Technical | Performance | |||||
[1] | 2020 | 47 | ✓ | ✓ | ✓ | ∂ | ✓ | Deprecated tools | ||
[3] | 2020 | 31 | ✓ | ✓ | ✓ | ∂ | Limited to only analysis of cost model, Many of the issues have been resolved by the simulators in their new versions | |||
[10] | 2021 | 3 | ✓ | ✓ | ∂ | ✓ | Deprecated tools and no performance comparison | |||
[11] | 2018 | 114 | ✓ | ✓ | ∂ | ∂ | ✓ | Deprecated simulators and limited to a conceptual approach | ||
[12] | 2022 | 2 | ∂ | ∂ | ∂ | ∂ | Comprehensive overview and does not provide any practical comparisons | |||
[13] | 2022 | 0 | ∂ | ✓ | ✓ | Comprehensive overview and lacks to provide any practical comparisons | ||||
[14] | 2020 | 15 | ✓ | ✓ | ✓ | ✓ | ∂ | Most of the issues mentioned have been resolved by simulators, limited performance analysis | ||
[15] | 2020 | 52 | ✓ | ✓ | ✓ | ✓ | ✓ | ∂ | Deprecated simulators, Lack of performance analysis | |
[16] | 2020 | 85 | ✓ | ✓ | ✓ | ∂ | ∂ | Limited focus of simulators, Lack of applicability details | ||
[17] | 2019 | 107 | ✓ | ✓ | ∂ | ∂ | Deprecated simulators, Lack to provide details on most of the technical features, no performance analysis | |||
[18] | 2019 | 31 | ✓ | ✓ | ✓ | Lack of Non-technical comparisons, Deprecated tools | ||||
[19] | 2016 | 38 | ✓ | ✓ | Limited to cloud simulators | |||||
[20] | 2020 | 5 | ✓ | ✓ | ✓ | Limited comparisons, Deprecated tools | ||||
[21] | 2021 | 5 | ✓ | ∂ | The considered tools mostly belongs to IoT technologies | |||||
Our Work | N/A | N/A | ✓ | ✓ | ∂ | ✓ | ✓ | ✓ | ✓ | - |
Simulators | First Release | Latest Release | Stars | Citations | Paper | GitHub | Release Frequency | Response Frequency | Installation |
---|---|---|---|---|---|---|---|---|---|
iFogSim | 2016 | 2016 | 168 | 1168 | [29] | [29] | deprecated | low | ✓ |
iFogSim2 | 2022 | 2022 | 44 | 8 | [35] | [36] | low | high | ✓ |
FogNetSim++ | 2018 | 2018 | 8 | 114 | [11] | [38] | low | low | ✓ |
EdgeCloudSim | 2018 | 2020 | 297 | 364 | [24] | [39] | moderate | low | ✓ |
FogComputingSim | 2019 | 2019 | 15 | N/A | [40] | [41] | low | N/A | ✗ |
PureEdgeSim | 2019 | 2022 | 85 | 21 | [42] | [42] | moderate | high | ✓ |
YAFS | 2019 | 2021 | 55 | 111 | [22] | [43] | moderate | high | ✓ |
LEAF | 2021 | 2022 | 59 | 6 | [23] | [44] | moderate | high | ✓ |
Simulators | Language | Documentation | Graphical Support | Migration Support | Mobility/ Locationaware Support | Energyaware Model | Costaware Model | Microservices Support | Future Works |
---|---|---|---|---|---|---|---|---|---|
iFogSim * | Java | ✓ | ✓ | ✓ | ✓ | ✓ | N/A | ||
iFogSim2 * | Java | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Monetary-based policies, support for distributed ledgers and federated machine learning |
FogNetSim++ *** | C++ | ✓ | ✓ | ✓ | ✓ | Support for virtual machine (VM) migration and interoperability | |||
EdgeCloudSim * | Java | ✓ | ✓ | ✓ | Add a hand-off mechanism to decrease the task failures | ||||
FogComputingSim ** | Java | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | Refining support for mobility patterns based on real datasets and other mobile communication technologies (e.g., Wi-Fi) | |
PureEdgeSim * | Java | ✓ | ✓ | ✓ | Support for the registry and the VM migrations | ||||
YAFS | Python | ✓ | ✓ | ✓ | ✓ | ✓ | Power-aware management policies, Controlling the computational capacity of the resources and improvements in the nomenclature. | ||
LEAF | Python or Java | ✓ | ✓ | Time-based and location-based calculations of the carbon emissions and electricity costs |
Metrics | Simulators | ||||||||
---|---|---|---|---|---|---|---|---|---|
iFogSim | iFogSim2 | FogNetSim++ | EdgeCloudSim | FogComputingSim | PureEdgeSim | YAFS | LEAF | ||
CPU consumption | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
Memory consumption | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
Bandwidth consumption | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
Energy consumption | Infrastructure Nodes | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |
Network | ✓ | ✓ | ✓ | ✓ | |||||
Application | ✓ | ||||||||
Technology | ✓ | ✓ | |||||||
Carbon emissions | |||||||||
Deployment cost | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Latency | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | ||
Execution time | ✓ | ✓ | ✓ | ✓ | ✓ | ✓ | |||
CPU time | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Network time | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Migration time | ✓ | ✓ | ✓ | ||||||
Failed tasks | ✓ | ✓ | ✓ | ✓ | ✓ | ||||
Waiting time | ✓ | ✓ | ✓ | ||||||
Link availability | |||||||||
Node availability | ✓ |
Resources/Layer | Cloud | Fog | IoT |
---|---|---|---|
CPU | 44,800 MIPS | 2800 MIPS | 1000 MIPS |
Memory | 40 GB | 4 GB | 1 GB |
Bandwidth | 10 GB | 1 GB | 100 MB |
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Fahimullah, M.; Philippe, G.; Ahvar, S.; Trocan, M. Simulation Tools for Fog Computing: A Comparative Analysis. Sensors 2023, 23, 3492. https://doi.org/10.3390/s23073492
Fahimullah M, Philippe G, Ahvar S, Trocan M. Simulation Tools for Fog Computing: A Comparative Analysis. Sensors. 2023; 23(7):3492. https://doi.org/10.3390/s23073492
Chicago/Turabian StyleFahimullah, Muhammad, Guillaume Philippe, Shohreh Ahvar, and Maria Trocan. 2023. "Simulation Tools for Fog Computing: A Comparative Analysis" Sensors 23, no. 7: 3492. https://doi.org/10.3390/s23073492
APA StyleFahimullah, M., Philippe, G., Ahvar, S., & Trocan, M. (2023). Simulation Tools for Fog Computing: A Comparative Analysis. Sensors, 23(7), 3492. https://doi.org/10.3390/s23073492