All resources with their required attributes are stored in CSV files.
The first three columns of all CSV are the following:
ID: Identifies a particular paper, type
Integer.Author: The author(s) of a particular paper, type
String.Year: The year of the publication, type
Integer.All other columns follow the pattern <Attribute>
(Example: Location) or
<Attribute>_<Specialization> (Example:
Location_GPS).
Only two characteristics attributes are possible:
y: Means “yes”, in terms of used or
required.n: Means “no”, in terms of not used or
not required.Properties:
Cloud: If nodes of type cloud are required.Fog: If nodes of type fog are required.Edge: If nodes of type edge are required.Gateway: If nodes of type gateway are required.Layer: Specifies that nodes are assigned a layer
number. This is required, for example, when layers are further
horizontally partitioned. Example: There is a fog layer 1 and are fog
layer 2.Role: Specifies that nodes are assigned a particular
role. Example: There are fog control nodes.Location_Place: The location of a particular node is
required. Example: A node is located in “Frankfurt, Germany”.Location_GPS: The GPS coordinates of a particular node
are required. Example: The GPS coordinates are described with longitude
and latitude.Group: It is required to group nodes, for example,
nodes in a particular region are grouped logically.CPU_MIPS: The CPU capabilities are required in MIPS.
Example: 1000 MIPS.CPU_Cores: The CPU capabilities in number of cores are
required. Example: 8 cores.CPU_Threads: The CPU capabilities in number of threads
are required. Example: 16 cores.CPU_GFlop: The CPU capabilities in GFlop are
required.CPU_Frequency: The CPU capabilities in terms of
frequency are required. Example: 2.0 GHz.CPU_Shares: The CPU capabilities in shares are
required. Example: 1000m.CPU_Slots: The CPU capabilities in terms of slots are
required. Example: 10 Slots are available. This value is normally
calculated out of other values.Memory_Capacity: The available memory is required in
MB, GB, or TB. Example: 5 GB.Memory_Slots: The memory resources in terms of slots
are required. Example: 10 Slots are available. This value is normally
calculated out of other values.Disk_Capacity: The available disk space is required in
MB, GB, or TB. Example: 500 GB.Disk_Slots: The available disk space in terms of slots
is required. Example: 10 Slots are available. This value is normally
calculated out of other values.Network_Capacity: The available network resources are
required. This is usually the available bandwidth provided by the
network interfaces of a particular node. Example: 1000 Mbps
download/upload.nodesStaticProps <- read.csv2("csv/nodes-static-properties.csv")| ID | Author | Year | Cloud | Fog | Edge | Gateway | Layer | Role | Location_Place | Location_GPS | Group | CPU_MIPS | CPU_Cores | CPU_Threads | CPU_GFlop | CPU_Frequency | CPU_Shares | CPU_Slots | Memory_Capacity | Memory_Slots | Disk_Capacity | Disk_Slots | Network_Capacity |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | Yigitoglu et al. | 2017 | y | y | y | n | y | n | n | n | y | n | n | n | n | n | n | n | y | n | n | n | n |
| 2 | Brogi and Forti | 2017 | y | y | n | n | n | n | y | n | n | n | n | n | n | n | n | n | y | n | n | n | n |
| 3 | Brogi et al. | 2017 | y | y | n | n | n | n | n | n | n | n | y | n | n | n | n | n | y | n | y | n | n |
| 4 | Skarlat et al. | 2017 | y | y | n | n | n | y | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
| 5 | Souza et al. | 2016 | y | y | n | n | y | n | n | n | y | n | n | n | n | n | n | y | n | y | n | y | n |
| 6 | Xia et al. | 2018 | y | n | y | y | y | n | n | n | y | n | n | n | y | n | n | n | y | n | y | n | n |
| 7 | Velasquez et al. | 2017 | y | y | n | n | n | n | n | n | n | n | n | n | n | n | n | n | n | n | n | n | n |
| 8 | Amarasinghe et al. | 2018 | y | n | y | n | y | n | n | n | y | n | y | y | n | y | n | n | y | n | n | n | n |
| 9 | Benamer et al. | 2018 | y | y | n | y | n | n | n | n | y | y | n | n | n | n | n | n | y | n | n | n | n |
| 10 | Lera et al. | 2019 | y | y | n | y | n | n | n | n | y | n | n | n | n | n | n | y | n | y | n | y | n |
| 11 | Naas et al. | 2018 | y | y | n | n | y | y | n | n | y | n | n | n | n | n | n | n | n | n | y | n | n |
| 12 | Naas et al. | 2017 | y | y | n | n | y | y | n | n | y | n | n | n | n | n | n | n | n | n | y | n | n |
| 13 | Ouyang et al. | 2017 | n | y | n | n | n | n | n | n | n | n | n | n | n | y | n | n | n | n | n | n | n |
| 14 | De Maio and Brandic | 2018 | y | n | y | n | y | n | y | y | y | y | y | n | n | n | n | n | y | n | y | n | n |
| 15 | Gargees et al. | 2016 | y | y | n | n | n | n | n | n | y | n | y | n | n | y | n | n | y | n | y | n | n |
| 16 | Hong et al. | 2017 | n | y | n | n | n | n | y | y | y | n | n | n | n | n | y | n | y | n | y | n | n |
| 17 | Venticinque and Amato | 2019 | y | y | n | n | n | y | n | n | y | n | n | n | n | n | n | n | n | n | n | n | y |
| 18 | Faticanti et al. | 2019 | y | y | n | n | n | n | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
| 19 | Guerrero et al. | 2019 | y | y | n | y | n | y | n | n | n | n | n | n | n | n | n | y | n | y | n | y | y |
| 20 | Faticanti et al. | 2020 | y | y | n | n | y | y | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
| 21 | Faticanti et al. | 2020a | y | y | n | n | y | y | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
| 22 | Armani et al. | 2021 | y | n | y | n | y | n | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
| 23 | Alencar et al. | 2022 | y | y | n | n | n | n | n | n | n | n | y | n | n | n | n | n | y | n | y | n | y |
| 24 | Fu et al. | 2021 | y | n | y | n | y | n | n | n | n | n | y | n | n | n | n | n | y | n | n | n | y |
| 25 | Lv et al. | 2022 | n | n | y | n | n | n | n | n | n | n | y | n | n | n | n | n | y | n | y | n | n |
| 26 | Kaur et al. | 2022 | y | y | y | y | n | n | n | n | y | n | n | n | n | n | n | y | n | n | n | n | n |
| 27 | Mehran et al. | 2022 | y | y | y | n | n | y | n | n | y | y | n | n | n | n | n | n | y | n | y | n | n |
Properties:
CPU_Util: Represents the current CPU utilization.
Example: 0.95.Memory_Util: Represents the current memory utilization.
Example: 0.40.Disk_Util: Represents the current disk utilization in
terms of used storage. Example: 0.35. This is not I/O
utilization.Network_Tx: Amount of transmitted traffic via the
network interface. Example: 1500 byte.Network_Rx: Amount of received traffic via the network
interfaces. Example: 1800 byte.Energy: Consumed energy by the node. Example: 1500 J.
Different values are possible based on custom calculation steps proposed
by various authors.Costs: Costs for running the node, usually expressed in
$.Workloads: List of allocated workloads on a particular
node.nodesDynamicProps<- read.csv2("csv/nodes-dynamic-properties.csv")The data columns are following this pattern:
<Relation>_<Metric>, for example
CC_RTD, which describes that the cloud layer uses,
requires, or obtains the Round-trip delay (RTD).
Relations:
CC: Between cloud nodesCF: Between cloud and fog nodesCE: Between cloud and edge nodesCU: Between cloud and end usersFF: Between fog nodesFE: Between fog and edge nodesFU: Between fog and end usersEE: Between edge nodesEU: Between edge nodes and end usersUU: Between end usersAll relations can indicate both directions equivalently, for example,
CF means cloud-to-fog and fog-to-cloud interactions.
Metrics are listed below.
Metrics:
RTD: Round-trip delayAdditional columns:
RTD_MEASURING_STATIC: Indicates how the RTD is
obtained.
y: Means “yes”, RTD is used, required, or obtained in a
static way. It does not change over time.n: Means “no” usage of RTD.RTD_MEASURING_DYNAMIC: Indicates how the RTD is
obtained.
y: Means “yes”, RTD is used, required, or obtained in a
dynamic way. It does change over time.n: Means “no” usage of RTD.linksRTDProps <- read.csv2("csv/links-round-trip-delay-properties.csv")Metrics:
E2E: End-to-end delayAdditional columns:
E2E_MEASURING_STATIC: Indicates how the E2E is
obtained.
y: Means “yes”, E2E is used, required, or obtained in a
static way. It does not change over time.n: Means “no” usage of E2EE2E_MEASURING_DYNAMIC: Indicates how the E2E is
obtained.
y: Means “yes”, E2E is used, required, or obtained in a
dynamic way. It does change over time.n: Means “no” usage of E2E.linksE2EProps <- read.csv2("csv/links-end-to-end-delay-properties.csv")Metrics:
BW: BandwidthAdditional columns:
BW_MEASURING_STATIC: Indicates how the BW is obtained.
y: Means “static”, BW is used, required, or obtained in
a static way. It does not change over time.n: Means “no” usage of BW.BW_MEASURING_DYNAMIC: Indicates how the BW is obtained.
y: Means “dynamic”, BW is used, required, or obtained
in a dynamic way. It does change over time.n: Means “no” usage of BW.linksBWProps <- read.csv2("csv/links-bandwidth-properties.csv")# hop propertiesThe following resources are not covered so far:
[1] E. Yigitoglu, M. Mohamed, L. Liu, and H. Ludwig, “Foggy: A Framework for Continuous Automated IoT Application Deployment in Fog Computing,” in 2017 IEEE International Conference on AI & Mobile Services (AIMS), Honolulu, HI, USA: IEEE, Jun. 2017, pp. 38–45. doi: 10.1109/AIMS.2017.14.
[2] A. Brogi and S. Forti, “QoS-Aware Deployment of IoT Applications Through the Fog,” IEEE Internet Things J., vol. 4, no. 5, pp. 1185–1192, Oct. 2017, doi: 10.1109/JIOT.2017.2701408.
[3] A. Brogi, S. Forti, and A. Ibrahim, “How to Best Deploy Your Fog Applications, Probably,” in 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), Madrid, Spain: IEEE, May 2017, pp. 105–114. doi: 10.1109/ICFEC.2017.8.
[4] O. Skarlat, M. Nardelli, S. Schulte, and S. Dustdar, “Towards QoS-Aware Fog Service Placement,” in 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), Madrid, Spain: IEEE, May 2017, pp. 89–96. doi: 10.1109/ICFEC.2017.12.
[5] V. B. C. Souza, W. Ramirez, X. Masip-Bruin, E. Marin-Tordera, G. Ren, and G. Tashakor, “Handling service allocation in combined Fog-cloud scenarios,” in 2016 IEEE International Conference on Communications (ICC), Kuala Lumpur, Malaysia: IEEE, May 2016, pp. 1–5. doi: 10.1109/ICC.2016.7511465.
[6] Y. Xia, X. Etchevers, L. Letondeur, T. Coupaye, and F. Desprez, “Combining hardware nodes and software components ordering-based heuristics for optimizing the placement of distributed IoT applications in the fog,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau France: ACM, Apr. 2018, pp. 751–760. doi: 10.1145/3167132.3167215.
[7] K. Velasquez, D. P. Abreu, M. Curado, and E. Monteiro, “Service placement for latency reduction in the internet of things,” Ann. Telecommun., vol. 72, no. 1–2, pp. 105–115, Feb. 2017, doi: 10.1007/s12243-016-0524-9.
[8] G. Amarasinghe, M. D. de Assuncao, A. Harwood, and S. Karunasekera, “A Data Stream Processing Optimisation Framework for Edge Computing Applications,” in 2018 IEEE 21st International Symposium on Real-Time Distributed Computing (ISORC), Singapore: IEEE, May 2018, pp. 91–98. doi: 10.1109/ISORC.2018.00020.
[9] A. R. Benamer, H. Teyeb, and N. Ben Hadj-Alouane, “Latency-Aware Placement Heuristic in Fog Computing Environment,” in On the Move to Meaningful Internet Systems. OTM 2018 Conferences, H. Panetto, C. Debruyne, H. A. Proper, C. A. Ardagna, D. Roman, and R. Meersman, Eds., in Lecture Notes in Computer Science, vol. 11230. Cham: Springer International Publishing, 2018, pp. 241–257. doi: 10.1007/978-3-030-02671-4_14.
[10] I. Lera, C. Guerrero, and C. Juiz, “Availability-Aware Service Placement Policy in Fog Computing Based on Graph Partitions,” IEEE Internet Things J., vol. 6, no. 2, pp. 3641–3651, Apr. 2019, doi: 10.1109/JIOT.2018.2889511.
[11] M. I. Naas, L. Lemarchand, J. Boukhobza, and P. Raipin, “A graph partitioning-based heuristic for runtime IoT data placement strategies in a fog infrastructure,” in Proceedings of the 33rd Annual ACM Symposium on Applied Computing, Pau France: ACM, Apr. 2018, pp. 767–774. doi: 10.1145/3167132.3167217.
[12] M. I. Naas, P. R. Parvedy, J. Boukhobza, and L. Lemarchand, “iFogStor: An IoT Data Placement Strategy for Fog Infrastructure,” in 2017 IEEE 1st International Conference on Fog and Edge Computing (ICFEC), Madrid: IEEE, May 2017, pp. 97–104. doi: 10.1109/ICFEC.2017.15.
[13] T. Ouyang, Z. Zhou, and X. Chen, “Follow Me at the Edge: Mobility-Aware Dynamic Service Placement for Mobile Edge Computing,” IEEE J. Select. Areas Commun., vol. 36, no. 10, pp. 2333–2345, Oct. 2018, doi: 10.1109/JSAC.2018.2869954.
[14] V. De Maio and I. Brandic, “First Hop Mobile Offloading of DAG Computations,” in 2018 18th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGRID), Washington, DC, USA: IEEE, May 2018, pp. 83–92. doi: 10.1109/CCGRID.2018.00023.
[15] R. Gargees et al., “Incident-Supporting Visual Cloud Computing Utilizing Software-Defined Networking,” IEEE Trans. Circuits Syst. Video Technol., vol. 27, no. 1, pp. 182–197, Jan. 2017, doi: 10.1109/TCSVT.2016.2564898.
[16] H.-J. Hong, P.-H. Tsai, A.-C. Cheng, M. Y. S. Uddin, N. Venkatasubramanian, and C.-H. Hsu, “Supporting Internet-of-Things Analytics in a Fog Computing Platform,” in 2017 IEEE International Conference on Cloud Computing Technology and Science (CloudCom), Hong Kong: IEEE, Dec. 2017, pp. 138–145. doi: 10.1109/CloudCom.2017.45.
[17] S. Venticinque and A. Amato, “A methodology for deployment of IoT application in fog,” J Ambient Intell Human Comput, vol. 10, no. 5, pp. 1955–1976, May 2019, doi: 10.1007/s12652-018-0785-4.
[18] F. Faticanti, F. De Pellegrini, D. Siracusa, D. Santoro, and S. Cretti, “Cutting Throughput with the Edge: App-Aware Placement in Fog Computing,” in 2019 6th IEEE International Conference on Cyber Security and Cloud Computing (CSCloud)/ 2019 5th IEEE International Conference on Edge Computing and Scalable Cloud (EdgeCom), Paris, France: IEEE, Jun. 2019, pp. 196–203. doi: 10.1109/CSCloud/EdgeCom.2019.00026.
[19] C. Guerrero, I. Lera, and C. Juiz, “Evaluation and efficiency comparison of evolutionary algorithms for service placement optimization in fog architectures,” Future Generation Computer Systems, vol. 97, pp. 131–144, Aug. 2019, doi: 10.1016/j.future.2019.02.056.
[20] F. Faticanti, M. Savi, F. D. Pellegrini, P. Kochovski, V. Stankovski, and D. Siracusa, “Deployment of Application Microservices in Multi-Domain Federated Fog Environments,” in 2020 International Conference on Omni-layer Intelligent Systems (COINS), Barcelona, Spain: IEEE, Aug. 2020, pp. 1–6. doi: 10.1109/COINS49042.2020.9191379.
[21] F. Faticanti, F. De Pellegrini, D. Siracusa, D. Santoro, and S. Cretti, “Throughput-Aware Partitioning and Placement of Applications in Fog Computing,” IEEE Trans. Netw. Serv. Manage., vol. 17, no. 4, pp. 2436–2450, Dec. 2020, doi: 10.1109/TNSM.2020.3023011.
[22] V. Armani, F. Faticanti, S. Cretti, S. Kum, and D. Siracusa, “A Cost-Effective Workload Allocation Strategy for Cloud-Native Edge Services,” 2021, doi: 10.48550/ARXIV.2110.12788.
[23] D. Alencar, C. Both, R. Antunes, H. Oliveira, E. Cerqueira, and D. Rosario, “Dynamic Microservice Allocation for Virtual Reality Distribution With QoE Support,” IEEE Trans. Netw. Serv. Manage., vol. 19, no. 1, pp. 729–740, Mar. 2022, doi: 10.1109/TNSM.2021.3076922.
[24] K. Fu et al., “QoS-Aware and Resource Efficient Microservice Deployment in Cloud-Edge Continuum,” in 2021 IEEE International Parallel and Distributed Processing Symposium (IPDPS), Portland, OR, USA: IEEE, May 2021, pp. 932–941. doi: 10.1109/IPDPS49936.2021.00102.
[25] W. Lv et al., “Microservice Deployment in Edge Computing Based on Deep Q Learning,” IEEE Trans. Parallel Distrib. Syst., pp. 1–1, 2022, doi: 10.1109/TPDS.2022.3150311.
[26] K. Kaur, F. Guillemin, V. Q. Rodriguez, and F. Sailhan, “Latency and network aware placement for cloud-native 5G/6G services,” in 2022 IEEE 19th Annual Consumer Communications & Networking Conference (CCNC), Las Vegas, NV, USA: IEEE, Jan. 2022, pp. 114–119. doi: 10.1109/CCNC49033.2022.9700582.
[27] N. Mehran, D. Kimovski, and R. Prodan, “MAPO: A Multi-Objective Model for IoT Application Placement in a Fog Environment,” in Proceedings of the 9th International Conference on the Internet of Things, Bilbao Spain: ACM, Oct. 2019, pp. 1–8. doi: 10.1145/3365871.3365892.