This repository contains code and instruction to reproduce the experiments presented in the paper
"A Methodology and Simulation-based Toolchain for Estimating Deployment Performance of Intelligent Collective Services at the Edge"
by Roberto Casadei, Giancarlo Fortino, Danilo Pianini, Andrea Placuzzi, Claudio Savaglio, and Mirko Viroli.
The dataset contains the navigation measurements obtained in the indoor experiment field. The volunteers move on the whole 4th floor of the Building D of Dong Jiu Teaching classes at Huazhong University of Science and Technology. Meanwhile, the experimental area consists of a total area of 717 m 2. These datasets were used and can be used to test and validate the radio map database updating-based localization positioning algorithm through the RSSI signals space.
This is a dataset of client-server Round Trip Time delays of an actual cloud gaming tournament run on the infrastructure of the cloud gaming company Swarmio Inc. The dataset can be used for designing algorithms and tuning models for user-server allocation and server selection. To collect the dataset, tournament players were connected to Swarmio servers and delay measurements were taken in real time and actual networking conditions.
For the main dataset, the 189 players and the 9 servers were distributed among 4 different regions: North America, South America, Europe, East Asia. The 9 servers were located in the following cities with their acronyms in the dataset:
- Santa Clara (nasc),
- Chicago (nach),
- Dallas (nada),
- Toronto (nato),
- Brazil (sabr),
- London (uk),
- Amsterdam (nl),
- Hong Kong (hk),
- Singapore (sg).
Each of the 189 players were able to connect to each of the 9 servers. The following data is registered for each player:
- User Identifier (in the field: user_id)
- Time of access (in the field: timestamp)
- Longitude (in the field: longitude)
- Latitude (in the field: latitude)
- IP Address (in the field: address)
- Access Support Network or Internet Service Provider (in the field: asn_org)
In the dataset file main-dataset.json, every record contains the network delay measurements from a particular player to each of the 9 servers. It should be noted that the URLs and the IP addresses of the servers are provided in a separate file main-dataset-servers.json.
The user ID is a unique 32-character identifier that is generated for each player; for example, 5193b0e1-2412-4338-ac8d-6f519049aa77. The time of access is based on the Unix timestamp which is counted in seconds January 1, 1970; for example, 1528484445170. Longitude and latitude are based on the geo-location of the player; for example, "longitude": "121.0409", "latitude": "14.5832". The Access Support Network is the ISP network in which the player is registered, for example Rogers Communications Canada Inc, Philippine Long Distance Telephone Company, AT&T Services Inc., tec.
Each measurement consisted of sending 11 packets from the player to the server, and the following measurements were obtained (all in ms):
- Median latency/delay (in the field: latency)
- Delay jitter (in the field: jitter)
- Minimum obtained delay (in the field: min)
- Maximum obtained delay (in the field: max)
- Average obtained delay (in the field: avr)
It should be noted that out of the 9 servers, only the 1st server (“nl”) was used for testing the connection, and that can be noted from the field “testing” having the value of “1”. Therefore, the value of “stats” for the first server will have no measurements.
For the secondary dataset, we set up 11 different servers: 1 server owned by Swarmio Media in Toronto and 10 servers using the AWS cloud in the following locations:
- North Virginia,
- Northern California,
- Sydney, AU
The same script as the main dataset was run in the Swarmio client software of 67 players. This time, each server sent 8 packets to each player, and only the average delay was recorded and stored.
The secondary dataset consists of the JSON file secondary-dataset.json, where the keys are the names of the servers, and the values contain a list of the delays to the 67 players. The players IPs are provided in order in a separate file secondary-dataset-users.json. It is also possible to reuse the code that was used to retrieve the measurements in the file HostsUsersRTT.py . The IP addresses of the 11 servers can also be accessed in the file secondary-dataset-servers.json where the key of the record will have the name of the server; for example “N Virginia”, and the value will have the IP address of the server
In contrast to the main dataset, the secondary dataset contains only the delay between the servers and the players whereas the main dataset has more information such as the geo-location and the ISP. This makes the secondary dataset more suitable for testing and verification due to having a single label with only 2 features (IP addresses and city names), while the main dataset contains more features and measurements suitable for training and inference.
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The data containing red team activities is divided into three sets, each corresponding to the three days of evaluation: 23Sep19, 24Sep19, and 25Sep19. The fourth set (23Sep19-night) contains no threats and contains data from the first night of evaluations, when clients were left running unattended overnight to collect additional baseline data.
During the initial one thousand client test, each mainframe server hosted fifty Windows clients. Half of the clients were taken down from each server for data collection, reducing the number of clients to five hundred, which resulted in a client machine naming continuity gap (e.g. Sys001-Sys025, Sys051-Sys075, …, Sys951-Sys975).
A full description of the contents, including message formats and file structure can be found in the OpTC-data-release.md file attached to this page and included in the root directory of the OpTC.tar.gz.
5G technologies have enabled new applications on a heterogeneous and distributed infrastructure edge which unifies hardware, network and software aimed at digital enabling. Based on the requirements of Industry 4.0, this infrastructure is developed using the cloud and fog computing sharing model, which should meet the needs of service level agreements in a convenient and optimized way, requiring an orchestration mechanism for the dynamic resource allocation.