Cloud Computing
Internet telephony consists of a combination of hardware and software that enables you to
use the Internet as the transmission medium for telephone calls. For users who have free,
or fixed-price Internet access, Internet telephony software essentially provides free
telephone calls anywhere in the world. In its simplest form, PC-to-PC Internet telephony
can be as easy as hooking up a microphone to your computer and sending your voice
through a cable modem to a person who has Internet telephony software that is
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The Multi-Server Multi-User computation offloading dataset is a dataset based on the scenario of multi-server multi-user binary computing offloading. It is characterized by the connection status between users and edge servers, user task information, and server computational resource information. The solution aims to minimize the total cost of power consumption and latency of all tasks. The labels are the offloading decisions of user tasks and the computational resource allocation of edge servers. The features and labels of this dataset are graph-structured.
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This dataset results from a 5-month-long Cloud Telescope Internet Background Radiation collection experiment conducted during the months of October 2023 until February 2024.
A total amount of 130 EC2 instances (sensors) were deployed across all the 26 commercially available AWS regions at the time, 5 sensors per region.
A Cloud Telescope sensor does not serve information. All traffic arriving to the sensor is unsolicited, and potentially malicious. Sensors were configured to allow all unsolicited traffic.
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The softwarization and virtualization of the fifth-generation (5G) cellular networks bring about increased flexibility and faster deployment of new services. However, these advancements also introduce new vulnerabilities and unprecedented attack surfaces. The cloud-native nature of 5G networks mandates detecting and protecting against threats and intrusions in the cloud systems.
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This dataset contains CoreMark result on azure Virtual machines in west Europe and Poland central regions. The dataset covers all available virtual machine types in given region form 2 CPU up to 20 CPU's. The dataset consists of VM type, actual processor that was running the CoreMark workload, number of iterations per second, total ticks, length of test is seconds, compiler version of the Microsoft Azure region that was chosen for the test. The dataset has 4772 row which is equal to the number of experiments run on each Virtual machine.
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Alibaba Cluster Trace (cluster-trace-v2018) . The dataset comprises metadata and runtime information concern-ing 4K machines, 71K online services, and 4M batch jobs over an 8-day horizon. Compared with the cluster-trace-v2017 dataset, this dataset features a longer sampling period, a larger number of workloads, and more fine-grained directed acyclic graph (DAG) dependency information.
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This dataset contains the source data and experimental result data required by the tarp project. The cover depicts our proposed adaptive resource allocation approach based on graph neural networks for optimizing qos-aware interactive microservices in cloud computing. This method uses DAG topology to extract the global characteristics of microservices, and adaptively generates microservice resource allocation strategies, which can effectively use microservice resources while ensuring the quality of service.
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This dataset is composed of 2000 time-series (1000 Read and 1000 Write) realized from the much larger cloud storage workload released to the research community by the Alibaba group. The original dataset can be download from here: (https://github.com/alibaba/block-traces).
This original dataset collected over 31 days contains read/write data for 1000 storage volumes. The schema for each file given the file names and columns per file is explained:
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This dataset plays a pivotal role in facilitating efficient resource management strategies, catering to the complex needs of modern Fog/Cloud environments. It comprises comprehensive information regarding machine configurations, task requirements, and bandwidth allotments. These details are indispensable for optimizing resource utilization, ensuring tasks are assigned to suitable machines based on their capabilities, and managing bandwidth allocation to prevent bottlenecks and maximize network efficiency.
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These datasets are collected from the tests that were performed for decentralized synchronization among collaborative robots via 5G and Ethernet networks using with/without causal message ordering. These files have different names depending on the connection type and causality type. For example, 5G_with_causality.txt file stores the test results which were performed on a public 5G network using causal message ordering for different cobot groups like 5,10,20,30,40. The test results for each robot group are separated in each txt file.
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