Cloud Computing

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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This dataset results from a 47-day Cloud Telescope Internet Background Radiation collection experiment conducted during the months of August and September 2023.
A total amount of 260 EC2 instances (sensors) were deployed across all the 26 commercially available AWS regions at the time, 10 sensors per region.

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This dataset results from a month-long cloud-based Internet Background Radiation observation conducted in May 2023.
A sensor fleet comprised of 26 EC2 compute instances was deployed within Amazon Web Services across their 26 commercially available regions, 1 sensor per region.

The dataset contains 21,856,713 incoming packets, out of which 17,008,753 are TCP datagrams, 3,076,855 are ICMP packets and the remainder, 1,770,418 are UDP messages.

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We utilized Digital Ocean's cloud service, setting up three Linux virtual machines, each with 1vCPU, 1GB of memory, and a 10GB disk. The architecture included an API gateway for routing requests to a stateless application service backed by a database for storing application data. The application operates the service under a fluctuating workload generated by a load-testing script to simulate real-world usage scenarios. The target source or the application service is integrated with Prometheus, a monitoring tool for gathering system metrics.

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The Reflection Server Tuning dataset contains HiPerConTracer latency measurements performed in a lab setup. The purpose of the dataset is to measure the latency and jitter effects of  firewalls and Linux kernel tuning.

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The data set was used to produce three figures (Figs. 4, 5, and 6) in the experimental section of a manuscript submitted for review to the IEEE Communications Magazine. The submission, titled Lightweight Determinism in Large-Scale Networks, describes a novel approach to the realization of network determinism in packet-switched networks of generic size and topology.

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The advent of the Internet of Things has increased the interest in automating mission-critical processes from domains such as smart cities. These applications' stringent Quality of Service (QoS) requirements motivate their deployment through the Cloud-IoT Continuum, which requires solving the NP-hard problem of placing the application's services onto the infrastructure's devices. Moreover, as the infrastructure and application change over time, the placement needs to continuously adapt to these changes to maintain an acceptable QoS.

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