CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing

Jayakumar, Vinodh Kumaran; Arbat, Shivani; Kim, In Kee; Wang, Wei

doi:10.1109/ic2e55432.2022.00027

Cited by 6 publications

(1 citation statement)

References 38 publications

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“…Dang-Quang and Yoo (2021) introduce a proactive custom autoscaler for Kubernetes, leveraging a Bidirectional Long Short-term Memory (Bi-LSTM) model for precise HTTP workload prediction, inspired by the Monitor-Analyze-Plan-Execute (MAPE) loop [23]. Similar work presented by Jayakumar et al (2022) [24]. Toka et al (2020) employs various LSTM based forecast methods, dynamically selects the most suitable forecasting approach to match real-time request dynamics [25].…”

Section: Related Workmentioning

confidence: 99%

Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms

Pintye,

Kovács,

Lovas

2024

Preprint

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With the increasing utilisation of machine-learning algorithms, autoscaling methods have the potential to provide more sophisticated control mechanisms for the cloud application operators over virtualized resources in terms of provisioning. This paper introduces machine learning-based autoscaling approach introducing several improvements including metric selection, proactivity, various enhancements of the neural network among others. Our enhanced machine learning models enables the autoscaler algorithm to react more quickly to sudden load changes, increase proactivity while using fewer resources and reducing Quality of Service (QoS) violations for cloud-based services. The comprehensive measurements indicate that QoS violations may reduce by up to 80%, while the level of resource utilization either remains constant or decreases slightly (by 3-4%) in certain applications. In cases where there was no reduction in QoS violations, the utilization of resources saw a significant decline, falling between 20-50%. The proposed changes have been analyzed and tested under various conditions, representing 3 distinct and common use cases in cloud environments. This developed process has been implemented on the science cloud of the Hungarian Research Network supporting the operation of the infrastucture that hosts over 300 scientific projects.

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Section: Related Workmentioning

confidence: 99%

Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms

Pintye,

Kovács,

Lovas

2024

Preprint

View full text Add to dashboard Cite

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Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

Pintye,

Kovács,

Lovas

2024

J Grid Computing

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Performance and cost-effectiveness are sustained by efficient management of resources in cloud computing. Current autoscaling approaches, when trying to balance between the consumption of resources and QoS requirements, usually fall short and end up being inefficient and leading to service disruptions. The existing literature has primarily focuses on static metrics and/or proactive scaling approaches which do not align with dynamically changing tasks, jobs or service calls. The key concept of our approach is the use of statistical analysis to select the most relevant metrics for the specific application being scaled. We demonstrated that different applications require different metrics to accurately estimate the necessary resources, highlighting that what is critical for an application may not be for the other. The proper metrics selection for control mechanism which regulates the requried recources of application are described in this study. Introduced selection mechanism enables us to improve previously designed autoscaler by allowing them to react more quickly to sudden load changes, use fewer resources, and maintain more stable service QoS due to the more accurate machine learning models. We compared our method with previous approaches through a carefully designed series of experiments, and the results showed that this approach brings significant improvements, such as reducing QoS violations by up to 80% and reducing VM usage by 3% to 50%. Testing and measurements were conducted on the Hungarian Research Network (HUN-REN) Cloud, which supports the operation of over 300 scientific projects.

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DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-Clouds with Dynamic Power-Budgets

Hewage,

Ilager,

Rodriguez

et al. 2023

2023 IEEE 16th International Conference on Cloud Computing (CLOUD)

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Cloud platforms commonly exploit workload temporal flexibility to reduce their carbon emissions. They suspend/resume workload execution for when and where the energy is greenest. However, increasingly prevalent delay-intolerant realtime workloads challenge this approach. To this end, we present a framework to harvest green renewable energy for real-time workloads in cloud systems. We use Renewables-driven cores in servers to dynamically switch CPU cores between real-time and low-power profiles, matching renewable energy availability. We then develop a VM Execution Model to guarantee running VMs are allocated with cores in the real-time power profile. If such cores are insufficient, we conduct criticality-aware VM evictions as needed. Furthermore, we develop a VM Packing Algorithm to utilize available cores across the data center. We introduce the Green Cores concept in our algorithm to convert renewable energy usage into a server inventory attribute. Based on this, we jointly optimize for renewable energy utilization and reduction of VM eviction incidents. We implement a prototype of our framework in OpenStack as openstack-gc. Using an experimental openstack-gc cloud and a large-scale simulation testbed, we expose our framework to VMs running RTEval, a real-time evaluation program, and a 14-day Azure VM arrival trace. Our results show: i) a 6.52× reduction in coefficient of variation of real-time latency over an existing workload temporal flexibilitybased solution, and ii) a joint 79.64% reduction in eviction incidents with a 34.83% increase in energy harvest over the stateof-the-art packing algorithms. We open source openstack-gc at https://github.com/tharindu-b-hewage/openstack-gc.

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CloudBruno: A Low-Overhead Online Workload Prediction Framework for Cloud Computing

Cited by 6 publications

References 38 publications

Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms

Comprehensive enhancements for machine-learning based cloud resource orchestration algorithms

Enhancing Machine Learning-Based Autoscaling for Cloud Resource Orchestration

DEMOTS: A Decentralized Task Scheduling Algorithm for Micro-Clouds with Dynamic Power-Budgets

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