OMBM-ML: efficient memory bandwidth management for ensuring QoS and improving server utilization

Sung, Hanul; Min, Jeesoo; Koo, Donghun; Eom, Hyeonsang

doi:10.1007/s10586-020-03191-2

Cited by 5 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, the authors of [2] and [13] proposed a novel scheduling of scientific workflows, while in [19] QoS and cost optimization of cloud resource allocation is discussed. Sung et al [28] describe Optimized Memory Bandwidth Management Machine Learning to manage resources for latency-critical workloads and the authors of [1] describe an algorithm that evaluates resource utilization requirements for incoming tasks. Although this method does not require load prediction, the solution must know the incoming requests' resource demands, which may create a big disadvantage where load data are not well defined.…”

Section: Related Workmentioning

confidence: 99%

“…The idea is to pay attention to the specific characteristics of data patterns. Based on Incoming request [12] Online incremental learning X modelling [26] Learning automata X [8] Time-series with queuing theory X [31] Time-series analysis X X Real-life QoS data with artificially generated load data Incoming traffic as time series [20] RNNs and LSTM X Dataset from Afry, 30 minutes long forecast [27] Holt-Winters, ARIMA and LSTM X One-step forecasting Scheduling [14] Random Forest X incoming task to the [2,13] Scheduling of scientific workflows X available resources [19] QoS and cost optimization X [28] Optimized Memory Bandwidth Management Machine Learning X [1] Evaluation of resource utilization X Artificial intelligence [25] Autoscaling of network resources X techniques [10] RNNs X Dataset partially from PlanetLab [33] General framework for a VM reservation plan X Dataset from Wikipedia [16] Random Forest and ARIMA X Dataset from EMPRES-I [21] Multilayer Perceptron X Dataset from IPTV [32] Deep learning model X Dataset from PlanetLab [30] LSTM, Random Forest, linear regression and Gaussian process regression X QoS-driven resource [7] Incoming task analysis X Dataset from WSDream allocation [17] Incoming task analysis X [6] Iterative QoS prediction model X [29] Time series prediction X Dataset from Amazon and Google [22][23][24] Multi-stage optimization process with sophisticated data-cleaning, monitoring and scaling mechanisms…”

Section: Preliminary Researchmentioning

confidence: 99%

See 1 more Smart Citation

Data-Driven Adaptive Prediction of Cloud Resource Usage

2023

View full text Add to dashboard Cite

Predicting computing resource usage in any system allows optimized management of resources. As cloud computing is gaining popularity, the urgency of accurate prediction is reduced as resources can be scaled on demand. However, this may result in excessive costs, and therefore there is a considerable body of work devoted to cloud resource optimization which can significantly reduce the costs of cloud computing. The most promising methods employ load prediction and resource scaling based on forecast values. However, prediction quality depends on prediction method selection, as different load characteristics require different forecasting mechanisms. This paper presents a novel approach that incorporates data-driven adaptation of prediction algorithms to generate short- and long-term cloud resource usage predictions and enables the proposed solution to readjust to different load characteristics as well as both temporary and permanent usage changes. First, preliminary tests were performed that yielded promising results – up to 36% better prediction quality. Subsequently, a fully autonomous, multi-stage optimization solution was proposed. The proposed approach was evaluated using real-life historical data from various production servers. Experiment results demonstrate 9.28% to 80.68% better prediction quality when compared to static algorithm selection.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Preliminary Researchmentioning

confidence: 99%

Data-Driven Adaptive Prediction of Cloud Resource Usage

2023

View full text Add to dashboard Cite

show abstract

“…e study focuses only on the IP traffic network and does not mention specifically the application of ML to the enhancement of the QoS of IoT devices. Furthermore, many studies have discussed the use of ML techniques to enhance the QoS, such as [163][164][165][166][167][168].…”

Section: For Enhanced Qos In Iot Environmentsmentioning

confidence: 99%

Machine Learning for Smart Environments in B5G Networks: Connectivity and QoS

Alsamhi

Almalki

Al-Dois

et al. 2021

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

The number of Internet of Things (IoT) devices to be connected via the Internet is overgrowing. The heterogeneity and complexity of the IoT in terms of dynamism and uncertainty complicate this landscape dramatically and introduce vulnerabilities. Intelligent management of IoT is required to maintain connectivity, improve Quality of Service (QoS), and reduce energy consumption in real time within dynamic environments. Machine Learning (ML) plays a pivotal role in QoS enhancement, connectivity, and provisioning of smart applications. Therefore, this survey focuses on the use of ML for enhancing IoT applications. We also provide an in-depth overview of the variety of IoT applications that can be enhanced using ML, such as smart cities, smart homes, and smart healthcare. For each application, we introduce the advantages of using ML. Finally, we shed light on ML challenges for future IoT research, and we review the current literature based on existing works.

show abstract