Elastic Resource Provisioning Using Data Clustering in Cloud Service Platform

Fei, Bowen; Zhu, Xiaomin; Liu, Daqian; Chen, Junjie; Bao, Weidong; Liu, Ling

doi:10.1109/tsc.2020.3002755

Cited by 20 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many approaches focus on workload intensity forecasting. Herbst et al (2014);Fei et al (2020); Erradi et al (2021); Feng et al (2022) forecast the workload intensity by choosing suitable regression models including the Elastic Net regression model, the boosting decision tree regression prediction model, the Ridge regression model,the Lasso regression model , and the Multi-Layer Perceptron. Time series forecasting approaches including Telescope and Prophet are also applied to forecast the context-tailored workload intensity (Schulz et al, 2021).…”

Section: Workload Intensity Modelingmentioning

confidence: 99%

“…Moreover, LWS explores different strategies of workload intensity modeling and combines them for workload simulation which can be intervened effectively for quick, extensible, and intervenable workload generation. Besides, some other approaches (Herbst et al, 2014;Fei et al, 2020;Erradi et al, 2021;Schulz et al, 2021;Feng et al, 2022) attempt to forecast future workloads based on time series forecasting and workload specification. Different from these approaches, LWS provides the e2e framework for workload simulation rather than specific forecasts or analysis for concrete systems.…”

Section: Differences Between Lws and Existing Approachesmentioning

confidence: 99%

“…At the same time, due to the heterogeneous, asynchronous, and independent nature, the operation and management of cloud-native systems and microservice-based applications are difficult and have become the focus of research. Prominent research topics are performance testing for satisfying performance requirements such as latency, throughput, and scalability in performance-critical software systems (Denaro et al, 2004;Helali Moghadam et al, 2019), dynamic resource provisioning and scheduling for maximizing resource utilization (Caglar et al, 2018;Fei et al, 2020), and Artificial Intelligence for IT Operations (AIOps) that enhances the quality and reliability of IT service offerings (Notaro et al, 2021). Essential and real workloads which represent incoming user requests in specific time intervals are the premise and basis of the above research topics (ögele et al, 2018;Abdullah et al, 2020;Notaro et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Han¹,

Du²,

Xu³

et al. 2023

Preprint

View full text Add to dashboard Cite

Microservice-based applications and cloud-native systems have been widely applied in large IT enterprises. The operation and management of microservice-based applications and cloud-native systems have become the focus of research. Essential and real workloads are the premise and basis of prominent research topics including performance testing, dynamic resource provisioning and scheduling, and AIOps. Due to the privacy restriction, the complexity and variety of workloads, and the requirements for reasonable intervention, it is difficult to copy or generate real workloads directly. In this paper, we formulate the task of workload simulation and propose a framework for Log-based Workload Simulation (LWS) in session-based application systems. First, LWS collects session logs and transforms them into grouped and wellorganized sessions. Then LWS extracts the user behavior abstraction based on a relational model and the intervenable workload intensity by three methods from different perspectives. LWS combines the user behavior abstraction and the workload intensity for simulated workload generation and designs a domain-specific language for better execution. The experimental evaluation is performed on an open-source cloud-native application and a public real-world e-commerce workload. The experimental results show that the simulated workload generated by LWS is effective and intervenable.

show abstract

Section: Workload Intensity Modelingmentioning

confidence: 99%

Section: Differences Between Lws and Existing Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Han¹,

Du²,

Xu³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Moreover, the majority of IoT devices are equipped with minimal resources and rely heavily on remote servers to process their tasks by offloading content and computation through the core network. Ideally, the IoTs can rely on the cloud-based servers and process various offloaded tasks with efficient cloud resource planing [3] or cloud-assisted heterogeneous networks [4]. However, cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to many challenges.…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities

Ale,

Zhang,

King

et al. 2021

Preprint

View full text Add to dashboard Cite

A smart city improves operational efficiency and comfort of living by harnessing techniques such as the Internet of Things (IoT) to collect and process data for decision making. To better support smart cities, data collected by IoT should be stored and processed appropriately. However, IoT devices are often task-specialized and resource-constrained, and thus, they heavily rely on online resources in terms of computing and storage to accomplish various tasks. Moreover, these cloud-based solutions often centralize the resources and are far away from the end IoTs and cannot respond to users in time due to network congestion when massive numbers of tasks offload through the core network. Therefore, by decentralizing resources spatially close to IoT devices, mobile edge computing (MEC) can reduce latency and improve service quality for a smart city, where service requests can be fulfilled in proximity. As the service demands exhibit spatial-temporal features, deploying MEC servers at optimal locations and allocating MEC resources play an essential role in efficiently meeting service requirements in a smart city. In this regard, it is essential to learn the distribution of resource demands in time and space. In this work, we first propose a spatio-temporal Bayesian hierarchical learning approach to learn and predict the distribution of MEC resource demand over space and time to facilitate MEC deployment and resource management. Second, the proposed model is trained and tested on real-world data, and the results demonstrate that the proposed method can achieve very high accuracy. Third, we demonstrate an application of the proposed method by simulating task offloading. Finally, the simulated results show that resources allocated based upon our models' predictions are exploited more efficiently than the resources are equally divided into all servers in unobserved areas.

show abstract

A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems

Ghobaei‐Arani

2020

Soft Comput

View full text Add to dashboard Cite

Elastic Resource Provisioning Using Data Clustering in Cloud Service Platform

Cited by 20 publications

References 36 publications

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

LWS: A Framework for Log-based Workload Simulation in Session-based SUT

Spatio-Temporal Bayesian Learning for Mobile Edge Computing Resource Planning in Smart Cities

A workload clustering based resource provisioning mechanism using Biogeography based optimization technique in the cloud based systems

Contact Info

Product

Resources

About