Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Chen, Xing; Lin, Junxin; Lin, Bing; Xiang, Tao; Zhang, Ying; Huang, Gang

doi:10.1002/cpe.4463

Cited by 19 publications

(15 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Raibulet et al proposed a taxonomy for evaluating SAS [108] which includes evaluation scope, evaluation time, evaluation mechanism, evaluation perspective, and evaluation type. The study proposed by Chen et.al [18] shows that using a genetic algorithm for predicting Quality of Service (QoS) provided higher accuracy results. The aforementioned works focus on the general development of SAS.…”

Section: Related Workmentioning

confidence: 99%

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Saputri

Lee

2020

IEEE Access

View full text Add to dashboard Cite

Self-adaptive systems have been studied in software engineering over the past few decades attempting to address challenges within the field. There is a continuous significant need to fully understand the behavior and characteristics of the systems that operate in dynamic environments. By learning the behavior pattern of the environment, we can avoid unnecessary adaptations imbalance efforts for adaptation. As such, there exist research in the area of machine learning aimed at understanding dynamic environments regarding self-adaptive systems. Objective: This study aims to help software practitioners to address adaptation concerns by performing a systematic literature review that provides a comprehensive overview of using machine learning (ML) in self-adaptive systems. We summarize state-ofthe-art Of the ML approaches used to handle self-adaptation to help software engineers in the proper selection of ML techniques based on the adaptation concern. Method: This review examines research published between 2001 and 2019 on ML implementation in self-adaptive systems, focusing on the adaptation aspects and purposes. The review was conducted by analyzing major scientific databases that resulted in 78 primary studies from 315 papers from an automatic search. Result: Finally, this study recommends three future research directions to enhance the application of machine learning in self-adaptive systems.

show abstract

Section: Related Workmentioning

confidence: 99%

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

Saputri

Lee

2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…To this end, it is necessary to design an effective QoS prediction model [29]- [31]. Based on our previous work in [32], the QoS prediction model can be defined as…”

Section: Problem Formulationmentioning

confidence: 99%

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

et al. 2020

Self Cite

View full text Add to dashboard Cite

Cloud-based software services necessitate adaptive resource allocation with the promise of dynamic resource adjustment for guaranteeing the Quality-of-Service (QoS) and reducing resource costs. However, it is challenging to achieve adaptive resource allocation for software services in complex cloud environments with dynamic workloads. To address this essential problem, we propose an adaptive resource allocation strategy for cloud-based software services with workload-time windows. Based on the QoS prediction, the proposed strategy first brings the current and future workloads into the process of calculating resource allocation plans. Next, the particle swarm optimization and genetic algorithm (PSO-GA) is proposed to make runtime decisions for exploring the objective resource allocation plan. Using the RUBiS benchmark, the extensive simulation experiments are conducted to validate the effectiveness of the proposed strategy on improving the performance of resource allocation for cloud-based software services. The simulation results show that the proposed strategy can obtain a better trade-off between the QoS and resource costs than two classic resource allocation methods. INDEX TERMS Cloud-based software services, resource allocation, QoS prediction, workload-time windows, particle swarm optimization, genetic algorithm.

show abstract

“…Siddiqa et al propose a novel Energy Efficient Greedy Routing Protocol (EEGRP) that can save energy issue to secure energy for Green Computing and Cloud before moving to full‐scale virtualization and Cloud services. Chen et al present a self‐learning and self‐adaptive approach to resource allocation for cloud‐based software services, and this approach can significantly help in improving resource utilization.…”

Section: Introductionmentioning

confidence: 99%

Foreword to the special issue of green cloud computing: Methodology and practice

Zheng

Rong²,

Badarch

2019

Concurrency and Computation

View full text Add to dashboard Cite

Self‐learning and self‐adaptive resource allocation for cloud‐based software services

Cited by 19 publications

References 34 publications

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

The Application of Machine Learning in Self-Adaptive Systems: A Systematic Literature Review

PSO-GA-Based Resource Allocation Strategy for Cloud-Based Software Services With Workload-Time Windows

Foreword to the special issue of green cloud computing: Methodology and practice

Contact Info

Product

Resources

About