Performance prediction of parallel applications: a systematic literature review

Flores-Contreras, Jesus; Durán-Limón, Héctor A.; Chavoya, Arturo; Almanza-Ruiz, Sergio H.

doi:10.1007/s11227-020-03417-5

Cited by 6 publications

(3 citation statements)

References 115 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, in [2] a systematic literature review of performance prediction methods for parallel applications is presented that includes analytic and non-analytic methods and indicates future research trends and some unsolved issues. In particular, this work shows that performance prediction has been applied on a wide range of domains and reviews 82 different approaches developed between 2005 and 2020.…”

Section: Related Workmentioning

confidence: 99%

“…This problem is well known in literature as performance prediction and it has been addressed since 2005 with different techniques based on the continuous collection of resource consumption data such as CPU and memory usage. In [2], a systematic review of performance prediction of parallel applications is provided. The authors observe that in the 81.7% of considered papers, the estimation is based on analytic methods in which the corresponding equations are either manually or automatically derived mostly by stochastic linear and non-linear approaches.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

2023

View full text Add to dashboard Cite

With the advent of Industry 4.0, the demand of high computing power for tasks such as data mining, 3D rendering, file conversion and cryptography is continuously growing. To this extent, distributed and decentralized environments play a fundamental role by dramatically increasing the amount of available resources. However, there are still several issues in the existing resource sharing solutions, such as the uncertainty of task running time, the renting price and the security of transactions. In this work, we present a blockchain-enabled task assignment platform by performance prediction based on Hyperledger Fabric, an open-source solution for private and permissioned blockchains in enterprise contexts that outperforms other technologies in terms of modularity, security and performance. We propose a model-free deep reinforcement learning framework to predict task runtime in agents current load state while the agent is engaged in multiple concurrent tasks. In addition, we let clients choose between prediction accuracy and price saving on each request. This way, we implicitly give inaccurate agents a chance to get assignments by competing in price rather than in time, allowing them to collect new experiences and improve future predictions. We conduct extensive experiments to evaluate the performance of the proposed scheme.INDEX TERMS Blockchain, cloud, deep reinforcement learning (DRL), resource sharing.

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

2023

View full text Add to dashboard Cite

show abstract

“…The elaboration of the search string was based on the method used in [25]. First several nouns were extracted from the research questions.…”

Section: Abstract and Search Stringsmentioning

confidence: 99%

Performance interference of co-allocated applications: a systematic literature review

Urzua-Barrientos,

Duran-Limon,

Mora

2023

Preprint

View full text Add to dashboard Cite

Several efforts have been made to study the nature of performance interference in co-allocated applications running in different environments. Such performance interference degrades the performance of co-allocated applications. Many researchers propose techniques, methods and approaches that use mathematical equations, machine learning algorithms or other kind of techniques to predict/analyze performance degradation. Hardware counters, virtual machine monitor metrics and third-party tools are commonly used to provide the input data for prediction techniques. Performance prediction/analysis is useful for understanding, preventing, and minimizing performance degradation. This paper presents a systematic literature review of methods for the prediction/analysis of performance degradation due to performance interference of co-allocated applications, which were published in the open literature during the period 2006-2023. We classified and categorized each analyzed article depending on the method used and the environment employed, among others features. In addition, we identify some directions and trends in performance interference, as well as some unresolved issues.

show abstract

Parallel protein multiple sequence alignment approaches: a systematic literature review

2022

View full text Add to dashboard Cite

Performance prediction of parallel applications: a systematic literature review

Cited by 6 publications

References 115 publications

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

A Deep Reinforcement Learning Approach for Competitive Task Assignment in Enterprise Blockchain

Performance interference of co-allocated applications: a systematic literature review

Parallel protein multiple sequence alignment approaches: a systematic literature review

Contact Info

Product

Resources

About