Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386869640> ?p ?o ?g. }
Showing items 1 to 70 of
70
with 100 items per page.
- W4386869640 endingPage "14" @default.
- W4386869640 startingPage "1" @default.
- W4386869640 abstract "As a lightweight and flexible infrastructure solution, containers have increasingly been used for application deployment on a global scale. By rapidly scaling containers at different locations, the deployed applications can handle dynamic workloads from the worldwide user community. Existing studies usually focus on the (dynamic) container scaling within a single data center or the (static) container deployment across geo-distributed data centers. This article studies an increasingly important container scaling problem for application deployment in geo-distributed clouds. Reinforcement learning (RL) has been widely used in container scaling due to its high adaptability and robustness. To handle high-dimensional state spaces in geo-distributed clouds, we propose a deep RL algorithm, named <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>DeepScale</i> , to auto-scale containerized applications. <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>DeepScale</i> innovatively utilizes multi-step predicted future workloads to train a holistic scaling policy. It features several newly designed algorithmic components, including a domain-tailored state constructor and a heuristic-based action executor. These new algorithmic components are essential to meet the requirements of low deployment costs and achieve desirable application performance. We conduct extensive simulation studies using real-world datasets. The results show that <italic xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>DeepScale</i> can significantly outperform an industry-leading scaling strategy and two state-of-the-art baselines in terms of both cost-effectiveness and constraint satisfaction." @default.
- W4386869640 created "2023-09-20" @default.
- W4386869640 creator A5013615287 @default.
- W4386869640 creator A5030936094 @default.
- W4386869640 creator A5037154800 @default.
- W4386869640 creator A5092903927 @default.
- W4386869640 date "2023-01-01" @default.
- W4386869640 modified "2023-09-27" @default.
- W4386869640 title "Auto-Scaling Containerized Applications in Geo-Distributed Clouds" @default.
- W4386869640 doi "https://doi.org/10.1109/tsc.2023.3317262" @default.
- W4386869640 hasPublicationYear "2023" @default.
- W4386869640 type Work @default.
- W4386869640 citedByCount "0" @default.
- W4386869640 crossrefType "journal-article" @default.
- W4386869640 hasAuthorship W4386869640A5013615287 @default.
- W4386869640 hasAuthorship W4386869640A5030936094 @default.
- W4386869640 hasAuthorship W4386869640A5037154800 @default.
- W4386869640 hasAuthorship W4386869640A5092903927 @default.
- W4386869640 hasConcept C104317684 @default.
- W4386869640 hasConcept C105339364 @default.
- W4386869640 hasConcept C111919701 @default.
- W4386869640 hasConcept C120314980 @default.
- W4386869640 hasConcept C127413603 @default.
- W4386869640 hasConcept C134306372 @default.
- W4386869640 hasConcept C185592680 @default.
- W4386869640 hasConcept C2524010 @default.
- W4386869640 hasConcept C2781018962 @default.
- W4386869640 hasConcept C33923547 @default.
- W4386869640 hasConcept C36503486 @default.
- W4386869640 hasConcept C41008148 @default.
- W4386869640 hasConcept C55493867 @default.
- W4386869640 hasConcept C63479239 @default.
- W4386869640 hasConcept C78519656 @default.
- W4386869640 hasConcept C79974875 @default.
- W4386869640 hasConcept C99844830 @default.
- W4386869640 hasConceptScore W4386869640C104317684 @default.
- W4386869640 hasConceptScore W4386869640C105339364 @default.
- W4386869640 hasConceptScore W4386869640C111919701 @default.
- W4386869640 hasConceptScore W4386869640C120314980 @default.
- W4386869640 hasConceptScore W4386869640C127413603 @default.
- W4386869640 hasConceptScore W4386869640C134306372 @default.
- W4386869640 hasConceptScore W4386869640C185592680 @default.
- W4386869640 hasConceptScore W4386869640C2524010 @default.
- W4386869640 hasConceptScore W4386869640C2781018962 @default.
- W4386869640 hasConceptScore W4386869640C33923547 @default.
- W4386869640 hasConceptScore W4386869640C36503486 @default.
- W4386869640 hasConceptScore W4386869640C41008148 @default.
- W4386869640 hasConceptScore W4386869640C55493867 @default.
- W4386869640 hasConceptScore W4386869640C63479239 @default.
- W4386869640 hasConceptScore W4386869640C78519656 @default.
- W4386869640 hasConceptScore W4386869640C79974875 @default.
- W4386869640 hasConceptScore W4386869640C99844830 @default.
- W4386869640 hasLocation W43868696401 @default.
- W4386869640 hasOpenAccess W4386869640 @default.
- W4386869640 hasPrimaryLocation W43868696401 @default.
- W4386869640 hasRelatedWork W15267691 @default.
- W4386869640 hasRelatedWork W1688445866 @default.
- W4386869640 hasRelatedWork W1999930627 @default.
- W4386869640 hasRelatedWork W2015855483 @default.
- W4386869640 hasRelatedWork W2026856333 @default.
- W4386869640 hasRelatedWork W2329229441 @default.
- W4386869640 hasRelatedWork W2369674902 @default.
- W4386869640 hasRelatedWork W2624990117 @default.
- W4386869640 hasRelatedWork W3152278044 @default.
- W4386869640 hasRelatedWork W2186020809 @default.
- W4386869640 isParatext "false" @default.
- W4386869640 isRetracted "false" @default.
- W4386869640 workType "article" @default.