Relevance feedback based online learning model for resource bottleneck prediction in cloud servers
Sign Up to like & getrecommendations! Published in 2020 at "Neurocomputing"
DOI: 10.1016/j.neucom.2020.04.080
Abstract: Abstract Cloud servers are highly prone to resource bottleneck failures. In this work, we propose an ensemble learning model to build LSTM-based multiclass classifier for resource bottleneck identification. The workload at cloud servers is highly… read more here.
Keywords: cloud servers; online learning; resource bottleneck; model ... See more keywords
Task Offloading in Heterogeneous Mobile Cloud Computing: Modeling, Analysis, and Cloudlet Deployment
Sign Up to like & getrecommendations! Published in 2018 at "IEEE Access"
DOI: 10.1109/access.2018.2812144
Abstract: In this paper, we consider a heterogeneous mobile cloud computing (HMCC) system that consists of remote cloud servers, local cloudlets, task offloading mobile devices (TMDs), non-task offloading MDs (NTMDs), and radio access networks such as… read more here.
Keywords: cloud servers; task offloading; analysis; cloud ... See more keywords
Decentralized Self-Auditing Scheme With Errors Localization for Multi-Cloud Storage
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Dependable and Secure Computing"
DOI: 10.1109/tdsc.2021.3075984
Abstract: With the popularity of cloud storage, increasing users begin to outsource data to the cloud. In order to resist possible data analysis for centralized outsourced data and improve the fault tolerance, users prefer to distribute… read more here.
Keywords: decentralized self; cloud storage; cloud; self auditing ... See more keywords
A Bi-Objective Learn-and-Deploy Scheduling Method for Bursty and Stochastic Requests on Heterogeneous Cloud Servers
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Parallel and Distributed Systems"
DOI: 10.1109/tpds.2022.3196475
Abstract: In this article, we consider the dynamic allocation of bursty requests stochastically arriving at heterogeneous servers with uncertain setup times. Lower expected response time and less power consumption are desirable objectives of users and service… read more here.
Keywords: time; learn deploy; bursty; launch ... See more keywords