scholarly journals Toward Bio-Inspired Auto-Scaling Algorithms: An Elasticity Approach for Container Orchestration Platforms

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 52139-52150
Author(s):  
Jose Herrera ◽  
German Molto
Keyword(s):  
Container Orchestration ◽  
Auto Scaling ◽  
Scaling Algorithms

scholarly journals BUILDING SPATIOTEMPORAL CLOUD PLATFORM FOR SUPPORTING GIS APPLICATION

2015 ◽  
Vol II-4/W2 ◽  
pp. 55-62 ◽  
Author(s):  
W. W. Song ◽  
B. X. Jin ◽  
S. H. Li ◽  
X. Y. Wei ◽  
D. Li ◽  
...  
Keyword(s):  
High Performance ◽  
Image Data ◽  
Geospatial Data ◽  
Cloud Platform ◽  
Geospatial Information ◽  
Gis Application ◽  
Government Decision ◽  
Auto Scaling ◽  
Scaling Algorithms ◽  
Thematic Data

Traditional geospatial information platforms are built, managed and maintained by the geoinformation agencies. They integrate various geospatial data (such as DLG, DOM, DEM, gazetteers, and thematic data) to provide data analysis services for supporting government decision making. In the era of big data, it is challenging to address the data- and computing- intensive issues by traditional platforms. In this research, we propose to build a spatiotemporal cloud platform, which uses HDFS for managing image data, and MapReduce-based computing service and workflow for high performance geospatial analysis, as well as optimizing auto-scaling algorithms for Web client users’ quick access and visualization. Finally, we demonstrate the feasibility by several GIS application cases.

Resource allocation, scheduling and auto-scaling algorithms for enhancing the performance of cloud using Grey Wolf Optimization and Fuzzy rules

2020 ◽  
Vol 39 (5) ◽  
pp. 7449-7467
Author(s):  
I. George Fernandez ◽  
J. Arokia Renjith
Keyword(s):  
Resource Allocation ◽  
Resource Utilization ◽  
Fuzzy Rules ◽  
Grey Wolf ◽  
Grey Wolf Optimization ◽  
Cloud Server ◽  
Auto Scaling ◽  
Machine Allocation ◽  
Virtual Machine Allocation ◽  
Scaling Algorithms

Cloud computing technology is playing a major role in the industry and real-life, for providing fast services such as data sharing and allocating the cloud resources that are paid and truly required. In this scenario, the cloud users are scheduled according to the rule-based systems for attempting to automate the matching between computing requirements and resources. Even though, the majority auto-scaling algorithms only helped as indicators for simple resource utilization and also not considered both cloud user needs and budget concerns. For this purpose, we propose a new model which is the combination of auto-scaling algorithms, resource allocation and scheduling for allocating the appropriate resources and scheduled them. This model consists of three new algorithms namely Grey Wolf Optimization and Fuzzy rules based Resource allocation and Scheduling Algorithm (GWOFRSA), Auto-Scaling Algorithm for Cloud based Web Application (ASACWA) and Auto-Scaling Algorithm for handling Distributed Computing Tasks (ASADCT). Here, we introduce new auto-scaling algorithms for enhancing the performance of cloud services. In this work, the optimization technique is used to predict the cloud server workload, resource requirements and it also uses fuzzy rules for monitoring the resource utilization and the size of virtual machine allocation process. According to the workload prediction, the completion time is estimated for each cloud server. The experiments are conducted by using a simulator called CloudSim environment of Java programming and compared with the existing works available in this direction in terms of resource utilization and enhance the cloud performance with better Quality of Service of Virtual Machine allocation, Missed Deadline, Demand Satisfaction, Power Utilization, CPU Load and throughput.

scholarly journals Energy-aware auto-scaling algorithms for Cassandra virtual data centers

2017 ◽  
Vol 20 (3) ◽  
pp. 2065-2082 ◽  
Author(s):  
Emiliano Casalicchio ◽  
Lars Lundberg ◽  
Sogand Shirinbab
Keyword(s):  
Data Centers ◽  
Energy Aware ◽  
Auto Scaling ◽  
Scaling Algorithms ◽  
Virtual Data

scholarly journals TOSCA-Based and Federation-Aware Cloud Orchestration for Kubernetes Container Platform

2019 ◽  
Vol 9 (1) ◽  
pp. 191 ◽  
Author(s):  
Dongmin Kim ◽  
Hanif Muhammad ◽  
Eunsam Kim ◽  
Sumi Helal ◽  
Choonhwa Lee
Keyword(s):  
Service Providers ◽  
Research Work ◽  
Cloud Service ◽  
Daunting Task ◽  
Cloud Service Providers ◽  
Service Areas ◽  
Sample Application ◽  
Container Orchestration ◽  
Auto Scaling ◽  
Cloud Applications

Kubernetes, a container orchestration tool for automatically installing and managing Docker containers, has recently begun to support a federation function of multiple Docker container clusters. This technology, called Kubernetes Federation, allows developers to increase the responsiveness and reliability of their applications by distributing and federating container clusters to multiple service areas of cloud service providers. However, it is still a daunting task to manually manage federated container clusters across all the service areas or to maintain the entire topology of cloud applications at a glance. This research work proposes a method to automatically form and monitor Kubernetes Federation, given application topology descriptions in TOSCA (Topology and Orchestration Specification for Cloud Applications), by extending the orchestration tool that automatizes the modeling and instantiation of cloud applications. It also demonstrates the successful federation of the clusters according to the TOSCA specifications and verifies the auto-scaling capability of the configured system through a scenario in which the servers of a sample application are deployed and federated.

Hybrid Auto-Scaling Mechanism for Dynamic Load Change

2017 ◽  
Vol 137 (3) ◽  
pp. 521-531
Author(s):  
Yoko Hirashima ◽  
Kenta Yamasaki ◽  
Tomohiro Morimura ◽  
Norihisa Komoda
Keyword(s):  
Dynamic Load ◽  
Load Change ◽  
Auto Scaling

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

Minimally Empirical Double Hybrid Functionals Trained Against the GMTKN55 Database: revDSD-PBEP86-D4, revDOD-PBE-D4, and DOD-SCAN-D4

2019 ◽  
Author(s):  
Golokesh Santra ◽  
Nitai Sylvetsky ◽  
Gershom Martin
Keyword(s):  
Substantial Improvement ◽  
Viable Alternative ◽  
Mean Absolute Deviation ◽  
Dispersion Correction ◽  
Training Set ◽  
Weighted Mean ◽  
Absolute Deviation ◽  
Hybrid Functionals ◽  
Scaling Algorithms

We present a family of minimally empirical double-hybrid DFT functionals parametrized against the very large and diverse GMTKN55 benchmark. The very recently proposed wB97M(2) empirical double hybrid (with 16 empirical parameters) has the lowest WTMAD2 (weighted mean absolute deviation over GMTKN55) ever reported at 2.19 kcal/mol. However, our xrevDSD-PBEP86-D4 functional reaches a statistically equivalent WTMAD2=2.22 kcal/mol, using just a handful of empirical parameters, and the xrevDOD-PBEP86-D4 functional reaches 2.25 kcal/mol with just opposite-spin MP2 correlation, making it amenable to reduced-scaling algorithms. In general, the D4 empirical dispersion correction is clearly superior to D3BJ. If one eschews dispersion corrections of any kind, noDispSD-SCAN offers a viable alternative. Parametrization over the entire GMTKN55 dataset yields substantial improvement over the small training set previously employed in the DSD papers.

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