Hyper-Convergence Storage Framework for EcoCloud Correlates
Source abstract
Abstract
Cloud computing is an emerging domain that is capturing global users from all walks of life—the corporate sector, government sector, and social arena as well. Various cloud providers have offered multiple services and facilities to this audience and the number of providers is increasing very swiftly. This enormous pace is generating the requirement of a comprehensive ecosystem that shall provide a seamless and customized user environment not only to enhance the user experience but also to improve security, availability, accessibility, and latency. Emerging technology is providing robust solutions to many of our problems, the cloud platform is one of them. It is worth mentioning that these solutions are also amplifying the complexity and need of sustenance of these rapid solutions. As with cloud computing, new entrants as cloud service providers, resellers, tech-support, hardware manufacturers, and software developers appear on a daily basis. These actors playing their role in the growth and sustenance of the cloud ecosystem. Our objective is to use convergence for cloud services, software-defined networks, network function virtualization for infrastructure, cognition for pattern development, and knowledge repository. In order to gear up these processes, machine learning to induce intelligence to maintain ecosystem growth, to monitor performance, and to become able to make decisions for the sustenance of the ecosystem. Workloads may be programmed to “superficially” imitate most business applications and create large numbers using lightweight workload generators that merely stress the storage. In today's current IT environment, when many enterprises use the cloud to service some of their application demands, a different performance testing technique that assesses more than the storage is necessary. Compute and storage are merged into a single building block with HCI(Hyper-converged infrastructure), resulting in a huge pool of compute and storage resources when clustered with other building blocks. The novelty of this work to design and test cloud storage using the measurement of availability, downtime, and outage parameters. Results showed that the storage reliability in a hyper-converged system is above 92%.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Cloud Computing and Resource Management · IoT and Edge/Fog Computing · Advanced Data Storage Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (17)
Show source-provided references
1. IMeter: An integrated VM power model based on performance profiling10.1016/j.future.2013.07.008 ↗
2. An ontology for heterogeneous resources management interoperability and HPC in the cloud10.1016/j.future.2018.05.086 ↗
3. IOFollow: Improving the performance of VM live storage migration with IO following in the cloud10.1016/j.future.2018.08.036 ↗
4. Optimizing the performance of optimization in the cloud environment: An intelligent auto-scaling approach10.1016/j.future.2019.07.042 ↗
5. HAS: Hybrid auto-scaler for resource scaling in cloud environment10.1016/j.jpdc.2018.04.016 ↗
6. Multi-objective optimization for rebalancing virtual machine placement10.1016/j.future.2017.08.027 ↗
7. Smart VM co-scheduling with the precise prediction of performance characteristics10.1016/j.future.2016.11.022 ↗
8. A proactive autoscaling and energy-efficient VM allocation framework using online multi-resource neural network for cloud data center10.1016/j.neucom.2020.08.076 ↗
9. Standardization of cloud security using mamdani fuzzifier
10. Multi-tier workload consolidations in the cloud: Profiling, modeling and optimization
11. VM scaling and load balancing via cost optimal MDP solution
12. Architectural design of cloud applications: a performance-aware cost minimization approach
13. Small-scale spectrum aggregation and sharing10.1109/JSAC.2016.2604999 ↗
14. Levitin · IEEE Transaction Server Computing · 2020 · 1374 · 1
15. Auto-scaling web applications in clouds: A cost-aware approach10.1016/j.jnca.2017.07.012 ↗
16. Cloud forensics-tool development studies & future outlook10.1016/j.diin.2016.05.001 ↗
17. Improving cloud gaming experience through mobile edge computing10.1109/MWC.2019.1800440 ↗