Modeling Cloud Storage: A Proposed Solution to Optimize Planning for and Managing Storage as a Service
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@Article{JICS-11-070,
author = {Anita L. Timmons, Pavel Fomin and James Wasek},
title = {Modeling Cloud Storage: A Proposed Solution to Optimize Planning for and Managing Storage as a Service},
journal = {Journal of Information and Computing Science},
year = {2024},
volume = {11},
number = {1},
pages = {070--080},
abstract = {Cloud-computing service providers are currently viewed as the best solution to the global need
for massive data systems because of their superior flexibility, scalability, and cost benefits. Cloud computing
that is enabled by virtualized services is still constrained, however, by the capacities of the underlying
physical systems that are combined into sharable pools of resources. The next challenge for computation
systems will arise when even the cloud is not sufficient. What comes after cloud migration and adoption? In
this paper, we examine how service providers can manage cloud storage resources and costs when the
amount of collected data to be retained grows exponentially, to the point that it strains even virtualized
resource capacities. We assess the analytical frameworks being developed to identify which storage
architectures can best accommodate the specific needs of large data storage consumers. We also investigate
the areas in which these fail to fully address the problem, and propose solutions. We argue that a cloud
storage framework that addresses data volume, data growth trends over time, and requirements for storage
management will enable service providers to manage cloud storage resources and costs in such a manner that
the cloud will continue to offer the greatest benefits for the storage of massive data systems.
},
issn = {1746-7659},
doi = {https://doi.org/},
url = {http://global-sci.org/intro/article_detail/jics/22530.html}
}
TY - JOUR
T1 - Modeling Cloud Storage: A Proposed Solution to Optimize Planning for and Managing Storage as a Service
AU - Anita L. Timmons, Pavel Fomin and James Wasek
JO - Journal of Information and Computing Science
VL - 1
SP - 070
EP - 080
PY - 2024
DA - 2024/01
SN - 11
DO - http://doi.org/
UR - https://global-sci.org/intro/article_detail/jics/22530.html
KW - Cloud storage, big data, cloud storage architecture, surface response methodology
AB - Cloud-computing service providers are currently viewed as the best solution to the global need
for massive data systems because of their superior flexibility, scalability, and cost benefits. Cloud computing
that is enabled by virtualized services is still constrained, however, by the capacities of the underlying
physical systems that are combined into sharable pools of resources. The next challenge for computation
systems will arise when even the cloud is not sufficient. What comes after cloud migration and adoption? In
this paper, we examine how service providers can manage cloud storage resources and costs when the
amount of collected data to be retained grows exponentially, to the point that it strains even virtualized
resource capacities. We assess the analytical frameworks being developed to identify which storage
architectures can best accommodate the specific needs of large data storage consumers. We also investigate
the areas in which these fail to fully address the problem, and propose solutions. We argue that a cloud
storage framework that addresses data volume, data growth trends over time, and requirements for storage
management will enable service providers to manage cloud storage resources and costs in such a manner that
the cloud will continue to offer the greatest benefits for the storage of massive data systems.
Anita L. Timmons, Pavel Fomin and James Wasek. (2024). Modeling Cloud Storage: A Proposed Solution to Optimize Planning for and Managing Storage as a Service.
Journal of Information and Computing Science. 11 (1).
070-080.
doi:
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