WPAR: A Weight-Based Metadata Management Strategy for Petabyte-Scale Object Storage Systems

2007 ◽  
Author(s):  
Wujuan Lin ◽  
Qingsong Wei ◽  
Bharadwaj Veeravalli
Keyword(s):  
Management Strategy ◽  
Storage Systems ◽  
Metadata Management ◽  
Object Storage

A Cloud-Aware Distributed Object Storage System to Retrieve Large Data via HTML5-Enabled Web Browsers

2016 ◽  
pp. 25-45 ◽  
Author(s):  
Ahmet Artu Yıldırım ◽  
Dan Watson
Keyword(s):  
High Performance ◽  
Storage Systems ◽  
Storage System ◽  
Large Data ◽  
Web Browsers ◽  
Cloud Infrastructure ◽  
Scalable Storage ◽  
Distributed Object ◽  
Object Storage ◽  
Restful Web Service

Major Internet services are required to process a tremendous amount of data at real time. As we put these services under the magnifying glass, It's seen that distributed object storage systems play an important role at back-end in achieving this success. In this chapter, overall information of the current state-of –the-art storage systems are given which are used for reliable, high performance and scalable storage needs in data centers and cloud. Then, an experimental distributed object storage system (CADOS) is introduced for retrieving large data, such as hundreds of megabytes, efficiently through HTML5-enabled web browsers over big data – terabytes of data – in cloud infrastructure. The objective of the system is to minimize latency and propose a scalable storage system on the cloud using a thin RESTful web service and modern HTML5 capabilities.

Understanding the latency distribution of cloud object storage systems

2019 ◽  
Vol 128 ◽  
pp. 71-83
Author(s):  
Yi Su ◽  
Dan Feng ◽  
Yu Hua ◽  
Zhan Shi
Keyword(s):  
Storage Systems ◽  
Latency Distribution ◽  
Object Storage

Stochastic power management strategy for hybrid energy storage systems to enhance large scale wind energy integration

2020 ◽  
Vol 31 ◽  
pp. 101650 ◽  
Author(s):  
Linda Barelli ◽  
Dana-Alexandra Ciupageanu ◽  
Andrea Ottaviano ◽  
Dario Pelosi ◽  
Gheorghe Lazaroiu
Keyword(s):  
Energy Storage ◽  
Power Management ◽  
Management Strategy ◽  
Large Scale ◽  
Storage Systems ◽  
Energy Integration ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Power Management Strategy ◽  
Wind Energy Integration

Backup mainframe data to object storage systems

2019 ◽  
Author(s):  
Gil Peleg ◽  
Offer Baruch ◽  
Dori Polotsky
Keyword(s):  
Storage Systems ◽  
Object Storage

scholarly journals HBA: Distributed Metadata Management for Large Cluster-Based Storage Systems

2008 ◽  
Vol 19 (6) ◽  
pp. 750-763 ◽  
Author(s):  
Yifeng Zhu ◽  
Hong Jiang ◽  
Jun Wang ◽  
Feng Xian
Keyword(s):  
Storage Systems ◽  
Large Cluster ◽  
Metadata Management

Object Storage Systems Support for Database

2007 ◽  
Author(s):  
Tan Zhipeng ◽  
Feng Dan ◽  
Tu Xudong ◽  
He Fei
Keyword(s):  
Storage Systems ◽  
Object Storage

scholarly journals Power Management Control Strategy Based on Artificial Neural Networks for Standalone PV Applications with a Hybrid Energy Storage System

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 902 ◽  
Author(s):  
João Faria ◽  
José Pombo ◽  
Maria Calado ◽  
Sílvio Mariano
Keyword(s):  
Energy Storage ◽  
Power Management ◽  
Management Strategy ◽  
Storage Systems ◽  
Li Ion Batteries ◽  
Pv System ◽  
Hybrid Energy ◽  
Hybrid Energy Storage ◽  
Power Management Strategy ◽  
Li Ion

Standalone microgrids with photovoltaic (PV) solutions could be a promising solution for powering up off-grid communities. However, this type of application requires the use of energy storage systems (ESS) to manage the intermittency of PV production. The most commonly used ESSs are lithium-ion batteries (Li-ion), but this technology has a low lifespan, mostly caused by the imposed stress. To reduce the stress on Li-ion batteries and extend their lifespan, hybrid energy storage systems (HESS) began to emerge. Although the utilization of HESSs has demonstrated great potential to make up for the limitations of Li-ion batteries, a proper power management strategy is key to achieving the HESS objectives and ensuring a harmonized system operation. This paper proposes a novel power management strategy based on an artificial neural network for a standalone PV system with Li-ion batteries and super-capacitors (SC) HESS. A typical standalone PV system is used to demonstrate and validate the performance of the proposed power management strategy. To demonstrate its effectiveness, computational simulations with short and long duration were performed. The results show a minimization in Li-ion battery dynamic stress and peak current, leading to an increased lifespan of Li-ion batteries. Moreover, the proposed power management strategy increases the level of SC utilization in comparison with other well-established strategies in the literature.

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