Strategizing secured image storing and efficient image retrieval through a new cloud framework

Noor, Jannatun; Salim, Saiful Islam; Islam, Aminul

doi:10.1016/j.jnca.2021.103167

Cited by 14 publications

(5 citation statements)

References 39 publications

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“…Most of the studies focus on the metrics -throughput and latency. Besides, study [7] works on throughput, latency, and concurrency whereas our proposed architecture improves the throughput, latency, and concurrency and makes the system more fault-tolerant. By adopting a completely new methodology, we can achieve long-term object storage sustainability by analyzing the proper middleware placement and removing orphan garbage data regularly using a deletion daemon.…”

Section: B Comparative Analysismentioning

confidence: 99%

“…Efficient and smoother video streaming, dynamic adaptive streaming, etc., are some special features, which need both cloud server-side and streaming server-side operation management. For achieving these features, studies [7], [8], [34] mainly focus on the faster and more secure management of media files through designing middlewares. The increasing demand for video streaming services over mobile networks has outpaced the capacity of wireless links to handle the growing traffic load.…”

Section: A On-demand Video Segmentation and Middleware Placementmentioning

confidence: 99%

“…A use case for the authority for our focused context is shown in Figure 1. To serve such use cases, promising multimedia-based cloud systems are now emerging [7], [8]. These systems often use various open-source Object Storage Systems (OSS) for faster and easier access to image and video-type data, which are the two foremost ingredients in multimedia communication over the Internet.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

RemOrphan: Object Storage Sustainability Through Removing Offline-Processed Orphan Garbage Data

Noor,

Al-Nabhan,

Islam

2023

IEEE Access

Self Cite

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Nowadays, extremely large amounts of structured and unstructured types of data are stored in public, private, and hybrid cloud storage using object storage systems. Among these, storing multimedia data such as image, video, and audio pose unique challenges and long-term effects on object storage Sustainability. Three such challenges are smoother and more efficient video streaming, middleware placement for media processing, and lastly, management of orphan garbage data. In order to tackle these challenges, this paper presents a generalized architecture for smooth and efficient management as well as retrieval of multimedia data in cloud systems. To do so, first, we propose a new middleware package in the object server for supporting smooth video streaming and on-demand playable video segment. Here, we demonstrate that video segment download time improves by up to 30% when segmentation is done in the object server rather than in the proxy server. After, we focus on how to find orphan garbage data on media cloud storage and to what extent they can hamper data retrieval. Specifically, we present a generalized architecture named 'RemOrphan' for detecting the orphan garbage data using OpenStack Swift hash Ring and scripts. We deploy a private media cloud SPMS and find that around 35% data can be orphan garbage data. Due to the huge amount of orphan data, rsync replication needs higher time and more network overhead which hampers the system's sustainability. We lower around 25% sync delay and 30% network overhead after deploying a deletion daemon to remove the orphan garbage data.

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Section: B Comparative Analysismentioning

confidence: 99%

Section: A On-demand Video Segmentation and Middleware Placementmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

RemOrphan: Object Storage Sustainability Through Removing Offline-Processed Orphan Garbage Data

Noor,

Al-Nabhan,

Islam

2023

IEEE Access

Self Cite

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“…Further, to improve the query performance, the authors proposed a content-aware and bandwidth-conscious multi-resolution-based image data replica selection method and a correlated data caching algorithm. J. Noor et al proposed a novel framework for secure and faster image retrieval in the cloud environment [17]. The authors have resized the images to several dimensions to make them compatible with the remote devices, incorporated encryption-decryption for the secure storage and then added a new middleware named iBuck for faster and smooth image retrieval.…”

Section: Content-based Medical Image Retrieval In Cloud Environmentmentioning

confidence: 99%

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

Tiwari

Pant

2022

ACM Trans. Multimedia Comput. Commun. Appl.

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In this paper, a brownfield Internet of Medical Things network is introduced for imaging data that can be easily scaled out depending on the objectives, functional requirements and the number of facilities and devices connected to it. This is further used to develop a novel Content-based Medical Image Retrieval framework. The developed framework uses DenseNet-201 architecture for generating the image descriptors. Then for classification, the optimized Deep Neural Network model has been configured through a population-based metaheuristic Differential Evolution. Differential Evolution iteratively performs the joint optimization of hyperparameters and architecture of Deep Neural Networks. The competence of the proposed model is validated on three publicly available datasets: Brain Tumor MRI dataset, Covid-19 Radiography database, and Breast Cancer MRI dataset and by comparing it with selected models over different aspects of performance evaluation. Results show that the convergence rate of the proposed framework is very fast, and it achieves at least 97.28% accuracy across all the models.

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“…This system is scalable, designed for durability, and ensures availability for the entire dataset. Swift proves ideal for unstructured data that may expand significantly [2]. It stores every data piece as an object, unlike file-based or block storage systems that save data as files.…”

mentioning

confidence: 99%

Sherlock in OSS: A Novel Approach of Content-Based Searching in Object Storage System

Noor,

Sakif,

Mondal

et al. 2024

IEEE Access

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Cloud-based Object Storage Systems (OSS) are known for their scalability, durability, availability, and concurrency. However, there is a gap in open-source OSS for a straightforward way for users and administrators to conduct data searches within object storage without fully utilizing the cloud infrastructure.In our research, we present Sherlock, a novel Content-Based Searching (CoBS) framework. Sherlock enhances search capabilities by using extra information from images and documents, incorporating this information into an Elasticsearch-powered database to enable content-driven searches. The framework operates through a two-stage process. First, it classifies the incoming data by type, directing images to an object detection model and processing documents for keyword extraction. Then, Elasticsearch catalogs the extracted data, facilitating searches based on content. The effectiveness of our searches is largely dependent on the precision of these models, which we improve by training them on large-scale datasets: the Microsoft COCO Dataset for multimedia content and the SemEval2017 Dataset for text documents. We further test our system's performance by integrating it with the open-source OSS, OpenStack Swift, and conducting realworld experiments with image uploads to evaluate how our model performs within Swift's object storage environments.

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Strategizing secured image storing and efficient image retrieval through a new cloud framework

Cited by 14 publications

References 39 publications

RemOrphan: Object Storage Sustainability Through Removing Offline-Processed Orphan Garbage Data

RemOrphan: Object Storage Sustainability Through Removing Offline-Processed Orphan Garbage Data

Optimized Deep-Neural Network for Content-based Medical Image Retrieval in a Brownfield IoMT Network

Sherlock in OSS: A Novel Approach of Content-Based Searching in Object Storage System

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