Video Storage System Research Articles

Using Deep Reinforcement Learning (DRL) for minimizing power consumption in Video-on-Demand (VoD) storage systems

As video streaming services such as Netflix become popular, resolving the problem of high power consumption arising from both large data size and high bandwidth in video storage systems has become important. However, because various factors, such as the power characteristics of heterogeneous storage devices, variable workloads, and disk array models, influence storage power consumption, reducing power consumption with deterministic policies is ineffective. To address this, we present a new deep reinforcement learning (DRL)-based file placement algorithm for replication-based video storage systems, which aims to minimize overall storage power consumption. We first model the video storage system with time-varying streaming workloads as the DRL environment, in which the agent aims to find power-efficient file placement. We then propose a proximal policy optimization (PPO) algorithm, consisting of (1) an action space that determines the placement of each file; (2) an observation space that allows the agent to learn a power-efficient placement based on the current I/O bandwidth utilization; (3) a reward model that assigns a greater penalty for increased power consumption for each action; and (4) an action masking model that supports effective learning by preventing agents from selecting unnecessary actions. Extensive simulations were performed to evaluate the proposed scheme under various solid-state disk (SSD) models and replication configurations. Results show that our scheme reduces storage power consumption by 5% to 25.8% (average 12%) compared to existing benchmark methods known to be effective for file placement.

Touch surgery to improve surgical performance with a robotic system in a public hospital in Central America

Since 2012 robotic surgery has been available in Panama. In 2022, the Hugo robot became available in a public hospital. The adoption of a new robotic platform can be difficult. We reviewed our experience in implementing this robotic system for urologic surgery in a public hospital in Central America using Touch surgery, a cloud-based video storage and analytics system, available on smartphones. We use the Touch surgery reports to compare time in minutes between the 5 first and the last 5 radical prostatectomy surgeries.

Video File Allocation for Wear-Leveling in Distributed Storage Systems With Heterogeneous Solid-State-Disks (SSDs)

With the advent of new large-capacity solid-state disks (SSDs) such as quad-level-cells (QLC), SSD arrays can be effectively used in video storage systems that require large-capacity storage space. Typically, SSD manufacturers specify a drive-writes-per-day (DWPD) metric, which is the ratio of bytes written per day to the total capacity in bytes, to ensure an SSD’s specified lifetime; it is important to limit the number of write operations by considering the DWPD for each SSD. We propose a new video file allocation technique to effectively manage the heterogeneous DWPD characteristics of SSDs in distributed storage systems. To express the degree of wear-leveling for heterogeneous SSDs, we first introduce the concept of ADWD, which is the actual number of bytes written per day compared to DWPD. We then propose two algorithms for file placement and migration. The file placement algorithm places files greedily based on the bandwidth-to-space ratio (BSR) of each file and SSD to balance the bandwidth usage and storage of the SSD. The file migration algorithm moves files from overloaded to underloaded SSDs to meet bandwidth limit requirements while minimizing the overall ADWD as a result of migration, and then migrates additional popular files to improve SSD bandwidth utilization. To use these algorithms in actual distributed file systems, we implemented a suite of tools for file placement and migration in the Hadoop distributed file system (HDFS). Experimental results show that the proposed algorithm reduces the mean of ADWD by 35.44% and its standard deviation by 69.78% compared to the benchmark methods on average.

Energy-Saving SSD Cache Management for Video Servers with Heterogeneous HDDs

Dynamic adaptive streaming over HTTP (DASH) technique, the most popular streaming method, requires a large number of hard disk drives (HDDs) to store multiple bitrate versions of many videos, consuming significant energy. A solid-state drive (SSD) can be used to cache popular videos, thus reducing HDD energy consumption by allowing I/O requests to be handled by an SSD, but this requires effective HDD power management due to limited SSD bandwidth. We propose a new SSD cache management scheme to minimize the energy consumption of a video storage system with heterogeneous HDDs. We first present a technique that caches files with the aim of saving more HDD energy as a result of I/O processing on an SSD. Based on this, we propose a new HDD power management algorithm with the goal of increasing the number of HDDs operated in low-power mode while reflecting the heterogeneous HDD power characteristics. For this purpose, it assigns a separate parameter value to each I/O task based on the ratio of HDD energy to bandwidth and greedily selects the I/O tasks handled by the SSD within limits on its bandwidth. Simulation results show that our scheme consumes between 12% and 25% less power than alternative schemes under the same HDD configuration.

A novel efficient algorithm for duplicate video comparison in surveillance video storage systems

Video surveillance system is playing an inevitable role in monitoring the world in uncountable and unimaginable aspects. There are numerous systems has been adopted to monitor. There are various application like forest monitoring system, border monitoring system, home security system, traffic monitoring in which the surveillance captured video data are stored for surveillance processing immediately or later. The stored video data are identical even for every minute, hour and this lead to need of more capacity of storage just for duplicate storage unintentionally. Some methods have been proposed to remove duplicate video in storage server. There must be good and efficient algorithm has to be offered. This work is proposed a novel duplicate video identification algorithm called Intelligent Duplicate Check Algorithm (IDCA) for removing duplicate video data without discarding any unique video data. The IDCA algorithm improvised duplicate identification considerable percentage and thus efficient duplicate free storage may be achieved.

Data-Pattern Enabled Self-Recovery Low-Power Storage System for Big Video Data

The growing popularity of powerful mobile devices such as smart phones and tablet devices has resulted in the exponential growth of demand for video applications. However, due to the large video data size and intensive computation, mobile video applications require frequent embedded memory access, which consumes a large amount of power and limits battery life. In this paper, we present a low-cost self-recovery video storage system by investigating meaningful data patterns hidden in big video data, by introducing data mining techniques to the hardware design process. We propose a two-dimensional data-pattern approach to explore horizontal data-association and vertical data-correlation characteristics. Such data relationship discovery and pattern identification enable a new dimension for the hardware design space and bring self-recovery ability to memories in the presence of bitcell failures. Based on the identified optimal data patterns, we present a low-cost and efficient SRAM design to enable data self-recovery at low voltages. A 45nm 32 kb SRAM is implemented that delivers good video quality at near-threshold voltage (0.5 V) with negligible area overhead (7.94 percent).

Centralized Duplicate Removal Video Storage System with Privacy Preservation in IoT.

In recent years, the Internet of Things (IoT) has found wide application and attracted much attention. Since most of the end-terminals in IoT have limited capabilities for storage and computing, it has become a trend to outsource the data from local to cloud computing. To further reduce the communication bandwidth and storage space, data deduplication has been widely adopted to eliminate the redundant data. However, since data collected in IoT are sensitive and closely related to users’ personal information, the privacy protection of users’ information becomes a challenge. As the channels, like the wireless channels between the terminals and the cloud servers in IoT, are public and the cloud servers are not fully trusted, data have to be encrypted before being uploaded to the cloud. However, encryption makes the performance of deduplication by the cloud server difficult because the ciphertext will be different even if the underlying plaintext is identical. In this paper, we build a centralized privacy-preserving duplicate removal storage system, which supports both file-level and block-level deduplication. In order to avoid the leakage of statistical information of data, Intel Software Guard Extensions (SGX) technology is utilized to protect the deduplication process on the cloud server. The results of the experimental analysis demonstrate that the new scheme can significantly improve the deduplication efficiency and enhance the security. It is envisioned that the duplicated removal system with privacy preservation will be of great use in the centralized storage environment of IoT.

A Retrieval Optimized Surveillance Video Storage System for Campus Application Scenarios

This paper investigates and analyzes the characteristics of video data and puts forward a campus surveillance video storage system with the university campus as the specific application environment. Aiming at the challenge that the content-based video retrieval response time is too long, the key-frame index subsystem is designed. The key frame of the video can reflect the main content of the video. Extracted from the video, key frames are associated with the metadata information to establish the storage index. The key-frame index is used in lookup operations while querying. This method can greatly reduce the amount of video data reading and effectively improves the query’s efficiency. From the above, we model the storage system by a stochastic Petri net (SPN) and verify the promotion of query performance by quantitative analysis.

A Case Study of Automation Management System of Damaged Container in the Port Gate

컨테이너 선박의 대형화에 따라 컨테이너터미널 내에서 동일한 시간 내에 처리해야 하는 물동량이 계속적으로 증가하고 있다. 컨테이너터미널에서는 수동처리 방식의 한계를 극복함과 동시에 지속적으로 운영측면에서 발생하는 비용을 줄이기 위하여 컨테이너터미널의 자동화 시스템을 도입하고 있다. 왜냐하면 컨테이너터미널의 게이트 반출 입 업무를 수행함에 있어 수작업 처리 시 부정확한 자료 처리로 혼란 요소가 다소 발생하고 있다. 바코드 시스템에서 스캐너에 바코드를 직접 인식시켜야하기 때문에 처리 시간소요가 다소 길고 불편함 있어 이러한 문제점을 보안하여 게이트 자동화 시스템 RFID시스템은 차량 번호를 인식하는 기술을 이용하여 반출 입 처리 소요시간 단축하여 효율적인 게이트 업무 수행을 할 수 있다. 한편, 데미지 컨테이너 체크를 수동으로 진행하는 경우 게이트 반입 할때 컨테이너의 손상여부 확인 후 수기로 게이트 작업자가 직접 문서을 작성하여 보관하기 때문에 고객이 데미지 컨테이너 증거자료를 요청할때 증거자료로 제출하기에 불충분하여 고객으로부터 크레임을 받는 경우가 발생하였다. 또한, 게이트 작업자 및 인력에 의한 컨테이너 관리의 어려움 발생하였고 게이트 주변 인력의 컨테이너 관리 안전사고에 위험이 항상 노출되어 있었다. 본 논문에서 고찰하는 게이트 데미지 컨테이너 자동화 시스템을 도입하게 되면 영상 저장 시스템에 의한 컨테이너 관리가 가능하다. 컨테이너 손상정보가 시스템에 저장되어 있기 때문에 고객이 데미지 컨테이너 자료를 요청 시 시스템에 저장 되어 있는 데미지 컨테이너 영상을 검색하여 증거자료 제출이 가능하고 고객으로부터의 크레임 대비가 가능하다. 또한, 게이트 관리 인력 업무의 통합으로 인력 감소 및 게이트 부근 안전사고 위험이 감소하였다. As container vessels get larger, container terminals are also likely to grow. The problem that arises is that the growing volume should be handled in the same amount of time as before. Container terminals are introducing an automation system in order to overcome the limitations of existing manual methods and to continuously reduce operating expenses. Because, Manual handling of carrying containers gate in and out of terminals causes inaccurate data, which results in confusion. An alternative is for containers to be labeled with barcodes that can be scanned into a system with a scanner, but this takes quite a long time and is inconvenient. A RFID system, also known as a gate automation system, can solve these problems by reducing the time of gate management with a technology that detects number identification plates, helping operators more efficiently perform gate management work. Having said that, with this system, when container damage is detected, gate operators make and keep documents manually. These documents, which are insufficient evidence in proving container damage, result in customer claims. In addition, it is difficult for gate operators and other workers to manage containers, exposing them to danger and accidents. This study suggests that if an automation system is introduced at gates, containers can be managed by a video storage system in order to better document damage The video system maintains information on container damage, allowing operators the ability to search for videos they need upon customer request, also allowing them to be better prepared for customer claims. In addition, this system reduces necessary personnel and risk of accidents near gates by integrating a wide range of work.

Approximate Storage of Compressed and Encrypted Videos

The popularization of video capture devices has created strong storage demand for encoded videos. Approximate storage can ease this demand by enabling denser storage at the expense of occasional errors. Unfortunately, even minor storage errors, such as bit flips, can result in major visual damage in encoded videos. Similarly, video encryption, widely employed for privacy and digital rights management, may create long dependencies between bits that show little or no tolerance to storage errors. In this paper we propose VideoApp, a novel and efficient methodology to compute bit-level reliability requirements for encoded videos by tracking visual and metadata dependencies within encoded bitstreams. We further show how VideoApp can be used to trade video quality for storage density in an optimal way. We integrate our methodology into a popular H.264 encoder to partition an encoded video stream into multiple streams that can receive different levels of error correction according to their reliability needs. When applied to a dense and highly error-prone multi-level cell storage substrate, our variable error correction mechanism reduces the error correction overhead by half under the most error-intolerant encoder settings, achieving quality/density points that neither compression nor approximation can achieve alone. Finally, we define the basic invariants needed to support encrypted approximate video storage. We present an analysis of block cipher modes of operation, showing that some are fully compatible with approximation, enabling approximate and secure video storage systems.

Approximate Storage of Compressed and Encrypted Videos

The popularization of video capture devices has created strong storage demand for encoded videos. Approximate storage can ease this demand by enabling denser storage at the expense of occasional errors. Unfortunately, even minor storage errors, such as bit flips, can result in major visual damage in encoded videos. Similarly, video encryption, widely employed for privacy and digital rights management, may create long dependencies between bits that show little or no tolerance to storage errors. In this paper we propose VideoApp, a novel and efficient methodology to compute bit-level reliability requirements for encoded videos by tracking visual and metadata dependencies within encoded bitstreams. We further show how VideoApp can be used to trade video quality for storage density in an optimal way. We integrate our methodology into a popular H.264 encoder to partition an encoded video stream into multiple streams that can receive different levels of error correction according to their reliability needs. When applied to a dense and highly error-prone multi-level cell storage substrate, our variable error correction mechanism reduces the error correction overhead by half under the most error-intolerant encoder settings, achieving quality/density points that neither compression nor approximation can achieve alone. Finally, we define the basic invariants needed to support encrypted approximate video storage. We present an analysis of block cipher modes of operation, showing that some are fully compatible with approximation, enabling approximate and secure video storage systems.

基于64位操作系统的超高速视频存储系统

The design of high speed digital video storage system based on 64-bit operation system is discussed because ultra high frame-rate and high pixel number digital cameras are widely used in the field of optical measurement.This system uses high performance PC as hardware platform and high capacity memory is carried.The software puts image and appendix data into cache in memory during the storage process and after the process the software writes the data into hard disks array.The choke point in unstability of data transfer speed of the increasing number of hard disks is overcomed.According to application the bandwidth can exceed 800 Mbyte/s and the expected result is obtained.

Design and configuration rationales for digital video storage and delivery systems

Design and configuration rationales for digital video storage and delivery systems

Evaluation of a Video Storage System for Intrusion Detections

The paper reports an evaluation of a video storage system used in conjunction with closed circuit televisions for surveillance purposes. When multiple intrusions occur simultaneously, video storage permits the user to assess intrusions that might otherwise be missed. A study of specific display characteristics was undertaken to optimize the effectiveness of the video storage system.