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Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure - WP00211_v01_A4

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Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure - WP00211_v01_A4

  1. 1. W H I T E PA P E R INTELLIGENT STORAGE ENABLES NEXT GENERATION SURVEILLANCE & SECURITY INFRASTRUCTURE Shifting from Network Centric to Storage Centric Video Surveillance Architectures
  2. 2. 2 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure CONTENTS Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 State of the art: camera numbers soar, together with demand for storage and video analytics . . . 4 Typical VMS architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 The invention of the NVR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Known issues of NVR based architectures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 Video storage shifts to IT real scale solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 Video Analytics drive the design of future video surveillance architectures . . . . . . . . . . . . . . . . . . . . 7 From alarm management to predictive analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Video alarm triggers: computer aided surveillance . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Assisted video search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Evolution towards predictive analytics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 Video analytics complexity defeat the traditional NVR architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 How is Quantum taking the challenges of volume and performance and still lowering costs? . . . . 11 Examples of typical large video surveillance systems with Quantum StorNext® . . . . . . . . . . . . . . . . 11 Example 1:1000 cameras - disk storage only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 30 Days Near Line Tape Archive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 Example 1:10 000 cameras - disk storage only . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12 30 Days Near Line Tape Archive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 180 Days Near Line Tape Archive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 Powering the StorNext platform with high-performance data management software . . . . . . . . . . . 16 File system . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 Single namespace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 The power of tiered storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Extended online storage: Capacity and ready access . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 Tape archives: Cost-effective long-term storage . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 StorNext Storage Manager . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 Policy Management Include – Policy Class . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 StorNext Distributed Data Mover . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 StorNext LAN/SAN unified architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 The future of video recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
  3. 3. 3 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure SCALING FROM 1,000 TO 10,000 CAMERAS WITH ONE UNIQUE SYSTEM The role of storage in video surveillance systems has always been of uttermost importance. But it has evolved, from an evidence preservation mean to an evidence search tool, and soon as the main data repository for predictive analytics. Old analog systems are now being replaced with massively multi camera IT systems. Several years ago, video tapes were very important in storage and securing video. They were rapidly replaced with computer disks. But necessary storage volumes keep growing as video resolution increases. 1K, 4K, 8K megapixel cameras require such gigantic storage space that Network Video Recorders can hardly handle more than a handful of them simultaneously. This white paper explains how this situation can be efficiently circumvented, showing a progressive migration path from the current state of the industry’s IP video solutions, up to tomorrow’s storage centric architectures. STORNEXT - VS FROM QUANTUM: SIMPLY THE BEST VIDEO SURVEILLANCE STORAGE & ANALYTICS SHARED FILE SYSTEM ABOUT QUANTUM Founded in 1980, Quantum is a leading expert dedicated to data management solutions with scale-out storage, archive and data protection, providing intelligent solutions for capturing, sharing and preserving digital assets over the entire data lifecycle. Our commitment to advancing technology starts with our customers. As we hear customer challenges and changing business needs, it inspires continued development that adds meaningful value from streamlining high performance data management workflows, enabling mission-critical data analysis and improving efficiency in the data center. QUANTUM STORNEXT: VIDEO IN THE DNA Because of the unique attributes of the Quantum StorNext® 5 file system, more video is stored and managed by Quantum than on any other platform. Thousands of Media & Entertainment companies rely on Quantum to capture, store, share, and retrieve video content at far higher bit rates and frame sizes than those required by video surveillance. In fact, Quantum recognizes video data as a unique data type and treats incoming video specifically to optimize the workflow for video applications— making the entire workflow faster and more efficient. 
  4. 4. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 4 INTRODUCTION Most of the big metropolitan surveillance systems will exceed 5 to 10 thousand cameras in the next few years by cumulating body worn cameras, stationary cameras, and vehicles cameras, to name a few sources. This fact brings up an important consideration about scalability and optimizing the management of the ever growing storage utilization required to store the surveillance video. As volumes become larger, the need for efficient search tools and smart video indexing strategies has become more stringent. As video surveillance turns to IT, managers in charge of surveillance systems look for better scalability and maintainability, which they hardly find in current generations of infrastructures. On the contrary, managing growth of a video surveillance system often means multiplying cameras, multiplying NVR servers, mapping cameras on NVRs, replicating servers, and above all coping with a permanent race forward where storage equipment is not fitted to address new needs in data volume or processing power. Hence, the three challenges of new video surveillance infrastructures are clearly identified: scalability, performance and cost. In this whitepaper, we will address these three topics with a simple yet powerful solution. We will show that it is possible to circumvent the camera number growth impact on overall system maintainability and provide a solid framework for deploying massive video analytics while still lowering the cost of the global storage system by adopting tiered storage on LTO tapes in a near line robotic tape library archive. We will introduce the video storage system as the key element in the predictive analytics architecture, bridging the gap between data hungry algorithms and immense volumes of stored video footage. STATE OF THE ART: CAMERA NUMBERS SOAR, TOGETHER WITH DEMAND FOR STORAGE AND VIDEO ANALYTICS As quality video surveillance deployment standardizes, surveillance cameras are increasingly used as critical sensors in a number of situations. Video storage has a key role to ensure that information captured by the ever increasing numbers of cameras is kept safe and accessible, not only in case of investigation, but also because the need for systematic video analytics is growing exponentially every day. “Surveillance systems have come a long way from the days of fixed cameras and video tape devices. Since the introduction of IP-based cameras in the 1990s, the industry has steadily migrated away from analog systems to network solutions. Camera technology also continues to get smarter—with on-board analytics, higher resolutions, and faster frame rates—resulting in a massive increase in video data, and with it, the need for better storage infrastructures. Consider this: a two-megapixel camera running at 30 fps (frame per second) generates approximately 10 gigabytes of data every day (assuming H.264 compression at 1024 kbps). Fifty of those cameras would generate approximately 183 terabytes of data per year. Replace those with Ultra HD 4K cameras, and the total jumps to 730 terabytes of data per year.” Wayne Arvidson, Video surveillance VP, Quantum
  5. 5. 5 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure TYPICAL VMS ARCHITECTURE The current view of “Network Centric Video Surveillance” is inherited from the early days of Closed Circuit Television where cameras were bound directly to a matrix switch that was in charge of sending camera signals to analog monitors and tape recorders. Since 2000, IP video has enabled huge scale changes but the old idea of multiplexing and de-multiplexing camera signals has just been translated into the digital age and most of the systems are still depending on real-time transmission of video streams over the network from cameras to Network Video Recorders (NVRs) and from cameras to visualization systems. In the early view of a network centric video surveillance, the key concept is the video “stream”. This object is the stream of data that is generated by a camera device in real-time that is sent over the network using tcp-ip protocols i.e. RTSP. There are lots of limitations and problems related to the fragility and the “real-time-ness” of live video streams. As transient objects they are ephemeral and the risk of not being able to capture them or transmit them is not null. Moreover, by essence real-time video cannot be replayed which puts a lot of pressure on processing capacity for real-time analytics. VMS VISUALIZATION VMS ALARM MANAGEMENT VMS STORAGE MANAGEMENT IP NETWORK Metadata Generation Stream Processing Stream Acquisition Figure 1: Functions of a typical VMS
  6. 6. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 6 THE INVENTION OF THE NVR When video surveillance transitioned to IP in the 2000s, the compression codecs were H263 (SIP videoconference), MPEG2, Wavelets and MJPEG. It is only in 2004 that the MPEG4 standard had begun to emerge and be considered as the unified codec for video surveillance applications. This has enabled the emergence of VMS vendors, independent from camera manufacturers. Each VMS comes with its own flavor of video recorder, video display, and alarm based management. Video storage is achieved through a storage server process that collects video streams from cameras and copies them to a disk, using a specific file management protocol. The physical server where the storage server process is executed is called the Network Video Recorder (NVR). It is a simple, distributed, elegant way of storing digital video on IP networks. It is also the most important bottleneck of the architecture. In fact, many limitations affect an NVR—as a server it has a network throughput limitation, a CPU limitation, an I/O limitation, and a disk space limitation. These limitations create bottlenecks that are obstacles to scalability when systems grow bigger as cameras multiply and camera resolution increases. KNOWN ISSUES OF NVR BASED ARCHITECTURES Network bottleneck NVRs are single device architectures with network throughput limited by network cards I/O bottleneck NVRs are storing video on local disks which have limited I/O capacity. They can’t scale out over a fixed disk throughput limit to handle output streams and input cameras Hardware single point of failure NVRs must be replicated to offer full hardware reliability. A single point of failure in the NVR means recording stops for all cameras managed by this NVR. Video analytics processing bottlenecks For video analytics that work on recorded video streams, the CPU capacity of the NVR is limiting the number of streams that can be served. The NVR is a bottleneck in the analytics processing Clustered, partial view of the overall system Global system-wide correlations are not possible for analytics running on a single NVR, which only has a limited view on a specific cluster of cameras. Figure 2: NVR inherent limitations NVR SERVER (LOCAL DISKS) NVR SERVER (LOCAL DISKS) NVR SERVER (LOCAL DISKS) Camera Onboard Processing NVR Processing Encoding Process RTSP Streaming NVR Recording Process Local File System IP Transmission
  7. 7. 7 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure VIDEO STORAGE SHIFTS TO IT REAL SCALE SOLUTIONS In over 15 years, the security industry has invented the NVR, standardized video compression, created distributed architectures that take full advantage of network centric vision, and standardized the interfaces in open industry forums like ONVIF which has issued specifications for a specific G profile for NVRs and S profile for IP cameras. The video surveillance industry has made its way from tape recorders to Digital Video Recorders up to the Network Video Recorders with one single idea in mind: preserve evidences and allow their recollection in case of an unexpected event. Now that the shift to IT systems is now complete, Moore’s law is having an exponential effect on video surveillance systems as well. While camera efficiency for preventing impunity is demonstrated and camera acceptance by population is widely verified, huge deployments are underway in major cities around the world with cumulated numbers of cameras for public transports, circulation and traffic control already exceeding 10,000 cameras. Technical infrastructure challenges are posed in terms of networks, processing power and storage space. Besides, image interpretation complexity and decision making keep the human operator in the loop. The situation becomes paradoxical as human capacity to process video continuously shows weaknesses, but its ability to interpret scenes remains considerably superior to the computer. A new and vast field of application development is at the crossroad of ergonomics and augmented reality. Computer assisted video surveillance slowly emerges as a response to a simple finding: we are capable of spreading many more surveillance cameras than we are able to monitor. VIDEO ANALYTICS DRIVE THE DESIGN OF FUTURE VIDEO SURVEILLANCE ARCHITECTURES After more than 20 years of intense R&D, video content analysis has shown to provide industrial solutions that have solid track records in security and law enforcement applications. This is clear with license plate recognition which is commonly used for automated speed enforcement or parking access control. It is also remarkable for face recognition, which was a complex dilemma that has been met today with an impressively high level of maturity. A broad range of applications use video analytics to complement the human operator in the supervision task. Interestingly, most of these applications are common to Homeland Security, Defense and Law Enforcement as they participate in the day-to-day fight against terrorism. Pattern recognition, change detection, and integrated extraction tools are now available and ready to leverage massive amounts of recorded video.
  8. 8. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 8 FROM ALARM MANAGEMENT TO PREDICTIVE ANALYTICS Today’s video analytics are used on both live and recorded video in two distinct scenarios: video alarm triggers and assisted video search. They are integrated with the VMS and interoperate with alarm management and video search user interfaces. They are mostly used in manual mode. VIDEO ALARM TRIGGERS: COMPUTER AIDED SURVEILLANCE In this scenario, the video analytic process is filtering the real-time video stream and it triggers an alarm when an expected situation is detected. The most simple of these video analytic filters include camera tampering, tail gating, counting, enter/exit/stop, directional detection, dwell, removed or abandoned object, colors, etc. Those very simple algorithms filter only one video stream and many camera manufacturers embed them directly on the IP camera board itself. Nevertheless, the most complex algorithms, requiring metadata from different cameras and involving access to matching databases, for recognition purpose, need CPU power and lots of disks I/O and must reside on adequate servers. ASSISTED VIDEO SEARCH Video Analytics are not always used in real-time. Filters that prove efficient to detect certain types of situations may also be used to filter out sequences of interest in large video archives. The filter is processed by the NVR in charge of managing the recorded video feed. The following schema shows a classification of video analytics according to the complexity of their inner algorithm and the number of cameras that are required in a typical alarm based scenario. Figure 3: Analytics classification according to algorithm complexity and number of video feeds Crowding/Congestion Detection Traffic Control Theft Detection Fire/Smoke Detection Traffic Control Face Recognition/Masking Vehicle & People Tracking Complex Behavior Recognition Camera Maintenance and Tampering License Plate Recognition NumberofCameras-I/OIntensive 12 10 8 6 4 2 0 Complexity (CPU Intensive) 0 2 4 6 8 10 12 Analytics Classification
  9. 9. 9 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure EVOLUTION TOWARDS PREDICTIVE ANALYTICS Having video analytics to support real-time detection of abnormal situations or even to help search through video evidence during investigations is an absolute must. But the idea that such systems could to some extent participate to actually avoiding abnormal situations is compelling. A large part of R&D is now dedicated to automating video analytics and leveraging their results with statistical tools that can actually predict the occurrence of specific situations. This has a direct impact on the IT architecture of video surveillance systems that must take into account data extraction from video footage, data correlation, and storage. The need for an analytic oriented middleware is pressing, together with a persistent data layer backed-up by a solid storage system. Analytics factory, the art of collecting metadata Video analytics middleware is highly necessary to manage the wide variety of algorithms available and the communication of data that they will extract. As recognition algorithms reach an industrial grade, it becomes interesting to apply them systematically on video feeds to generate metadata that enriches the contextual information about the footage and facilitates indexing for forensic searches. The dedicated architecture needs to be designed around the analytic middleware, allowing multiple types of concurrent analytics to access and analyze recorded video, gathering all resulting data for further processing. VIDEO ANALYTICS COMPLEXITY DEFEAT THE TRADITIONAL NVR ARCHITECTURE While basic video analytics can be straightforward to implement and require very few resources in term of CPU or even I/O, complex analytics must take into account transactional data from multiple cameras in order to establish correlations in time and space. Specific searches may be necessary in video which have been moved to archive, or on sets of cameras which are not attached to the same NVRs. A high level of complexity remains in the distributed aspect of today’s video surveillance architectures. A centralized and unified view of the data is necessary to enable complex analytics to fuse data from all cameras and achieve the pattern matching that will be of interest for security operations. The clustered architecture of NVRs limits their use in environments that require global system view and correlations. A need for a unified video recorder that is integrated with analytics at the scale of the overall system is pushing the limits of traditional VMS systems to clear the boundaries between storage system, analytics, and VMS. The NVR, once an integrated component of the VMS, is shifting to the role of a tiered service capable of swiftly delivering storage, retrieval, and streaming video feeds to the other components of the system.
  10. 10. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 10 Figure 4: Unified Video Storage and Video Analytics integration with VMS architecture Figure 5: Analytics shift from alarm to rich video metadata information generation Metadata Generation Stream Processing Stream Acquisition VMS VISUALIZATION VMS ALARM MANAGEMENT VMS STORAGE MANAGEMENT StorNext 5HA Scale-Out Converged Primary Storage 16Gb FC Channel SAN 10GbE & 1GbE NAS Options (NFS / SMB) XCELLIS • Public • Private (Lattus enabled) • Extended Nearline QUANTUM Q-CLOUD Automated Policy-Based Tiering (On-Premise, Off-Premise & Cloud) • Near-Line Tape Archive • AES 256Bit Encryption • WO RM • Offline Vaulting • Data Integrity Validation QUANTUM SCALAR i500 LTO TAPE LIBRARIES SAN ANALYTIC CLIENTS LAN ANALYTIC CLIENTS IP Transmission StorNext 5HA Scale-Out Converged Primary Storage 16Gb FC Channel SAN 10GbE & 1GbE NAS Options (NFS / SMB) Analytics Server NVR Server XCELLIS Video Analytic Processing Video Streaming Quantum Storage Processing Metadata Generation Recognition Matching Detecting Stream Acquisition NVR Playback Process Quantum LAN/SAN Client Quantum Physical Storage
  11. 11. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 11 QUANTUM STORNEXT LOWERS COSTS WITHOUT PERFORMANCE TRADE-OFFS Quantum conquers these two challenges by smoothly scaling to cope with the large ingestion network that collects video from very large sets of cameras, while also providing the streaming power to distribute video feeds to visualization and analytics layers. A typical large video surveillance system would involve: • 1000 cameras from a large ingestion network • 10% display streams (cameras displayed to operators) • 10 to 100% analytics streams (cameras processed by analytics) EXAMPLES OF TYPICAL LARGE VIDEO SURVEILLANCE SYSTEMS WITH QUANTUM STORNEXT Example 1 - 1,000 Cameras: 1,000 cameras - 30 Days on Disk Storage Parameter Value Number of cameras 1000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Disk 30 Days Required Usable Capacity 648 TB’s Required Throughput Ingest BW ~ 250 MB per sec 1,000 cameras - 7 Days on Disk Storage Parameter Value Number of cameras 1000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Disk 7 Days Required Usable Capacity 160 TB’s Required Throughput Ingest BW ~ 250 MB per sec
  12. 12. 1,000 cameras - 30 Days on Near Line Tape Archive Parameter Value Number of cameras 1000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Tape 30 Days Required Usable Capacity 648 TB’s Total Number of LTO 7 Storage Slots 110 Storage Slots LTO 7 Tape Drive R/W Bandwidth ~ 300 MB per sec per drive LTO-7 Drives Qty 2 = 600 MB per sec per drive AEL i500 14U 133 Active Slots, Qty 2 IBM LTO-7 Drives 1,000 cameras - 180 Days on Near Line Tape Archive Parameter Value Number of cameras 1000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Tape 180 Days Required Usable Capacity 4,000 TB’s Total Number of LTO 7 Storage Slots 700 Storage Slots LTO 7 Tape Drive R/W Bandwidth ~ 300 MB per sec per drive LTO-7 Drives Qty 4 = 1.2 GB per sec per drive AEL i6K 700 Active Slots, Qty 4 IBM LTO 7 Drives Example 2 - 10,000 Cameras 10,000 cameras - 30 Days on Disk Storage Parameter Value Number of cameras 10,000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Disk 30 Days Required Usable Capacity 6,480 TB’s or 6.5 PB’s Required Throughput Ingest BW ~ 2.5 GB per sec 10,000 cameras - 7 Days on Disk Storage Parameter Value Number of cameras 10,000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Disk 7 Days Required Usable Capacity 1,512 TB’s or 1.52 PB’s Required Throughput Ingest BW ~ 2.5 GB per sec 12 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure
  13. 13. 10,000 cameras - 30 Days on Near Line Tape Archive Parameter Value Number of cameras 10,000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Tape 30 Days Required Usable Capacity 6,480 TB’s or 6.5 PB’s Total Number of LTO 7 Storage Slots 1,100 Storage Slots LTO Tape Drive R/W Bandwidth ~ 300 MB per sec per drive LTO-7 Drives Qty 10 = 3 GB per sec per drive 10,000 cameras - 180 Days on Near Line Tape Archive Parameter Value Number of cameras 10,000 AVG Throughput per camera (Bit Rate) 2Mbps Number of Days Retention on Tape 180 Days Required Usable Capacity 38,880 TB’s or 38.9 PB’s Total Number of LTO 7 Storage Slots 6500 LTO Tape Drive R/W Bandwidth ~ 300 MB per sec per rive LTO-7 Drives Qty 10 = 3 GB per sec per drive Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 13 VMS SERVER / NVR 1 VMS SERVER / NVR 2 VMS SERVER / NVR 3 Video Ingest Network Scale out • Near-Line Tape Archive • AES 256Bit Encryption • WO RM • Offline Vaulting • Data Integrity Validation QUANTUM SCALAR i500 LTO TAPE LIBRARIES StorNext 5HA Scale-Out Converged Primary Storage 16Gb FC Channel SAN 10GbE & 1GbE NAS Options (NFS / SMB) XCELLIS • Public • Private (Lattus enabled) • Extended Nearline QUANTUM Q-CLOUD Various Connectivity Options Automated Policy-Based Tiering (On-Premise, Off-Premise & Cloud) Figure 6: Tiered storage lowers costs without quality or performance tradeoffs
  14. 14. 1,000 cameras 30 days 650TB Storage Only - One Copy Storage & Protection - Two Copies Disk 30 Days 7 Days 30 Days x2 7 Days Tape None 30 Days None 30 Days x2 Ratio versus full Disk 1 0.94 1 0.69 Saving with Tiered Storage - 6% - 31% 180 days 4PB Storage Only - One Copy Storage & Protection - Two Copies Disk 180 Days 7 Days 180 Days x2 7 Days Tape None 180 Days None 180 Days x2 Ratio versus full Disk 1 0.45 1 0.46 Saving with Tiered Storage - 55% - 54% 10,000 cameras 30 days 6.5PB Storage Only - One Copy Storage & Protection - Two Copies Disk 30 Days 7 Days 30 Days x2 7 Days Tape None 30 Days None 30 Days x2 Ratio versus full Disk 1 0.68 1 0.53 Saving with Tiered Storage - 32% - 47% 180 days 40PB Storage Only - One Copy Storage & Protection - Two Copies Disk 180 Days 7 Days 180 Days x2 7 Days Tape None 180 Days None 180 Days x2 Ratio versus full Disk 1 0.28 1 0.32 Saving with Tiered Storage - 72% - 68% As storage capacity need increases due to the numbers of cameras, the retention period length or the need to have a 2nd protection copy increases even more the saving provided by a tiered storage. Such architecture can help save from 30% up to 70% on acquisition cost alone as shown on the tables above Since not all costs have been taken into account in the comparisons (for example are support/maintenance, installation, datacenter footprint and power/cooling consumption costs), file tape will provide additional saving on OpEx such as power and cooling. Figure 7: Tiered tape storage lowers costs of archive video storage Quantum’s solution for video surveillance offers an extensive number of advantages for today’s and more interestingly for tomorrow’s large video surveillance installation challenges. 14 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure
  15. 15. Primarily, StorNext solution is intrinsically a video file sharing system that has demonstrated the ability to ingest, manage, and share video files of extremely large sizes between large numbers of concurrent users. StorNext file system optimizes performance of the video storage to guarantee streamlined viewing and simultaneous analytic access to the video recordings will be served in the most efficient and timely manner. StorNext is also a policy based data management solution that transparently handles the movements of video data from primary disk to extended object storage or tape libraries. Eventually, the LAN/SAN mixed client types allow using the best of both worlds, keeping in mind objectives of interoperability with network equipment as well as efficiency of new analytics services. LAN/SAN Clients will enable the customer to quickly add more analytic processing capability by increasing the access performance to storage (SAN Client) or parallelizing the process across multiple servers that require slower storage access (LAN clients) The role of storage shifts from commodity to predominant, ensuring both data security and data access in environments where size has a direct impact on maintainability. Figure 8: Quantum’s Unified Video Archival system enables unlimited incoming and outgoing streams between LAN and SAN Networks Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 15 Recording Processes Ingestion Streaming Streaming Moving Video Storage & Movement SAN LAN Analytics Visualization Archiving Extended Storage Tape Cloud X 1,000 X10,000, ... 100% 100% 10%
  16. 16. POWERING THE STORNEXT PLATFORM WITH HIGH-PERFORMANCE DATA MANAGEMENT SOFTWARE StorNext data management software seamlessly integrates multiple tiers of storage systems into a single environment. It provides file system management, creates a single namespace, offers multi-protocol communications support, includes a policy engine, and facilitates efficient data movement across the tiers. FILE SYSTEM The StorNext file system is a high-performance shared file system that delivers rapid, low-latency access to files and content across the multi-tier storage environment. Streaming: The StorNext file system is the industry’s fastest streaming file system. It can meet the most extreme requirements for fast ingest and access, optimizing throughputs and increasing the number of cameras that can be managed simultaneously. Built to drive the underlying storage hardware to its maximum potential, StorNext takes advantage of highly scalable configurations, aggregated performance from multiple arrays, optimal data placement, and I/O. While other storage solutions might have limitations on the speed of a single stream, StorNext can deliver high performance whether supporting numerous small streams or big streams of file transfer. Figure 9: Substituting NVRs disks with Quantum unlimited storage space Shared file system: StorNext provides a shared file system, enabling video to be accessible to many operators and analytics applications simultaneously. With traditional NVRs, each storage volume is mounted and owned by a single server. This approach allows a server to read from or write to the file system quickly—but there are drawbacks. For example, pairing each server with a storage volume can leave storage capacity under utilized and force you to acquire additional capacity. In addition, this approach restricts access to a server’s storage volume to the users and applications that can access that particular server. 16 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure IP Transmission StorNext 5HA Scale-Out Converged Primary Storage 16Gb FC Channel SAN 10GbE & 1GbE NAS Options (NFS / SMB) XCELLIS NVR SERVER Camera Onboard Processing NVR Processing Quantum Storage Processing Encoding Process RTSP Streaming NVR Recording Process Quantum LAN/SAN Client Quantum Physical Storage
  17. 17. The StorNext file system allows multiple servers to share a common storage pool within a single namespace. Client systems send requests to the workflow director and receive block-level information that correspond to the file request. Clients then communicate directly with storage; the workflow director is out of band of the data path and moves on to the next request. This direct communication approach allows for tremendous simultaneous shared access and high-performance deterministic file delivery. StorNext also includes cache coherency mechanisms to help ensure data is coherent and up-to-date even when data is changed by multiple clients. These mechanisms limit the required metadata operations by having the clients communicate with only the metadata system for certain requests, such as file creation, deletes, or allocations. Portability: The StorNext file system is POSIX compliant and can be accessed by a wide array of clients systems, whether they are running Windows, Linux, UNIX, or Mac operating systems. Consequently, you can extend access to data to a broad array of users without requiring them to alter their existing IT environment or workflows. High availability: The StorNext High Availability (HA) feature allows you to operate a redundant server that can quickly assume control of the primary server’s operations in the event of software, hardware and network failures. The StorNext HA feature is a special StorNext configuration with improved availability and reliability. The configuration consists of two servers, shared disks and tape libraries. StorNext is installed on both work-flow director node servers. One of the servers is dedicated as the initial primary server and the other the initial standby server. StorNext File System and Storage Manager run on the primary server. The standby server runs StorNext File System and special HA supporting software. The StorNext failover mechanism allows the StorNext services to be automatically transferred from the current active primary server to the standby server in the event of the primary server failure. The roles of the servers are reversed after a failover event. Only one of the two servers is allowed to control and update StorNext metadata and databases at any given time. The HA feature enforces this rule by monitoring for conditions that might allow conflicts of control that could lead to data corruption. SINGLE NAMESPACE StorNext provides a single namespace for all storage tiers to simplify the user experience, accelerate file system performance, and improve efficiency. With StorNext, users can view and seamlessly search for video sequences located anywhere on the entire multi-tier environment, including primary, secondary, tape, and cloud tiers. They can find the data they need quickly and easily, no matter where it resides. The StorNext single namespace implementation also enhances efficiency. Because client systems can communicate directly with storage after placing requests through the workflow director, you eliminate communications overhead. Some hierarchical storage management (HSM) solutions attempt to integrate distinct storage volumes. The HSM system creates a layer above those distinct volumes, which keeps track of where data resides and moves data from one namespace to another as needed. A key problem with this approach is that it introduces a layer between clients and storage. When clients connect to the storage environment, they are actually connecting to the HSM system. Adding that level of abstraction above storage introduces latency and reduces performance. StorNext eliminates that added layer and avoids the performance issues the layer can create. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 17
  18. 18. THE POWER OF TIERED STORAGE Archiving policies have been implemented into VMS and achieve video file movements between “live” and “archive” storage systems. This level of service is natively integrated into the StorNext system that uses four levels of archival media (tiers of storage) : the disks, the extended online with object storage, the robotic tape libraries, and eventually the cloud backup. EXTENDED ONLINE STORAGE: CAPACITY AND READY ACCESS The extended online—or “secondary”—storage tier provides capacity for video that you need to keep online, ensuring it remains readily available to users. While the high cost of primary storage often restricts scalability, the lower cost of extended online storage enables you to scale this tier to very large petabyte sizes and frees primary storage for more performance-intensive operations. QUANTUM LATTUS The StorNext platform uses Quantum Lattus™ object storage as an option as an option for this secondary extended online tier. Lattus combines extreme scalability and low latency with data durability to protect file based data from loss. Scalability: Lattus object storage can scale from hundreds of terabytes to hundreds of petabytes, making it ideal for large-scale repositories of engineering, design, 3D modeling, audio, video, medical, geospatial/satellite, and office files. With Lattus, you can preserve all that data for the long term. Most importantly for sensitive security applications, when scaling, you can add storage or expand to multi- site configurations without disruption or downtime. The intelligent system automatically spreads data—in the background—across the environment and sites. Low latency: Lattus delivers excellent near-online file retrieval. It can offer ready access to large-scale repositories, even when that access is required infrequently. Data durability: Lattus also provides outstanding data durability and data protection. Self-healing capabilities continuously check data for bit errors and correct them as necessary. If a drive fails, the Lattus system automatically moves data to a working drive. You can also capitalize on multi-geo support to spread data across multiple, geographically dispersed sites. This approach helps avoid data loss even in the event of a site disaster. Using object storage rather than a RAID approach further helps reduce downtime. While large RAID environments take time to be rebuilt after a failure, Lattus object storage can recover quickly even as the size of the environment grows. TAPE ARCHIVES: COST-EFFECTIVE LONG-TERM STORAGE The tape archive is the most cost-effective capacity tier for archiving video. With file based tape, you can store massive amounts of video over longer periods of retention. To scale a tape storage environment’s capacity, you can simply add more tape cartridges, or additional drives can be provisioned to accommodate increased ingest or data retrieval. 18 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure
  19. 19. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 19 Tape offers substantial cost savings compared with primary or extended online storage tiers, though it does not deliver the same level of performance or accessibility of those tiers. To access and work with data stored on tape, applications must transfer data from tape to a disk-based tier. The speed of that process depends on a variety of factors, including the location of the tape, the number of tape drives provisioned, and the availability of the tape for the system’s robot. Because the physical attributes of tapes and tape systems are slow to change, you could continue to use tapes for decades. As new LTO standards are introduced, tape systems typically offer backwards compatibility, allowing you to read tapes from the previous two generations of LTO standards and even write with the prior LTO standard. The evolution of LTO standards, meanwhile, enables you to continue to achieve greater and greater value from the tape archive tier over time. You can accommodate rapidly growing capacity needs, control costs, and provide faster access to archived material. USING TAPE TO STORE VIDEO IS BOTH SECURE AND COST EFFECTIVE WITH LIMITED TRADE-OFF ON ACCESS TIME STORNEXT INTELLIGENT TAPE ARCHIVES StorNext intelligent tape archive solutions—including StorNext Archived Enabled Library archives and Scalar® tape libraries—combine low-cost, high-capacity storage with best-of-breed monitoring to help ensure long-term data protection while streamlining management.
  20. 20. 20 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure STORNEXT STORAGE MANAGER StorNext Storage Manager (SNSM) enhances the StorNext solution by reducing the cost of long term data retention, without sacrificing accessibility. SNSM sits on top of SNFS and utilizes intelligent data movers to transparently locate data on multiple tiers of storage. This enables customers to store more files at a lower cost, without having to reconfigure applications to retrieve data from disparate locations. Instead, applications continue to access files normally and SNSM automatically handles data access – regardless of where the file resides. As data movement occurs, SNSM also performs a variety of data protection services to guarantee that data is safeguarded both on site and off site. Figure 10: StorNext transparent data moving POLICY MANAGEMENT INCLUDES – POLICY CLASS • Rules to determine behavior of data • Applied at directory level • Spans file systems when applied to multiple directories • Time-based movement of data • Number of copies (1 to 4) steer data copies to tape, extended on-line and cloud storage tiers • Checksum creation/validation • Automatic stores to secondary tiers or schedule based • Disk to Disk relocation within primary file system • Stub files Day 0 Day 30 Day 60 Day 90 Day 120 Day 150 Data moves based on your rules Applied at directory level Spans File System Supports Multiple Copies Disk-to-Disk moves within Primary Storage Works through SAN, LAN, and NAS clients File remains visible in original location and indicates archive and offline status File Arrives Create Backup Still Untouched Remove from Primary Untouched Files Move to Archive
  21. 21. Intelligent Storage Enables Next Generation Surveillance & Security Infrastructure | WHITE PAPER 21 STORNEXT DISTRIBUTED DATA MOVER With Quantum Distributed Data Mover, the data movement operations from primary workflow storage to extended storage and archive or cloud storage are handled transparently by the metadata controller. The Distributed Data Mover feature provides the following benefits: • Concurrent utilization of shared StorNext tape and disk tier resources by multiple Distributed Data Mover hosts • Scalable data streaming • Flexible, centralized configuration of data movement • Dynamic Distributed Data Mover host reconfiguration • Support for StorNext File System storage disks (SDisks) • Works on HA systems without additional configuration Figure 11: StorNext data management through tiered storage devices STORNEXT LAN/SAN UNIFIED ARCHITECTURE StorNext can provide SAN connectivity through Fibre Channel and iSCSI or IP over InfiniBand for applications that need fast access, as well as distributed LAN client (DLC) for SAN-like connections over Ethernet. DLC is an efficient protocol that provides a NAS-like interface to shared storage. With DLC, servers on an IP network can connect to StorNext volumes through SAN clients designated as clustered gateway servers. These gateways are SAN clients that connect directly to the shared storage pool over Fibre Channel and/or iSCSI, but primarily service DLC I/O instead of running customer applications. • Use cases: DLC is ideal for organizations that have farms of servers running analytics for systematic recognition of people or vehicles, all needing to access a shared set of files, but not at Fibre Channel speeds. DLC is especially attractive to customers with high-performance computing (HPC) and rendering projects where a large dataset is broken into segments and processed by multiple servers. This is the typical case for image recognition in very high definition photographs. DLC is also used when customers have a variety of performance needs and want to mix SAN and NAS characteristics. In addition, it offers a fast way to access data when users do not have Fibre Channel connectivity available. VIDEO STORAGE High-performance, multi-tier storage solutions for enabling productivity and efficiency EXTENDED STORAGE Massively scalable storage systems for safely preserving an organization’s most strategic assets VIDEO ARCHIVE Highly efficient and fast systems for protecting an organization’s critical operational data CLOUD Services that integrate the Cloud as an on-demand, off-premise storage location for backup and archive
  22. 22. 22 WHITE PAPER | Intelligent Storage Enables Next Generation Surveillance Security Infrastructure • Performance: While DLC might sound similar to traditional Network File System (NFS)/Common Internet File System (CIFS) data sharing, it is unique in that it uses a specialized block-based IP protocol designed for higher per-stream performance and resilient communication. This specialized protocol is optimized for StorNext and can achieve near line-rate throughputs over standard IP network connections. DLC can achieve nearly three times the throughput of NFS on the same network. To keep performance optimized, DLC load balances data requests across all available network interfaces on the client and across all available gateways. • Resiliency: The protocol also enables resiliency by having each DLC automatically attach to multiple clustered gateways. DLC will use all available gateways for traffic; if one of the gateways fails, DLC just stops sending I/O to that individual gateway. Figure 12: LAN / SAN mixed architectures allows fine performance tuning for all storage processes THE FUTURE OF VIDEO RECORDING As demonstrated previously in this article, NVR architectures suffer many limitations, either in disk or in processing capacity. We have proposed an architecture that mitigates the disk capacity bottleneck by replacing the NVR internal disks by a link to StorNext file management system. This proves more efficient in a number of ways including scalability, availability, and security. Still the need for an NVR server that controls the storage of a camera video feed to the disks is necessary. But why use an NVR if the camera can be empowered to store its video directly to the storage system? NVR are necessary to organize video files on the storage spaces and to manage recording/playback requests. They also process filters to fasten searches on their data. If we assume that the cameras can write directly to the storage system, and if the storage system provides the right interface with the video analytics systems, the need for the NVR becomes less evident. The VMS directory still has to identify each camera in the system, but the mapping of each camera on a specific NVR is not necessary anymore. VMS SERVER / NVR 1 VMS SERVER / NVR 2 VMS SERVER / NVR 3 Video Ingest Network Various Connectivity Options Automated Policy-Based Tiering (On-Premise, Off-Premise Cloud) Scale Out • Near-Line Tape Archive • AES 256Bit Encryption • WO RM • Offline Vaulting • Data Integrity Validation QUANTUM SCALAR LTO TAPE LIBRARIES StorNext 5HA Scale-Out Converged Primary Storage 16Gb FC Channel SAN 10GbE 1GbE NAS Options (NFS / SMB) XCELLIS StorNext 5HA Scale-Out NAS Archive Storage Solution QUANTUM ARTICO ARCHIVE GATEWAY Block-based NTFS - iSCSi FC Options Primary Storage Solution QUANTUM QXS HYBRID STORAGE QUANTUM VIDEO SURVEILLANCE APPLIANCE Server Platform (Optional) • Public • Private (Lattus enabled) • Extended Nearline QUANTUM Q-CLOUD
  23. 23. Furthermore, the growing interest for complementary video wearable devices is changing the way video surveillance is operated. Systematic video analytics processing, metadata generation, and predictive situational awareness processing force the integration of stationary cameras footage with mobile devices footage into a unified video storage system, capable of feeding the most complex rules based on machine learning algorithms with video sequences of interest. The utmost critical situations for homeland security require that no compromise be made, no evidence lost, and no correlation missed between the smallest detail from a body worn camera into a routine visit and the metadata captured from downtown urban security cameras. People and vehicle tracking, in real-time and forensic mode, require that many different video streams from geographically diverse locations and sensor type be analyzed and confronted by the same logic. To that extent, DLC provides a portable, interoperable and scalable solution to video sensor integration, from the body worn camera to the stationary camera, no matter the camera sensor operating system, be it Linux or Windows. StorNext DLC integrates seamlessly with video sensors to provide a unified view of the video storage system. Figure 13: DLC based MDTS NVR-less recording CONCLUSION Storage systems have made their way into the IP video surveillance ecosystem and are now a central component. Not only do they guarantee the protection of critical data, but also enable ongoing creation of massive video analytic environments to showcase the value of stored video for extracting actionable intelligence and allowing true situational awareness to emerge. The relentless growth of the number of video feeds and storage space has an end. The complete and efficient dynamic video coverage that mitigates risks, be they anticipated or unexpected. Quantum provides a cornerstone of the solution, with a coherent vision of a storage system; StorNext is the only unified video surveillance storage solution in the industry, with cost efficient and definitive path across scalability and performance issues.  Intelligent Storage Enables Next Generation Surveillance Security Infrastructure | WHITE PAPER 23 IP Transmission Camera Onboard Processing Quantum Storage Processing Encoding Process RTSP Streaming Quantum LAN Client LAN Client Quantum Physical Storage
  24. 24. Intelligent Storage Enables Next Generation Surveillance Security Infrastructure | WHITE PAPER 24 ©2016 Quantum Corporation. All rights reserved. Quantum, the Quantum logo, Lattus, Scalar, StorNext and Xcellis are either registered trademarks or trademarks of Quantum Corporation and its affiliates in the United States and/or other countries. All other trademarks are the property of their respective owners. WP00211A-v01 April 2016 www.quantum.com/video-surveillance ABOUT QUANTUM Quantum is a leading expert in scale-out storage, archive and data protection, providing solutions for sharing, preserving and accessing digital assets over the entire lifecycle. For security and law enforcement professionals facing challenges created by more cameras, higher resolutions, and increasingly complex analytics, Quantum’s Video Surveillance Solution is an intelligent, scalable storage platform that provides a simple to manage foundation which can grow under a single file system, designed specifically for video applications.

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