Huawei OceanStor UDS Massive Storage System Technical White Paper

Transcription

1 Huawei OceanStor UDS Massive Storage System Technical White Paper Issue 1.1 Date HUAWEI TECHNOLOGIES CO., LTD.

2 Copyright Huawei Technologies Co., Ltd All rights reserved. No part of this document may be reproduced or transmitted in any form or by any means without prior written consent of Huawei Technologies Co., Ltd. Trademarks and Permissions and other Huawei trademarks are trademarks of Huawei Technologies Co., Ltd. All other trademarks and trade names mentioned in this document are the property of their respective holders. Notice The purchased products, services and features are stipulated by the contract made between Huawei and the customer. All or part of the products, services and features described in this document may not be within the purchase scope or the usage scope. Unless otherwise specified in the contract, all statements, information, and recommendations in this document are provided "AS IS" without warranties, guarantees or representations of any kind, either express or implied. The information in this document is subject to change without notice. Every effort has been made in the preparation of this document to ensure accuracy of the contents, but all statements, information, and recommendations in this document do not constitute a warranty of any kind, express or implied. Huawei Technologies Co., Ltd. Address: Website: Huawei Industrial Base Bantian, Longgang Shenzhen People's Republic of China

3 Contents Contents 1 Executive Summary Introduction Product Composition Product Features Exascale Scalability SoD Architecture High Security and Reliability Low TCO Experience Solution 1: Massive Resource Pool Typical Needs and Problems Facing Customers Solution Software and Hardware Configurations Benefits Solution 2: Centralized Backup Typical Needs and Problems Facing Customers Solution Software and Hardware Configurations Benefits Solution 3: Web Disk Typical Needs and Problems Facing Customers Solution Software and Hardware Configurations Solution Network Benefits Solution 4: Centralized Active Archiving Typical Needs and Problems Facing Customers Solution Software and Hardware Configurations Solution Network Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 3

4 Technical White Pater Contents Benefits Conclusion Acronyms and Abbreviations... 45

5 1 Executive Summary 1 Executive Summary As the IT industry develops, data amount soars at an unprecedented speed. A brand-new storage system is required for the reliable storage of massive data. Massive storage systems come into being. This document describes HUAWEI UDS massive storage system (UDS for short) in terms of product composition, application scenario, and advantage. With large capacity, high reliability, and outstanding scalability, the UDS brings unique values to customers. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 5

6 2 Introduction 2 Introduction As the IT industry evolves, people's life becomes closely related to the IT. Data, as the cornerstone and most important assets in the IT industry, is growing at an unprecedented speed. Big data now becomes a trend. All industries call for secure and reliable storage of massive data. HUAWEI UDS massive storage system is developed to address problems and challenges facing customers. The UDS features: Industry-leading scale-out distributed storage architecture and the distributed hash table (DHT) algorithm Diversified external interfaces compatible with Amazon Simple Storage Service (S3) interfaces Multi-level data protection technologies such as Multiple Copies (MC) and Erasure Code (EC) With large capacity, high reliability, easy maintenance, and flexible scalability, the UDS applies to scenarios of massive data storage and centralized backup, and supports an exascale capacity and a secure, reliable, efficient, and converged architecture.

7 Based on industry trends and a thorough understanding of customer needs, Huawei releases the UDS, a massive storage system designed specifically for the big data market. The UDS employs the DHT-based scale-out storage architecture and multiple data protection technologies such as EC and MC to ensure data security, and provides unified external interfaces for the access of multiple types of services, meeting massive data storage requirements. 3.1 Product Composition The UDS consists of access nodes (A-Node for short) and universal distributed storage nodes (UDSNs). A-Nodes are used for data scheduling, that is, distributing data requests from upper-layer services to UDSNs. UDSNs are used for data storage. To meet the requirement of massive data storage, A-Nodes and UDSNs are deployed in high availability (HA) clusters, namely, the access cluster and the storage cluster. 0 shows the components deployed in a UDS cabinet. Figure 3-1 Components deployed in a UDS cabinet Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 7

8 A-Nodes: two T3200 servers UDSNs: seven (flexibly deployed based on the load bearing capability of equipment rooms and power consumption requirements) Access switches: two S6724 (S6724 for the enterprise market/s6324 for the carrier market), full 10GE An A-Node is used to process and control access requests initiated by clients, establish object transmission channels, and manage metadata. A-Nodes can be clustered. When the amount of concurrent access requests is large, new A-Nodes can be added to improve request processing capabilities, thereby eliminating data processing bottlenecks. Figure 3-2 shows the appearance of an A-Node. Figure 3-2 Appearance of an A-Node Specifications Disk type Value SATA, SAS, NL SAS, and SSD Max. number of disks 12 Max. capacity per disk AC power supplies Power consumption 4 TB 100 V to 127 V or 200 V to 240 V, 1+1 power supply redundancy Without service disks: 350 W Max. power consumption: 650 W Dimensions 86.1 mm x 446 mm x 585 mm (2 U) Weight 18.5 kg (unloaded) A UDSN is used to store, replicate, and ensure consistency of data and metadata. A UDSN contains innovative smart disks. Unlike traditional disks, smart disks combine disk drives and

9 CPUs to provide improved data processing capabilities. The storage capacity of the UDS can be expanded by adding UDSNs. Figure 3-3 shows the appearance of a UDSN. Figure 3-3 Appearance of a UDSN Specifications Disk type Value SATA Max. number of disks 75 Max. capacity per disk 4 TB AC power supplies 100 V to 127 V or 200 V to 240 V, 2+2 or 1+1 power supply redundancy Max. power consumption 1350 W Dimensions mm x 446 mm x 790 mm (4 U) Weight 45.2 kg (unloaded) 97.7 kg (fully-loaded) The UDS provides massive data storage capabilities by A-Node and UDSN clusters and cross-cabinet capacity expansion. UDS cabinets are connected by service switches S6724 (S6724 for enterprise markets and S6324 for carrier markets) and core switches over a full 10GE network, as shown in Figure 3-4. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 9

10 Figure 3-4 UDS system network diagram 3.2 Product Features As a massive storage system, the UDS applies to big data scenarios where upper-layer services vary and data amount soars. To meet the increasing requirement of data security, the UDS provides a secure, reliable, massive, efficient, and converged storage architecture to cope with challenges in the big data era Exascale Scalability For customers who do not want to purchase a large storage capacity during initial deployment, but will expand the system capacity with the service growth, the UDS reduces their initial investment and provides flexible capacity scalability. For customers whose service systems carry heavy workloads, the UDS provides a massive storage resource pool to eliminate storage capacity bottlenecks DHT Based on the elastic DHT, DHT-based one-off addressing, and key technologies such as decentralized architecture, stateless access cluster, and metadata hashing, the UDS provides massive storage capacities scalable to exabyte level. The UDS uses the DHT-based hash algorithm to divide and address all storage units' address space and then maps the divided address space to the DHT ring. Each storage unit stores data as objects and can be located by its address space. Upon data object read/write, each data object stored on a storage unit is located using the one-off hash algorithm addressing.

11 A DHT ring has an infinite address space and is elastic in size (as shown in Figure 3-5) by changing the partition size. Theoretically, a DHT ring supports an extremely large number of storage units, laying a solid foundation for exascale capacity expansion. Figure 3-5 Elastic DHT ring that supports infinite node expansion The UDS provides the following advantages based on the DHT: 1 Metadata is evenly in virtual space comprising all physical nodes, enabling infinite storage expansion. 2 Data is equivalently accessed on each two nodes in a point-to-point approach, shortening latency of central node index query and eliminating performance bottlenecks. 3 Storage capacity can be gradually expanded on demand. The DHT technical principles are as follows: Each storage unit (smart disk) corresponds to a physical node and has a unique ID. In the UDS, data has a key and is stored by the key's hash value. The hash value of a key corresponds to a storage unit. Hash values of all keys reside in integer range [0, ]. When the UDS is being initialized, this integer range is divided into multiple same size partitions, each of which contains the same number of hash value integers. Each partition represents the same hash space. The capacity of each physical node is usually divided into 20 to 40 partitions. Each partition corresponds to a virtual node. Data in a partition is stored onto the corresponding virtual node. The UDS maintains and updates a mapping table between partitions (or virtual nodes) and physical nodes. The DHT ring is an integer range of 0 to Each virtual mode is mapped to the DHT ring and each key of data is mapped to a virtual node. The hash value (data) of a key is stored to the corresponding virtual node. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 11

12 After the UDS is expanded, for example, new physical nodes are added, the number of hash space partitions keeps unchanged but the mappings between virtual nodes and physical nodes are updated automatically. The DHT ring has an infinite address space therefore supports unlimited virtual nodes. By adjusting mappings between virtual nodes and physical nodes, unlimited physical nodes can be added. Figure 3-6 uses a storage cluster with four physical nodes (each physical node contains five virtual nodes) as an example to describe the DHT technical principles. Figure 3-6 DHT technical principles The hash space (0 to ) is divided into N same-size partitions. In the preceding figure, the hash space is divided into 20 partitions from P0 to P19. Each partition contains the same number of hash values. The hash value of each key is mapped to a partition. For example, the hash value of key k1 is mapped to partition P0. A to T in the preceding figure represent 20 virtual nodes. Data in a partition is stored onto the corresponding virtual node. For example, data represented by the key whose hash value is mapped to P0 is stored to virtual node A. Similarly, data represented by the key whose hash value is mapped to P1 is stored to virtual node B. Physical nodes 1, 2, 3, and 4 that represent physical storage units (smart disks) provide persistent data processing capabilities. A physical node has a mapping relationship with virtual nodes. Usually, a physical node corresponds to multiple virtual nodes. This mapping relationship is similar to that between partitions and physical nodes. The number of partitions is determined when the UDS is being initialized and keeps unchanged after the number of physical nodes increases. The change in partition quantity will cause the number of hash values to change in each partition. As a result, data in each partition

13 and node will be relocated. Therefore, the number of hash partitions is kept unchanged to avoid data relocation. Physical nodes can be added or removed online based on capacity requirements. The number of partitions does not change with that of physical nodes in the SoD cluster, but mappings between partitions and physical nodes are automatically updated after the number of physical nodes is changed. As shown in the preceding figure, physical nodes 1, 2, 3, and 4 correspond to five virtual nodes (partitions) respectively. After the new physical node 5 is added, each physical node corresponds to four virtual nodes (partitions). Figure 3-7 Mappings between physical nodes and partitions after a physical node is added After a new physical node is added, four partitions are allocated to the new physical node. Therefore, 1/5 data of the cluster is migrated to the new node. Based on the DHT algorithm, if the UDS has M physical nodes and a new node is added, 1/(M+1) data of the UDS is migrated after partitions are reallocated to all physical nodes. Similarly, if a node is faulty or removed from the UDS, 1/M data of the UDS is migrated after the partition reallocation. Figure 3-8 shows mappings between physical nodes and partitions after physical node 4 is removed. Figure 3-8 Mappings between physical nodes and partitions after a physical node is removed Decentralized Architecture The UDS has two logical clusters: the access cluster and the storage cluster, which consist of A-Nodes and UDSNs respectively. An A-Node provides access to the object-based storage service. It also processes and controls access requests initiated by clients, establishes object transmission channels, and manages metadata. A UDSN is used to store, replicate, and ensure consistency of data and metadata. Figure 3-9 shows the DHT algorithm-based equivalent point-to-point data access between A-Nodes and UDSNs. In the UDS, an A-Node can directly access any UDSN for data read/write based on the DHT-based addressing. Unlike traditional storage systems, this way of data access in the UDS does not rely on central nodes, which shortens the latency of data index query and eliminates access bottlenecks. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 13

14 Figure 3-9 Decentralized architecture for equivalent point-to-point access Smart Disk The UDS uses smart disks as storage units, which are also regarded as physical nodes. A smart disk contains a disk drive, energy-saving Advanced RISC Machines (ARM) chip, small-capacity memory, and Ethernet ports. Each smart disk is allocated a dedicated IP address to connect to switches and communicate with other smart disks in a distributed and interconnected network, as shown in Figure The UDS capacity can be expanded by adding smart disks, enabling fine-grain capacity expansion at the disk-level. Figure 3-10 Decentralized architecture for equivalent point-to-point access Stateless Cluster Each smart disk has fixed data access throughput. Therefore, the throughput of the UDS can linearly grow with the number of smart disks. For details, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Smart Disks. In the UDS, A-Nodes are networked in the access cluster. Based on the object-based storage technology and the DHT algorithm-based one-off addressing, A-Nodes that are loosely coupled with UDSNs can work as stateless service nodes. An A-Node can process any service

15 Metadata Hashing MDC requests allocated to it after load balancing. Unlike traditional storage systems where the number of nodes used for processing service requests is limited due to state synchronization and locking mechanisms, the UDS can have an unlimited number of A-Nodes in its access cluster theoretically, eliminating architecture bottlenecks that hinder exabyte-level capacity expansion. The UDS does not have dedicated metadata nodes. Instead, metadata services are provided by A-Nodes, which distribute metadata slices evenly onto UDSNs in the same way as common data based on the DHT algorithm. When the number of concurrent access requests soars, metadata service requests are distributed to A-Nodes in the access cluster for load balancing and A-Nodes can be added on demand to improve the request processing capability, preventing a bottleneck from occurring. The UDS provides the Multiple Data Center (MDC) feature to centrally schedule and manage multiple DCs across regions. To meet different capacity requirements, the UDS can be expanded from several terabytes to exabytes on demand. Figure 3-11 Centralized scheduling and management of multiple DCs across regions SoD Architecture As shown in Figure 3-11, the UDS can synchronize data between cross-regional DCs, customize data copy policies based on service level agreements (SLAs), and preferentially access data on the nearest DC. The MDC feature ensures exascale capacity expansion in terms of scalability, reliability, and operability. Sea of Disks (SoD) is an innovative and decentralized storage architecture dedicated to processing massive unstructured data that is much more frequently read than written. With the DHT algorithm-based addressing, a large number of power-saving and cost-effective smart disks are consolidated into a decentralized cluster with unified software and hardware. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 15

16 I/O Process Based on the SoD architecture, the UDS provides outstanding availability, scalability, and maintainability. High performance is delivered while costs and energy consumption are cut down. The SoD architecture comprises the access cluster and the storage cluster. The access cluster consists of A-Nodes that process external requests. A-Nodes have powerful computing capabilities. Therefore, the access cluster provides computing-intensive services such as request access, user authentication, data slicing, data aggregation, and data routing. The storage cluster consists of UDSNs whose computing capability is inferior to that of A-Nodes. A UDSN comprises power-saving and energy-efficient smart disks. Each smart disk provides key-value interfaces. All user data is stored in the storage cluster as data slices after being processed by the access cluster. Data slices and partitions in the storage cluster are divided and routed based on the DHT. The DHT determines the location where a data slice is stored. Therefore, the storage cluster can be regarded as a DHT ring. The data write process is as follows: 1 Request access: The client sets up connections with an A-Node of the UDS and transmits data to that A-Node.

17 2 Storage policy selection: The A-Node determines the data storage policy based on preset configurations. 3 Data slicing: If the amount of data transmitted from the client exceeds 1 MB, the A-Node divides the data into multiple slices of 1 MB each. 4 Data route: The A-Node writes the data slices into the storage cluster based on the DHT. 1 Request access: The client sets up connections with an A-Node of the UDS and sends a data read request to that A-Node. 2 Data routing: The A-Node locates the partition where the requested data resides based on the DHT, and obtains the address of the smart disk where the partition resides. 3 Data repair: If any data slice is damaged, the A-Node repairs the data slice based on the specified data storage policy. 4 Data aggregation: The A-Node aggregates data slices to the original data and sends the data to the client. Buffers are reserved in A-Nodes for data slicing and aggregation. 1 Data write: After dividing data into slices, an A-Node buffers some data slices and writes data slices to different UDSNs to speed up data write. 2 Data read: An A-Node anticipates the range where the data requested by the client resides and then reads data slices consecutively from smart disks onto the buffer to speed up data read. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 17

18 Request Access To achieve the optimal throughput with the minimum resources, A-Nodes automatically adjusts buffer sizes and the volume of data concurrently read from or written to smart disks based on connection speeds and data volume of clients. The access cluster of the UDS provides standard S3 interfaces and rich S3 ecosystems (including tools, development packages, and third-party software integration). Amazon S3 is the de facto standard in the cloud storage field. Based on the HTTP protocol, Amazon S3 provides mature interfaces complying with the Representational State Transfer (REST) protocol. S3 interfaces are easy-to-use, reliable, stateless, and easy to be accessed via networks in nature. S3 interfaces define a data model consisting of three layers: user, bucket, and object. 1 A user in the UDS can own and manage buckets and objects. 2 A bucket is the container of objects, similar to a folder in a file system. A bucket can contain multiple objects but no other buckets. 3 An object is a set of data, similar to a file in a file system. The UDS data model abandons the traditional nested directory structure. A single bucket is able to house hundreds of millions of objects and is easy to expand. This flat storage structure is highly suitable to unstructured data Storage Policy The UDS defines a user model consisting of three layers: account, group, and user. 1 An account owns resources in the UDS, and corresponds to an individual user, enterprise, or organization. 2 A user uses resources in the UDS. An account can create multiple users and grant users permission for different resources. Usually, a user corresponds to an enterprise or an employee of an organization. 3 A group is a collection of users. An account can create multiple groups, add users to different groups, and grant groups permission for different resources. One user can belong to different groups. Users in a group inherit all permission of the group. Usually, a group corresponds to an enterprise or a sub-department or sub-organization. The UDS provides flexible storage policies. A storage policy determines the reliability, availability, security, and space occupation of data. Upon receiving data access requests from users, the access cluster of the UDS reads user configurations from the user server and then determines a data storage policy accordingly. 1. Multiple Copies (MC) The MC storage policy generates multiple copies for a piece of data. Each data copy is stored onto different physical nodes in the same storage cluster. Even if some data copies are completely damaged or lost, users can still access the other data copies. This storage policy provides high redundancy and reliability, but consumes large storage space. The MC storage policy works based on a quorum mechanism. This mechanism defines a group of replication parameters, which are called NWR for short.

19 Data Routing N: indicates the number of data copies. A piece of data has N copies in the UDS. W: indicates the number of data copies that are successfully written onto the UDS. Only after W data copies are successfully written, the UDS returns a write success message to the user. R: indicates the number of data copies that are successfully read from the UDS. Only after R copies of the requested data are successfully read, the data is returned to the user. The default MC policy adopted by the UDS is NWR = 322, that is, each piece of data has two copies and data read and write require at least two copies respectively. This storage policy strikes a balance between reliability and data consistency, applicable to most reliability-demanding scenarios. With this storage policy, the UDS's access cluster slices user data into pieces, and each piece is replicated to multiple copies. The data copies are then written onto different data partitions of physical nodes in the DHT. The UDS is environment-aware, storing data copies onto physical environments independent from each other to improve data reliability and availability. For example, copies of the same data are stored onto different storage cabinets, enclosures, and physical nodes, to tolerate more fault scenarios. 2. Erasure Code (EC) The EC storage policy generates redundant data for a piece of data. If a piece of data is partially damaged or lost, the UDS can use its redundant data to reconstruct or repair the damaged data. The EC storage policy ensures high data reliability and consumes less storage space, striking a balance between reliability and economy. After the access cluster divides data into slices, consecutive M slices comprise an EC group. Based on the EC storage policy, the UDS generates N parity data slices for the EC group. The data slices and parity slices are stored onto a consecutive group of data partitions in the storage cluster. In doing so, the data slices are stored onto different physical nodes, improving the data reliability. As long as the number of damaged data slices does not exceed N, the access cluster is able to restore the damaged slices using the other ones. 3. Data stored in different DCs for cross-region disaster recovery The Multiple Data Center (MDC) policy is configured by the unit of bucket. If an MDC policy is enabled for a bucket, the access cluster writes the data from the bucket to the local storage cluster and its data copies to UDS systems in other data centers. The MDC policy supports asynchronous replication. Data is first written onto the local storage cluster and then a background asynchronous replication task is initiated to replicate the data to a remote data center. If this task fails, the UDS initiates the task again after a periodic background scan. For details, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Reliability. The UDS routes data slices based on the DHT. After the UDS is initialized, mappings between data partitions of the storage cluster and physical nodes are determined and recorded. At the same time, the UDS maps the data partitions evenly to a hash space residing in the range of [0, ]. The next number after is back to 0. Therefore, the hash space is a ring. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 19

20 Data Repair When reading a data slice, an A-Node first uses the consistency hash algorithm to calculate the hash value of the data slice based on its key. The hash value is also stored in the hash space. Therefore, both data slices and data partitions reside on the same logical hash ring. The consistency hash algorithm stores each data slice onto the first data partition next to the data slice in counterclockwise direction. After obtaining a data partition, the A-Node can locate the physical node where the data partition resides; thereby the routing of the data slice is completed. If the MC storage policy is used, N copies of a data slice are stored. After locating the first data partition, the A-Node locates the other N-1 data partitions clockwise along the logical ring. The other N-1 data partitions are where the other N-1 data copies are stored. If the EC storage policy is used, an A-Node locates the first data partition for the first data slice of the EC group and then addresses other data partitions according to specific addressing rules. For details, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Reliability. If data slices are found damaged when clients read data on the UDS, A-Nodes repair the data slices based on the storage policy. This is done to ensure the correctness and reliability of the data read by the clients. MC EC If the data slice that is stored based on the MC policy is damaged, A-Nodes attempt to read one of its copies to repair the data. If the data slice that is stored based on the EC policy is damaged, A-Nodes attempt to read the EC group where the data slice resides. The intact data slices and parity data slices in the EC group are used to repair the damaged data slice. For details, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Reliability. MDC If the data slice that is stored based on the MDC policy is damaged, A-Nodes attempt to read the desired data slice from the backup data center and use the data slice to repair the damaged one. In addition to read repair, the UDS constantly scans and verifies data in the cluster and restores damaged data in the background if any errors are detected. Background repair is classified into two levels: object-level and slice-level. Object-level A-Nodes constantly scan data in the cluster, calculate the digest of the object, and compare the digest with the correct digest stored in the metadata. If the object data is incorrect, the UDS repairs the object data using the repair mechanism applied to data read. Slice-level Cluster Management Smart disks of the UDS constantly scan the data that they carry and repair incorrect data slices by using the anti-entropy mechanism. For details about the anti-entropy mechanism, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Smart Disks. 1. Access cluster

21 The access cluster of the UDS is stateless and idempotent. Stateless: A-Nodes do not use data layout information to process requests. Each request can be processed by any A-Node. Therefore, requests are independent from each other. Idempotent: A client can send the same request for multiple times, and these attempts have no adverse impact. Therefore, users can send a failed request repeatedly until the request is successfully processed. Most of UDS's external interfaces are idempotent. The access cluster is decentralized and can be regarded as a loose couple of stateless A-Nodes. Adding or removing A-Nodes from the access cluster has no impact on the other nodes in the cluster. The access cluster's DNS implements load balancing and health checks on A-Nodes in the cluster. Faulty A-Nodes can be discovered in a timely manner and removed from the address list. 2. Storage cluster The storage cluster is also decentralized. UDSNs synchronize information with each other using the gossip protocol. Inspired by the form of gossip seen in social networks, the gossip protocol is used to transmit information in large-scale clusters. When being initialized, each UDSN in the storage cluster obtains the cluster information that records the status of each node in the cluster. A UDSN periodically selects another UDSN at random and synchronizes cluster information with the latter. After being synchronized, cluster information on both nodes is combined and updated. When synchronizing cluster information using the gossip protocol, a UDSN uses the protected health information (PHI) fault detector to check the status of other UDSNs. The PHI fault detector anticipates the time window of the next synchronization of a UDSN based on previous synchronization. If cluster information of a UDSN is not synchronized as anticipated, the PHI fault detector considers the UDSN to be faulty. When an A-Node attempts to write data onto a UDSN that is unavailable due to faults, the data will be written onto another UDSN temporarily and then written back to the intended UDSN after the faults are rectified. This data write process is called the hinted handoff mechanism. For details about the hinted handoff mechanism, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Smart Disks. When a UDSN detects another UDSN to be faulty, it records the fault in a local status table. The access cluster periodically obtains status tables from UDSNs. When detecting that a UDSN is faulty for a long time, the access cluster removes the UDSN from the storage cluster and updates cluster information. The updated cluster information is then transmitted to all UDSNs in the storage cluster through the gossip protocol. After a UDSN is removed from the storage cluster, data partitions on this UDSN are automatically migrated to the other UDSNs in the storage cluster. According to the consistency hash algorithm, only 1/N data needs to be migrated after a UDSN is removed from the storage cluster consisting of N UDSNs. When a small number of UDSNs are added to the storage cluster, data partitions on the other UDSNs in the storage cluster are automatically migrated to the newly added UDSNs. According to the consistency hash algorithm, only 1/N+1 data needs to be migrated after a UDSN is added to the storage cluster consisting of N UDSNs High Security and Reliability As the IT develops, data gradually becomes the most important asset of a company and data loss has an unpredictable adverse impact on a company. Therefore, increasing importance is Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 21

22 attached to data protection. The UDS employs multi-tenant, Multiple Copies (MC), Erasure Code (EC), and multiple data consistency technologies to ensure data security and reliability in massive data storage scenarios High Reliability Clusters 1. Storage cluster In the storage cluster, data on any faulty smart disk (caused by a man-made or mechanical error) can be recovered to other smart disks automatically. A faulty smart disk can be removed from the UDS system without affecting data availability and data slices on smart disks. Unlike a traditional storage system whose RAID group degrades when a member disk is faulty, the UDS works correctly when any disk is faulty. Immediate maintenance of a faulty disk is not needed in the UDS. Instead, all faulty smart disks can be replaced at a time and need to be maintained only after certain conditions are met, for example: The failure rate or the capacity usage reaches the preset threshold, and critical alarms are generated to inform users of replacing disks in batches or expanding system capacity. For example, the disk failure rate reaches 6% or the capacity usage exceeds 80%. You can configure the disk failure rate and capacity usage based on site requirements. This threshold-triggered maintenance prolongs the maintenance period and reduces maintenance costs. All damaged or slow disks are batch replaced based on a preset periodic maintenance schedule. This schedule-based maintenance reduces the possibility of system faults. After smart disks are replaced, data slices are evenly relocated to all smart disks based on the intelligent balancing algorithm, prolonging disk lifespan, lowering disk failure rate, reducing data loss, and improving system reliability. 2. Access cluster All A-Nodes comprise a distributed access cluster via load balancers. Instead of controlling or saving layout information about data and metadata, A-Nodes use the DHT-based hash algorithm to calculate data storage locations. This is a breakthrough in storage structure. Data layout controlled and recorded by control nodes or engines in traditional storage architecture is no longer required in the UDS structure where A-Nodes determine data routes based on rules. This change greatly simplifies data processing and resolves the bottleneck in cluster scalability and reliability. Nodes in a cluster are freed from complex synchronization and lock mechanisms that restrict a cluster's node quantity and affects node consistency and reliability. The decentralized architecture adopted by the UDS eliminates adverse impact on system availability when any A-Node is faulty due to human or mechanical errors Multi-Level Data Protection 1. Smart disk level: disk lifecycle management Focusing on how to lower disk failure rates and control impacts brought by disk faults, the UDS disk lifecycle management adopts end-to-end lifecycle management technologies such as disk detection, disk repair, disk failure control, and pre-reconstruction. The smart disk lifecycle management greatly reduces disk failure rates, prolongs disk lifespan, and has the following advantages:

23 Automatic hardware control: The UDS employs the self-managed smart disk architecture that enables self-monitoring, self-management, and selective data synchronization of smart disks, remarkably improving system reliability while lowering hardware failure rates. Lowered disk failure rate: With the disk lifecycle management, the UDS obtains disk status in real time, separates and repairs physical and logical bad sectors in a timely manner, and implements weight management of smart disks, striking a balance between system data and service loads and prolonging disk lifespan. 2. UDSN level: EC redundancy algorithm The Erasure Code (EC) redundancy algorithm developed by Huawei is a superset of traditional RAID. Different from traditional storage systems using RAID consisting of fixed member disks, the UDS uses EC to consolidate all disks into a unified storage resource pool. Each time data is written onto or read from the UDS, disks are automatically selected at random and form a temporary RAID group onto which data blocks and parity blocks are written. Compared with traditional RAID, temporary RAID improves the overall system performance and resource utilization. The innovative DHT-based EC algorithm enables the UDS to provide: Longer data durability: When the EC policy is configured to M:N = 15:6, data durability can reach thirteen nines, minimizing data loss risks. Faster data reconstruction speed: The UDS distributes data objects to different smart disks based on the hash algorithm. Multiple disks are involved in the reconstruction, increasing the capacity for concurrent reconstruction and reducing the time for reconstructing 1 TB data to four hours. This has greatly reduced the reconstruction time and improved system reliability. Improved reconstruction efficiency: Data reconstruction is based on objects and only damaged objects are reconstructed. Undamaged objects or empty regions are not processed. In this way, the data reconstruction rate is greatly increased. Global hot spare and batch disk replacement: The UDS uses global hot spare space instead of hot spare disks for data reconstruction. When the failure rate of smart disks reaches the threshold, the UDS will send alarms for disk replacement. In the UDS, immediate maintenance is not needed, reducing quantities of spare parts and upgrading cycles and saving inputs of maintenance personnel. Eliminated data recovery restrictions and improved EC recoverability: Compared with the traditional RAID algorithm, the refined EC reconstruction algorithm can effectively eliminate restrictions on the quantity of damaged data. Any damaged data block can be recovered fully, which enhances data durability and system reliability. More flexible data redundancy and improved disk utilization: The available EC policy configuration for tenants is as follows: M (the number of data blocks) ={3/6/9/12/15}, N (the number of parity blocks) = {1/2/3/6}. The EC policy, which is defined based on user environments, has direct impacts on data durability and provides users with flexible redundancy ratio configuration and more choices of data durability. The EC algorithm can flexibly control disk utilization and reduce costs. For example, when M is 12 and N is 3, the disk utilization is almost 80%. Enhanced storage management efficiency and lowered costs: Users do not need to spend much time on storage planning because all the disks automatically form a unified storage pool. Users only need to insert new disks to expand the system capacity. The UDS automatically distributes data to each disk. Intelligent reconstruction of EC groups and lighter system load: The UDS intelligently determines the range and size of the damaged data and temporarily reconstructs EC Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 23

24 groups to refine the data to be recovered, which reduces system load and improves recovery efficiency. 3. Data center level: cross-regional disaster recovery and failover The UDS provides the Multiple Data Center (MDC) feature that enables users to access the massive storage system on the nearest DC, maximizing resource utilization and reducing investment in storage. The UDS also provides flexible cross-regional data redundancy policies to prevent data loss in the event of a disaster or unexpected fault occurred on the active DC. Upon the breakdown of an active DC, the UDS quickly resumes services of the active DC on a standby DC, minimizing the service interruption duration and ensuring service continuity. The UDS also provides the load balancing technology for resource management, enabling users to maximize their resource utilization and improve the return on investment (ROI). The multi-tenant-based MDC feature has the following advantages: Unified resource scheduling: Multiple DCs are globally virtualized and consolidated to a unified resource pool for improving resource utilization. In addition, the MDC feature uses policy-based scheduling to ensure preferential data access on the nearest DC. SLA policy-based control: SLA policies are used to control DR paths, number of DR backups, and quality of service (QoS), supporting customers' service choice decisions and maintaining an optimal balance between services and resources. Cross-regional DR: The MDC feature uses HTTP/REST interfaces to perform DR among DCs. Data stored on a local DC can be backed up and verified on a remote DC. Data is transmitted between DCs over optimized networks, remarkably enhancing the DR efficiency. DCs back up for each other to optimize resource utilization. For details, see the HUAWEI OceanStor UDS Massive Storage System Technical White Paper Reliability Continuous Data Detection and Repair 1. Short-term fault handling The UDS defines a special intermediate state: short-term fault state. If the UDS detects a short-term fault, it will start the fault diagnosis mechanism and try to recover the fault. If the recovery fails and the fault persists for more than X (X is user-configurable, for example, 15 minutes), the fault goes to the permanent fault state and the node where the fault occurs exits from the DHT ring. The UDS then starts the data recovery policy to recover damaged data. The short-term fault handing technology has the following advantages: Improved data and performance stability: Only the faults that cannot be detected and recovered by the short-term handling technology are considered permanent faults and require adjustment of the DHT ring, reducing the workload for data recovery and migration. Transparent to upper-layer services and enhanced business continuity: The short-term fault handling mechanism is invisible to upper-layer services, ensuring business continuity and durability. Efficient system performance utilization and increased data recovery efficiency: After a fault is considered as a permanent fault, the UDS starts the data rebalancing process and evenly distributes damaged data to multiple nodes through effective data distribution control.

25 2. Data integrity The UDS provides multi-level data integrity protection measures and supports four-level data repair from track, slice, object, to data center. The data integrity protection measures effectively ensure end-to-end data durability and have the following advantages: Full integrity protection and improved data durability: The UDS provides end-to-end repair measures for damaged data at four levels from track, partition, object, to data center. Progressive recovery measures and reduces resources used in data recovery: Based on the degree of data damage, the UDS tries to recover the data in a range as small as possible to reduce resources used in data recovery. 3. End-to-end consistency The UDS supports the end-to-end data consistency check at the application level, object level, slice level, and physical level. With the four-level data consistency check, the UDS ensures that no silence errors occur during the data writing process (from the time data is sent by end users to the time data is written onto the disk). At each level, once data inconsistency is detected, the UDS can repair or resend the data quickly. The end-to-end data consistency check greatly improves data security and has the following advantages: Data will not be damaged in storage and transmission, ensuing data correctness. Possible malicious data tampering from internal personnel of cloud storage service providers is prevented, increasing data security End-to-End Data Security The UDS ensures data security in terms of data transfer, data integrity, identity authentication, data access control, and data encryption. 1. Data transfer The UDS provides object-based storage interfaces that are compatible with Amazon S3 interfaces and supports Representational State Transfer (REST) interfaces. Users can upload SSL-encrypted data to the UDS in a DC using a Huawei or third-party terminal. 2. Identity authentication The UDS uses access key ID (AK) and secret access key (SK) to authenticate user identities. The keyed-hash message authentication code algorithm (HMAC) is used in authentication. Based on the HMAC algorithm, a key and a message is input and a message summary is output. Each client user has a pair of AK and SK. The AK is public and identifies a unique user. The SK is used for calculating signatures. Client users are required to keep the SK safe. An operation request sent by a client user contains the user's AK and a signature calculated using the SK (the signature is calculated based on the HMAC-SHA1.) Upon receiving the request, the UDS checks the AK and SK stored on it and calculates a signature using the SK. Then the UDS compares the obtained signature with the one in the request. If the two signatures are consistent, the authentication succeeds. Figure 3-12 shows the process of identity authentication. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 25

26 Figure 3-12 Identity authentication 3. Object access control The UDS provides a flexible and secure data access mechanism that allows users to set different access control policies based on bucket and object configurations. Available access control policies are: READ, WRITE, READ_ACP (users are granted the permission to read the access control policy), WRITE _ACP, and FULL_CONTROL. 4. Static data encryption The current version of UDS does not support data encryption in the cloud. If sensitive data is to be stored in the cloud, you are advised to upload the data after encrypting it locally. In the cloud scenario, keys for encrypted data are kept on clients. Figure 3-13 shows the process of data encryption in a non-cloud scenario. Figure 3-13 Data encryption in a non-cloud scenario 5. Data integrity The UDS uses digital signatures to ensure data integrity during transfer. The current version of UDS supports object integrity signatures and the later versions will support slice integrity

27 3.2.4 Low TCO signatures. The integrity of a data slice is automatically verified by the UDS and the integrity verification of a data object must be supported by client applications. 6. Data durability Energy Saving The UDS provides % data availability and % data durability. Apart from servers and network devices that consume about half of a DC's energy, storage devices also consume a large portion of a DC's energy. As the number of storage devices increases, more equipment room space is occupied and a larger amount of energy will be consumed. With 4 TB enterprise-class disks equipped with ARM chips, each 4 U 75-slot UDSN in the UDS provides a 300 TB capacity. A single UDS cabinet can house up to 525 disks and provides a 2.1 PB capacity. Compared with x86 servers providing the same computing or storage capacity, the UDS halves the CPU power consumption and equipment room space occupation with an average power consumption of 4.2 W/TB, ranking the top in the industry. Moreover, the UDS employs the intelligent CPU frequency control and intelligent fan speed control technologies to maintain lower power consumption of idle storage units. With the high-density and power-saving design, the UDS lowers the power consumption of an equipment room by 45%, providing a comprehensive energy-saving storage solution Automated Management The UDS provides a graphical management system (as shown in Figure 3-14) that automatically manages topologies, alarms, configurations, performance, logs, and users. Moreover, the UDS can be automatically deployed and upgraded, without the need of manual intervention and service interruption. The automatic upgrade and deployment enable service transparency and simplify system deployment, upgrade, and capacity expansion, greatly improving the management efficiency while lowering management costs. In the UDS, the minimum maintenance unit can be an ARM-based smart disk. Disk failures have minor impact on services. Immediate maintenance is not needed. Instead, faulty smart disks can be batch replaced after the disk failure rate reaches the preset threshold. This zero touch maintenance reduces the quantity of spare parts and upgrade cycles and saves inputs of maintenance personnel. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 27

28 Figure 3-14 Graphical management page Open Interfaces The UDS provides object-based storage interfaces that are compatible with Amazon S3 interfaces and supports HTTP/HTTPS Representational State Transfer (REST) interfaces. With the open interfaces, the UDS opens its storage space to various types of customer applications. The UDS's underlying storage space can be accessed using standard protocols, regardless of data storage location and data format. Moreover, as the carrier for the multi-tenant services and multiple instances, the UDS provides customers with different levels of secure SLA and QoS services tailored to different scenarios, enhancing customer competitiveness.

29 4 Experience 4 Experience With the IT development, the amount of unstructured data increases exponentially, and various types of services come out, posing demanding requirements on reliability and openness of storage systems. Traditional storage systems fail to meet such requirements. Based on an in-depth analysis of customer needs, Huawei launches the UDS, a massive storage system that helps customers resolve existing or pending issues of storing the huge amount of data. Apart from providing massive storage capacity, the UDS supports the access of multiple types of services by providing interfaces compatible with Amazon S3 interfaces. Based on its broad compatibility, the UDS can be tailored to various application scenarios, boasting success stories in multiple vertical industries. Currently, the UDS can be used in the massive resource pool solution and the centralized backup solution. 4.1 Solution 1: Massive Resource Pool Typical Needs and Problems Facing Customers As the IT industry develops by leaps and bounds, various applications come out. These applications are closely related to our daily life. People have been accustomed to resolving most of their routine issues on the Internet, for example, sending s, visiting e-communities, and paying bills online. These changes bring forth the following new situations to the storage industry: The data volume is growing rapidly and is estimated to reach 35 ZB by Data that needs to be stored changes from traditional structured data (database type data) to unstructured data (such as electronic bills). Sources of data become increasingly diversified, covering various services like SMS, micro blogs, medical images, and scientific data. Diversified data storage poses higher requirements on data reliability. Different storage systems are used to store data from different types of services. Storage vendors provide complex storage management systems for self interests. As a result, customers have to arrange quite a number of IT management personnel to maintain heterogeneous storage systems and networks, causing the TCO to soar. The maintenance cost remains high as the number of storage devices increases. Storage devices keep working for a long time at high power consumption and measures are taken to adjust working temperature and dissipate heat in equipment rooms, leading to constant rise of electricity fees in data centers. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 29

30 4 Experience Solution To cope with the preceding challenges, customers require a new generation of storage system that works in new a pattern. It is expected that storage systems of the next generation have the following features: Massive data storage Infinite storage capacity expansion without affecting performance Storage for data of various services Highly reliable Thin provisioning for flexible space expansion Simple storage management Efficient and energy-saving The UDS provides the massive resource pool solution to resolve the preceding problems and meet the preceding needs. The solution is oriented to various upper-layer services, providing customers with storage capacity for various types of service data. The solution requires that upper-layer services comply with interface specifications, as the following described: The UDS provides unified storage space to store data of various services, including files, videos, and images. The UDS adopts the scale-out architecture for flexible capacity expansion, allowing the system capacity to be easily expanded from the minimum capacity of 448 TB to exabyte level for massive data storage. The performance is improved in line with capacity expansion to prevent performance bottlenecks caused by increasing data. Online capacity expansion is supported to ensure hitless services. Adopting the high-density design, the UDS provides 30% more device space than x86 peers. The energy consumption of UDS equipment rooms is 30% lower than that consumed by equipment rooms accommodating traditional storage devices. All UDS nodes are clustered and multiple data protection technologies (such as MC and EC) and automatic fault detection and repair technologies are employed to ensure system reliability. With its external interfaces compatible with Amazon S3 interfaces, the UDS can interconnect with multiple services (such as file and image services) in various scenarios. Each service has its specific storage space needs. The UDS supports thin provisioning that allows flexible space expansion, meeting storage needs of different services while preventing a waste of storage space caused by space assignment by fixed quota. The UDS employs S3 authentication, data encryption, and access control to ensure data security. The UDS provides device administrators with a web UI management tool that manages cloud storage devices and upper-layer services in a unified manner. Administrators no longer need to use different tools to manage different services. This unified management means frees administrators from heavy management work and lowers the TCO. Service operators no longer need to manage complex storage devices but concentrate on service operation. In this way, more values are created. Figure 4-1 shows the massive resource pool scenario.

31 4 Experience Figure 4-1 Massive resource pool scenario Software and Hardware Configurations This section provides a list that describes the devices, interfaces, and software to be configured. [Sample is omitted.] Table 4-1 Software and hardware configurations of the massive resource pool solution Location Hardware/Software Model Quantity Remarks Equipment room in XXX DC A-Node RH UDSN UDSN 14 SATA disk 2 TB 224 Service switch S Management switch S Cabinet 1 Optical module 4 Distributed storage software 1 Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 31

32 4 Experience System management software 1 License Benefits The UDS massive resource pool solution enables customers to reliably store massive data and simplify complex storage management, creating greater values. The benefits of the solution include: Massive capacity: The UDS provides storage for massive data and a storage capacity up to exabyte level. Security and reliability: To ensure multi-level security and reliability from storage nodes, cabinets, to data centers, the UDS employs user authentication, data transmission encryption, data integrity check, MC, and EC to protect data in an all-around way. Low TCO: The UDS employs power-saving ARM chips to reduce energy consumption per capacity and green technologies such as disk spin-down and intelligent fan speed control to lower the power consumption of the entire system. Besides, a unified web management page is provided to centrally manage all massive storage devices. This unified management means simplifies storage management and cuts down investment in IT personnel training and device maintenance, reducing the TCO. 4.2 Solution 2: Centralized Backup Typical Needs and Problems Facing Customers Backup is an important means for data protection. It is widely used in various application scenarios and vertical industries, for example, file backup, database backup, banking data backup, and transportation data backup. Media used by backup systems vary. The backup media can be tapes, virtual tape libraries (VTLs), CD-ROMs, and disk arrays. All mainstream storage vendors launch their backup storage products, for example, Symantec's NetBackup and Backup Exec, and CommVault's Simpana. However, traditional backup systems have the following disadvantages: 1 Dedicated personnel must be arranged to manage and maintain tapes of physical tape libraries. 2 Physical tape libraries must be maintained periodically. 3 Data can only be recovered to the state since the last tape backup. 4 Data in physical tapes can only be sequentially accessed, causing long backup and recovery windows. 5 Power consumption of physical tape libraries is low but that of VTL is high. 6 Tape libraries, particular VTLs, have limited capacities. 7 Both physical and virtual tape libraries cannot be infinitely expanded. New devices have to be purchased. 8 Tape drives and robot arms of physical tape libraries may be damaged and storage array engines and disks of VTLs may be faulty.

33 4 Experience Solution 9 Physical tape libraries must be carefully relocated. To cope with the preceding challenges, customers require a new backup solution that has the following capabilities: High reliability: Storage of backup data is secure and reliable, and backup data is highly available in recovery. Ease of manage: A GUI-based web management system is provided to manage all backup tasks and hardware devices in a unified manner. Self checks are periodically initiated and alarms are automatically reported once faults are detected. A backup task-oriented service process is set up to simplify backup and recovery. Easy expansion: Capacities can be expanded from the initial minimum configurations to large capacities without affecting performance. Low cost: Massive data can be backed up at low power consumption and TCO. The UDS provides the centralized backup solution to resolve the preceding problems and meet the preceding needs. The solution is oriented to massive data scenarios, providing customers with a comprehensive backup solution for ensuring data security. The solution requires that upper-layer services comply with interface specifications. Currently, the UDS has passed the interface tests of Symantec's NetBackup and CommVault's CV backup software. Later, the UDS will participate in more interface tests of new backup software. The cloud backup solution is described as follows: The UDS adopts the scale-out architecture for flexible capacity expansion, allowing the system capacity to be easily expanded from the minimum capacity of 448 TB to exabyte level for massive data storage. The performance is improved in line with capacity expansion to prevent performance bottlenecks caused by increasing data. Underlying massive storage space interwork with upper-layer backup software for massive data backup solutions. All UDS nodes are clustered and multiple data protection technologies (such as MC and EC) and automatic fault detection and repair technologies are employed to ensure system reliability. The underlying massive resource pool provides highly reliable and low-cost storage space. Backup resources are allocated on demand to make full use of storage capacities. Data can be recovered to a specific point-in-time without the need of sequential access, saving recovery time and improving recovery efficiency. Upper-layer backup services support various backup types such as files, databases, and applications. The entire solution can be automatically and quickly deployed across regions. Backup resources across regions can be managed and schedules in a unified manner. Solution components and services can be centrally managed. Figure 4-2 shows the scenario of the centralized backup solution. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 33

34 4 Experience Figure 4-2 Scenario of the centralized backup solution Software and Hardware Configurations This section provides a list that describes the devices, interfaces, and software to be configured. [Sample is omitted.] Table 4-2 Software and hardware configurations of the centralized backup solution Location Hardware/Software Model Quantity Remarks Equipment room in XXX DC A-Node RH UDSN UDSN 14 SATA disk 2 TB 224 Service switch S Management switch S Cabinet 1 Optical module 4 Distributed storage software Desktop data backup software 1 1

35 4 Experience System management software 1 License 10 Backup servers are provided by customers and are not listed in the preceding table Benefits The UDS backup solution meets customers' requirements for massive data backup by combining a massive storage system with upper-layer backup software. It also reliably protects customers' data by providing diversified data reliability mechanisms. This solution is economical, efficient, and easy-to-manage, saving customers' costs and creating larger value for customers. Massive capacity: The UDS provides storage for massive data and a storage capacity up to exabyte level. Low TCO: The UDS employs power-saving ARM chips to reduce energy consumption per capacity and green technologies such as disk spin-down and intelligent fan speed control to lower the power consumption of the entire system. Compared with VTLs, the UDS enables the same backup software to provide a larger storage capacity, without the need to add new backup software for capacity expansion, lowering the TCO. High reliability: The UDS provides multiple data protection technologies such as MC, EC, and MDC to ensure data reliability. Data can be recovered when any storage device, cabinet, or data center is faulty, minimizing the recovery point objective (RPO). Besides, all nodes in the UDS are deployed in clusters such as service cluster, switch cluster, and storage cluster, eliminating single points of failure. Data can be restored upon lost and the recovery time is not affected by any device fault, minimizing the recovery time objective (RTO). Ease of manage: A GUI-based web management system is provided to manage all backup tasks and hardware devices in a unified manner. Self checks are periodically initiated and alarms are automatically reported once faults are detected. A backup task-oriented service process is set up to simplify backup and recovery. High efficiency: Storage capacities are allocated on demand and expanded dynamically, making full use of storage space and improving storage efficiency. 4.3 Solution 3: Web Disk Typical Needs and Problems Facing Customers With the in-depth development of the information society, individuals and enterprises have a greater need of information sharing and exchanging. Existing information sharing platforms fail to meet challenges brought by the increasing amount of data and diversified data storage services. The challenges are as follows: Individual and enterprise data increases rapidly and more types of data come out. Enterprises are in urgent need of collaborative office and data backup. Individual or enterprise data can hardly be accessed from mobile terminals such as mobile phones. Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 35

36 4 Experience Solution Enterprise branches are scattered in different locations, causing difficulty in sharing data. Diversified methods of data sharing cannot be centrally managed. Data security must be ensured during access to individual and enterprise data over the Internet. An all-in-one solution is called for to store individual and enterprise data. To cope with the preceding challenges, customers require a new data access solution that has the following features: For cloud service providers: Provides multiple access methods, such as data access from browsers, PCs and mobile phone clients. Supports routine O&M, self-service, metadata billing, and interconnection with network management systems. For large- and medium-sized enterprises, governments, and scientific institutions: Implements desktop data protection. Provides a unified platform to store mission-critical information assets. Ensures that data is securely accessed anytime anywhere. Meets the requirements of on-demand data sharing among employees and branches. The UDS provides the web disk solution to resolve the preceding problems and meet the preceding needs. The solution uses the object-based storage technology to provide terminal users with online storage services over IP-based networks. The solution is cost effective and flexibly scalable, and provides a strong consolidation capability. It customizes user-oriented and large-capacity individual DCs that are secure, speedy, and easy-to-use, providing enterprise users with secure, reliable, economical, and easy-to-use web services that can be quickly deployed during production and collaborative office. The cloud backup solution is described as follows: Underlying storage system consists of loosely coupled A-Nodes and UDSNs. A-Nodes are used for data scheduling, that is, distributing data requests from upper-layer services to UDSNs. UDSNs are used for data storage. A-Nodes and UDSNs are deployed in high availability clusters. 4 U 75-slot UDSNs support enterprise-class SATA disks. With the scale-out architecture, the capacity of the UDS can be flexibly expanded from the initial minimum 300 TB to exabyte level. Performance can grow in line with capacities, which eliminates performance bottlenecks caused by data growth. Moreover, the UDS capacity can be expanded online without interrupting services. Multiple access means are provided and the web disk clients support multiple operating systems and browsers. File sharing and synchronization are supported to meet requirements of data sharing among multiple users and groups. Interfaces compatible with Amazon S3 interfaces are provided for interconnection with third-party services. A comprehensive operation management system provides multi-level administrator management, self-services such as web disk service subscription, activation, and termination, and metadata of traffic and capacity billing.

37 4 Experience Comprehensive web disk management tools are provided to centrally manage underlying storage resources covering system monitoring, logs, and alarms, simplifying administrators' management work. Figure 4-3 shows the scenario of the web disk solution. Figure 4-3 Scenario of the web disk solution Software and Hardware Configurations [Sample is omitted.] Table 4-3 Software and hardware configurations of the web disk solution Location Hardware/Software Model Quantity Remarks Equipment room in XXX DC A-Node T UDSN UDSN 4 Smart disk 4 TB 300 Service switch S6724 (for enterprise markets)/s6 324 (for carrier markets) 2 Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 37

38 4 Experience Cloud storage service-system data node Cloud storage service-computing node Service switch for web disks S5700 (for enterprise markets)/s5 300 (for carrier markets) Cabinet 2 Optical module 12 Optical fiber 12 UDS massive storage system software per terabyte license (0 TB to 500 TB) UDS massive storage system software per terabyte license (501 TB to 1000 TB) UDS massive storage system software per terabyte license (1001 TB to 5000 TB) HUAWEI cloud storage web disk user access subsystem software license per node Increases with the system capacity. For details, see the quotation template. 200 Increases with the system capacity. For details, see the quotation template. 4

39 4 Experience HUAWEI cloud storage web disk data storage subsystem software license per node Solution Network This section provides the topology of the solution. Figure 4-4 shows an example topology. You can modify the topology based on actual networks and upper-layer services. Figure 4-4 Network diagram of the web disk solution Benefits The UDS web disk solution customizes massive storage systems for web disk services and client management, meeting customers' requirements for an integrated E2E solution. The web disk solution not only applies to carriers for external operation but also to enterprises for internal use. For cloud service providers: Enhanced customer loyalty More revenue streams from new services Web-based storage platforms that integrate various services to improve competitiveness For medium- and large-sized enterprises, governments, and scientific institutions: E2E data security Cross-region file sharing platform for higher working efficiency Permission- and domain-based management and organization structure import for agile adaptation to service changes Issue 01 ( ) Huawei Proprietary and Confidential Copyright Huawei Technologies Co., Ltd.. 39