Windows : A Highly Available Cloud Storage Service with Strong Consistency
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Windows (WAS) is a cloud storage system that provides customers the ability to store seemingly limitless amounts of data for any duration of time. WAS customers have access to their data from anywhere at any time and only pay for what they use and store. In WAS, data is stored durably using both local and geographic replication to facilitate disaster recovery. Currently, WAS storage comes in the form of Blobs (files), Tables (structured storage), and Queues (message delivery). In this paper, we describe the WAS architecture, global namespace, and data model, as well as its resource provisioning, load balancing, and replication systems.
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Categories and Subject Descriptors
D.4.2 [Operating Systems]: Storage Management—Secondary storage; D.4.3 [Operating Systems]: File Systems Management—Distributed file systems; D.4.5 [Operating Systems]: Reliability—Fault tolerance; D.4.7 [Operating Systems]: Organization and Design—Distributed systems; D.4.8 [Operating Systems]: Performance—Measurements
Algorithms, Design, Management, Measurement, Performance, Reliability.
Cloud storage, distributed storage systems, Windows Azure.
Windows (WAS) is a scalable cloud storage system that has been in production since November 2008. It is used inside Microsoft for applications such as social networking search, serving video, music and game content, managing medical records, and more. In addition, there are thousands of customers outside Microsoft using WAS, and anyone can sign up over the Internet to use the system.
WAS provides cloud storage in the form of Blobs (user files), Tables (structured storage), and Queues (message delivery). These three data abstractions provide the overall storage and
workflow for many applications. A common usage pattern we see is incoming and outgoing data being shipped via Blobs, Queues providing the overall workflow for processing the Blobs, and intermediate service state and final results being kept in Tables or Blobs.
An example of this pattern is an ingestion engine service built on Windows Azure to provide near real-time Facebook and Twitter search. This service is one part of a larger data processing pipeline that provides publically searchable content (via our search engine, Bing) within 15 seconds of a Facebook or Twitter user’s posting or status update. Facebook and Twitter send the raw public content to WAS (e.g., user postings, user status updates, etc.) to be made publically searchable. This content is stored in WAS Blobs. The ingestion engine annotates this data with user auth, spam, and adult scores; content classification; and classification for language and named entities. In addition, the engine crawls and expands the links in the data. While processing, the ingestion engine accesses WAS Tables at high rates and stores the results back into Blobs. These Blobs are then folded into the Bing search engine to make the content publically searchable. The ingestion engine uses Queues to manage the flow of work, the indexing jobs, and the timing of folding the results into the search engine. As of this writing, the ingestion engine for Facebook and Twitter keeps around 350TB of data in WAS (before replication). In terms of transactions, the ingestion engine has a peak traffic load of around 40,000 transactions per second and does between two to three billion transactions per day (see Section 7 for discussion of additional workload profiles).
In the process of building WAS, feedback from potential internal and external customers drove many design decisions. Some key design features resulting from this feedback include:
Strong Consistency – Many customers want strong consistency: especially enterprise customers moving their line of business applications to the cloud. They also want the ability to perform conditional reads, writes, and deletes for optimistic concurrency control  on the strongly consistent data. For this, WAS provides three properties that the CAP theorem  claims are difficult to achieve at the same time: strong consistency, high availability, and partition tolerance (see Section 8).
Global and /Storage – For ease of use, WAS implements a global namespace that allows data to be stored and accessed in a consistent manner from any location in the world. Since a major goal of WAS is to enable storage of massive amounts of data, this global namespace must be able to address exabytes of data and beyond. We discuss our global namespace design in detail in Section 2.
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Disaster Recovery – WAS stores customer data across multiple data centers hundreds of miles apart from each other. This redundancy provides essential data recovery protection against disasters such as earthquakes, wild fires, tornados, nuclear reactor meltdown, etc.
Multi-tenancy and Cost of Storage – To reduce storage cost, many customers are served from the same shared storage infrastructure. WAS combines the workloads of many different customers with varying resource needs together so that significantly less storage needs to be provisioned at any one point in time than if those services were run on their own dedicated hardware.
We describe these design features in more detail in the following sections. The remainder of this paper is organized as follows. Section 2 describes the global namespace used to access the WAS Blob, Table, and Queue data abstractions. Section 3 provides a high level overview of the WAS architecture and its three layers: Stream, Partition, and Front-End layers. Section 4 describes the stream layer, and Section 5 describes the partition layer. Section 6 shows the throughput experienced by Windows Azure applications accessing Blobs and Tables. Section 7 describes some internal Microsoft workloads using WAS. Section 8 discusses design choices and lessons learned. Section 9 presents related work, and Section 10 summarizes the paper.
2. Global Partitioned Namespace
A key goal of our storage system is to provide a single global namespace that allows clients to address all of their storage in the cloud and scale to arbitrary amounts of storage needed over time. To provide this capability we leverage DNS as part of the storage namespace and break the storage namespace into three parts: an account name, a partition name, and an object name. As a result, all data is accessible via a URI of the form:
The AccountName is the customer selected account name for accessing storage and is part of the DNS host name. The AccountName DNS translation is used to locate the primary storage cluster and data center where the data is stored. This primary location is where all requests go to reach the data for that account. An application may use multiple AccountNames to store its data across different locations.
In conjunction with the AccountName, the PartitionName locates the data once a request reaches the storage cluster. The PartitionName is used to scale out access to the data across storage nodes based on traffic needs.
When a PartitionName holds many objects, the ObjectName identifies individual objects within that partition. The system supports atomic transactions across objects with the same PartitionName value. The ObjectName is optional since, for some types of data, the PartitionName uniquely identifies the object within the account.
This naming approach enables WAS to flexibly support its three data abstractions2. For Blobs, the full blob name is the PartitionName. For Tables, each entity (row) in the table has a
2 APIs for Windows Azure Blobs, Tables, and Queues can be found here:
primary key that consists of two properties: the PartitionName and the ObjectName. This distinction allows applications using Tables to group rows into the same partition to perform atomic transactions across them. For Queues, the queue name is the PartitionName and each message has an ObjectName to uniquely identify it within the queue.
3. High Level Architecture
Here we present a high level discussion of the WAS architecture and how it fits into the Windows Platform.
3.1 Windows Platform
The Windows platform runs many cloud services across different data centers and different geographic regions. The Windows Controller is a resource provisioning and management layer that provides resource allocation, deployment/upgrade, and management for cloud services on the Windows Azure platform. WAS is one such service running on top of the Fabric Controller.
The Fabric Controller provides node management, network configuration, health monitoring, starting/stopping of service instances, and service deployment for the WAS system. In addition, WAS retrieves network topology information, physical layout of the clusters, and hardware configuration of the storage nodes from the Fabric Controller. WAS is responsible for managing the replication and data placement across the disks and load balancing the data and application traffic within the storage cluster.
3.2 WAS Architectural Components
An important feature of WAS is the ability to store and provide access to an immense amount of storage (exabytes and beyond). We currently have 70 petabytes of raw storage in production and are in the process of provisioning a few hundred more petabytes of raw storage based on customer demand for 2012.
The WAS production system consists of Storage Stamps and the Location Service (shown in Figure 1).
Access Blobs, Tables and Queues for account
Account Management DNS
Stream Layer Intra-Stamp Replication
Stream Layer Intra-Stamp Replication
Figure 1: High-level architecture
Storage Stamps – A storage stamp is a cluster of N racks of storage nodes, where each rack is built out as a separate fault domain with redundant networking and power. Clusters typically range from 10 to 20 racks with 18 disk-heavy storage nodes per rack. Our first generation storage stamps hold approximately 2PB of raw storage each. Our next generation stamps hold up to 30PB of raw storage each.
To provide low cost cloud storage, we need to keep the storage provisioned in production as highly utilized as possible. Our goal is to keep a storage stamp around 70% utilized in terms of capacity, transactions, and bandwidth. We try to avoid going above 80% because we want to keep 20% in reserve for (a) disk short stroking to gain better seek time and higher throughput by utilizing the outer tracks of the disks and (b) to continue providing storage capacity and availability in the presence of a rack failure within a stamp. When a storage stamp reaches 70% utilization, the location service migrates accounts to different stamps using inter-stamp replication (see Section 3.4).
Location Service (LS) – The location service manages all the storage stamps. It is also responsible for managing the account namespace across all stamps. The LS allocates accounts to storage stamps and manages them across the storage stamps for disaster recovery and load balancing. The location service itself is distributed across two geographic locations for its own disaster recovery.
WAS provides storage from multiple locations in each of the three geographic regions: North America, Europe, and Asia. Each location is a data center with one or more buildings in that location, and each location holds multiple storage stamps. To provision additional capacity, the LS has the ability to easily add new regions, new locations to a region, or new stamps to a location. Therefore, to increase the amount of storage, we deploy one or more storage stamps in the desired location’s data center and add them to the LS. The LS can then allocate new storage accounts to those new stamps for customers as well as load balance (migrate) existing storage accounts from older stamps to the new stamps.
Figure 1 shows the location service with two storage stamps and the layers within the storage stamps. The LS tracks the resources used by each storage stamp in production across all locations. When an application requests a new account for storing data, it specifies the location affinity for the storage (e.g., US North). The LS then chooses a storage stamp within that location as the primary stamp for the account using heuristics based on the load information across all stamps (which considers the fullness of the stamps and other metrics such as network and transaction utilization). The LS then stores the account metadata information in the chosen storage stamp, which tells the stamp to start taking traffic for the assigned account. The LS then updates DNS to allow requests to now route from the name https://AccountName.service.core.windows.net/ to that storage stamp’s virtual IP (VIP, an IP address the storage stamp exposes for external traffic).
3.3 Three Layers within a Storage Stamp
Also shown in Figure 1 are the three layers within a storage stamp. From bottom up these are:
Stream Layer – This layer stores the bits on disk and is in charge of distributing and replicating the data across many servers to keep data durable within a storage stamp. The stream layer can be thought of as a distributed file system layer within a stamp. It understands files, called “streams” (which are ordered lists of large storage chunks called “extents”), how to store them, how to replicate them, etc., but it does not understand higher level object constructs or their semantics. The data is stored in the stream layer, but it is accessible from the partition layer. In fact, partition servers (daemon processes in the partition layer) and stream servers are co-located on each storage node in a stamp.
Partition Layer – The partition layer is built for (a) managing and understanding higher level data abstractions (Blob, Table, Queue), (b) providing a scalable object namespace, (c) providing transaction ordering and strong consistency for objects, (d) storing object data on top of the stream layer, and (e) caching object data to reduce disk I/O.
Another responsibility of this layer is to achieve scalability by partitioning all of the data objects within a stamp. As described earlier, all objects have a PartitionName; they are broken down into disjointed ranges based on the PartitionName values and served by different partition servers. This layer manages which partition server is serving what PartitionName ranges for Blobs, Tables, and Queues. In addition, it provides automatic load balancing of PartitionNames across the partition servers to meet the traffic needs of the objects.
Front-End (FE) layer – The Front-End (FE) layer consists of a set of stateless servers that take incoming requests. Upon receiving a request, an FE looks up the AccountName, authenticates and authorizes the request, then routes the request to a partition server in the partition layer (based on the PartitionName). The system maintains a Partition Map that keeps track of the PartitionName ranges and which partition server is serving which PartitionNames. The FE servers cache the Partition Map and use it to determine which partition server to forward each request to. The FE servers also stream large objects directly from the stream layer and cache frequently accessed data for efficiency.
3.4 Two Replication Engines
Before describing the stream and partition layers in detail, we first give a brief overview of the two replication engines in our system and their separate responsibilities.
Intra-Stamp Replication (stream layer) – This system provides synchronous replication and is focused on making sure all the data written into a stamp is kept durable within that stamp. It keeps enough replicas of the data across different nodes in different fault domains to keep data durable within the stamp in the face of disk, node, and rack failures. Intra-stamp replication is done completely by the stream layer and is on the critical path of the customer’s write requests. Once a transaction has been replicated successfully with intra-stamp replication, success can be returned back to the customer.
Inter-Stamp Replication (partition layer) – This system provides asynchronous replication and is focused on replicating data across stamps. Inter-stamp replication is done in the background and is off the critical path of the customer’s request. This replication is at the object level, where either the whole object is replicated or recent delta changes are replicated for a given account. Inter-stamp replication is used for (a) keeping a copy of an account’s data in two locations for disaster recovery and (b) migrating an account’s data between stamps. Inter-stamp replication is configured for an account by the location service and performed by the partition layer.
Inter-stamp replication is focused on replicating objects and the transactions applied to those objects, whereas intra-stamp replication is focused on replicating blocks of disk storage that are used to make up the objects.
We separated replication into intra-stamp and inter-stamp at these two different layers for the following reasons. Intra-stamp replication provides durability against hardware failures, which occur frequently in large scale systems, whereas inter-stamp replication provides geo-redundancy against geo-disasters, which
are rare. It is crucial to provide intra-stamp replication with low latency, since that is on the critical path of user requests; whereas the focus of inter-stamp replication is optimal use of network bandwidth between stamps while achieving an acceptable level of replication delay. They are different problems addressed by the two replication schemes.
Another reason for creating these two separate replication layers is the namespace each of these two layers has to maintain. Performing intra-stamp replication at the stream layer allows the amount of information that needs to be maintained to be scoped by the size of a single storage stamp. This focus allows all of the meta-state for intra-stamp replication to be cached in memory for performance (see Section 4), enabling WAS to provide fast replication with strong consistency by quickly committing transactions within a single stamp for customer requests. In contrast, the partition layer combined with the location service controls and understands the global object namespace across stamps, allowing it to efficiently replicate and maintain object state across data centers.
4. Stream Layer
The stream layer provides an internal interface used only by the partition layer. It provides a file system like na
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