The Nutanix platform currently uses a replication factor (RF) to ensure data redundancy and availability in the case of a node or disk failure. As explained in the Architectural Design section, OpLog acts as a staging area to absorb incoming writes onto a low-latency SSD tier. Upon being written to the local OpLog the data is synchronously replicated to another Nutanix CVM’s OpLog before being acknowledged (Ack) as a successful write to the host. This ensures that the data exists in at least two independent locations and is fault tolerant. All nodes participate in OpLog replication to eliminate any “hot nodes” and ensuring linear performance at scale.
Data is then asynchronously drained to the extent store where the RF is implicitly maintained. In the case of a node or disk failure the data is then re-replicated among all nodes in the cluster to maintain the RF.
Below we show an example of what this logically looks like:
Being a converged (compute+storage) platform, I/O and data locality is key to cluster and VM performance with Nutanix. As explained above in the I/O path, all read/write IOs are served by the local Controller VM (CVM) which is on each hypervisor adjacent to normal VMs. A VM’s data is served locally from the CVM and sits on local disks under the CVM’s control. When a VM is moved from one hypervisor node to another (or during a HA event) the newly migrated VM’s data will be served by the now local CVM. When reading old data (stored on the now remote node/CVM) the I/O will be forwarded by the local CVM to the remote CVM. All write I/Os will occur locally right away. NDFS will detect the I/Os are occurring from a different node and will migrate the data locally in the background allowing for all read I/Os to now be served locally. The data will only be migrated on a read as to not flood the network.
Below we show an example of how data will “follow” the VM as it moves between hypervisor nodes:
Metadata is at the core of any intelligent system and is even more critical for any filesystem or storage array. In terms of NDFS there are a few key structs that are critical for its success: it has to be right 100% of the time (aka. “strictly consistent”), it has to be scalable, and it has to perform, at massive scale. As mentioned in the architecture section above, NDFS utilizes a “ring like” structure as a key-value store which stores essential metadata as well as other platform data (eg. stats, etc.). In order to ensure metadata availability and redundancy a RF is utilized among an odd amount of nodes (eg. 3, 5, etc.).
Upon a metadata write or update the row is written to a node in the ring and then replicated to n number of peers (where n is dependent on cluster size). A majority of nodes must agree before anything is committed which is enforced using the paxos algorigthm. This ensures strict consistency for all data and metadata stored as part of the platform.
Below we show an example of a metadata insert/update for a 4 node cluster:
Performance at scale is also another important struct for NDFS metadata. Contrary to traditional dual-controller or “master” models, each Nutanix node is responsible for a subsest of the overall platform’s metadata. This eliminates the the traditional bottlenecks by allowing metadata to be served and manipulated by all nodes in the cluster. A consistent hashing scheme is utilized to minimize the redistribution of keys during cluster size modifications (aka. “add/remove node”)
When the cluster scales (eg. from 4 to 8 nodes), the nodes are inserted throughout the ring between nodes for “block awareness” and reliability.
Below we show an example of the metadata “ring” and how it scales:
The Nutanix Distributed Filesystem has a feature called ‘Shadow Clones’ which allows for distributed caching of particular vDisks or VM data which is in a ‘multi-reader’ scenario. A great example of this is during a VDI deployment many ‘linked clones’ will be forwarding read requests to a central master or ‘Base VM’. In the case of VMware View this is called the replica disk and is read by all linked clones and in XenDesktop this is called the MCS Master VM. This will also work in any scenario which may be a multi-reader scenario (eg. deployment servers, repositories, etc.).
Data or I/O locality is critical for the highest possible VM performance and a key struct of NDFS. With Shadow Clones, NDFS will monitor vDisk access trends similar to what it does for data locality. However in the case there are requests occurring from more than two remote CVMs (as well as the local CVM), and all of the requests are read I/O, the vDisk will be marked as immutable. Once the disk has been marked as immutable the vDisk can then be cached locally by each CVM making read requests to it (aka Shadow Clones of the base vDisk).
This will allow VMs on each node to read the Base VM’s vDisk locally. In the case of VDI, this means the replica disk can be cached by each node and all read requests for the base will be served locally. NOTE: The data will only be migrated on a read as to not flood the network and allow for efficient cache utilization. In the case where the Base VM is modified the Shadow Clones will be dropped and the process will start over. Shadow clones are disabled by default (as of 3.5) and can be enabled/disabled using the following NCLI command: ncli cluster edit-params enable-shadow-clones=true
Below we show an example of how Shadow Clones work and allow for distributed caching:
Elastic Dedupe Engine
The Elastic Dedupe Engine is a software based feature of NDFS which allows for data deduplication at that capacity (HDD) and performance (SSD/Memory) tiers. Sequential streams of data are fingerprinted during ingest using a SHA-1 hash at a 4K granularity. This fingerprint is only done on data ingest and is then stored persistently as part of the written block’s metadata. Contrary to traditional approaches which utilize background scans, requiring the data to be re-read, Nutanix performs the fingerprint in-line on ingest. For duplicate data that can be deduplicated in the capacity tier the data does not need to be scanned or re-read, essentially duplicate copies can be removed.
Below we show an example of how the Elastic Dedupe Engine scales and handles local VM I/O requests:
Fingerprinting is done during data ingest of data with an I/O size of 64K or greater. Intel acceleration is leveraged for the SHA-1 computation which accounts for very minimal CPU overhead. In cases where fingerprinting is not done during ingest (eg. smaller I/O sizes), fingerprinting can be done as a background process.
The Elastic Deduplication Engine spans both the capacity disk tier (HDD), but also the performance tier (SSD/Memory). As duplicate data is determined, based upon multiple copies of the same fingerprints, a background process will remove the duplicate data using the NDFS MapReduce framework (curator). For data that is being read, the data will be pulled into the NDFS Content Cache which is a multi-tier/pool cache. Any subsequent requests for data having the same fingerprint will be pulled directly from the cache. To learn more about the Content Cache and pool structure, please refer to the ‘Content Cache’ sub-section in the I/O path overview, or click HERE.
Below we show an example of how the Elastic Dedupe Engine interacts with the NDFS I/O path:
Networking and I/O
The Nutanix platform does not leverage any backplane for inter-node communication and only relies on a standard 10GbE network. All storage I/O for VMs running on a Nutanix node is handled by the hypervisor on a dedicated private network. The I/O request will be handled by the hypervisor which will then forward the request to the private IP on the local CVM. The CVM will then perform the remote replication with other Nutanix nodes using its external IP over the public 10GbE network. For all read requests these will be served completely locally in most cases and never touch the 10GbE network.
This means that the only traffic touching the public 10GbE network will be NDFS remote replication traffic and VM network I/O. There will however be cases where the CVM will forward requests to other CVMs in the cluster in the case of a CVM being down or data being remote. Also, cluster wide tasks such as disk balancing will temporarily generate I/O on the 10GbE network.
Below we show an example of how the VM’s I/O path interacts with the private and public 10GbE network:
Reliability and resiliency is a key, if not the most important, piece to NDFS. Being a distributed system NDFS is built to handle component, service and CVM failures. In this section I’ll cover how CVM “failures” are handled (I’ll cover how we handle component failures in future update). A CVM “failure” could include a user powering down the CVM, a CVM rolling upgrade, or any event which might bring down the CVM. NDFS has a feature called autopathing where when a local CVM becomes unavailable the I/Os are then transparently served by another CVM.
The hypervisor and CVM communicate using a private 192.168.5.0 network on a dedicated vSwitch (more on this above). This means that for all storage I/Os these are happening to the internal IP addresses on the CVM (192.168.5.2). The external IP address of the CVM is used for remote replication and for CVM communication.
Below we show an example of what this looks like:
In the event of a local CVM failure the local 192.168.5.2 addresses previously hosted by the local CVM is unavailable. NDFS will automatically detect this outage and will redirect these I/Os to another CVM in the cluster over 10GbE. The re-routing is done transparently to the hypervisor and VMs running on the host. This means that even if a CVM is powered down the VMs will still continue to be able to perform I/Os to NDFS. NDFS is also self healing meaning it will detect the CVM has been powered off and will automatically reboot or power-on the local CVM. Once the local CVM is back up and available, traffic will then seamlessly be transferred back and served by the local CVM.
Below we show a graphical representation of how this looks for a failed CVM:
NDFS is designed to be a very dynamic platform which can react to various workloads as well as allow heterogeneous node types: compute heavy (3050, etc.) and storage heavy (60X0,etc.) to be mixed in a single cluster. Ensuring uniform distribution of data is an important item when mixing nodes with larger storage capacities.
NDFS has a native feature called disk balancing which is used to ensure uniform distribution of data throughout the cluster. Disk balancing works on a node’s utilization of its local storage capacity and is integrated with NDFS ILM. It’s goal is to keep utilization uniform among nodes once the utilization has breached a certain threshold.
Below we show an example of a mixed cluster (3050 + 6050) in a “unbalanced” state:
Disk balancing leverages the NDFS Curator framework and is run as a scheduled process as well as when a threshold has been breached (eg. local node capacity utilization > n %). In the case where the data is not balanced Curator will determine which data needs to be moved and will distribute the tasks to nodes in the cluster.
In the case where the node types are homogeneous (eg. 3050) utilization should be fairly uniform. However, if there are certain VMs running on a node which are writing much more data then others there can become a skew in the per node capacity utilization. In this case disk balancing would run and move the coldest data on that node to other nodes in the cluster.
In the case where the node types are heterogeneous (eg. 3050 + 6020/50/70), or where a node may be used in a “storage only” mode (not running any VMs), there will likely be a requirement to move data.
Below we show an example the mixed cluster after disk balancing has been run in a “balanced” state:
In some scenarios customers might run some nodes in a “storage only” state where only the CVM will run on the node who’s primary purpose is bulk storage capacity.
Below we show an example of how a storage only node would look in a mixed cluster with disk balancing moving data to it from the active VM nodes:
Software-Defined Controller Architecture
As mentioned above (likely numerous times), the Nutanix platform is a software based solution which ships as a bundled software + hardware appliance. The controller VM is where the vast majority of the Nutanix software and logic sits and was designed from the beginning to be a extensible and pluggable architecture.
A key benefit to being software defined and not relying upon any hardware offloads or constructs is around extensibility. Like with any product life cycle there will always be advancements and new features which are introduced. By not relying on any custom ASIC/FPGA or hardware capabilities, Nutanix can develop and deploy these new features through a simple software update. This means that the deployment of a new feature (say deduplication) can be deployed by upgrading the current version of the Nutanix software. This also allows newer generation features to be deployed on legacy hardware models.
For example, say you’re running a workload running a older version of Nutanix software on a prior generation hardware platform (eg. 2400). The running software version doesn’t provide deduplication capabilities which your workload could benefit greatly from. To get these features you perform a rolling upgrade of the Nutanix software version while the workload is running, and whala you now have deduplication. It’s really that easy.
Similar to features, the ability to create new “adapters” or interfaces into NDFS is another key capability. When the product first shipped it solely supported iSCSI for I/O from the hypervisor, this has now grown to include NFS and SMB. In the future there is the ability to create new adapters for various workloads and hypervisors (HDFS, etc.). And again, all deployed via a software update.
This is contrary to mostly all legacy infrastructure as a hardware upgrade or software purchase was normally required to get the “latest and greatest” features. With Nutanix it’s different, since all features are deployed in software they can run on any hardware platform, any hypervisor and be deployed through simple software upgrades.
Below we show a logical representation of what this software-defined controller framework looks like:
Storage Tiering and Prioritization
The Disk Balancing section above talked about how storage capacity was pooled among all nodes in a Nutanix cluster and that ILM would be used to keep hot data local. A similar concept applies to disk tiering in which the cluster’s SSD and HDD tiers are cluster wide and NDFS ILM is responsible for triggering data movement events.
A local node’s SSD tier is always the highest priority tier for all I/O generated by VMs running on that node, however all of the cluster’s SSD resources are made available to all nodes within the cluster. The SSD tier will always offer the highest performance and is a very important thing to manage for hybrid arrays.
The tier prioritization can be classified at a high-level by the following:
Specific types of resources (eg. SSD, HDD, etc.) are pooled together and form a cluster wide storage tier. This means that any node within the cluster can leverage the full tier capacity, regardless if it is local or not.
Below we show a high level example of how this pooled tiering looks:
A common question is what happens when a local node’s SSD becomes full? As mentioned in the Disk Balancing section a key concept is trying to keep uniform utilization of devices within disk tiers. In the case where a local node’s SSD utilization is high, disk balancing will kick in to move the coldest data on the local SSDs to the other SSDs throughout the cluster. This will free up space on the local SSD to allow the local node to write to SSD locally instead of going over the network. A key point to mention is that all CVMs and SSDs are used for this remote I/O to eliminate any potential bottlenecks and remediate some of the hit by performing I/O over the network.
The other case is when the overall tier utilization breaches a specific threshold [curator_tier_usage_ilm_threshold_percent (Default=75)] where NDFS ILM will kick in and as part of a Curator job will down-migrate data from the SSD tier to the HDD tier. This will bring utilization within the threshold mentioned above or free up space by the following amount [curator_tier_free_up_percent_by_ilm (Default=15)], which ever is greater. The data for down-migration is chosen using last access time.
In the case where the SSD tier utilization is 95%, 20% of the data in the SSD tier will be moved to the HDD tier (95% –> 75%). However, if the utilization was 80% only 15% of the data would be moved to the HDD tier using the minimum tier free up amount.
NDFS ILM will constantly monitor the I/O patterns and (down/up)-migrate data as necessary as well as bring the hottest data local regardless of tier.