What I have presented are just three examples where File copy was not going to be a good predictor of storage issues. If you really want to know if your storage is the issue, use disk speed and do not run in production. Its just as likely that all the other processes running on the server are the problem, then just the storage.
Using the parameters in Table 3, you can have DiskSpd display data concerning events from an NT Kernel Logger trace session. Because event tracing (ETW) carries additional overhead, this is turned off by default.
Your registry fix at least stopped our host servers from BSOD'ing, but there's still something wrong. Might be helpful to point MS at your old case number so they can see why I made that registry change.My behavior now is that after the backup completes, the Checkpoint (.avhdx file[s]) "disappears" from the Hyper-V GUI (and from Powershell), but it does not actually get applied to the "real" .vhdx file(s). (In our case, the VM has 3 disks so I have 3 .vhdx, and 3 .avhdx from a Checkpoint). There is an entry in the Hyper-V VMMS log showing where the checkpoint attempted to merge and then failed due to an access violation. I've logged the whole thing with ProcMon, but it does not really help tracing at the file handle level - you can see that various processes add and remove handles to the files, but they don't seem to have any unique identifiers, which makes it nearly impossible to follow the "life cycle" of a given handle.
Even more frustrating is that for my environment, this failure/problem only seems to happen on two VMs, and the only real commonality is that they both have multiple virtual disks, and they both see a fair amount of disk activity (one is a standard Windows file server, the other is running a timeclock app that uses a "flat file" database and does a lot of read/write on that data). I have plenty of other Server 2016 VMs that backup perfectly fine, so other than the high disk activity, I have no clue what could be causing this. Worth noting, our backing storage is a Pure Storage array via 10Gb iSCSI for at least one of the VMs, so even though it is "busy," latency is still low and throughput should be more than sufficient.
The devicemapper driver uses block devices dedicated to Docker and operates atthe block level, rather than the file level. These devices can be extended byadding physical storage to your Docker host, and they perform better than usinga filesystem at the operating system (OS) level.
Production hosts using the devicemapper storage driver must use direct-lvmmode. This mode uses block devices to create the thin pool. This is faster thanusing loopback devices, uses system resources more efficiently, and blockdevices can grow as needed. However, more setup is required than in loop-lvmmode.
Warning: Changing the storage driver makes any containers you have already created inaccessible on the local system. Use docker save to save containers, and push existing images to Docker Hub or a private repository, so you do not need to recreate them later.
The procedure below creates a logical volume configured as a thin pool touse as backing for the storage pool. It assumes that you have a spare blockdevice at /dev/xvdf with enough free space to complete the task. The deviceidentifier and volume sizes may be different in your environment and youshould substitute your own values throughout the procedure. The procedure alsoassumes that the Docker daemon is in the stopped state.
If you run into repeated problems with thin pool, you can set the storage optiondm.min_free_space to a value (representing a percentage) in/etc/docker/daemon.json. For instance, setting it to 10 ensuresthat operations fail with a warning when the free space is at or near 10%.See thestorage driver options in the Engine daemon reference.
The /var/lib/docker/devicemapper/metadata/ directory contains metadata aboutthe Devicemapper configuration itself and about each image and container layerthat exist. The devicemapper storage driver uses snapshots, and this metadatainclude information about those snapshots. These files are in JSON format.
When you start Docker with the devicemapper storage driver, all objectsrelated to image and container layers are stored in/var/lib/docker/devicemapper/, which is backed by one or more block-leveldevices, either loopback devices (testing only) or physical disks.
Memory usage: the devicemapper uses more memory than some other storagedrivers. Each launched container loads one or more copies of its files intomemory, depending on how many blocks of the same file are being modified atthe same time. Due to the memory pressure, the devicemapper storage drivermay not be the right choice for certain workloads in high-density use cases.
Use volumes for write-heavy workloads: Volumes provide the best and mostpredictable performance for write-heavy workloads. This is because they bypassthe storage driver and do not incur any of the potential overheads introducedby thin provisioning and copy-on-write. Volumes have other benefits, such asallowing you to share data among containers and persisting even when norunning container is using them.
Event Tracing for Windows (ETW) is an advanced debugging feature provided by Microsoft that allows you to create customized event tracing using a provider-consumer model.For more information on how ETW works, refer to About Event Tracing on Microsoft Docs.
Shared storage is an important topic in WFSC operations.Whether you are running SCSI, iSCSI, or FiberChannel, you may need visibility on input and output operations and the hardware state of your storage adapters.This provider will help you monitor storage operations on cluster shared storage.
Survey data shows that at least half of all enterprise data center Hadoop projects are stalled and that only 20% are actually making into production. This presentation looks at the problems with Hadoop that enterprise data center administrators encounter and how the storage environment can be used to fix at least some of these problems.
This talk looks at the implications to Hadoop of future server hardware - and to start preparing for them. What would a pure SSD Hadoop filesystem look like, and how to get there via a mixed SSD/HDD storage hierarchy? What impact would that have on ingress, analysis and HBase? What could we do do better if network bandwidth and latency became less of a bottleneck, and how should interprocess communication change? Would it make the graph layer more viable? What would massive arrays of WIMPy cores mean -or a GPU in every sever. Will we need to schedule work differently? Will it make per-core RAM a bigger issue? Finally: will this let us scale Hadoop down?
"The most expensive storage purchased is that which causes the deployment of another Data Center." George Crump, President & Founder Storage-Switzerland In a world of more, more, more, using 'less' to store all of it, is a crucial skill, which translates to a real competitive advantage for an organization.
Data deduplication and compression are no longer storage optimizations relegated to backup. They have become mainstream in primary and high performance (flash) storage. In this BOF session, we will discuss how to build a Linux storage appliance using standard Linux components (XFS, LVM2, and Linux iSCSI) and Permabit Albireo Virtual Data Optimizer (VDO). Whether you are designing cloud storage, backup solutions, or high performance flash arrays, this discussion will show you how to build a storage-optimized product in matter of hours.
Serial Attached SCSI (SAS) is the connectivity solution of choice for disk drives and JBODs in the data center today. SAS connections are getting faster while storage solutions are getting larger and more complex. Data center configurations and disaster recovery solutions are demanding longer cable distances. This is making it more and more difficult or impossible to configure systems using passive copper cables. This presentation discusses the application, limitations and performance of passive copper, active copper and optical SAS cabling options available today and those likely to be available in the next few years.
Fred Knight is a Principal Engineer in the CTO Office at NetApp. Fred has over 35 years of experience in the computer and storage industry. He currently represents NetApp in several National and International Storage Standards bodies and industry associations, including T10 (SCSI), T11 (Fibre Channel), T13 (ATA), IETF (iSCSI), SNIA, and FCIA. He is the chair of the SNIA Hypervisor Storage Interfaces working group, the primary author of the SNIA HSI White Paper, the author of the new IETF iSCSI update RFC, and the editor for the T10 SES-3 standard. Fred has received the INCITS Technical Excellence Award for his contributions to both T10 and T11. He is also the developer of the first native FCoE target device in the industry. At NetApp, he contributes to technology and product strategy and serves as a consulting engineer to product groups across the company. Prior to joining NetApp, Fred was a Consulting Engineer with Digital Equipment Corporation, Compaq, and HP where he worked on clustered operating system and I/O subsystem design.
Over the past year, we have integrated our storage solution with a number of cloud and object storage APIs, including Amazon S3, WebDAV, OpenStack, and HDFS. While these protocols share much commonality, they also differ in meaningful ways which complicates the design of a cross-protocol compatibility layer. In this presentation, we detail how the various storage protocols are the same, how they differ, and what design decisions were necessary to build an underlying storage API that meets the requirements to support all of them. Further, we consider the lessons learned and provide recommendations for developing cloud storage APIs such as CDMI.
Cloud systems promise virtually unlimited, on-demand increases in storage, computing, and bandwidth. As companies have turned to cloud-based services to store, manage and access big data, it has become clear that this promise is tempered by a series of technical bottlenecks: transfer performance over the WAN, HTTP throughput within remote infrastructures, and size limitations of the cloud object stores. This session will discuss principles of cloud object stores, using examples of Amazon S3, Microsoft Azure, and OpenStack Swift, and performance benchmarks of their native HTTP I/O. It will share best practices in orchestration of complex, large-scale big data workflows. It will also examine the requirements and challenges of such IT infrastructure designs (on-premise, in the cloud or hybrid), including integration of necessary high-speed transport technologies to power ultra-high speed data movement, and adoption of appropriate high-performance network-attached storage systems. 2b1af7f3a8