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Tony Pearson is a Master Inventor and Senior Software Engineer for the IBM Storage product line at the
IBM Executive Briefing Center in Tucson Arizona, and featured contributor
to IBM's developerWorks. In 2016, Tony celebrates his 30th year anniversary with IBM Storage. He is
author of the Inside System Storage series of books. This blog is for the open exchange of ideas relating to storage and storage networking hardware, software and services. You can also follow him on Twitter @az990tony.
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If you store your VMware bits on external SAN or NAS-based disk storage systems, this post is for you. The subject of the post, VM Volumes, is a potential storage management game changer!
Fellow blogger Stephen Foskett mentioned VM Volumes in his [Introducing VMware vSphere Storage Features] presentation at IBM Edge 2012 conference. His session on VMware's storage features included VMware APIs for Array Integration (VAAI), VMware Array Storage Awareness (VASA), vCenter plug-ins, and a new concept he called "vVol", now more formally known as VM Volumes. This post provides a follow-up to this, describing the VM Volumes concepts, architecture, and value proposition.
"VM Volumes" is a future architecture that VMware is developing in collaboration with IBM and other major storage system vendors. So far, very little information about VM Volumes has been released. At VMworld 2012 Barcelona, VMware highlights VM Volumes for the first time and IBM demonstrates VM Volumes with the IBM XIV Storage System (more about this demo below). VM Volumes is worth your attention -- when it becomes generally available, everyone using storage arrays will have to reconsider their storage management practices in a VMware environment -- no exaggeration!
But enough drama. What is this all about?
(Note: for the sake of clarity, this post refers to block storage only. However, the VM Volumes feature applies to NAS systems as well. Special thanks to Yossi Siles and the XIV development team for their help on this post!)
The VM Volumes concept is simple: VM disks are mapped directly to special volumes on a storage array system, as opposed to storing VMDK files on a vSphere datastore.
The following images illustrate the differences between the two storage management paradigms.
You may still be asking yourself: bottom line, how will I benefit from VM Volumes?
Well, take a VM snapshot for example. With VM Volumes, vSphere can simply offload the operation by invoking a hardware snapshot of the hardware volume. This has significant implications:
VM-Granularity: Only the right VMs are copied (with datastores, backing up or cloning individual-VM portions of hardware snapshot of a datastore would require more complex configuration, tools and work)
Hardware Offload: No ESXi server resources are consumed
XIV advantage: With XIV, snapshots consume no space upfront and are completed instantly.
Here's the first takeaway: With VM Volumes, advanced storage services (which cost a lot when you buy a storage array), will become available at an individual VM level. In a cloud world, this means that applications can be provisioned easily with advanced storage services, such as snapshots and mirroring.
Now, let's take a closer look at another relevant scenario where VM Volumes will make a lot of difference - provisioning an application with special mirroring requirements:
VM Volumes case: The application is ordered via the private cloud portal. The requestor checks a box requesting an asynchronous mirror. He changes the default RPO for his needs. When the request is submitted, the process wraps up automatically: Volumes are created on one of the storage arrays, configured with a mirror and RPO exactly as specified. A few minutes later, the requestor receives an automatic mail pointing to the application virtual machine.
Datastores case #1: As may be expected, a datastore that is mirrored with the special RPO does not exist. As a result, the automated workflow sets a pending status on the request, creates an urgent ticket to a VMware administrator and aborts. When the VMware admin handles that ticket, she re-assigns the ticket to the storage administrator, asking for a new volume which is mirrored with the special RPO, and mapped to the right ESXi cluster. The next day, the volume is created; the ticket is re-assigned to the storage admin, with the new LUN being pointed to. The VMware administrator follows and creates the datastore on top of it. Since the automated workflow was aborted, the admin re-assigns the ticket to the cloud administrator, who sometime later completes the application provisioning manually.
Datastores case #2: Luckily for the requestor, a datastore that is mirrored with the special RPO does exist. However, that particular datastore is consuming space from a high performance XIV Gen3 system with SSD caching, while the application does not require that level of performance, so the workflow requires a storage administrator approval. The approval is given to save time, but the storage administrator opens a ticket for himself to create a new volume on another array, as well as a follow-up ticket for the VMware admin to create a new datastore using the new volume and migrate the application to the other datastore. In this case, provisioning was relatively rapid, but required manual follow up, involving the two administrators.
Here's the second takeaway: With VM Volumes, management is simplified, and end-to-end automation is much more applicable. The reason is that there are no datastores. Datastores physically group VMs that may otherwise be totally unrelated, and require close coordination between storage and VMware administrators.
Now, the above mainly focuses on the VMware or cloud administrator perspective. How does VM Volumes impact storage management?
VM's are the new hosts: Today, storage administrators have visibility of physical hosts in their management environment. In a non-virtualized environment, this visibility is very helpful. The storage administrator knows exactly which applications in a data center are storage-provisioned or affected by storage management operations because the applications are running on well-known hosts. However, in virtualized environments the association of an application to a physical host is temporary. To keep at least the same level of visibility as in physical environments, VMs should become part of the storage management environment, like hosts. Hosts are still interesting, for example to manage physical storage mapping, but without VM visibility, storage administrators will know less about their operation than they are used to, or need to. VM Volumes enables such visibility, because volumes are provided to individual VMs. The XIV VM Volumes demonstration at VMworld Barcelona, although experimental, shows a view of VM volumes, in XIV's management GUI.
Here's a screenshot:
That's not all!
Storage Profiles and Storage Containers: A Storage Profile is a vSphere specification of a set of storage services. A storage profile can include properties like thin or thick provisioning, mirroring definition, snapshot policy, minimum IOPS, etc.
Storage administrators define a portfolio of supported storage services, maintained as a set of storage profiles, and published (via VASA integration) to vSphere.
VMware or cloud administrators define the required storage profiles for specific applications
VMware and storage administrators need to coordinate the typical storage requirements and the automatically-available storage services. When a request to provision an application is made, the associated storage profiles are matched against the published set of available storage profiles. The matching published profiles will be used to create volumes, which will be bound to the application VMs. All that will happen automatically.
Note that when a VM is created today, a datastore must be specified. With VM Volumes, a new management entity called Storage Container (also known as Capacity Pool) replaces the use of datastore as a management object. Each Storage Container exposes a subset of the available storage profiles, as appropriate. The storage container also has a capacity quota.
Here are some more takeaways:
New way to interface vSphere and storage management: Storage administrators structure and publish storage services to vSphere via storage profiles and storage containers.
Automated provisioning, out of the box: The provisioning process automatically matches application-required storage profiles against storage profiles available from the specified storage containers. There is no need to build custom scripts and custom processes to automate storage provisioning to applications
The XIV advantage:
XIV services are very simple to define and publish. The typical number of available storage profiles would be low. It would also be easy to define application storage profiles.
XIV provides consistent high performance, up to very high capacity utilization levels, without any maintenance. As a result, automated provisioning (which inherently implies less human attention) will not create an elevated risk of reduced performance.
Note: A storage vendor VASA provider is required to support VM Volumes, storage profiles, storage containers and automated provisioning. The IBM Storage VASA provider runs as a standalone service that needs to be deployed on a server.
To summarize the VM Volumes value proposition:
Streamline cloud operation by providing storage services at VM and application level, enabling end-to-end provisioning automation, and unifying VMware and storage administration around volumes and VMs.
Increase storage array ROI, improve vSphere scalability and response time, and reduce cloud provisioning lag, by offloading VM-level provisioning, failover, backup, storage migration, storage space recycling, monitoring, and more, to the storage array, using advanced storage operations such as mirroring and snapshots.
Simplify the adoption of VM Volumes using XIV, with smaller and simpler sets of storage profiles. Apply XIV's supreme fast cloning to individual VMs, and keep automation risks at bay with XIV's consistent high performance.
Until you can get your hands on a VM Volumes-capable environment, the VMware and IBM developer groups will be collaborating and working hard to realize this game-changing feature. The above information is definitely expected to trigger your questions or comments, and our development teams are eager to learn from them and respond. Enter your comments below, and I will try to answer them, and help shape the next post on this subject. There's much more to be told.
A client asked me to explain "Nearline storage" to them. This was easy, I thought, as I started my IBM career on DFHSM, now known as DFSMShsm for z/OS, which was created in 1977 to support the IBM 3850 Mass Storage System (MSS), a virtual storage system that blended disk drives and tape cartridges with robotic automation. Here is a quick recap:
Online storage is immediately available for I/O. This includes DRAM memory, solid-state drives (SSD), and always-on spinning disk, regardless of rotational speed.
Nearline storage is not immediately available, but can be made online quickly without human intervention. This includes optical jukeboxes, automated tape libraries, as well as spin-down massive array of idle disk (MAID) technologies.
Offline storage is not immediately available, and requires some human intervention to bring online. This can include USB memory sticks, CD/DVD optical media, shelf-resident tape cartridges, or other removable media.
Sadly, it appears a few storage manufacturers and vendors have been misusing the term "Nearline" to refer to "slower online" spinning disk drives. I find this [June 2005 technology paper from Seagate], and this [2002 NetApp Press Release], the latter of which included this contradiction for their "NearStore" disk array. Here is the excerpt:
"Providing online access to reference information—NetApp nearline storage solutions quickly retrieve and replicate reference and archive information maintained on cost-effective storage—medical images, financial models, energy exploration charts and graphs, and other data-intensive records can be stored economically and accessed in multiple locations more quickly than ever"
Which is it, "online access" or "nearline storage"?
If a client asked why slower drives consume less energy or generate less heat, I could explain that, but if they ask why slower drives must have SATA connections, that is a different discussion. The speed of a drive and its connection technology are for the most part independent. A 10K RPM drive can be made with FC, SAS or SATA connection.
I am opposed to using "Nearlne" just to distinguish between four-digit speeds (such as 5400 or 7200 RPM) versus "online" for five-digit speeds (10,000 and 15,000 RPM). The difference in performance between 10K RPM and 7200 RPM spinning disks is miniscule compared to the differences between solid-state drives and any spinning disk, or the difference between spinning disk and tape.
I am also opposed to using the term "Nearline" for online storage systems just because they are targeted for the typical use cases like backup, archive or other reference information that were previously directed to nearline devices like automated tape libraries.
Can we all just agree to refer to drives as "fast" or "slow", or give them RPM rotational speed designations, rather than try to incorrectly imply that FC and SAS drives are always fast, and SATA drives are always slow? Certainly we don't need new terms like "NL-SAS" just to represent a slower SAS connected drive.
Here I am, day 11 of a 17-day business trip, on my last leg of the trip this week, in Kuala Lumpur in Malaysia. I have been flooded with requests to give my take on EMC's latest re-interpretation of storage virtualization, VPLEX.
I'll leave it to my fellow IBM master inventor Barry Whyte to cover the detailed technical side-by-side comparison. Instead, I will focus on the business side of things, using Simon Sinek's Why-How-What sequence. Here is a [TED video] from Garr Reynold's post
[The importance of starting from Why].
Let's start with the problem we are trying to solve.
Problem: migration from old gear to new gear, old technology to new technology, from one vendor to another vendor, is disruptive, time-consuming and painful.
Given that IT storage is typically replaced every 3-5 years, then pretty much every company with an internal IT department has this problem, the exception being those companies that don't last that long, and those that use public cloud solutions. IT storage can be expensive, so companies would like their new purchases to be fully utilized on day 1, and be completely empty on day 1500 when the lease expires. I have spoken to clients who have spent 6-9 months planning for the replacement or removal of a storage array.
A solution to make the data migration non-disruptive would benefit the clients (make it easier for their IT staff to keep their data center modern and current) as well as the vendors (reduce the obstacle of selling and deploying new features and functions). Storage virtualization can be employed to help solve this problem. I define virtualization as "technology that makes one set of resources look and feel like a different set of resources, preferably with more desirable characteristics.". By making different storage resources, old and new, look and feel like a single type of resource, migration can be performed without disrupting applications.
Before VPLEX, here is a breakdown of each solution:
Non-disruptive tech refresh, and a unified platform to provide management and functionality across heterogeneous storage.
Non-disruptive tech refresh, and a unified platform to provide management and functionality between internal tier-1 HDS storage, and external tier-2 heterogeneous storage.
Non-disruptive tech refresh, with unified multi-pathing driver that allows host attachment of heterogeneous storage.
New in-band storage virtualization device
Add in-band storage virtualization to existing storage array
New out-of-band storage virtualization device with new "smart" SAN switches
SAN Volume Controller
HDS USP-V and USP-VM
For IBM, the motivation was clear: Protect customers existing investment in older storage arrays and introduce new IBM storage with a solution that allows both to be managed with a single set of interfaces and provide a common set of functionality, improving capacity utilization and availability. IBM SAN Volume Controller eliminated vendor lock-in, providing clients choice in multi-pathing driver, and allowing any-to-any migration and copy services. For example, IBM SVC can be used to help migrate data from an old HDS USP-V to a new HDS USP-V.
With EMC, however, the motivation appeared to protect software revenues from their PowerPath multi-pathing driver, TimeFinder and SRDF copy services. Back in 2005, when EMC Invista was first announced, these three software represented 60 percent of EMC's bottom-line profit. (Ok, I made that last part up, but you get my point! EMC charges a lot for these.)
Back in 2006, fellow blogger Chuck Hollis (EMC) suggested that SVC was just a [bump in the wire] which could not possibly improve performance of existing disk arrays. IBM showed clients that putting cache(SVC) in front of other cache(back end devices) does indeed improve performance, in the same way that multi-core processors successfully use L1/L2/L3 cache. Now, EMC is claiming their cache-based VPLEX improves performance of back-end disk. My how EMC's story has changed!
So now, EMC announces VPLEX, which sports a blend of SVC-like and Invista-like characteristics. Based on blogs, tweets and publicly available materials I found on EMC's website, I have been able to determine the following comparison table. (Of course, VPLEX is not yet generally available, so what is eventually delivered may differ.)
Scalable, 1 to 4 node-pairs
One size fits all, single pair of CPCs
SVC-like, 1 to 4 director-pairs
Works with any SAN switches or directors
Required special "smart" switches (vendor lock-in)
SVC-like, works with any SAN switches or directors
Broad selection of IBM Subsystem Device Driver (SDD) offered at no additional charge, as well as OS-native drivers Windows MPIO, AIX MPIO, Solaris MPxIO, HP-UX PV-Links, VMware MPP, Linux DM-MP, and comercial third-party driver Symantec DMP.
Limited selection, with focus on priced PowerPath driver
Invista-like, PowerPath and Windows MPIO
Read cache, and choice of fast-write or write-through cache, offering the ability to improve performance.
No cache, Split-Path architecture cracked open Fibre Channel packets in flight, delayed every IO by 20 nanoseconds, and redirected modified packets to the appropriate physical device.
SVC-like, Read and write-through cache, offering the ability to improve performance.
Space-Efficient Point-in-Time copies
SVC FlashCopy supports up to 256 space-efficient targets, copies of copies, read-only or writeable, and incremental persistent pairs.
Like Invista, No
Remote distance mirror
Choice of SVC Metro Mirror (synchronous up to 300km) and Global Mirror (asynchronous), or use the functionality of the back-end storage arrays
No native support, use functionality of back-end storage arrays, or purchase separate product called EMC RecoverPoint to cover this lack of functionality
Limited synchronous remote-distance mirror within VPLEX (up to 100km only), no native asynchronous support, use functionality of back-end storage arrays
Provides thin provisioning to devices that don't offer this natively
Like Invista, No
SVC Split-Cluster allows concurrent read/write access of data to be accessed from hosts at two different locations several miles apart
I don't think so
PLEX-Metro, similar in concept but implemented differently
Non-disruptive tech refresh
Can upgrade or replace storage arrays, SAN switches, and even the SVC nodes software AND hardware themselves, non-disruptively
Tech refresh for storage arrays, but not for Invista CPCs
Tech refresh of back end devices, and upgrade of VPLEX software, non-disruptively. Not clear if VPLEX engines themselves can be upgraded non-disruptively like the SVC.
Heterogeneous Storage Support
Broad support of over 140 different storage models from all major vendors, including all CLARiiON, Symmetrix and VMAX from EMC, and storage from many smaller startups you may not have heard of
Invista-like. VPLEX claims to support a variety of arrays from a variety of vendors, but as far as I can find, only DS8000 supported from the list of IBM devices. Fellow blogger Barry Burke (EMC) suggests [putting SVC between VPLEX and third party storage devices] to get the heterogeneous coverage most companies demand.
Back-end storage requirement
Must define quorum disks on any IBM or non-IBM back end storage array. SVC can run entirely on non-IBM storage arrays
HP SVSP-like, requires at least one EMC storage array to hold metadata
SVC 2145-CF8 model supports up to four solid-state drives (SSD) per node that can treated as managed disk to store end-user data
Invista-like. VPLEX has an internal 30GB SSD, but this is used only for operating system and logs, not for end-user data.
In-band virtualization solutions from IBM and HDS dominate the market. Being able to migrate data from old devices to new ones non-disruptively turned out to be only the [tip of the iceberg] of benefits from storage virtualization. In today's highly virtualized server environment, being able to non-disruptively migrate data comes in handy all the time. SVC is one of the best storage solutions for VMware, Hyper-V, XEN and PowerVM environments. EMC watched and learned in the shadows, taking notes of what people like about the SVC, and decided to follow IBM's time-tested leadership to provide a similar offering.
EMC re-invented the wheel, and it is round. On a scale from Invista (zero) to SVC (ten), I give EMC's new VPLEX a six.
The IBM Challenge was a big success. One of the contestants, Ken Jennings, [welcomes our new computer overlords]. Congratulations are in order to the IBM Research team who pulled off this Herculean effort!
Some folks have poked fun at some of the odd responses and wager amounts from the IBM Watson computer during the three-day tournament. Others were surprised as I was that the impressive feat was done with less than 1TB of stored data. Here is what John Webster wrote in CNET yesterday, in hist article [What IBM's Watson says to storage systems developers]:
"All well and good. But here's what I find most interesting as a result of what IBM has done in response to the Grand Challenge that motivated Watson's creators. We know, from Tony Pearson's blog, that the foundation of Watson's data storage system is a modified IBM SONAS cluster with a total of 21.6TB of raw capacity. But Pearson also reveals another very significant, and to me, surprising data point: "When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, the actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1 Terabyte."
What Pearson just said is that the data set Watson actually uses to reach his push-the-button decision would fit on a 1TB drive. So much for big data?"
To better appreciate how difficult the challenge was, and how a small amount of data can answer a billion different questions, I thought I would cover Business Intelligence, Data Retrieval and Text Mining concepts.
"In this paper, business is a collection of activities carried
on for whatever purpose, be it science, technology,
commerce, industry, law, government, defense, et cetera.
The communication facility serving the conduct of a business
(in the broad sense) may be referred to as an intelligence
system. The notion of intelligence is also defined
here, in a more general sense, as the ability to apprehend
the interrelationships of presented facts in such a way as
to guide action towards a desired goal."
Ideally, when you need "Business Intelligence" to help you make a better decision, you perform data retrieval from a structured database for the specific information you are looking for. In other cases, you might be looking for insight, patterns or trends. In that case, you go "data mining" against your structured databases.
Here's a simple example. John runs a fruit stand. One day, he kept track of how many apples and oranges were bought by men and women. How many questions can we ask against this small set of data? Let's count them:
How many apples were sold to men?
How many apples were sold to women?
How many oranges were sold to men?
How many oranges were sold to women?
But wait! For each row and column, we can combine them into totals.
How many apples were sold in total?
How many oranges were sold in total?
How many fruit in total were sold to men?
How many fruit in total were sold to women?
How many fruit in total were sold?
But wait, there's more! Each row and column can be evaluated for relative percentages, as well as percentages of each cell compared to the total. You could make five relevant pie-charts from this data. This results in 16 more questions, such as:
Of the fruit purchased by men, what percentage for apples?
Of all the apples purchased, what percentage by women?
And that's not including more ethereal questions, such as:
Are there gender-specific preferences for different types of fruit?
What type of fruit do men prefer?
This is just for a small set, two market segments (by gender) and two products (apples and oranges). However, if you have many market segments (perhaps by age group, zip code, etc.) and many products, the number of queries that can be supported is huge. For small sets of data, you can easily do this with a spreadsheet program like IBM Lotus Symphony or Microsoft Excel.
But why limit yourself to two dimensions? The above example was just for one day's worth of activity, if John captures this data for every day for historical and seasonal trending, it can be represented as a three-dimensional cube. The number of queries becomes astronomical. This is the basis for Online Analytical Processing (OLAP), and three-dimensional tables are often referred to as [OLAP cubes].
Back in 1970, IBM invented the Structured Query Language [SQL], and today, nearly all modern relational databases support this, including IBM DB2, Informix, Microsoft SQL Server, and Oracle DB. SQL poses two challenges. First, you had to structure the data in advance to the way you expect to perform your ad-hoc queries. Deciding the groups and categories in advance can limit the way information is recorded and captured.
Second, you had to be skilled at SQL to phrase your queries correctly to retrieve the data you are after. What ended up happening was that skilled SQL programmers would develop "canned reports" with fixed SQL parameters, so that less-skilled business decision makers could base their decisions from these reports.
IBM has fully integrated stacks to help process structured data, combining servers, storage, and advanced analytics software into a complete appliance. IBM offers the [Smart Analytics System] for robust, customized deployments, and recently acquired [Netezza] for pre-configured, and more rapid deployments.
However, the bigger problem is that more than 80 percent of information is not structured!
Semi-structured data like email provides some searchable fields like From and Subject. The rest of the information is unstructured, such as text files, photographs, video and audio. To look for specific information in unstructured sources can be like looking for a needle in a haystack, and trying to get insight, patterns or trends involves text mining.
This, in effect, is what IBM Watson was able to perform so well this week. Finding the needle in the haystacks of unstructured data from 200 million pages of text stored in its system, combined with the ability to apprehend the interrelationships of meaning and subtle nuance, resulted in an impressive technology demonstration. Certainly, this new technology will be powerful for a variety of use cases across a broad set of industries!
Tonight PBS plans to air Season 38, Episode 6 of NOVA, titled [Smartest Machine On Earth]. Here is an excerpt from the station listing:
"What's so special about human intelligence and will scientists ever build a computer that rivals the flexibility and power of a human brain? In "Artificial Intelligence," NOVA takes viewers inside an IBM lab where a crack team has been working for nearly three years to perfect a machine that can answer any question. The scientists hope their machine will be able to beat expert contestants in one of the USA's most challenging TV quiz shows -- Jeopardy, which has entertained viewers for over four decades. "Artificial Intelligence" presents the exclusive inside story of how the IBM team developed the world's smartest computer from scratch. Now they're racing to finish it for a special Jeopardy airdate in February 2011. They've built an exact replica of the studio at its research lab near New York and invited past champions to compete against the machine, a big black box code -- named Watson after IBM's founder, Thomas J. Watson. But will Watson be able to beat out its human competition?"
Like most supercomputers, Watson runs the Linux operating system. The system runs 2,880 cores (90 IBM Power 750 servers, four sockets each, eight cores per socket) to achieve 80 [TeraFlops]. TeraFlops is the unit of measure for supercomputers, representing a trillion floating point operations. By comparison, Hans Morvec, principal research scientist at the Robotics Institute of Carnegie Mellon University (CMU) estimates that the [human brain is about 100 TeraFlops]. So, in the three seconds that Watson gets to calculate its response, it would have processed 240 trillion operations.
Several readers of my blog have asked for details on the storage aspects of Watson. Basically, it is a modified version of IBM Scale-Out NAS [SONAS] that IBM offers commercially, but running Linux on POWER instead of Linux-x86. System p expansion drawers of SAS 15K RPM 450GB drives, 12 drives each, are dual-connected to two storage nodes, for a total of 21.6TB of raw disk capacity. The storage nodes use IBM's General Parallel File System (GPFS) to provide clustered NFS access to the rest of the system. Each Power 750 has minimal internal storage mostly to hold the Linux operating system and programs.
When Watson is booted up, the 15TB of total RAM are loaded up, and thereafter the DeepQA processing is all done from memory. According to IBM Research, "The actual size of the data (analyzed and indexed text, knowledge bases, etc.) used for candidate answer generation and evidence evaluation is under 1TB." For performance reasons, various subsets of the data are replicated in RAM on different functional groups of cluster nodes. The entire system is self-contained, Watson is NOT going to the internet searching for answers.