While VMware moved away from client hypervisors, they had to agree that an end user compute device strategy must encompass non-VDI. Their Mirage technology can be considered desktop virtualization, but it is not a client hypervisor. Client hypervisor vendors such as Citrix (who subsumed Virtual Computer’s NxTop) , MokaFive, Parallels, Virtual Bridges and joined by Zirtu. Organisations like WorldView look to innovate on desktop vitualization through containers rather than full virtualization.
Tablets. Touch Screen capable laptops. Hybrid devices with detachable screens. The netbook might be dead, or they could just be resting. The presence of tablets has undeniably shaken the netbook market but businesses still need powerful, capable laptops.
Bring Your Own Pencil aside – there is still a need to manage “stuff”: still large and small organisations who need to manage the delivery of IT including the end device. The question remains how are devices, and the all important data and applications on them, managed? Hosted and session based desktops have their place – but offline capable device requirements will remain. Is Intelligent Desktop Virtualization the same as client hypervisors?
In, Building a Management Stack for Your Software Defined Data Center, we proposed a reference architecture for how one might assemble the suite of management components that will be needed to manage a Software Defined Data Center (SDDC). In this post we take a look at the need for that management suite to be supported by a multi-vendor big data datastore, and take a look at who might provide such a data store.
The Need for a Multi-Vendor Management Data Store in Your SDDC Management Stack
So why you ask, will a SDDC require a set of management products that will in turn require a multi-vendor big data back end. The reasons are as follows:
The whole point of moving the configuration and management of compute, memory, networking and storage out of the respective hardware and into a software layer abstracted from the hardware is to allow for configuration and management changes to happen both more quickly and more automatically (which means very quickly). Each configuration change or policy change is going to create a blizzard of management data.
If you look at all of the horizontal boxes in our Reference Architecture (below) each one of them along with the vertical IT Automation box will be generating data.
The rate of change in the SDDC will be high enough so as to require fine grained and very frequent monitoring at every layer of the infrastructure.
Just combining the number of layers with the rate of change with the need for fine grained and high frequency monitoring (5 minutes is an eternity) creates a big data problem.
Finally, the need to be able to to cross layer root cause analytics (where in the software or hardware infrastructure is the cause of that application response time problem) means that the root cause analysis process has to cross domains and layers. This in and of itself calls for a common data store across management products.
The Software Defined Data Center Management Stack Reference Architecture
Who Could Provide the Multi-Vendor Big Data Repository?
There are two basic criteria for being able to provide such a repository. The first is that you have to have one, or have the intention to build one. The second is that since it is multi-vendor, you have to have the technical capability to, and the business model to partner with the vendors whose products will feed this datastore. The rest of this post is entirely speculative in nature as it is based upon who could do what, not upon who is doing what. To be clear, no vendor listed below has told us anything about what they intend to do in this regard. The rest of this post is based entirely upon what people are shipping today and the author’s speculation as to what might be possible.
If there is one vendor who has an early lead in filling this role it would be Splunk. Splunk is in fact the only vendor on the planet from whom you can purchase an on-premise big data datastore, which is today based upon shipping and available products being populated by data from management products from other vendors. In fact if you go to SplunkBase do searches on things like APM, monitoring, security, and operations you will find a wide variety of Splunk written and vendor written applications that feed data into Splunk. Now it is important to point that today, when a vendor like ExtraHop Networks or AppDynamics feeds their data into Splunk they are not making Splunk in THE back end datastore for their products. They are just feeding a subset and a copy of their data into Splunk. But this is a start, and it puts Splunk further down this road than anyone else. Needless to say, if the vision of the multi-vendor datastore is correct, and Splunk is to become the or one of the vendors who provides this, then Splunk is going to have to entice a considerable number of software vendors to trust Splunk to perform a role that no vendor today trusts any other vendor to perform.
“Another area of focus for an open networking ecosystem should be defining a framework for common storage and query of real time and historical performance data and statistics gathered from all devices and functional blocks participating in the network. This is an area that doesn’t exist today. Similar to Quantum, the framework should provide for vendor specific extensions and plug-ins. For example, a fabric vendor might be able to provide telemetry for fabric link utilization, failure events and the hosts affected, and supply a plug-in for a Tool vendor to query that data and subscribe to network events”.
Needless to say, it is highly unlikely that VMware would choose to make the current datastore for vCenter Operations into the “framework for the common storage and query of real time performance data“. Rather it is much more likely that VMware would build its own big data datastore with the people and the assets that VMware acquired when VMware acquired the Log Insight technology and team from Pattern Insight. VMware therefore clearly has the technology building blocks and the people to pull this off. You could also argue that they would not have make this acquisition if there were not intentions to go at least somewhat in this direction. The key challenge for VMware will then be the multi-vendor part. VMware has no relationship of technical cooperation with any other management software companies other than Puppet Labs, so this is clearly an area where VMware has a long way to go.
New Relic is the hands down market leader for monitoring Java, .NET, Ruby, Python, and PHP applications in the cloud. New Relic offers cloud hosted APM as a Service and has gone in four years from a brand new company to now having more organizations using its product than the rest of the APM industry combined. New Relic recently raised $75M from top tier investors and is rumored to be positioning itself for an IPO in the 2014-2015 timeframe. New Relic already makes its data available to third parties in its partner program via a REST API. It is not much of a stretch for New Relic to consider becoming the management platform in the cloud, partnering with adjacent vendors and becoming a vendor of the multi-vendor cloud datastore. Again all of this is pure speculation at this point.
The Pivotal Initiative
The Pivotal Initiative is a new company formed with assets and people from EMC and VMware lead by former VMware CEO Paul Maritz. These assets consist of the application platform PaaS products from VMware (Gemfire, Spring, vFabric and CloudFoundry), and the big data assets fromEMC (GreenPlum). The stated ambition is to deliver a way to build and deploy big data applications that is radically better than the incumbent method and tackle giants like IBM, Microsoft, and Oracle in the process. This means that the focus of both the application development assets and the big data assets is most likely to be upon solving business problems for customers, not IT management problems for customers. However, it would not be inconceivable for a third party company to license these technologies from Pivotal and build an offering targeting the multi-vendor management stack use case.
Consider the possibility that this multi-vendor big data datastore is in fact non on-premise, but in the cloud. If you are willing to consider that possibility, then it is not much of a stretch to consider that CloudPhysics a vendor with cloud hosted (delivered as a service) operations management solutions might step into this fray. One of the key reasons that CloudPhysics may be able to provide something of extraordinary value is that the company has a strategy of applying Google quality analytics to Google size data sets. The analytics come from a world class team of people some of whom previously worked at Google. The data today is collected by virtual appliances installed at CloudPhysic’s customer sites (in their respective VMware environments). If CloudPhysics is already collecting data across customers and putting it in its cloud, it is not too huge a stretch to consider the possibility that other vendors who also deliver their value as a service could partner up with CloudPhysics, combine their respective sets of data, and produce a 1+1=3 scenario for joint customers.
AppNeta is today a market leading vendor of a cloud hosted service, PathView Cloud, that measures the performance of the wide area network in between the users and branch offices of an enterprise and the enterprises back end data center. The back end is a true big data back end, built around true big data technologies. AppNeta is branching out into APM with its TraceView offering. But network performance data and application performance data are just parts of the compete set of data that will get generated by the SDDC and about the SDDC by various management products. AppNeta does not today have a partner program to attract third party data to its management data cloud, but who knows what the future holds.
Boundary is an APM vendor with a cloud hosted big data back end that today focuses upon collecting the statistics from the network layer of the operating system that support applications running in clouds. If you think of New Relic as the vendor who is monitoring your application in the cloud, you can think of Boundary as the vendor who should be monitoring the interaction of your operation system underlying your application with the cloud. Boundary has no partner program today, and no ability to add third party vendor data to its cloud datastore today, but again who knows what the future might hold.
The SDDC and the Cloud are going to require a new SDDC Management Stack that will need to be based upon a multi-vendor big data datastore. There will likely be on-premise and cloud hosted version of these datastores. Splunk, VMware, New Relic, The Pivotal Initiative, CloudPhysics, AppNeta, and Boundary are all excellent hypothetical suppliers of such a datastore.
Soon the backup power will be available for our new datacenter and the redesign to make use of VMware vCloud Suite is nearing completion. Soon, our full private cloud will be ready for our existing workloads. These workloads however now run within a XenServer based public cloud. So the question is, do we stay in a poorly performing public cloud (mentioned in our Public Cloud Reality series) or move back to our own private cloud? As the Clash put it “Should I Stay or Should I Go Now.” Continue reading Public Cloud Reality: Do we Stay or Do We Go?→
Let us pick up where we left off in part III in our look beyond the covers to help answer the question of which is the best HDD to use.
Power and energy
Power consumption will vary based on size and type of HDD, along with different usage. For example, during power-up there is a larger amount of energy being used vs. when the drive is idle (not reading or writing) yet still spinning, or actively reading and writing. With intelligent power management (IPM), inactive drives can go into lower power usage modes with variable performance. IPM includes the ability to vary the amount of power used to level of performance with different steps or levels. This is different from some first generation MAID solutions based on desktop class drives that were either on or off with subsequent performance issues. While an HDD requires power to spin the platters, once those are in motion, less power is required; however, energy is needed for the read write heads and associated electronics.
This leads to a common myth or misperception that HDDs consume a lot of energy because they are spinning. There is energy being used to keep the platters moving, however power is also required for the electronics to manage the drives interface, read write heads and other functions. With IPM leaving the drive spinning or reducing the rotational speed can help save power, so to can disabling or putting into low power mode the associated processors and control electronics.
As a comparison SSD, drives are often touted as not drawing as much energy compared to an HDD, which is true. However, SSDs do in fact consume electricity and get warm as they also have electronics and control processors similar to HDDs. If you do not believe this, put an SSD to work and feel it over time as it heats up. Granted that is an apple to oranges comparisons, however my point is that there is more to energy savings with HDDs than simply focusing on the rotational speeds. Speaking of energy savings, typical enterprise class drives are in the 4 to 8 watts or a fraction of what they were only a few years ago. Notebook, laptop and workstation drives can be in the single watt to a few watts in power usage range. Note that these numbers may be less than what some will talk about when comparing SSD and HDDs, or trying to make a point about HDDs and power consumption. The reason is this is a reduction from where just a few years ago when drives were in the “teens” in terms of watts per drive. For performance or active drives, compare those on a cost per activity per watt such as cost per IOP per watt, for inactive data then cost per capacity per watt can be relevant.
Given the large amount of data that can be stored on an HDD along with compliance and other concerns, drive level security is becoming more common. There are different types of drive level encryption including self-encrypting devices (SEDs) with some supporting FIPS level requirements. Drive level encryption depending on implementation can be used to off-load servers, workstations or storage systems from performing encrypt and decrypt functions.
The space capacity of the drives is determined by the aerial density (how many bits in a given amount of space) per platter, the size of the platter (3.5” are larger than 2.5”) and number of platters. For example at the same aerial density, more bits and bytes exist on a 3.5” vs. 2.5” device, and by adding more platters (along with read/write heads) the resulting taller height drive has even more space capacity. Drive space capacities include 4TB and smaller for 3.5” devices and TB plus sized for various 2.5” form factors. Watch out for “packaging” games where for example a drive is offered as say 4TB that are actually two separate 2TB drives in a common enclosure (no RAID or NAS or anything else).
The super parametric barrier effects keeps being delayed, first with perpendicular recording, now with shingled magnetic recording (SMR) and heat assisted magnetic recording (HAMR) all in the works. The super parametric barrier is the point where data bits can no longer safely (with data integrity) be stored and later used without introducing instability. Watch for more on SMR and HAMR in a later post when we look at new and emerging trends.
Speaking of space capacity, ever wonder where those missing bits and bytes disappeared on a HDD or SSD? First there is how it is measured, meaning decimal or base10 vs. binary base 2 for example one Gigabyte (GB) being one billion bytes vs. 1,024,000,000.00 bytes. These space capacities are before RAID or hypervisor or operating system and file system formatting overhead are added. There is also reserved space for bad block re vectoring which can be thought of as hot spare blocks for when the drive (HDD or SSD) detects something going bad. In addition to the bad block areas, there are also some reserved space that you will not be able to access that is kept for drive management, read/write head alignment and other things.
Speaking of large capacity drives, as mentioned earlier, rebuild operations with RAID configurations can take longer given more data to move. Good news is that some RAID systems or solutions can rebuild a 1TB or 2TB drive as fast as or faster than a 9GB drive from a decade ago. The catch is that there are more drives and they are getting larger with 3TB and 4TB shipping and larger ones in the works. Things you can do to minimize the impact of long rebuild times; include selecting the right type of drive that has better endurance, reliability and availability. This could mean that selecting a lower priced drive up front that is not as reliable could cost you down the road. Configuration including RAID level, number of parity drivers, and software, adapter, controller or storage system with ability to accelerate rebuilds can also make a difference.
Another impact of large capacity drives or large numbers of HDDs in general is how to securely erase them when decommissioning. That is assuming you are securely erasing them or taking other safeguards disposition vs. throwing in the garbage or giving them away. Self-encrypting devices (SEDs) normally associated with security can be part of a solution for some environments. Since SEDs can effectively erase the data stored on those by, removing the enablement key, instead of hours or days, for some environments secure erase can be in minutes or less.
There are various warranties on HDDs, those from the manufacture that may be the same as what an OEM or system integrator passes on to their customers. Some HDDs have a manufactures limited warranty of five years while others have shorter terms. Thus while a manufacture may offer a five year warranty, it can be up to the OEM or integrator to pass that warranty on, or in turn provider a shorter duration with different terms or price. Something to think about in terms of HDD warranties is that replacing them can mean sending your old device back in exchange for a new one. If you have sensitive or secure data on those devices, how will they be disposed of? An option is to not leverage return to vendor or manufacture warranties opting for self-disposition, or using self-encrypting devices (SEDs).
This wraps up this post, coming up next in part V we will look at what to use when, where along with other options and some trends.
EMC and VMware’s pivotal moment has officially spun off and the Pivotal Initiative, a big data and cloud platform company is slated to go public according to EMC CEO Joe Tucci while speaking with investors at an event in New York. EMC’s chief strategist and ex-CEO of VMware, Paul Maritz, who is leading the Pivotal Initiative, believes and expects it to be a billion dollar business within the next five years if they can get the $400 million initial investment needed to reach that goal. EMC will own 69 percent and VMware will own 31 percent with 1,250 employees and $300 million in revenue. Continue reading EMC and VMware’s Pivotal Moment→
Microsoft has announced that it will offer System Center Advisor for free to its customers in supporting countries. System Center Advisor is a cloud service that enables IT Professionals to proactively avoid problems resulting from server configuration issues. It can help you resolve issues faster by providing access to current and historical configuration data for a deployment. System Center Advisor can also assist in reducing downtime by providing suggestions for improvement and notifying users of key updates specific to their configuration Continue reading News: Microsoft System Center Advisor now a FREE service→