Archive for the ‘Big Data and Hadoop’ Category

Container Security using Artificial Intelligence & Machine Learning (AI/ML)

January 14, 2020 Leave a comment


The mayfly is an insect found in the Midwestern USA and Canada.  The existence of this  mayfly is ephemeral, its adult life is no more than 24 hours!  What does this have to do with containers?  Just like the mayfly a container is light-weight and has a lifespan that ranges between a few minutes and a few hours.  For instance, Netflix the online media-services provider runs over 1000 microservices in containers and most of these containers have a lifespan of 24 hours.


Why should you care about container security?  Tesla the auto-maker was in the news recently when hackers took over an unsecured Kubernetes console to run scripts to mine digital coins at Tesla’s expense.  This put at risk Tesla telemetry, mapping, and vehicle servicing data not to mention attracting unwanted bad publicity.

The short lives of containers means that it is not feasible for a DevOps or SecOps team to create a manual profile for a container.  So how should they secure containers? AI/ML based algorithms may provide a solution.   AI/ML algorithms have to be trained using large amounts of data on how the container’s behavior changes when the application environment changes.  Vendors like Sysdig will tell you that the way to train the algorithm is by using their hosted SaaS offering Sysdig Secure.  Sysdig Secure will provide DevOps with a snapshot of the container’s behavior and the DevOps or SecOps team can then create a run-time security policy for this container.  To enable this Sysdig provides a rule builder dubbed “Falco Rule Builder”.

Is Sysdig unique in using ML?  No, other competitors likeTwistLock also use ML to combine knowledge of the image and how it is deployed to understand what they term an “application’s DNA”.  This allows them to automatically build rules to compare this DNA against what a container is actually doing.

In conclusion, container security is getting so data intensive, that using AI/ML is the only logical choice.  Stay tuned for more container specific blogs.



Categories: Big Data and Hadoop

Cloud-native applications, Microservices and Containers

January 6, 2020 Leave a comment


In days bygone when terms like “cloud-native” were not part of IT lexicon, a traditional eCommerce application might have had a client application (traditional web app using HTML) talking to a server process where the web application could consist of modules like Identity, Catalog, Ordering, Basket, Marketing and Location.  All these server processes would use a single relational database.  Over time companies realized that monolithic application made maintenance cumbersome and the introduction of new functionality challenging.

In addition, traditional client web applications were joined by Mobile apps, Single Page Applications (SPA) web apps.  In this new model, the web application communicated via an API gateway to individual microservices for tasks like identify, Catalog, Ordering, Basket, Marketing and Locations.  Rather than relying on a single Relational database, this cloud-native application now had each micro-service using its own data store which could be anything ranging from a Relational to a NoSQL database.  These microservices in turn communicated with an event bus which acted as a publish/subscribe channel.

These micro-services were written using a variety of language and frame-works and ended up being packaged in light-weight containers rather than in virtual machines.  Now that each micro-service was maintained independently there was more efficient life cycle management of the entire application.

Containers were an evolution of virtual machines (VM).  Unlike a VM the containers didn’t install anything on the Operating system, they had shorter lifetimes, increased densities per host and a containerized application could start up almost instantly.

Security concerns around containers gave rise to an ecosystem of companies like TwistLock who identifies the expected behavior of a container and creates a white-list of processes, networking activities and storage practices so that any deviation from the baseline could be flagged as potentially malicious behavior.

getaway car

Other ecosystem vendors like Polyverse took advantage of the fact that a container could start in a fraction of a second to relaunch containerized applications in a “good state” every few seconds.  This meant that a potential hacker wouldn’t have enough time to exploit an application running in the container.  Reminds you of jewel thieves using multiple getaway cars doesn’t  it?  Stay tuned for more on containers, Docker, Kubernetes next..

Categories: Big Data and Hadoop

Blockchain and its implications

February 17, 2018 Leave a comment

What do mangoes, diamonds and orangutans have in common?  A technology called blockchain. How is that, you ask?  Read-on..


What is blockchain?

Banks which were the trusted middleman of yore gave way to virtual middlemen – exchanges like eBay.  As technology and human aspirations evolve eBay might give way to OpenBazaar (based on Ethereum blockchain) which cuts out the middleman and connects buyers directly to sellers.

The Economist describes blockchain as “a shared, trusted, public ledger that everyone can inspect, but which no single user controls.”  Bettina Warburg describes blockchain as a public registry, a decentralized database which stores a record of assets and transactions across a peer to peer network. Transactions are secured via cryptography, the transaction history is locked in blocks of data which are cryptographically linked and secured.  Record are replicated on every computer using the network.

Think of blockchain as a global platform which stores ownership of assets like home titles, financial contracts etc.. A blockchain has 3 components: a distributed network, a shared ledger and digital transactions

What are use cases for blockchain?

Binding contracts which don’t need a third party enforcer. Today you can contest a charge on your personal credit card but what if you want this level of personal enforcement without a credit card?  This is where blockchain could help.

Another use case relevant to our times is in identifying the source of food contamination during a food safety recall.  Governments can use blockchain as a tool against money laundering by criminal enterprises.

Blockchain provides a way to combat real-estate fraud in emerging markets where unscrupulous people may alter property records thus defrauding legitimate buyers. If property records were “tamper proof” and stored in a block chain then it becomes easier for a title insurer to access the records so they may clear a title.  Buyers could buy property sight unseen knowing that they are less likely to be defrauded.

Today a music content creator doesn’t have the flexibility to change prices for music by tiers of consumers as middlemen like youtube or spotify set the rules. By cutting out the middleman, blockchain empowers the music creator and improves the content creator’s chances of personally profiting from their creation.  Case in point: Imogene Heap released her music on the blockchain

Retail use cases for blockchain


Walmart used blockchain to track the movement of Mexican mangoes in 2.2 sec vs 7 days in trials. This could help greatly when there is an e coli or salmonella outbreak that could seriously impact children or adults with weakened immune systems by helping retail giants like Walmart identify the source of contaminated food before it does its damage.

Eco friendly implications of block chain?

monkey-orangutan-animal-face-52530As a consumer, you could potentially ensure that the palm oil used to fry your chips is not derived from a place like Borneo or Sumatra where orangutans are slaughtered mercilessly to clear the forest so palm trees may be planted.

You could confirm that the diamonds you bought for an engagement ring are not “blood diamonds”– mined in a war zone to finance an insurgency using gullible child soldiers as canon fodder.

chocolateAs a chocoholic you could confirm that dark chocolate bought in an upscale chocolatier is not tainted by cadmium or lead from being sourced in countries which don’t have lead-free gasoline

You can rest easy knowing that the shrimp you bought at a seafood store wasn’t derived from slave labor in Thailand.

Manufacturers of pet food can quickly identify the source of contamination in tainted and potentially poisonous pet-food before unsuspecting animals pay the price.

As you see, the potential benefits are endless.  Blockchain might yet prove to be the technology that changes our world beyond our wildest dreams..

Categories: Big Data and Hadoop

Predictive analytics & IoT in Healthcare

June 28, 2015 Leave a comment

GE CT scannerA Computerized Tomography (CT) scanner uses ionizing radiation in small doses to produce a diagnostic image – a cross sectional image of the human body. Increase the radiation dose above the minimum required level and you risk causing cancer in the patient. How do you find that right balance of minimum dose and optimal diagnostic image?

Consider how one CT scanner maker GE achieves this balance – Each GE CT scanner is connected to a web based tool called GE Dosewatch™ which gives hospitals a web-based radiation dose monitoring system that tracks a patient’s exposure to radiation from imaging devices. This means clinicians can reduce the cumulative radiation dose produced by a series of imaging procedures, while still delivering the image quality needed to diagnose and treat cancer. DoseWatch uses GE Predix™ (GE’s software platform for the industrial internet) which in turn bundles Pivotal software. Gazzang provides encryption and key management for the Pivotal app that is embedded within GE Predix.  You may wonder how secure wireless communications is achieved for such a solution? GE partners with AT&T and Verizon who aim to deliver a global SIM for secure machine-to-machine communications.

Meanwhile GE’s competitors namely Siemens and Toshiba are not sitting idle.  While GE partners with Pivotal, Siemens partners with Teradata and is deploying Teradata Unified Data Architecture (data warehouse appliance, discovery platform, Hadoop appliance) for a big data lake. Siemens also partners with SAP to use the HANA Cloud Platform (HCP) as the basis of its own cloud to derive insights from IoT machine data. Siemens has its own deviceWISE IOT Cloud software which appears to be their answer to GE’s Predix. Siemens has also invested in CyberFlow Analytics to secure the IoT.   Not to be outdone Toshiba has partnered with Microsoft so consumers with sensor-enabled Toshiba devices can access predictive analytics over Microsoft Azure IoT cloud infrastructure.  This intersection of healthcare, IoT, big data and predictive analytics is just a scratch on the surface of what is to come in the years ahead.


Cyber-security for IoT in Healthcare

June 26, 2015 Leave a comment

Cisco Systems predicts that 50 billion devices will be connected to the internet by the year 2020.  While the actual number is debatable it is a fact that today billions of devices are generating a cacophony of sensor data.  In the field of consumer healthcare, consider the Fitbit which monitors heart rates and sleep patterns. heart monitor It collects PIA information – names, email addresses, phone numbers, payment account info, height, weight and other biometric information and sends out location data 24×7 using Bluetooth technology.  Since most of the user data is sent over HTTP protocols, it is susceptible to hacking as explained here.  Fitbit relies on 3rd parties to protect this consumer data and since the data it collects is not officially termed as Personal Health Information (PHI), it is not bound by government regulations like HIPAA.  The same is true for products like NikeFuel.

Assume you are looking at the other end of the spectrum, an invalid patient confined to his/her home and using a programmable thermostat like NEST.  NestIt has been proven that NEST can be hacked.  In principle a cyber-attacker could subject the patient to extremes of heat and cold using their own home’s heating/cooling system!   Granted you need physical access to the NEST device – but this can be easily obtained by contractors, painters, cleaning crew!

Consider devices like insulin pumps and continuous glucose monitors.  These can be hacked by cyber-attackers who could potentially release an excess dose of insulin causing a severe drop in blood sugar levels resulting in the patient being rendered unconscious.

Security concerns are not limited to wearable devices and devices implanted in the patient’s body as a cardiac defibrillator at a place of work could be hacked to deliver excessively high levels of shock resulting in death.


Why is healthcare more susceptible to cyber-attack?  One reason is that unlike credit card hacks which can be spotted almost instantaneously by sophisticated fraud detection algorithms used by the major credit card vendors like Visa, Amex and Mastercard, health care related hacks could go undetected for a long time.  This gives the cyber criminals the luxury of doing harm or selling patient information on the black market without having to watch their backs.

What are healthcare companies doing to address this?  GE acquired Wurldtech to enhance cybersecurity for its devices deploying sensors.  While Wurldtech has focused on protecting Supervisory Control & Data Acquisition (SCADA) systems – which are IT systems used to manage power plants and refineries, the same technology could be re-purposed to protect GE wearable devices from cyber-attacks.   GE’s competitor Siemens has invested in cyber-security startups like CyActive and CounterTack.  Outside healthcare GE has a range of businesses whose products rely on sensors for their reliable operation:  air craft engines, gas turbines, locomotives. Hence GE purchased a 10% stake in Platform-as-a-Service (PaaS) vendor Pivotal and developed its own Predix software (essentially an operating system for industrial equipment) and plans to run Predix over Pivotal’ data lake. The goal is to derive insights which can predict and prevent problems before they occur.  While the big vendors like GE and Siemens are taking the right measures, the plethora of emerging wearable device makers must follow their lead or risk putting them and us at considerable risk in the years to come.

Data Lakes & Hadoop analytics

April 29, 2015 Leave a comment

Glacier lakeInitially coined in the Hadoop context, a “data lake” referred to the ability to consolidate data (structured and semi-structured) from different data silos (say from CRM, ERP, supply chain) in its native format into Hadoop.  You didn’t need to worry about schema, structure or other data requirements until the data needed to be queried or processed. For a typical eCommerce site it might be transactional data, data from marketing campaigns, clues from the online behavior of consumers.  The goal of an eCommerce site might be to analyze all this data and send out targeted coupons and other promotions to influence prospective buyers.

Later  software companies like Pivotal appropriated this term. The storage vendor behind Pivotal – EMC came up with its own marketing spin on a data lake which involved EMC ViPR with Isilon storage on the back-end.  Not to be outdone HDS acquired Pentaho and could make a claim that Pentaho actually coined the term “data lake”.   Microsoft marketing uses the term “Azure data lake” to refer to a central repository of data where data scientists could use their favorite tools to derive insights from the data.  The analyst firm Gartner cautioned that the lack of oversight (“governance” if you want to use big words) of what goes into the data lake could result in a “data swamp”.  Not to be outdone, Hortonworks (the company selling services around Apache Hadoop) counters with the argument that technologies like the Apache Knox gateway (security gateway available for Hadoop) enable a way to democratize access to corporate data in the data lake while maintaining compliance with corporate security policies.

Who actually uses a data lake today? Besides Google and Facebook? I’d be curious to know.  In the interim deriving insights via Hadoop analytics on data wherever it resides (whether it be on a NetApp FAS system or on some other networked storage) may be the right first step.  I’d welcome input from readers who use data lakes today to solve business related problems.

Internet of Things (IoT) and storage requirements

September 23, 2014 Leave a comment

On the news tonight I learned that the Indian spacecraft “Mangalyaan” built at a cost of $73.5M had reached Mars orbit after a 11 month trek, making India the first nation to succeed in its maiden attempt to send a spacecraft to Mars.  Granted spacecraft and Mars Rovers aren’t connected to the internet so can’t be categorized under the Internet of Things (IoT) today but who knows what can come to pass years from now…


The analyst firm Gartner claims that 26 billion IoT-ready products will be in service by the year 2020.  Sensors are everywhere (dare I say “ubiquitous”?) – from the helmets worn by football players to aircraft engines, from smart-watches to internet tablets, from luxury BMWs to microwave ovens, from smart thermostats made by “Nest”, from 9 million smart meters deployed by PG&E right here in California.    Companies like Fedex would like to embed sensors on all your packages so you can track their route to a destination from the comfort of your home office.  Appliance vendors like Samsung, LG, Bosch, Siemens are embedding sensors in your consumer appliances – LG and Bosch would like to take photos of your fast emptying refrigerator and send an email with a shopping list to your smartphone.  Granted this brings us to the realm of “nagging by appliance” ..   So it is going to be a cacophony of sensor data which thankfully we humans can’t hear but we will need to analyze, understand and act on.

Sensors by themselves aren’t very useful.  When was the last time you took notice of an auto-alarm blaring away on a busy street?  Sensors need actuators (devices which can convert the electrical signal from a sensor into a physical action)  Sensors may provide real time status updates in the form of data but there needs to be tools in place to analyze the data and make business decisions.  Examples of vendors who build such tools:

  • Keen IO – offers an API for custom analytics – what this means for you and me is that if a business isn’t happy with analytics using existing tools nor wants to built an entire analytics stack on their own, Keen IO meets them half way and allows your business to collect/analyze/visualize events from any device connected to the internet.
  • ThingWorx (acq by PTC whose head coined the term “product as a service”). He has a point here. In the past we signed up with cellular providers for 2 years of cellphone coverage – now vendors like Karma are offering a better model where you pay-as-you-go for Wifi – effectively relegating 2 year cell phone contracts to a thing of the past.
  • Axeda (acq by PTC) – whose cloud makes sense of machine generated messages from millions of machines owned by over 150 customers.
  • Arrayent whose Connect Platform is behind the toy company’s Mattel’s IoT Toy network enabling 8 year old girls to chat with other 8 year olds over the Mattel IM-Me messaging system
  • SmartThings (acq by Samsung) who offer an open platform to connect smart home devices
  • Neul (acq by Huawei) specialized in using thin slices of the spectrum to enable mobile operators to manage the IoT and profit from it.
  • Ayla Networks – hosts wifi based weather sensors for a Chinese company.

On the networked storage side, each storage vendor has a different view:  DataGravity recommends a selective approach to deciding which pieces of sensor data  (from potentially exabytes of unstructured data) to store.  EMC recommends customers buy EMC Elastic Cloud Storage appliances and store all sensor data on it (discarding nothing),  Nexenta claims that “software-defined-storage” is the savior of the IoT, SwiftStack claims that cloud-based IoT using OpenStack Swift is the way to go.

I think it is naïve to assume that all IoT data will need to be preserved or archived for years to come.   Data from sensors in aircraft engines may need to be preserved on low cost disk storage in the event of future lawsuits resulting from air crashes but there is little value in preserving a utility’s smart meter data for 7 years for regulatory reasons if the data can be analyzed in real time to understand consumer usage patterns, enable tiered pricing and the like. By the same reasoning does any vendor really need to preserve sensor data from my $100 home microwave unit for years to come?  However I see cloud providers focusing on IoT to need SSD as well as HDD based networked storage.

How about you?  What is your view wrt networked storage and IoT?  What unique capabilities do you feel need to be delivered to enable IoT in the cloud?  Any and all civil feedback is welcome.

Linux containers, Docker, Flocker vs. server virtualization

August 31, 2014 1 comment
Analogy to Linux containers

Analogy to Linux containers

In the past if your goal was to isolate applications (from a memory, disk I/O, network I/O resource and security perspective) on a physical server you had one choice – run your application over a guest OS over a hypervisor.  Each VM had a unique guest OS on top of which you had binaries/libraries and on top of these your applications.  The flip side of this solution is that if you ran 50 applications on a physical server you needed 50 VMs over the hypervisor with 50 instances of guest OS.   Fortunately for developers, Linux containers had a recent resurgence and offer you another alternative.  If you are an application developer and want to package your source code in Linux containers with the goal of being able to run it on any bare metal server or on any cloud provider, Docker (a startup with less than 50 employees) offers you a way to make a Linux container easy to create and manage.

Benefits of Docker container technology:

  • No need for many different guest operating systems on the same server. Instead you run a Docker engine over a Linux kernel (v3.8 or higher) on a 64-bit server and run your apps on binaries/libraries running over the Docker engine. This allows you to do away with the relatively expensive VMware licensing per server.
  • Lower performance penalty than with traditional hypervisors (Red Hat KVM, Citrix Xen or VMware ESXi).

This is ideally suited for apps that are stateless and do not write data to a file system.  At a high level, Docker containers make it easy to package and deploy applications over Linux.  Think of a container as a virtual sandbox which relies on the Linux OS on the host server without the need for a guest OS.  When an application moves from a container in host A to a container in host B the only requirement is that both hosts must have the same version of the Linux kernel.

You may ask, how do containers differ from virtual machines?  Containers and virtual machines (VM) both isolate workloads on a shared host.  Some would argue that containers don’t provide the levels of security one could have with using a VM.  VMs also allow you to run a Windows app on a Linux kernel something which is not possible with Docker containers.  Container technology is actively used by Google so much so that Google  released  Kubernetes into the open source community to help manage containers.

Rather than follow the model of the taxi industry which bitterly attacked ride sharing startup Uber, VMware is taking the high ground and embracing Linux containers and its proponent Docker – perhaps recalling Nietzsche’s words “That which does not kill us makes us stronger.”

You may wonder  – why aren’t enterprises embracing containers and Docker?  One issue with Linux containers is that if your application in the container needs access to data, the database hosting that data has to be housed elsewhere.  This means the enterprise has to manage two silos – the container itself and the database for the container.  This problem could be solved by giving every application running in a container its very own data volume where the database could be housed.  ClusterHQ,  an innovative startup offers  “Flocker” – a free and open source volume and container manager for Docker which aims to make data volumes in Direct Attached Storage (DAS) portable.  ClusterHQ’s future roadmap includes continuous replication, container migration and Distributed Resource Scheduler (DRS) like services – which sound eerily similar to the capabilities offered by VMware vMotion or DRS – causing VMware to put the brakes on an all-out embrace of the Docker ecosystem.  Perhaps VMware strategists recalled Billy Livesay’s song “Love can go only so far”

Another startup Altiscale is looking into the problem of how to run Hadoop applications within Docker containers.  In view of all this we can be sure of one thing, Linux containers and Docker are here to stay and its just a question of when (not if) enteprises begin adopting this new way of achieving multi-tenancy on a physical server.

PaaS – RedHat OpenShift or Pivotal One?

March 1, 2014 1 comment

Metaphor for PaaSIf you want to write applications but your IT staff isn’t equipped to handle maintenance of the underlying stack comprising Linux, Apache, MySQL, PHP, Python or Perl then Platform as a Service (PaaS) may be right for you.  For instance, you may be tasked by your employer with developing web applications involving ecommerce shopping carts and you may choose to use a tool like Ruby on Rails, in such cases you’re likely to gravitate to PaaS platforms like that of Heroku (now owned by

If you are an application developer who prefers to develop applications in Node.js, Ruby, Python, PHP, Perl or Java but don’t want to manage the LAMP stack on your own, you might consider RedHat OpenShift Online PaaS.  OpenShift PaaS is built on OpenShift Origin (which is open source), Red Hat Enterprise Linux (RHEL) and JBoss using the KVM hypervisor all hosted in Amazon Web Services (AWS). Unlike hardware-only bundles like the VCE Vblock™ or the Netapp FlexPod which combine compute, storage and networking into one product SKU, the PaaS service RedHat OpenShift Online adds the OS and middleware to the afore mentioned combination of compute, storage and networking.  However instead of selling you a hardware/software product to install/use at your site, OpenShift Online offers it all as a cloud service or PaaS.  If you like the PaaS concept but prefer to run it within your own datacenter RedHat offers you OpenShift Enterprise which is software designed for on-premise use.

Another option Pivotal One appears to enable development of web applications on scale but using a big data back-end.  Pivotal One uses the Spring framework and Cloud Foundry (an open source PaaS) as building blocks.  It offers many services including a Hadoop (HD) service, an analytics service and a MySQL service.  If you want to develop Hadoop applications but don’t want to deal with issues like deployment, security and networking then a managed service like HD Service might be right for you.  In addition you get the option to use familiar SQL queries to analyze petabytes size data sets stored in the HDFS layer of Hadoop.  Potentially, a big manufacturer like General Electric may decide that it wants a cloud based big data analytics platform to ingest data from sensors in millions of GE consumer devices (say microwave ovens, refrigerators, washing machines) deployed world-wide.  The idea being to ingest then analyze this sensor data (big data) in the cloud to give consumers advance warning of potential failures (microwave leaks?) or even entice consumers with an upgrade offer for the latest GE model.  Not so far-fetched considering that GE is an investor in Pivotal

What about the downside to PivotalOne?  In my mind it would be potential for vendor lock-in.  After all Pivotal is a spin-off of VMware and VMware’s parent EMC has had a death grip on proprietary storage over the years despite grudgingly paying lip service to new concepts like software defined storage (SDS) via projects like ViPR.  Are RedHat and Pivotal the only PaaS options out there?  Definitely not, other vendors like InfoChimps, Apprenda, CloudBees are also worth evaluating. So what do you think? Is PaaS in your cards?

OpenStack and solid state drives

October 20, 2013 Leave a comment

If you are a service provider or enterprise considering deploying private clouds using OpenStack (an open source alternative to VMware vCloud) then you are in the company of other OpenStack adopters like PayPal and eBay.  This article considers the value of SSDs to cloud deployments using OpenStack (not Citrix CloudStack or Eucalyptus).

cloudsBlock storage & OpenStack: If your public or private cloud is supporting a virtualized environment where you want up to a Terabyte of disk storage to be accessible from within a virtual machine (VM) such that it can be partitioned/formatted/mounted and stays persistent till the user deletes it, then your option for block storage is any storage for which OpenStack Cinder (an OpenStack project for managing storage volumes) supports a block storage driver.  Open source block storage options include:

Proprietary alternatives for OpenStack block storage include products from IBM, NetApp, Nexenta and SolidFire.

Object storage & OpenStack: On the other hand if your goal is to access multi terabytes of storage and you are willing to access it over a REST API and you want the storage to stay persistent till the user deletes it, then your open source options for object storage include:

  • Swift – A good choice if you plan to distribute your storage cluster across many data centers.  Here objects and files are stored on disk drives spread across numerous servers in the data center.  It is the OpenStack software that ensures data integrity & replication of this dispersed data
  • Ceph  – A good choice if you plan to have a single solution to support both block and object level access and want support for thin-provisioning
  • Gluster – A good choice if you want a single solution to support both block and file level access

Solid state drives (SSD) or spinning disk?

An OpenStack Swift cluster that has high write requirements would benefit from using SSDs to store metadata.  Zmanda (a provider of open source backup software) has run benchmarks to prove that SSD based Swift containers outperform HDD based Swift containers especially when the predominant operations are PUT and DELETE.  If you are a service provider looking to deploy a cloud based backup/recovery service based on OpenStack Swift and each of your customers is to have a unique container assigned to them, then you stand to benefit from using SSDs over spinning disks.

Turnkey options?

As a service provider if you are looking for an OpenStack cloud-in-a-box to compete with Amazon S3 consider vendors like MorphLabs.   They offer turn-key solutions on Dell servers with storage nodes running NexentaStor (commercial implementation of OpenSolaris and ZFS), KVM hypervisor, VMs running Windows or Linux as the guest OS all on a combination of SSDs and HDDs.  The use of SSDs allows MorphLabs to claim lower power consumption and price per CPU as compared to “disk heavy” (their term not mine) vBlock (from Cisco & EMC) and FlexPod (from NetApp) systems.

In conclusion if you are planning to deploy clouds based on OpenStack, SSDs offer you some great alternatives to spinning rust (oops disk).

Categories: Big Data and Hadoop