{"id":744,"date":"2017-04-04T04:38:45","date_gmt":"2017-04-03T20:38:45","guid":{"rendered":"http:\/\/microdium.net\/public\/2017\/04\/04\/cloud-economics-drive-the-it-infrastructure-of-tomorrow\/"},"modified":"2017-04-04T04:38:45","modified_gmt":"2017-04-03T20:38:45","slug":"cloud-economics-drive-the-it-infrastructure-of-tomorrow","status":"publish","type":"post","link":"https:\/\/www.microdium.com\/public\/2017\/04\/04\/cloud-economics-drive-the-it-infrastructure-of-tomorrow\/","title":{"rendered":"Cloud Economics drive the IT Infrastructure of Tomorrow"},"content":{"rendered":"
\"Cloud<\/div>\n
\n

By Louis Imershein, VP Products and Wayne Salpietro, Director of Marketing<\/strong><\/span><\/span><\/p>\n

Permabit Technology Corp<\/strong><\/span><\/span><\/p>\n

The cloud continues to dominate IT as businesses make their infrastructure decisions based on cost and agility. Public cloud, where shared infrastructure is paid for and utilized only when needed, is the most popular model today. However, more and more organizations are addressing security concerns by creating their own private clouds. As businesses deploy private cloud infrastructure, they are adopting techniques used in the public cloud to control costs. Gone are the traditional arrays and network switches of the past, replaced with software-defined data centers running on industry standard servers. <\/span><\/span><\/p>\n

Efficiency features make the cloud model more effective by reducing costs and increasing data transfer speeds. One such feature, which is particularly effective in cloud environments is inline data reduction. This is a technology that can be used to lower the costs of data in flight and at rest. In fact, data reduction delivers unique benefits to each of the cloud deployment models. <\/span><\/span><\/p>\n

Public Clouds<\/strong><\/span><\/span><\/p>\n

The public cloud\u2019s raison d\u2019etre is its ability to deliver IT business agility, deployment flexibility and elasticity. As a result, new workloads are increasingly deployed in public clouds. \u00a0<\/span><\/span>Worldwide public IT cloud service revenue in 2018 is predicted to be $127B.<\/span><\/span><\/span><\/span><\/a> \u00a0 <\/span><\/span><\/p>\n

Data reduction technology minimizes public cloud costs. For example, deduplication and compression typically cut capacity requirements of block storage in enterprise public cloud deployments by up to 6:1. \u00a0These savings are realized in reduced storage consumption and operating costs in public cloud deployments. \u00a0\u00a0<\/span><\/span><\/p>\n

Consider AWS costs employing data reduction; <\/span><\/span><\/p>\n

If you provision a 300 TB of EBS General Purpose SSD (gp2) storage for 12 hours per day over a 30 day month in a region that charges $0.10 per GB-month, you would be charged $15,000 for the storage.<\/span><\/span><\/p>\n

With data reduction, that monthly cost of $15,000 would be reduced to $2,500. \u00a0Over a 12 month period you will save $150,000. \u00a0 Capacity planning is a simpler problem when it is 1\/6<\/span><\/span>th<\/span><\/span><\/sup> its former size. \u00a0Bottom line, data reduction increases agility and reduces costs of public clouds.<\/span><\/span><\/p>\n

One data reduction application that can readily be applied in public cloud is <\/span><\/span>Permabit\u2019s Virtual Disk Optimizer (VDO)<\/a> which<\/span><\/span><\/span> is a pre-packaged software solution that installs and deploys in minutes on Red Hat Enterprise Linux and Ubuntu LTS Linux distributions.\u00a0To deploy VDO in Amazon AWS, the administrator provisions Elastic Block Storage (EBS) volumes, installs the VDO package into their VMs and applies VDO to the block devices represented for their EBS volumes.\u00a0 Since VDO is implemented in the Linux device mapper, it is transparent to the applications installed above it.<\/span><\/span><\/p>\n

As data is written out to block storage volumes, VDO applies three reduction techniques:<\/span><\/span><\/p>\n

    \n
  1. \n

    Zero-block elimination<\/strong><\/span><\/span> uses pattern matching techniques to eliminate 4 KB zero blocks<\/span><\/span><\/p>\n<\/li>\n

  2. \n

    Inline Deduplication<\/strong><\/span><\/span> eliminates 4 KB duplicate blocks<\/span><\/span><\/p>\n<\/li>\n

  3. \n

    HIOPS Compression\u2122<\/strong><\/span><\/span> compresses remaining blocks\u00a0<\/span><\/span><\/p>\n<\/li>\n<\/ol>\n

    \"cloud1\"<\/p>\n

    This approach results in remarkable 6:1 data reduction rates across a wide range of data sets.\u00a0 <\/span><\/span><\/p>\n

    Private Cloud<\/strong><\/span><\/span><\/p>\n

    Organizations see similar benefits when they deploy data reduction in their private cloud environments. Private cloud deployments are selected over public because they offer the increased flexibility of the public cloud model but keep privacy and security under their own control. IDC <\/a><\/span><\/span>predicts<\/a> in 2017 <\/span><\/span><\/span><\/span> $17.2B in infrastructure spending for private cloud, including on-premises and hosted private clouds. <\/span><\/span><\/p>\n

    One problem that data reduction addresses for the private cloud is that, when implementing private cloud, organizations can get hit with the double whammy of hardware infrastructure costs plus annual software licensing costs. For example, Software Defined Storage (SDS) solutions are typically licensed by capacity and their costs are directly proportional to hardware infrastructure storage expenses. Data reduction decreases storage costs because it reduces storage capacity consumption. For example, deduplication and compression typically cut capacity requirements of block storage in enterprise deployments by up to 6:1 or approximately 85%.<\/span><\/span><\/p>\n

    Consider a private cloud configuration with a 1 PB deployment of storage infrastructure and SDS. Assuming a current hardware cost of <\/span><\/span>$500 per TB for commodity server-based storage infrastructure with datacenter-class <\/a>SSDs<\/a><\/span><\/span><\/span><\/span> and a <\/span><\/span>cost of $56,000 per 512 TB for the SDS componen<\/span><\/span><\/span><\/span><\/a>t, users would pay $612,000 in the first year. In addition, software subscriptions are annual, over three years you will spend $836,000 for 1 PB of storage and over five years, $1,060,000.<\/span><\/span><\/p>\n

    The same configuration with 6:1 data reduction in comparison over five years will cost $176,667 for hardware and software resulting in $883,333 in savings. And that\u2019s not including the <\/span><\/span>additional substantial savings in power cooling and space<\/span><\/span><\/span><\/span><\/a>. As businesses develop private cloud deployments, they must be sure it has data reduction capabilities because the cost savings are compelling. <\/span><\/span><\/p>\n

    When implementing private cloud on Linux, the easiest way to include data reduction is with <\/span><\/span>Permabit Virtual Data Optimizer (VDO)<\/span><\/span><\/span><\/span><\/a>. VDO operates in the Linux kernel as one of many core data management services and is a device mapper target driver transparent to persistent and ephemeral storage services whether the storage layers above are providing object, block, compute, or file based access. <\/span><\/span><\/p>\n

    VDO – Seamless and Transparent Data Reduction <\/strong><\/span><\/span><\/p>\n

    \"cloud2\"<\/p>\n

    The same transparency applies to the applications running above the storage service level. Customers using VDO today realize savings up to 6:1 across a wide range of use cases. <\/span><\/span><\/p>\n

    Some workflows that benefit heavily from data reduction are;
    <\/span><\/span><\/p>\n