Data Reduction Removes Cloud Size Restrictions —
You’ve probably already made a commitment to the Cloud because it enables you to flexibly deliver your computing resources more quickly and at a lower cost. Why? Because your customers are steadily shifting towards Cloud-first strategies where they can rapidly adapt to market dynamics as they are no longer bound by legacy computing constraints. Public, private, or hybrid Clouds are choices every C-level executive is making today.
Each Cloud strategy is different. For example, public Cloud is shared with other businesses to yield economies of scale in Amazon Web Services (AWS), Google, Microsoft, while private Cloud is either on-premises or hosted by a public Cloud provider. Hybrid is a mix of both approaches, with different workloads deployed in each based on need. IDC reports that Clouds are rapidly evolving to become more trusted, more intelligent, and more specialized for specific industries and workloads.
The Cloud’s raison d’être, regardless of the deployment model used, is its ability to deliver overall agility, deployment flexibility, and elasticity. Cloud priorities today include moving more workloads to the Cloud, optimizing existing Cloud utilization, leveraging innovation, and enabling multi-Cloud deployments.
As telecommunications companies deploy Cloud infrastructures, they embrace efficiency techniques that have been developed and used by Amazon, Google, and Microsoft to control their public Cloud data center costs. They deploy efficiency models that increase data density and reduce footprint requirements, while dropping operating costs for power and cooling. As these extremely efficient models are deployed, legacy purpose-built hardware is being replaced with software-defined data centers running on industry-standard servers.
Software-defined data centers are also ushering in new opportunities to maximize efficiency in software. One such feature, which is particularly effective and critical in Cloud environments, is inline data reduction which includes deduplication and compression.
In addition to the platforms and software efficiency of Cloud, data reduction delivers a substantial impact on your budget by reducing the amount of data stored. It shrinks the amount of storage consumed, increases data density further, and lowers the costs of data at rest and in flight over your networks.
Cloud Agnostic Strategy
No matter which Cloud deployment you use, data reduction delivers economic benefits that make the Cloud business case more compelling.
Public Cloud Enthusiasts: Every day new workloads are being deployed in public Clouds. According to Forbes, Worldwide public IT Cloud service revenue in 2018 is predicted to be $127B.1 The economics that public Cloud delivers are undeniable. Amazon, Google, and Microsoft growth in the public Cloud is testament.
As you deploy a public Cloud environment, consider that data reduction technology shrinks public Cloud costs. For example, data reduction technology (deduplication and compression) typically cuts capacity requirements of block storage in enterprise public Cloud deployments by up to 85% (6:1). Take your Cloud block storage bill, divide by 6, and that is the business case.
Private Cloud Enthusiasts: Organizations see similar benefits when they deploy data reduction in private Clouds. In fact, IDC predicts $17.2B in infrastructure spending for private Cloud in 2017.2 This demand reflects requirements for Cloud’s increased efficiency, flexibility, privacy, performance, and security.
The telecommunications case for data reduction in private Cloud is based on reducing the cost of both storage hardware and excessive annual software licensing. For example, Software-Defined Storage (SDS) solutions are typically licensed by capacity and their costs are directly proportional to storage device expenses. Data reduction decreases storage costs because it reduces storage consumption.
Hybrid Cloud Enthusiasts: For many, this is the preferred Cloud deployment approach today because it addresses data security concerns while still leveraging Cloud efficiency. On-premises resources (private Cloud) combined with colocation and multiple public Clouds result in a highly redundant data environment. For example, IDC’s FutureScape report (IDC 259840) finds "Over 80% of enterprise organizations will commit to hybrid Cloud architectures, encompassing multiple public Cloud services, as well as private Clouds by the end of 2017."4
One consideration is whether to depend on a single Cloud storage provider, which can pose a significant risk to Service Level Agreement targets. Consider the widespread AWS S3 storage errors that occurred on February 28th 2017, where data was not available to clients for several hours.3 Businesses lost millions of dollars of revenue due to loss of data access. This highlights requirements for a "Cloud of Clouds" approach where data is redundantly distributed across multiple Clouds for data safety and near continuous accessibility. IDC forecasts that 85% of Cloud deployments will be multi-Cloud by 2018.4 Unfortunately, the Cloud of Clouds approach increases storage capacity cost (by having redundant copies in multiple Clouds), and adds the networking cost to move and sync data between Cloud deployments.
In hybrid Cloud deployments where data is replicated to the participating Clouds, data reduction multiplies capacity and cost savings. If 3 copies of the data are kept in 3 different Clouds, 3 times as much data is saved, and data movement between Clouds to sync them can be costly.
Data Reduction Is Whaaat?
OS-based data reduction solutions address public Cloud, private Cloud (including on-premises and hosted private Clouds), and the bandwidth challenges faced in hybrid Cloud environments. Data reduction can reduce storage requirements and network bandwidth consumption by as much as 85% (6:1 data reduction).
One data reduction application that can readily be applied in public, private, and hybrid Clouds is Permabit’s Virtual Disk Optimizer (VDO), a pre-packaged software solution that installs and deploys in minutes on Linux operating systems. Because it is deployed directly with Linux, it can be easily deployed in any public or private Cloud today.
Data reduction solutions generally combine several techniques to reduce data footprint. The ideal solution would employ 3 reduction techniques:
1. Zero-block elimination uses pattern matching techniques to eliminate zero data blocks.
2. Inline Deduplication eliminates duplicate data blocks.
3. HIOPS Compression™ compresses the remaining data blocks.
According to Right Scale, Telecommunications businesses are finding that the future of their computing infrastructure lies in the Cloud. Data reduction technologies enable Clouds — public, private, and hybrid — to deliver agility and elasticity at the lowest possible cost, making Cloud the deployment model of choice for IT infrastructure going forward.5
Endnotes
1. Roundup Of Cloud Computing Forecasts And Market Estimates, 2016.
https://www.forbes.com/sites/louiscolumbus/2016/03/13/roundup-of-Cloud-computing-forecasts-and-market-estimates-2016/#355835fe2187
2. Growth in Cloud IT Infrastructure Spending Will Accelerate in 2017 Driven by Public Cloud Datacenters and On-Premises Private Cloud Environments, According to IDC.
http://www.businesswire.com/news/home/20170113005749/en/Growth-Cloud-Infrastructure-Spending-Accelerate-2017-Driven
3. Amazon S3 Outage Has Broken A Large Chunk Of The Internet.
https://www.forbes.com/sites/ryanwhitwam/2017/02/28/amazon-s3-outage-has-broken-a-large-chunk-of-the-internet/#5be360f4c467
4. IDC FutureScape: Worldwide IT Industry 2017 Predictions.
https://www.idc.com/getdoc.jsp?containerId=US41883016
5. Cloud Computing Trends: 2017 State of the Cloud Survey.
https://www.rightscale.com/blog/cloud-indutry-insights/cloud-computing-Trends-2017-state-cloud-survey
About the Authors:
Save
Save
