These days the world operates in real-time all the time. Whether making airline reservations or getting the best deal from an online retailer, information is expected to be up to date with the best information at your fingertips. Businesses are expected to meet this requirement, whether they sell products or services. Having real-time, actionable information can dictate whether a business survives or dies. In-memory databases have become popular in these environments. The world's 24X7 real-time demands cannot wait for legacy ERP and CRM application rewrites. Companies such as SAP devised ways to integrate disparate databases by building a single super-fast uber-database that could operate with legacy infrastructure while simultaneously creating a new environment where real-time analytics and applications can flourish. These capabilities enable businesses to succeed in the modern age, giving forward-thinking companies a real edge in innovation.
SAP HANA is an example of an application environment that uses in-memory database technology and allows the processing of massive amounts of real-time data in a short time. The in-memory computing engine allows HANA to process data stored in RAM as opposed to reading it from a disk. At the heart of SAP HANA is a database that operates on both OLAP and OLTP database workloads simultaneously. SAP HANA can be deployed on-premises or in the cloud. Originally, on-premises HANA was available only as a dedicated appliance. Recently SAP has expanded support to best in class components through their SAP Tailored Datacenter Integration (TDI) program. In this solution profile, Taneja Group examined the storage requirements needed for HANA TDI environments and evaluated storage alternatives including the HPE 3PAR StoreServ All Flash. We will make a strong case as to why all-flash arrays like the HPE 3PAR version are a great fit for SAP HANA solutions.
Why discuss storage for an in-memory database? The reason is simple: RAM loses its mind when the power goes off. This means that persistent shared storage is at the heart of the HANA architecture for scalability, disaster tolerance, and data protection. The performance attributes of your shared storage dictate how many nodes you can cluster into a SAP HANA environment which in turn affects your business outcomes. Greater scalability capability means more real-time information is processed. SAP HANA workload shared storage requirements are unique in being write intensive with low latency for small files and sequential throughput performance for large files. However, the overall storage capacity is not extreme which makes this workload an ideal fit for all-flash arrays that can meet performance requirements with the smallest quantity of SSDs. Typically you would need 10X the equivalent spinning media drives just to meet the performance requirements, which then leaves you with a massive amount of capacity that cannot be used for other purposes.
In this study, we examined five leading all-flash arrays including the HPE 3PAR StoreServ 8450 All Flash. We found that that the unique architecture of the 3PAR array could meet HANA workload requirements with up to 73% fewer SSDs, 76% less power, and 60% less rack space.
The storage market is truly changing for the better with new storage architectures finally breaking the rusty chains long imposed on IT by traditional monolithic arrays. Vast increases in CPU power found in newer generations of servers (and supported by ever faster networks) have now freed key storage functionality to run wherever it can best serve applications. This freedom has led to the rise of all software-defined storage (SDS) solutions that power modular HyperConverged infrastructure (HCI). At the same time, increasingly affordable flash resources have enabled all-flash array options that promise both OPEX simplification and inherent performance gains. Now, we see a further evolution of storage that intelligently converges performance-oriented storage functions on each server while avoiding major problems with HyperConverged “single appliance” adoption.
Given the market demand for better, more efficient storage solutions, especially those capable of large scale, low latency and mixed use, we are seeing a new generation of vendors like Datrium emerge. Datrium studied the key benefits that hyperconvergence previously brought to market including the leverage of server-side flash for cost-effective IO performance, but wanted to avoid the all-in transition and the risky “monoculture” that can result from vendor-specific HCI. Their resulting design runs compute-intensive IO tasks scaled-out on each local application server (similar to parts of SDS), but persists and fully protects data on cost-efficient, persistent shared storage capacity. We have come to refer to this optimizing tiered design approach as “Server Powered Storage” (SPS), indicating that it can take advantage of the best of both shared and server-side resources.
Ultimately this results in an “Open Convergence” approach that helps virtualized IT environments transition off of aging storage arrays in an easier, flexible and more natural adoption path than with a fork-lift HyperConvergence migration. In this report we will briefly review the challenges and benefits of traditional convergence with SANs, the rise of SDS and HCI appliances, and now this newer “open convergence” SPS approach as pioneered by Datrium DVX. In particular, we’ll review how Datrium offers benefits ranging from elastic performance, greater efficiency (with independent scaling of performance vs. capacity), VM-centric management, enterprise scalability and mixed workload support while still delivering on enterprise requirements for data resiliency and availability.
Today we are seeing big impacts on storage from the huge increase in the scale of an organization’s important data (e.g. Big Data, Internet Of Things) and the growing size of virtualization clusters (e.g. never-ending VM’s, VDI, cloud-building). In addition, virtualization adoption tends to increase the generalization of IT admins. In particular, IT groups are focusing more on servicing users and applications and no longer want to be just managing infrastructure for infrastructure’s sake. Everything that IT does is becoming interpreted, analyzed, and managed in application/business terms, including storage to optimize the return on their total IT investment. To move forward, an organization’s storage infrastructure not only needs to grow internally smarter, it also needs to become both VM and application aware.
While server virtualization made a lot of things better for the over-taxed IT shop, delivering quality storage services in hypervisor infrastructures with traditional storage created difficult challenges. In response Tintri pioneered per-VM storage infrastructure. The Tintri VMstore has eliminated multiple points of storage friction and pain. In fact, it’s now becoming a mandatory checkbox across the storage market for all arrays to claim some kind of VM-centricity. Unfortunately, traditional arrays are mainly focused on checking off rudimentary support for external hypervisor APIs that only serve to re-package the same old storage. The best fit to today’s (and tomorrow’s) virtual storage requirements will only come from fully engineered VM-centric storage and application-aware approaches as Tintri has done.
However, it’s not enough to simply drop in storage that automatically drives best practice policies and handles today’s needs. We all know change is constant, and key to preparing for both growth and change is having a detailed, properly focused view of today’s large scale environments, along with smart planning tools that help IT both optimize current resources and make the best IT investment decisions going forward. To meet those larger needs, Tintri has rolled out a Tintri Analytics SaaS-based offering that applies big data analytical power to the large scale of their customer’s VMstore VM-aware metrics.
In this report we will look briefly at Tintri’s overall “per-VM” storage approach and then take a deeper look at their new Tintri Analytics offering. The new Tintri Analytics management service further optimizes their app-aware VM storage with advanced VM-centric performance and capacity management. With this new service, Tintri is helping their customers receive greater visibility, insight and analysis over large, cloud-scale virtual operations. We’ll see how “big data” enhanced intelligence provides significant value and differentiation, and get a glimpse of the payback that a predictive approach provides both the virtual admin and application owners.
Traditional backup storage is being challenged by the immense growth of data. These solutions including tape, RAID devices that are gated by controllers and dedicated storage appliances simply aren’t designed for today’s enterprise backup storage at petabyte levels, especially when that data lives in geographically distributed environments. This insufficiency is due in large part to inefficiency and limited data protection, as well as the limited scalability and the lack of flexibility of these traditional storage solutions.
These constraints can lead to multiple processes and many storage systems to manage. Storage silos develop as a result, creating complexity, increasing operational costs and adding risk. It is not unusual for companies to have 10-20 different storage systems to achieve petabyte storage capacity, which is inefficient from a management point of view. And if companies want to move data from one storage system to another, the migration process can take a lot of time and place even more demand on data center resources.
And the concerns go beyond management complexity. Companies face higher capital costs due to relatively high priced proprietary storage hardware, and worse, limited fault tolerance, which can lead to data loss if a system incurs simultaneous disk failures. Slow access speeds also present a major challenge if IT teams need to restore large amounts of data from tape while maintaining production environments. As a result, midsized companies, large enterprises and service providers that experience these issues have begun to shift to software-defined storage solutions and scale-out object storage technology that addresses the shortcomings of traditional backup storage.
Software-defined scale out storage is attractive for large-scale data backup because these storage solutions offer linear performance and hardware independence – two core capabilities that drive tremendous scalability and enable cost-effective storage solutions. Add to this the high fault tolerance of object storage platforms, and it’s easy to see why software-defined object storage solutions are rapidly becoming the preferred backup storage approach for petabyte-scale data environments. A recent Taneja Group survey underscores the benefits of software-defined scale out storage. IT professionals indicated that the top benefits of software-defined, scale-out architecture on industry standard servers are a high level of flexibility (34%), low cost of deployment (34%), modular scalability (32%), and ability to purchase hardware separate from software (32%).
Going a step further, the Scality backup storage solution built upon the Scality RING platform offers the rare combination of scalability, durability and affordability plus the flexibility to handle mixed workloads at petabyte-scale. Scality backup storage achieves this by supporting multiple file and object protocols so companies can backup files, objects and VMs, leveraging a scale-out file system that delivers linear performance as system capacity increases, offering advanced data protection for extreme fault tolerance, enabling hardware independence for better price performance and providing auto balancing that enables migration-free hardware upgrades.
In this paper, we will look at the limitations of backup appliances and Network-Attached Storage (NAS) and the key requirements for backup storage at petabyte-scale. We will also study the Scality RING software-defined architecture and provide an overview of the Scality backup storage solution.
We live in a digital world where online services, applications and data must always be available. Yet the modern data center remains very susceptible to interruptions. These opposing realities are challenging traditional backup applications and disaster recovery solutions and causing companies to rethink what is needed to ensure 100% uptime of their IT environments.
The need for availability goes well beyond recovering from disasters. Companies must be able to rapidly recover from many real world disruptions such as ransomware, device failures and power outages as well as natural disasters. Add to this the dynamic nature of virtualization and cloud computing, and it’s not hard to see the difficulty of providing continuous availability while managing a highly variable IT environment that is susceptible to trouble.
Some companies feel their backup devices will give them adequate data protection and others believe their disaster recovery solutions will help them restore normal business operations if an incident occurs. Regrettably, far too often these solutions fall short of meeting user expectations because they don’t provide the rapid recovery and agility needed for full business continuance.
Fortunately, there is a way to ensure a consistent experience in an inconsistent world. It’s called IT resilience. IT resilience is the ability to ensure business services are always on, applications are available and data is accessible no matter what human errors, events, failures or disasters occur. And true IT resilience goes a step further to provide continuous data protection (CDP), end-to-end recovery automation irrespective of the makeup of a company’s IT environment and the flexibility to evolve IT strategies and incorporate new technology.
Intrigued by the promise of IT resilience, companies are seeking data protection solutions that can withstand any disaster to enable a reliable online experience and excellent business performance. In a recent Taneja Group survey, nearly half the companies selected “high availability and resilient infrastructure” as one of their top two IT priorities. In the same survey, 67% of respondents also indicated that unplanned application downtime compromised their ability to satisfy customer needs, meet partner and supplier commitments and close new business.
This strong customer interest in IT resilience has many data protection vendors talking about “resilience.” Unfortunately, many backup and disaster recovery solutions don’t provide continuous data protection plus hardware independence, strong virtualization support and tight cloud integration. This is a tough combination and presents a big challenge for data protection vendors striving to provide enterprise-grade IT resilience.
There is however one data protection vendor that has replication and disaster recovery technologies designed from the ground up for IT resilience. The Zerto Cloud Continuity Platform built on Zerto Virtual Replication offers CDP, failover (for higher availability), end-to-end process automation, heterogeneous hypervisor support and native cloud integration. As a result, IT resilience with continuous availability, rapid recovery and agility is a core strength of the Zerto Cloud Continuity Platform.
This paper will explore the functionality needed to tackle modern data protection requirements. We will also discuss the challenges of traditional backup and disaster recovery solutions, outline the key aspects of IT resilience and provide an overview of the Zerto Cloud Continuity Platform as well as the hypervisor-based replication that Zerto pioneered.
For all the gains server virtualization has brought in compute utilization, flexibility and efficiency, it has created an equally weighty set of challenges on the storage side, particularly in traditional storage environments. As servers become more consolidated, virtualized workloads must increasingly contend for scarce storage and IO resources, preventing them from consistently meeting throughput and response time objectives. On top of that, there is often no way to ensure that the most critical apps or virtual machines can gain priority access to data storage as needed, even in lightly consolidated environments. With a majority (70+%) of all workloads now running virtualized, it can be tough to achieve strong and predictable app performance with traditional shared storage.
To address these challenges, many VMware customers are now turning to server-side acceleration solutions, in which the flash storage resource can be placed closer to the application. But server-side acceleration is not a panacea. While some point solutions have been adapted to work in virtualized infrastructures, they generally lack enterprise features, and are often not well integrated with vSphere and the vCenter management platform. Such offerings are at best band-aid treatments, and at worst second-class citizens in the virtual infrastructure, proving difficult to scale, deploy and manage. To provide true enterprise value, a solution should seamlessly deliver performance to your critical VMware workloads, but without compromising availability, workload portability, or ease of deployment and management.
This is where FlashSoft 4 for VMware vSphere 6 comes in. FlashSoft is an intelligent, software-defined caching solution that accelerates your critical VMware workloads as an integrated vSphere data service, while still allowing you to take full advantage of all the vSphere enterprise capabilities you use today.
In this paper we examine the technology underlying the FlashSoft 4 for vSphere 6 solution, describe the features and capabilities it enables, and articulate the benefits customers can expect to realize upon deploying the solution.