The SimpliVity Data Virtualization Engine. An Overview of the Data Architecture Powering SimpliVity s OmniCube

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1 The SimpliVity Data Virtualization Engine An Overview of the Data Architecture Powering SimpliVity s OmniCube

2 Table of Contents 1. Synopsis The SimpliVity Data Virtualization Engine: Addressing the Complexity Crisis at the Core The Broken System The 21 st Century Data Efficiency Panacea: Deduplication, Compression and Optimization (DCO ) State of the Deduplication Market Today SimpliVity s Data Virtualization Engine: A 21 st Century Efficiency and Mobilitycentric Data Architecture The Starting Point: Real-time Deduplication, Compression and Optimization without Impact to Performance OmniCube Accelerator Enhancing the Value through Optimization Tying It All Together: The OmniCube Global Federation OmniCube: Globally Federated Hyperconverged IT Infrastructure Summary... 9

3 1. Synopsis SimpliVity s OmniCube is the cure for two of today s most vexing problems in IT the extreme cost and complexity of the IT environment, and cross-site IT (the limitations of managing IT across sites including the Public Cloud). OmniCube is the industry s first globally federated and hyperconverged IT infrastructure platform. Designed and optimized for the virtualized environment, OmniCube provides a complete scalable and flexible IT infrastructure that meets the data center requirements for high availability, performance, and serviceability. In addition to these core server, storage and networking services, OmniCube delivers a complete set of advanced features, providing global protection and management of all the virtual machines within and across data centers, including the Cloud. Scalability is achieved when two or more OmniCube systems are deployed together, creating the OmniCube Global Federation, a massively scalable pool of shared resources that enables efficient data movement, extensive scalability, protection, disaster recovery, and enterpriseclass system availability. Figure 1: OmniCube Overview At the root of today s complexity crisis is an antiquated data architecture with an inherent course-grained data architecture (that was optimized for a world that predated server virtualization), and the need to span multiple sites and the Cloud, thus requiring far less data mobility than the modern IT environment demands. SimpliVity has comprehensively solved the problem by delivering a fundamentally new data architecture which powers the functionality of OmniCube known as the SimpliVity Data Virtualization Engine, or DVE. DVE deduplicates, compresses and optimizes all data (at inception, in real-time, once and forever), and provides a global framework for storing and managing the resulting fine-grained data elements across all tiers within a single system (DRAM, Flash, HDD), across all the datalifecycle phases (primary, backup, WAN, archive), across geographies, data centers and the Cloud. In doing so, DVE enables OmniCube to deliver the functionality that can only be achieved today through the management of more than a dozen disparate products and does it at a fraction of the cost and at an extreme reduction in complexity as compared to today s traditional infrastructure stack. The result is a first-of-its-kind product and global architecture featuring capabilities that break records in terms of TCO and functional scope (acquisition cost and operating cost). This paper will provide an in-depth look into the Data Virtualization Engine, and will demonstrate how the DVE powers all of the differentiated functionality of OmniCube. 1

4 2. The SimpliVity Data Virtualization Engine: Addressing the Complexity Crisis at the Core 1. The Broken System The architecture upon which the average data center is built dates back nearly 30 years. It is inflexible, inefficient, and incapable of supporting the modern business with its modern data mobility needs (see figure 2). Virtualization has brought tremendous value in consolidating resources and bringing some level of simplicity to the infrastructure, but its true potential remains blocked by the underlying course-grain data architecture, which acts as an anchor for the virtual machines (VMs) that are designed for mobility. The fact that these architectures inhibit virtualization and data mobility should come as no surprise, as they were optimized for a different world a world that predated server virtualization, the Cloud, and solid state drives (SSD). It was a world in which IT was tightly centralized within a single data center, and remote IT sites if they existed were run independently from the primary data center. The size of disk drives was small (3 GB), the cost of storage was very high, replication was a luxury very few could afford, and almost 100% of disaster recovery operations were optimized for using tape media. But while the underlying data architectures have changed very little, IT s role in business has changed dramatically. IT and the data that the IT team is chartered to protect is at the core of almost all businesses today, and as such, the demands on IT are ever-greater. To address the demands, IT teams have been forced to deploy technologies in disparate products as they could not be delivered within their existing infrastructure products. Thus, the last 10 years has seen a wave of specialty devices such as WAN optimization, disk-based backup, SSD storage devices, and Cloud gateways each delivering value, but collectively adding to the complexity crisis. Figure 2: Legacy Infrastructure Topology 2

5 2. The 21 st Century Data Efficiency Panacea: Deduplication, Compression and Optimization (DCO ) The need for a lighter data architecture one that fosters mobility rather than inhibits it has been clear for some time. Many have seen great promise in data deduplication and compression and have recognized that if done well, these technologies can facilitate lighterweight, mobile data structures. Optimization further holds promise as a means of intelligently managing data based on the anticipated usage of it by the applications it serves. Following are brief definitions of these technologies: A. Deduplication the process of finding and eliminating redundant data within a given data set in reference to the whole available repository holds great promise in delivering a light-weight, mobile data structure and therefore is seen as a key to solving the complexity crisis by addressing the root cause. B. Compression the process of finding and eliminating redundant data within a given data set, in relation to other data within the same dataset, is a simpler problem, but provides complimentary value. C. Optimization the intelligent treatment of data based on its anticipated use by an application. Systems that can identify file types and make real-time decisions about whether and where to store that data can achieve overall improved storage efficiency, performance, and bandwidth usage. Specifically, deduplication, compression and optimization have several key benefits that address the core requirements of today s data center: More efficient use of the SSD storage cache. A deduplication process that operates at the right point in the data stream can reduce the footprint on the cache, improving the overall system-wide performance. Dramatic bandwidth reduction on replication between sites. Twenty years ago, the IT organization was dedicated to a single primary data center, but today, almost all IT teams manage multiple sites. A fundamental requirement of the infrastructure, then, is fostering efficient data transfer among sites. Deduplicating data before it goes to a remote site makes the transfer itself more efficient and saves significant bandwidth resources. Enhanced data mobility. A fundamental principle of server virtualization is the mobility of the VMs, but course-grain data structures significantly block mobility in a traditional infrastructure environment. When the data is deduplicated, it is easier to move VMs from one server to another, and it is easier to move data in and out of the Cloud for the same reason. Efficient storage utilization. Required capacity can be reduced 2-3X in standard primary use cases based on the effective use of deduplication, compression, and optimization (DCO). Enhanced performance given that less actual data needs to be written to disk or read from disk. This is amplified in application environments such as VDI, where boot storm can generate multiple GB of random reads from disk. With DCO, that can be reduced to tens of MB. Enhanced time-to-data. Achieve faster access to data (by virtue of there being less physical data to move), when migrating data, or when recovering data from a remote site or from the Cloud. 3

6 The above list enumerates the great potential value of deduplication across a number of areas. This may be counter-intuitive given that deduplication technologies have historically been designed to optimize for HDD capacity. When introduced to the market in the mid-2000s, dedupe was designated entirely for backup. In this use case, optimizing for capacity is crucial, given massive redundancy of data and the ever increasing volume of data to be backed up and retained. In primary storage systems, optimizing for disk capacity is a relatively lower priority. HDD IOPs are a much more expensive system resource than HDD capacity. All of this points in one direction: 21 st century data has to be deduped, compressed and optimized at the primary storage level, and no later. When data is deduplicated across all tiers right from the point of inception, it has significant resource-saving ramifications all the way down the stream, and opens up the advanced functionality required for today s world. 3. State of the Deduplication Market Today Deduplication emerged as a hot technology for the data center in the mid-2000s, and has remained prominently used. However, thus far, deduplication has been designed by vendors as an isolated data-lifecycle phase-specific and resource-intensive operation that has been implemented in different products, by different vendors, each addressing a single specific problem: dedupe of backup data, or dedupe of WAN data, or dedupe of archive data. Despite the maturity of deduplication, and the great benefits of deduping primary data, no vendor has thus far comprehensively solved the dedupe challenge in primary data. Some products apply deduplication only within the SSD tier, and therefore only offer limited benefits in terms of overall efficiency. Others apply compression technology and incorrectly use the term dedupe. Because of the latency that dedupe may impose, many have deployed it as a postprocess, which severely limits other operations such as replication and backup. Most of these sub-optimal implementations are a result of adding deduplication to an existing legacy architecture, rather than developing it as the foundation for the overall 21 st Century architecture. The various fragmented work-arounds that vendors have delivered have varying levels of value, but fall short of solving the underlying problem. These approaches provide some value, but ultimately do not deliver a truly fine-grained and mobile data infrastructure. IT teams can be left with higher acquisition costs and even more complexity as they manage partial dedupe amidst their other infrastructure burdens. Given these noted challenges, no vendor has thus far been able to offer a dedupe apparatus that addresses all the dedupe opportunities and needs across the full life-cycle of data: from primary storage, to backup, to the WAN, archive and the Public Cloud. As a result, IT teams seeking the noted benefits have been forced to deploy multiple, disparate products, from different vendors, each necessitating separate training and separate on-going management. Yet despite the challenges of recent years it is clear that the future of IT infrastructure operations will depend heavily on an efficient and effective combination of deduplication, compression and optimization for primary production workloads. 4

7 4. SimpliVity s Data Virtualization Engine: A 21 st Century Efficiency and Mobility-centric Data Architecture Rather than taking an existing data architecture and trying to build-in deduplication, compression and optimization, SimpliVity took the inverse approach. As a first step, it designed the core technology that performs real-time deduplication and compression on primary data, in real-time, without impact to performance or latency (see below, the OmniCube Accelerator ), and built an entire globally federated data architecture around that foundation that manages the resulting fine-grained data elements across a Global Federation of systems. In doing so, it addressed all of the core requirements for truly effective deduplication, compression and optimization for the primary production infrastructure system and beyond: Real-time Once and forever (no need for a second pass, or hydration/dehydration inefficiencies) Across all tiers of data within a system Across all datasets Across all locations Including on the Public Cloud Without impacting performance In delivering DVE, SimpliVity is realizing the potential of well-implemented deduplication, compression, and optimization of primary data. In addition to disk capacity, DVE optimizes HDD IOPs, flash capacity, DRAM capacity, and WAN capacity. In so doing, DVE is going far beyond capacity efficiency. While it may not be intuitive- by performing real-time deduplication, compression, and optimization DVE improves system performance. With DVE, deduplication, compression, and optimization occur before data is written to the HDD, thus preserving the precious HDD IOPs. The boot storm is a great example. In a traditional storage platform, 100 Windows VMs booting at the same time will cause roughly 10,000MB of random disk reads. In the SimpliVity OmniCube, this same workload will cause roughly 100MB of reads as all of the data Windows reads to boot is common between the 100 VMs. This is a 100x savings in disk operations. Figure 3: DVE Deduplication, Compression, Optimization 5

8 1. The Starting Point: Real-time Deduplication, Compression and Optimization without Impact to Performance The DVE performs deduplication, compression and optimization in real-time, as the data is first written into the OmniCube datastore. This contrasts to a more prevalent approach called postprocess deduplication, which allows data to be written first without deduplication and at some later stage, performs the deduplication process. The big problem with post-processing deduplication is that it introduces a lag where there was none before. Businesses are presented with the choice to replicate data before deduplicating it or waiting to replicate until the dedupe process is complete. But neither option is sufficient: replicating before deduplicating defeats the purpose of deduplicating at all, and waiting to replicate can create RPO (Recovery Point Objective) issues. It may take so long to dedupe the data that by the time it is deduped and replicated, it no longer meets the RPO. Given the clear superiority (and elegance) of performing deduplication real-time, why is it unusual? In a word, performance. Deduplication is a resource intensive process. As data enters the system, it must be scanned, analyzed, compared to an index or table that has cataloged all existing blocks in the data set, and then acted upon (either deleted if redundant, or written if new). Pointers and indexes need to be updated in real-time such that the system can keep track of all data elements in all their locations, while maintaining an understanding of the full data sets (pre-deduplication) that have been stored in the system. The challenge is augmented if we wish to maximize data-efficiency by focusing the architecture of granular 4KB or 8KB data sets (which is the original size at which data is written by the application). A system managing 4KB blocks and ingesting data at 400MB/s needs to perform 100,000 such operations per second. Given the challenge, it is understandable that many vendors have opted to conduct this operation out-of-band, so as not to impact performance. This is a challenge that SimpliVity addressed head-on and resolved. 2. OmniCube Accelerator SimpliVity s real-time deduplication breakthrough is the OmniCube Accelerator, a specially architected SimpliVity PCIe module that processes all writes and manages the compute intensive tasks of deduplication and compression. All data that is written to the OmniCube datastore first passes through the OmniCube Accelerator at inception, as it is created. The practical effect of real-time deduplication is that the DVE ends up processing data elements that are between 4KB and 8KB in size, compared to the 10-20MB of traditional architectures, i.e. 2,000 times more efficient. The data is thus born to be mobile from the beginning, and remains so throughout its lifecycle within the OmniCube Global Federation. Within a given OmniCube system, deduplication makes each storage media tier more efficient DRAM, Flash, SSD, and HDD thereby dramatically lowering the cost of the system compared to traditional offerings. While deduplication within a single OmniCube system provides great efficiencies and cost savings, the additionally groundbreaking value of OmniCube lies in the Global Federation the network of connected OmniCube systems that provide High Availability (HA), resource sharing, simplified scale-out, and replication for VM movement and Disaster Recovery (DR). Additionally, with deduplication at the core, the DVE architecture has been designed and 6

9 optimized for managing a very large set of fine-grained data elements, across a Federation of systems that are both local (within the same data center) and remote (dispersed data centers), including the Public Cloud. For example, a modest Federation of a few systems contains tens of billions of deduped, compressed and optimized 4KB elements. The DVE enables this Federation to efficiently track and manage all elements, and make real-time decisions about which data elements to send via replication and which need not be sent due to the existence of an exact match at the destination site. Designing the overall data architecture around the deduplication, compression and optimization engine has ensured that the value of dedupe pervades all media, all tiers (primary, backup, and archive), and all locations. 3. Enhancing the Value through Optimization While dedupe is the fundamental core, the DVE further enhances the CAPEX and OPEX savings enabled with OmniCube by delivering remarkable efficiencies through operatingsystem and virtualization aware optimizations. The optimizations within OmniCube deliver similar effects to dedupe in a different way they identify data that need not be copied, or replicated, and take data-specific actions to improve the overall efficiency of the system. Given that OmniCube today is optimized for the VMware environment, most such optimizations stem from awareness of VMware specific content or commands. For example,.vswp 1 files, though important to the functionality of each individual VM, do not need to be backed up or replicated across sites. Thus, when preparing to backup or replicate a given VM from one site to another, the DVE recognizes the.vswp file associated with a VM, and eliminates that data from the transfer- saving time, bandwidth and capacity. Other optimizations are similar in nature leveraging DVE s ability to find and make real-time decisions on common data types within a VMware environment. 4. Tying It All Together: The OmniCube Global Federation With the data permanently in an efficient fine-grained state, and an overall architecture designed to manage and track billions of elements across a global network of systems, all of the core functionality of OmniCube is enabled: efficient data mobility within and across data centers, intelligent data movement within the Federation, data sharing for high availability, cache accelerated performance, cloud integration, and a single point of global management, that s automated and VM-centric 1.vswp files are swap files for guest VM memory, they are used by VMware if system memory is over-allocated..vswp files are overwritten when a VM boots, so there is no need to capture them in backups. OmniCube is built to recognize.vswp files and therefore skip the backup and replication process on those files. 7

10 Figure 4: OmniCube Global Federation The value of deduplication, compression and optimization is amplified in the Global Federation as the required bandwidth is reduced dramatically compared to non-deduplicating systems. Importantly, this also speeds-up the delivery of data from/to remote sites and the Cloud. Deduplication has traditionally been successful comparing incoming data to what lies within the local array, but struggles in a true global setting. In contrast, the DVE contains an advanced inter-node messaging system that allows OmniCube systems to communicate about the contents of their local data store. In essence, when replicating from one system to another, this allows each OmniCube system to know enough about what is on the remote systems to ensure that only unique data (free of operating systems and VMW commands and other overhead) is sent over across the wire. This inter-node communication can have dramatic effects. For example, in many cases within an OmniCube Federation, on the first replication of a given VM, very little data needs to traverse the wires. This is a radical departure compared to any other replicating system, which must always replicate a full copy of a given data set during the first replication. But in reality, any two VMs running common operating systems like Windows 2008 will have a large set of data elements in common. The DVE will recognize any such redundant data that already exists at the remote site, and ensure only truly unique data elements are sent. 8

11 5. OmniCube: Globally Federated Hyperconverged IT Infrastructure The new DVE architecture supports a globally comprehensive solution that dramatically consolidates and optimizes each of these new technologies into a single, efficient platform that scales elegantly within the data center, and extends scalability to data centers across the world including the Cloud. In short, the OmniCube is a hyperconverged, globally-federated IT infrastructure that is designed and optimized for the virtualized environment: Hyperconverged: OmniCube consolidates all core infrastructure functionality in addition to the functionality delivered today by over a dozen specialized devices and products into a single, scalable building block. Globally-federated: OmniCube systems are networked together to create a dynamic pool of shared resources within the data center, and an optimal inter-data center replication and DR solution. Optimized for virtualized environments: OmniCube has been designed and optimized to simplify the IT operations of the virtual environment. The OmniCube Global Federation provides a VM-centric and globally unified management framework to the IT team. From a single pane of glass, an administrator can view and manage all of the VMs including all backup copies in all of their locations across the globe. 6. Summary By focusing its new architecture (from the ground-up) on solving the legacy data-architecture problem, SimpliVity cured the disease rather than just addressing the symptom. SimpliVity created a novel global solution that s tuned for a virtualized, globally distributed and Cloud enabled IT operation. By delivering DVE at the heart of OmniCube, SimpliVity succeeds in delivering a Globally Federated Hyperconverged IT Infrastructure. In doing so, DVE overcomes the various barriers that have limited the usage and deployment of deduplication (in primary storage, in real-time, across all tiers within a system, across data centers and the Cloud, and fine-grained). The result is a radically simplified and dramatically lower cost infrastructure platform that delivers on the requirements for scalability, flexibility, performance elasticity, data mobility, global management, and Cloud integration that today s IT infrastructures require. 9

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