SNIA S10-210 : Storage Networking Management and Administration Exam

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Exam Number : S10-210
Exam Name : Storage Networking Management and Administration
Vendor Name : SNIA
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S10-210 test Format | S10-210 Course Contents | S10-210 Course Outline | S10-210 test Syllabus | S10-210 test Objectives

Exam Name : Storage Networking Management and Administration
Exam Number : S10-210 SCSE
Exam Duration : 90 minutes
Questions in test : 60
Passing Score : 67%
Recommended Training : Storage Networking Management and Administration
Exam Registration : Kryterion Testing Center
Real Questions : SNIA S10-210 Real Questions
VCE Practice Test : SNIA Certified Storage Engineer Practice Test

- Describe how to create storage allocations based on requirements
- Demonstrate knowledge of how to implement storage capacity planning
- Demonstrate knowledge of the storage monitoring process
- Demonstrate knowledge of how to provision storage
- Describe cloud storage concepts

Storage Networking
- Demonstrate knowledge of how to respond to storage network support issues - Demonstrate knowledge how to implement zoning best practices
- Identify the differences between transport protocols

- Describe how to administer and monitor host storage adapters
- Demonstrate knowledge of multipathing implementations
- Demonstrate knowledge of disk/volume management

- Demonstrate knowledge of securing management access to devices
- Demonstrate knowledge of encryption methods
- Demonstrate knowledge of securing storage access

Data Protection
- Describe how to use different types of replication
- Describe how to implement High Availability
- Describe backup/restore implementations

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SNIA Networking real Questions

what is a computational storage drive? an awful lot-essential assist for CPUs | S10-210 PDF Questions and Dumps

The inevitable slowing of Moore’s law has pushed the computing trade to undergo a paradigm shift from the traditional CPU-handiest homogeneous computing to heterogeneous computing. With this trade, CPUs are complemented by way of special-purpose, domain-specific computing fabric. As we’ve seen over time, this is neatly mirrored by using the enormous increase of hybrid-CPU/GPU computing, giant funding on AI/ML processors, broad deployment of SmartNIC, and greater these days, the emergence of computational storage drives.

not extraordinarily, as a new entrant into the computing panorama, the computational storage pressure sounds fairly unfamiliar to most americans and many questions naturally come up. what's a computational storage drive? the place should still a computational storage force be used? What variety of computational characteristic or capability should a computational storage power give?

Resurgence of an easy and decades-ancient idea

The essence of computational storage is to empower statistics storage gadgets with additional information processing or computing capabilities. Loosely speaking, any records storage device — developed on any storage technology, such as flash memory and magnetic recording — that can carry out any information processing tasks beyond its core data storage obligation will also be referred to as a computational storage power.

The primary idea of empowering data storage gadgets with extra computing capability is not at all new. It will also be traced back to more than two decades in the past during the clever reminiscence (IRAM) and clever disks (IDISKs) papers from Professor David Patterson’s neighborhood at UC Berkeley around 1997. fundamentally, computational storage complements host CPUs to kind a heterogeneous computing platform. 

Computational storage even stems back to when early educational research confirmed that such a heterogeneous computing platform can drastically increase the performance or power efficiency for quite a lot of purposes like database, graph processing, and scientific computing. however, the business selected not to adopt this idea for precise world functions easily as a result of outdated storage gurus might  now not justify the investment on this kind of disruptive concept within the presence of the regular CPU advancement. because of this, this subject has develop into generally dormant during the last two decades. 

fortunately, this conception lately got a big resurgence of pastime from both academia and industry. it's pushed by using two grand industrial tendencies:

  • there is a becoming consensus that heterogeneous computing must play an more and more vital role as the CMOS technology scaling is slowing down.
  • The colossal growth of high-speed, strong-state data storage technologies pushes the system bottleneck from records storage to computing.
  • The conception of computational storage natively suits these two grand tendencies. not relatively, we now have viewed a resurgent activity on this syllabu during the last few years, no longer handiest from academia however also, and arguably greater importantly, from the business. Momentum during this space was highlighted when the NVMe common committee lately commissioned a working group to prolong NVMe for helping computational storage drives, and SNIA (Storage Networking trade affiliation) shaped a working group on defining the programming mannequin for computational storage drives. 

    Computational storage in the real world

    As records facilities have become the cornerstone of contemporary advice expertise infrastructure and are accountable for the storage and processing of ever-exploding quantities of data, they're clearly the most desirable place for computational storage drives to birth the event towards precise world application. however, the important thing query here is how computational storage drives can top-quality serve the needs of data facilities.

    information facilities prioritize on charge rate reductions, and their hardware TCO (complete can charge of ownership) can most effective be reduced by way of two paths: more cost-effective hardware manufacturing, and better hardware utilization. The gradual-down of expertise scaling has compelled statistics centers to increasingly depend on the second direction, which naturally ends up in the present style in opposition t compute and storage disaggregation. regardless of the absence of the term “computation” from their job description, storage nodes in disaggregated infrastructure can be accountable for a wide array of heavy-duty computational tasks:

  • Storage-centric computation: cost savings demand the pervasive use of at-relaxation statistics compression in storage nodes. Lossless records compression is smartly familiar for its giant CPU overhead, chiefly because of the high CPU cache omit price caused by using the randomness in compression records circulate. in the meantime, storage nodes should make certain at-leisure facts encryption too. furthermore, facts deduplication and RAID or erasure coding can even be on the project checklist of storage nodes. All of these storage-centric initiatives demand a big volume of computing vigour.   
  • community-traffic-alleviating computation: Disaggregated infrastructure imposes a whole lot of application-stage computation initiatives onto storage nodes to be able to vastly alleviate the burden on inter-node networks. In specific, compute nodes could off-load definite low-level facts processing features like projection, option, filtering, and aggregation to storage nodes to be able to mostly in the reduction of the quantity of records that need to be transferred again to compute nodes. 
  • To in the reduction of storage node cost, it's crucial to off-load heavy computation hundreds from CPUs. compared to off-loading computations to separate standalone PCIe accelerators for frequent design observe, at once migrating computation into each storage pressure is a plenty greater scalable answer. furthermore, it minimizes statistics site visitors over reminiscence/PCIe channels, and avoids information computation and facts transfer hotspots. 

    The want for CPU off-loading naturally requires computational storage drives. apparently, storage-centric computation initiatives (in selected compression and encryption) are probably the most convenient pickings, or low-striking fruit, for computational storage drives. Their computation-intensive and glued-feature nature renders compression or encryption ideally suited for being applied as personalized hardware engines interior computational storage drives. 

    moving beyond storage-centric computation, computational storage drives could extra aid storage nodes to operate computation initiatives that aim to alleviate the inter-node community data site visitors. The computation initiatives in this class are software-elegant and hence require a programmable computing material (e.g., ARM/RISC-V cores or even FPGA) interior computational storage drives.

    It is clear that computation and storage inside computational storage drives have to cohesively and seamlessly work collectively so as to supply the best possible end-to-conclusion computational storage provider. in the presence of continual improvement of host-side PCIe and memory bandwidth, tight integration of computation and storage becomes much more crucial for computational storage drives. hence, it is crucial to integrate computing fabric and storage media control cloth into one chip. 

    Architecting computational storage drives

    At a look, a commercially plausible computational storage pressure should still have the structure as illustrated in figure 1 below. A single chip integrates flash memory handle and computing fabric that are connected by way of a high-bandwidth on-chip bus, and the flash memory handle textile can serve flash access requests from both the host and the computing fabric.

    Given the familiar at-relaxation compression and encryption in information facilities, computational storage drives must own compression and encryption with the intention to extra aid any application-level computation initiatives. hence, computational storage drives must strive to supply the superior-in-category support of compression and encryption, ideally in both in-line and off-loaded modes, as illustrated in figure 1.

    computational storage drive ScaleFlux

    determine 1: architecture of computational storage drives for data centers.

    For the in-line compression/encryption, computational storage drives enforce compression and encryption directly alongside the storage IO route, being clear to the host. For each write IO request, records go during the pipelined compression → encryption → write-to-flash route; for each and every read IO request, records go through the pipelined examine-from-flash → decryption → decompression course. Such in-line statistics processing minimizes the latency overhead brought on by using compression/encryption, which is incredibly eye-catching for latency-sensitive functions akin to relational databases.

    additionally, computational storage drives can also integrate extra compression and safety hardware engines to provide off-loading service via well-described APIs. security engines may consist of a considerable number of modules such as root-of-trust, random number generator, and multi-mode deepest/public key ciphers. The embedded processors are responsible for helping host CPUs on imposing a lot of community-traffic-alleviating functions.

    at last, it’s key to be aware that  an outstanding computational storage force should first be a very good storage machine. Its IO efficiency have to be at least similar to that of a traditional storage pressure. with out a superior basis of storage, computation becomes just about inappropriate and meaningless.

    Following the above intuitive reasoning and the naturally derived structure, ScaleFlux (a Silicon Valley startup company) has successfully launched the world’s first computational storage drives for records centers. Its products are being deployed in hyperscale and webscale information centers worldwide, helping statistics middle operators to cut back the system TCO in two methods:

  • Storage node charge discount: The CPU load discount enabled through ScaleFlux’s computational storage drives allows for storage nodes to reduce the CPU charge. therefore, without changing the compute/storage load on each storage node, you may at once set up computational storage drives to cut back the per-node CPU and storage cost. 
  • Storage node consolidation: One may leverage the CPU load discount and intra-node information site visitors reduction to consolidate the workloads of varied storage nodes into one storage node. meanwhile, the storage cost reduction enabled through computational storage drives largely increases the per-pressure storage density/means, which further supports storage node consolidation.
  • searching into the future

    The inevitable paradigm shift towards heterogeneous and area-particular computing opens a large door for opportunities and improvements. Natively echoing the knowledge of moving computation nearer to statistics, computational storage drives are destined to turn into an critical part in future computing infrastructure. pushed by using the business-huge standardization efforts (e.g., NVMe and SNIA), this rising enviornment is being actively pursued by means of further and further businesses. It will be exciting to look how this new disruptive expertise progresses and evolves over the following couple of years.

    Tong Zhang is co-founder and chief scientist at ScaleFlux.

    New Tech forum offers a venue to explore and discuss rising commercial enterprise know-how in extraordinary depth and breadth. The choice is subjective, according to our pick of the technologies we believe to be crucial and of ideal pastime to InfoWorld readers. InfoWorld doesn't settle for advertising collateral for booklet and reserves the appropriate to edit all contributed content material. send all inquiries to

    Copyright © 2021 IDG Communications, Inc.

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