|Exam Name||:||IBM Distributed Systems Storage Solutions Version 7|
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|Updated On||:||April 23, 2019|
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000-111 exam Dumps Source : IBM Distributed Systems Storage Solutions Version 7
Test Code : 000-111
Test Name : IBM Distributed Systems Storage Solutions Version 7
Vendor Name : IBM
Q&A : 269 Real Questions
No questions was requested that became out of these Q&A bank.
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IBM Cloud Migration manufacturing facility will support Fukui migrate vital know-how extra quickly and efficiently
ARMONK, N.Y., April 18, 2019 /PRNewswire/ -- IBM functions (NYSE: IBM) introduced an agreement with Japan-based mostly Fukui bank emigrate its distributed processing equipment to IBM Cloud to help power operational transformation to tackle consumer needs, and support the financial institution enhance its initiative to support local neighborhood building.
IBM corporation logo. (PRNewsfoto/IBM)extra
Banks are presently confronted with shifting buyer behaviors and expectations in areas like true-time banking, cellular capabilities, and access to finished financial facts. As consumer preferences turn into more customized, it is crucial for each worker to respond for that reason, without delay reply to alterations, and provide excessive first-class digital consumer service. Fukui financial institution is additionally riding its transformation to align with changes in its managerial environment, which requires a more bendy IT atmosphere that also bolster the bank's operational safety.
IBM Cloud gives a framework that allows for applications in a virtual infrastructure atmosphere to be migrated as is, so that it will aid Fukui financial institution build a cloud environment immediately.
As Fukui financial institution continues to movement data, functions and linked functions from its existing infrastructure to the cloud, it's the use of IBM Cloud Migration factory to take a multi-pronged approach to re-host, re-platform, re-architect, and modernize its infrastructure. through the use of IBM Cloud Migration manufacturing unit with automation and other tools, the bank is looking to in the reduction of migration prices and time.
The financial institution plans to start operation on IBM Cloud in the first half of this 12 months.
besides its core banking gadget, Fukui financial institution uses a number of hundred different disbursed techniques which have been built by means of its headquarters and numerous divisions for their individual operations.
The bank is adopting IBM Cloud to run its distributed processing programs and plans to migrate these mission-critical workloads using IBM Cloud for VMware solutions. With IBM Cloud for VMware options, Fukui bank can instantly migrate to the cloud without having to exchange existing tools or approaches and maintain the same level of control, safety and lines. The financial institution can additionally build a totally native subsequent-technology digital infrastructure ambiance on IBM Cloud, which ability future cloud usage, comparable to hybrid cloud adoption, can be greater with ease carried out.
IBM and Fukui bank signed this transaction in q4, 2018.
Conor GoldenIBM Media Relationscgolden@us.ibm.com845-323-6682
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ARMONK, N.Y., Nov. 2, 2016 /PRNewswire/ -- IBM (NYSE: IBM) has been recognized as a pacesetter for dispensed File programs and Object Storage in Gartner's 2016 Magic Quadrant for allotted File programs and Object Storage [ 1] .
according to Gartner, "companies in the Leaders quadrant have the optimum scores for his or her skill to Execute and Completeness of vision. A supplier in the Leaders quadrant has the market share, credibility, and advertising and sales capabilities obligatory to pressure the acceptance of new applied sciences. Market Leaders will customarily be in a position to execute strongly throughout numerous geographies with products that cover both allotted file programs and object storage offerings. they'll also have consistent fiscal efficiency, wide platform guide and flexible deployments fashions."
past this 12 months in Gartner's critical Capabilities for Object Storage [2 ] record, IBM received the highest scores for key use circumstances important to shoppers, together with: Analytics, Archiving and Cloud Storage. additional extending the IBM Cloud Object Storage answer providing, IBM recently announced new public cloud functions that complement or not it's scalable object storage on premises offering with wonderful hybrid cloud alternatives throughout the expansive community of IBM Cloud facts centers all over the world.
"IBM Spectrum Scale and IBM Cloud Object Storage have mixed to offer customers a versatile, scalable answer that may also be deployed on premises and also use public cloud functions," noted Ed Walsh, widely wide-spread supervisor of IBM Storage and application defined Infrastructure. "via automating the circulation of object storage to the cloud within Spectrum Scale, IBM's clear Cloud Tiering extends ordinary storage in cloud repositories with policy pushed, simplicity, safety, and handle."
Nuance: Managing R&D facts on HPC Grid with IBM Spectrum Scale To help its research and construction groups, voice and language options leader, Nuance Communications makes use of excessive efficiency computing (HPC) grids for the development of recent purposes – every little thing from speech awareness and imaging to cell-enabled and healthcare services. The crew tasked with presenting the infrastructure for its globally dispersed developers sought a solution to supply scale and anytime, anyplace accessibility, with regionalization and secure segmentation as integral, to the records stored inside its HPC grids.
"Spectrum Scale clear cloud tiering not best gives us the skill so as to add cloud and object storage with ease but additionally enables us to flow information seamlessly between file, object, and cloud," noted Bob Houvener, Sr. Dir world IT Infrastructure and advanced Computing, Nuance Communications, Inc. "we can movement in opposition t preserving a unified view of all our facts under a single safety and management infrastructure."
nowadays, Nuance has develop into more efficient in its use of storage each vertically, to scale to transforming into potential demands and horizontally to immediately control diverse storage forms, every with differing prices.
 Gartner, Magic Quadrant for allotted File programs and Object Storage, October 20, 2016. identity: G00307798, Analyst(s): Julia Palmer, Arun Chandrasekaran, Raj Bala.
[2 ] Gartner, important Capabilities for Object Storage document, March 31, 2016 id: G00271719; Analyst(s): Arun Chandrasekaran, Raj Bala, Garth Landers. *Use instances: Analytics, Archiving, Cloud Storage.
Gartner doesn't suggest any seller, product or provider depicted in its research publications, and does not recommend expertise clients to choose simplest those companies with the maximum scores or different designation. Gartner research publications consist of the opinions of Gartner's research company and should now not be construed as statements of truth. Gartner disclaims all warranties, expressed or implied, with admire to this analysis, together with any warranties of merchantability or health for a specific aim.
About IBM For more on IBM Storage, seek advice from www.ibm.com/techniques/storage.
For extra tips on IBM Cloud Object Storage, visit https://www.ibm.com/cloud-computing/items/storage/object-storage/.
Media Contact: Greg VitarelliIBM Media Relations1firstname.lastname@example.org
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companies are more and more unfolded throughout distributed suggestions technology environments. distinct purposes dispersed across multicloud computing buildings may require storage with numerous personalities to optimize performance and operations … and manage charge.
“It’s not the flamboyant home windows; it’s now not the fancy paint. but if that basis isn’t right, what occurs? The total constructing falls down,” talked about Eric Herzog (pictured), chief advertising officer and vice president of worldwide storage channels at IBM storage techniques and utility-described infrastructure.
Most groups are adopting multicloud recommendations. This capability they have got distinct types of workloads — legacy, monolithic, cloud-native and microservices-based mostly, to name a number of. They may additionally run in on-premises data facilities, virtualized environments, or in public cloud. One storage type could provide that solid foundation in a single ambiance however not an additional.
A storage misfit may effect in negative utility efficiency and overspending. IBM has refreshed its huge storage portfolio to satisfy the wants of workloads on-prem, in the cloud, and in all places in between.
In a two-part video collection, Herzog spoke with Peter Burris, host of theCUBE, SiliconANGLE Media’s cell livestreaming studio, at theCUBE’s studio in Palo Alto, California. They mentioned the storage wants of nowadays’s distinct workloads and IBM’s latest enhancements to its storage portfolio (watch finished video interviews with transcripts here and right here). (* Disclosure beneath.)
Watch Herzog’s first interview with theCUBE below:To cloud or now not to cloud? a price query
When profiling records for an excellent storage healthy, businesses should analyze performance needs, cost, security and utilization. information that is going to circulate around lots may additionally cost a corporation a bundle if they put it within the cloud, in response to Herzog.
“Going to a public cloud provider, which charge both for out and in of the information, that truly can also can charge more than purchasing an array on-prem,” he noted.
however, many groups may now not want any dataset to be permanently certain on-prem. IBM has made compatibility with all cloud suppliers a main intention of its storage refresh. Its cloud tiering enables clients to tier their on-prem storage arrays to their cloud instances.
“we will tier to the cloud. we are able to backup to the cloud. With object storage, we are able to vicinity [data] in the cloud. We’ve made the cloud, if you will, a seamless tier to the storage infrastructure for our shoppers,” he mentioned.
Watch Herzog’s 2nd interview with theCUBE beneath:statistics flies round go back and forth to cloud and again
additionally in the bang-for-your-buck category are IBM’s new non-volatile memory specific choices. Its Storwize portfolio now comprises the V7000, V5100, and other fashions with NVMe each interior the array and assist for NVMe over cloth.
“These solutions all deliver dramatic efficiency beneficial properties but brilliant fee coupon codes as well,” Herzog observed. “as an instance, the 5010E is not handiest twice as fast because the older 5010, but it surely occurs to be up to 25% less costly,” he pointed out. The V7000 and V5100 bring NVMe to market at a a whole lot lessen cost than rivals offer, he delivered.
Storewize arrays also come with award-successful Spectrum Virtualize utility that presents enterprise-category statistics services. These include image, replication, data-at-rest encryption, transparent tiering to cloud, migration, etc.
one other fresh addition is Spectrum Virtualize for Public Cloud. It sits in a VMware Inc. virtualized instantiation out on the public cloud issuer, giving commercial enterprise-class functionalities. It enables information emigrate or replicate between heterogeneous on-prem storage methods and IBM Cloud or Amazon web capabilities Inc. IBM plans to extend it to greater clouds sooner or later.be certain to take a look at more of SiliconANGLE’s and theCUBE’s dice Conversations. (* Disclosure: IBM Corp. subsidized this segment of theCUBE. Neither IBM nor different sponsors have editorial handle over content material on theCUBE or SiliconANGLE.) image: SiliconANGLE due to the fact that you’re right here …
… We’d like to tell you about our mission and how that you could assist us fulfill it. SiliconANGLE Media Inc.’s business model is based on the intrinsic price of the content, no longer advertising. not like many online publications, we don’t have a paywall or run banner promoting, as a result of we want to preserve our journalism open, devoid of influence or the need to chase traffic.The journalism, reporting and commentary on SiliconANGLE — along with are living, unscripted video from our Silicon Valley studio and globe-trotting video groups at theCUBE — take loads of complicated work, time and money. conserving the best excessive requires the help of sponsors who are aligned with our vision of advert-free journalism content material.
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Major car makers and technology companies convene to discuss how to make human mobility safer and greener for better quality of life SHANGHAI, April 17, 2019 /PRNewswire/ -- Today, members from the Mobility Open Blockchain Initiative (MOBI) and representatives from major technology companies and automakers convene at IBM's China Headquarters in Shanghai to discuss the way forward to accelerate the development of connected cars and autonomous mobility solutions. The first APAC MOBI Colloquium sets out to explore the current state of blockchain research and development, IoT solutions, and how these new technologies can make mobility greener, safer, and more accessible for China and the region. Today's vehicles are evolving from a mode of transport to moving data centers with onboard sensors and computers that capture information about the vehicle and its surroundings. Blockchains can unleash this data to power a new and more sustainable future.
In China, blockchain is undergoing a period of rapid growth and experimentation. The People's Bank of China recently backed and piloted a new blockchain-based trade finance platform, the judiciary adopted rules making blockchain evidence admissible in court, and the Agricultural Bank of China made a blockchain mortgage. The development of blockchain technology itself is key to the nation's technological advancement.
"Blockchain and decentralized ledger technology has great potential to make mobility greener, safer, and more accessible for cities around the world, especially for nations with rapid urbanization like China," said Chris Ballinger, CEO of MOBI. "MOBI is set up to promote the advancement of a future solution that combines smart cities and connected cars to make people's lives better. This is a big mission. It is not a task to be realized by a single company. By bringing together key stakeholders, experts, and partners, we believe MOBI is in the right position at the right time to advance this grand vision."
China has more than 300 million registered vehicles – almost the same number as people in the United States – and the number continues to rise.  10 out of the 25 most congested cities in the world are in mainland China.  Motor vehicle emissions have become a major source of air pollution in China. 
A new mobility solution is needed to ensure a sustainable future.
If this vision can be realized, the benefits it brings to our society are immense. Studies  have shown that autonomous vehicles can optimize fuel efficiency and reduce emissions by 60%. In the US alone, people in urban areas spend about 7 billion hours in traffic every year. That's 3.1 billion gallons of fuel wasted and $160 billion lost due to traffic congestion. Most importantly, by minimizing human error, autonomous vehicle solutions can reduce accidents by 90%.
All this is great but we still have a fair way ahead of us. According to a Rand Corp study,  1 trillion miles of self-driving data is needed in order for autonomous vehicle models to be accurate and safe. Waymo, the former Google autonomous car project, is driving 25,000 miles a day. At this rate, it would take close to 110,000 years to reach the Rand Corp mileage target.
"The linchpin is data," said Trent McConaghy, founder of Ocean Protocol. "We are bringing to MOBI a blockchain-powered platform that enables data to be shared in a safe, privacy-preserving, borderless fashion. Algorithms can travel to data to get trained without exposing the data or taking a copy. This is ideal for MOBI's partners and community to retain data privacy and ownership. For the first time we can unlock the value of data without unlocking data itself," McConaghy continues.
The MOBI Colloquium Shanghai brings together a diverse network of industry leaders and startups, providing them with a platform to share knowledge and innovate. Widespread adoption of blockchain technology cannot be achieved without the creation and agreement upon standards to regulate the ecosystem. By encouraging communication between differing industries, MOBI helps drive forward blockchain scalability. China's robust mobility, blockchain, IoT, and AI ecosystem offers tremendous opportunities for MOBI community members to create a brighter and safer future.
The first APAC MOBI Colloquium is hosted by IBM and co-organized by CPChain and Tribe Accelerator.
MOBI is a nonprofit foundation formed to accelerate adoption of and to promote standards in blockchain, distributed ledgers, and related technologies for the benefit of the mobility industry, consumers, and communities. MOBI and our partners are creating simple, standard and digital ways of identifying cars, people, and trips, of paying for mobility services, and securely exchanging and monetizing data in ways that preserve property rights and privacy.
MOBI is working with most of the world's large automakers and many mobility ecosystem players, along with many start-ups, non-profits, governments, NGOs, transit agencies, and technology companies. We are convinced that by working together we can make mobility services more efficient, affordable, greener, safer and less congested. MOBI is an open, inclusive body that acts as a 'trusted convener' and partner to entities in the emerging ecosystem of pay for use, on demand, connected, and increasingly autonomous mobility services. MOBI itself is technology and ledger agnostic.
About Ocean Protocol
Ocean Protocol is a decentralized data exchange protocol to unlock data for AI. Through blockchain technology and smart contracts, Ocean Protocol connects data providers and consumers, allowing data to be shared while guaranteeing traceability, transparency, and trust for all stakeholders involved. It allows data owners to give value to and have control over their data assets without being locked-in to any single marketplace. By bringing together decentralized blockchain technology, a data sharing framework, and an ecosystem for data and related services, Ocean Protocol is committed to kick-starting a new Data Economy that touches every single person, company and device, giving power back to data owners, enabling people to reap value from data to better our world. Visit oceanprotocol.com to find out more.
Cyber Physical Chain (CPChain) is a new distributed infrastructure for next generation IoT. CPChain intends to build a fundamental data platform for IoT systems in combination with distributed storage, encryption computation and blockchain technologies, providing the whole process solution from data acquisition, storage, sharing to application. CPChain is a promising solution to a series of challenges of the current "chimney architecture" of IoT systems, reducing connectivity cost of devices, protecting data privacy and maximizing the value of IoT data.
About Tribe Accelerator
Tribe Accelerator, the first Singapore government supported blockchain accelerator, is championing to be a neutral platform in driving collaboration and growth of the blockchain ecosystem. Together, these organizations, as an integral part of the critical growth of the global blockchain community, aim to create a collaborative space to encourage the exchange of ideas on the modernization of the mobility space.
Media Contact: Adria Stauber, Communications Managercommunication@dlt.mobi | www.dlt.mobi | @dltMOBI
[1 ] http://english.gov.cn/state_council/ministries/2018/10/17/content_281476349681232.htm  https://www.tomtom.com/en_gb/trafficindex/list?citySize=LARGE&continent=ALL&country=ALL [3 ] https://gbtimes.com/vehicle-emissions-become-major-source-of-air-pollution-in-china [4 ] https://onlinemasters.ohio.edu/blog/the-future-of-driving/ [5 ] https://www.rand.org/content/dam/rand/pubs/research_reports/RR1400/RR1478/RAND_RR1478.pdf
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IBM is announcing today an enhancement to its PowerAI software platform aimed at facilitating the practical scaling of AI models on today’s fastest GPUs. Scaling to 256 GPUs with its new distributed deep learning (DLL) library, IBM reports that it has bested previous records set by Google and Facebook on two well-known image recognition workloads.
“This is one of the bigger breakthroughs I have seen in a while in all of the deep learning industry announcements over the last six months,” said Patrick Moorhead, president and principal analyst of Moor Insights & Strategy. “The interesting part is that it is from IBM, not one of the web giants like Google, which means it is available to enterprises from on-prem use using OpenPower hardware and PowerAI software or even through cloud provider Nimbix.”
The crux of the announcement is a new communication algorithm developed by IBM Research scientists and encapsulated as a communication library, called PowerAI DDL. The library and APIs are available today as a technical preview to Power users as part of the PowerAI version 4.0 release. Other efforts to improve multi-node communication have tended to focus on only a single deep learning framework, so it’s notable that the PowerAI DDL is being integrated into multiple frameworks. Currently TensorFlow, Caffe and Torch are supported with plans to add Chainer.
Customers who don’t have their own Power systems can access the new PowerAI software via the Nimbix Power Cloud.
“Like the hyperscalers and large enterprises, Nimbix has been working to build distributed capability into deep learning frameworks and it just so happens that what IBM is announcing is effectively a turnkey software solution that implements that in multiple frameworks,” said Nimbix CEO Steve Hebert.
“This is truly an HPC technology,” he continued. “It’s taking some of the best software components of traditional HPC and marrying those up with AI and deep learning to be able to deliver that solution. Our platform is ideally suited for scaling out in the HPC sense, very low latency for codes that get that linear scaling of problem sizes. That means for deep learning we can start to tackle enterprise-class deep learning problems basically on day one. For this to become available to any company or consumer outside of [the big hyperscalers], like Google, Baidu, etc., it really democratizes access to everybody.”
The multi-ring communication algorithm within DDL is described (see IBM Research paper) as providing a good tradeoff between latency and bandwidth, as well as being adaptable to a variety of network configurations. The full method is proprietary but section 4 of the paper provides a fairly detailed description of the library and algorithm.
The current PowerAI DDL implementation is based on Spectrum MPI. “MPI provides many needed facilities, from scheduling processes to basic communication primitives, in a portable, efficient and mature software ecosystem” state the researchers, although they add the “core API can be implemented without MPI if desired.”
To evaluate the performance of its new PowerAI Distributed Deep Learning library, IBM performed two experiments using a cluster of 64 IBM “Minsky” Power8 SL822LC servers, each equipped with four Nvidia Tesla P100 GPUs connected through Nvidia’s high-speed NVLink interconnect. The systems occupied four racks (16 nodes each), connected via InfiniBand.
IBM reports that the combination of its Power hardware and software offers better communication overhead for the Resnet-50 neural network using Caffe than what Facebook recently achieved with the Caffe2 deep learning software. The IBM Research DDL software achieved an efficiency of 95 percent using Caffe on its 256-GPU Minsky cluster whereas Facebook achieved 89 percent scaling efficiency on a 256 NVIDIA P100 GPU accelerated DGX-1 cluster using the Caffe2 framework. Implementation differences that could affect the comparison, e.g., Caffe versus Caffe2, are discussed in the IBM Research paper.Scaling results using Caffe with PowerAI DLL to train a ResNet-50 model using the ImageNet-1K data set on 64 Power8 servers that have a total of 256 Nvidia P100 GPUs (Source: IBM)
In the second benchmark test, IBM Research reported a new image recognition accuracy of 33.8 percent for a Resnet-101 neural network trained on a very large data set (7.5 million images, part of the ImageNet-22k set). The previous record published by Microsoft in 2014 demonstrated 29.8 percent accuracy.
IBM Research fellow Hillery Hunter observed that a 4 percent increase in accuracy is a big leap forward as typical improvements in the past have been less than 1 percent.
Further, with IBM’s distributed deep learning approach, the ResNet-101 neural network model was trained in just seven hours, compared to the 10 days it took Microsoft took to train the same model. IBM reported a scaling efficiency of 88 percent.
Sumit Gupta, vice president of AI and HPC within IBM’s Cognitive Systems business unit, believes the increased speed and accuracy will be a huge boon to enterprise clients. “Part of challenge has been if it takes 16 days to train an AI model it’s not really practical,” he said. “You only have a few data scientists when you work in a large enterprise and you really need to make them productive so bringing down that 16 days to 7 hours makes data scientists much more productive.”
Certain applications are particularly time-constrained. “In security, military, fraud protection, and autonomous vehicles you often only have minutes or seconds to train a system to deal with a new exploit or problem but currently it generally takes days,” said market analyst Rob Enderle. “This effectively reduces days to hours, and provides a potential road map to get to minutes and even seconds.” It’s scenarios like these that make buying Power Systems to speed deep learning far easier to justify, he added.
The list of use cases seemingly grows longer by the day. Recommendation engines, credit card fraud detection, mortgage analysis, upsell/cross-sell to retail clients, shopping experience analysis are all getting a lot of attention from IBM’s customers.
“The giants like Microsoft and Google and others who have consumer apps, they obviously are getting on the consumer platform a lot of data all the time. So their use cases in many cases are very obvious, finding images of dogs in Google photos,” for example, said Gupta. “But we see enterprise clients have lots of data and lots of use cases they are now getting around to using these methods.”
The next step for IBM researchers is to document scaling beyond 256 GPUs as their current findings indicate that is feasible. “We don’t see a reason why the method would slow down when we double the size of the system,” said Gupta.
Supported by Alibaba, Baidu, Barclays, IBM, EMC, Intel and Other Industry Leaders, Alluxio Is the Next Major Innovation Out of UC Berkeley's AMPLab
SAN MATEO, CA--(Marketwired - Feb 23, 2016) - Alluxio (formerly known as Tachyon), the world's first memory-centric virtual distributed storage system, today announced its open source version 1.0 release. The vision for Alluxio is to become the de-facto storage unification layer for big data and other scale-out application environments in the same manner that Apache Spark became the standard computation layer.
Alluxio's memory-centric architecture provides orders of magnitude performance gains over existing solutions and superior manageability by allowing developers to interact with a single storage layer API without worrying about the configurations and complexities of underlying storage and file systems. Co-created by Haoyuan Li, CEO of Alluxio, Inc. and a founding committer of Spark, Alluxio ushers in the next generation of storage virtualization for petabyte scale computing.
"A storage unification layer that bridges computation frameworks and underlying storage systems is long overdue in the enterprise," said Haoyuan Li. "Alluxio is that unification layer with a memory-centric architecture. Alluxio enables any framework to access any data, from any storage at memory speeds."
Organizations can run any computation framework (e.g. Apache Spark, Apache MapReduce, Presto, etc.) with any storage system (e.g. Amazon S3, EMC, Google Cloud Storage, NetApp) and utilize any storage media (DRAM, SSD, HDD, etc.). As a memory-centric system, Alluxio yields orders of magnitude performance gains and manageability for existing configurations.
Only three years in existence, Alluxio has gained broad industry support as an open source project. With more than 200 contributors, 12,000 commits, and over 50 commercial organizations, Alluxio has surpassed many other open source projects in the same timeframe. Alluxio runs in production at some of the largest cloud providers for petabyte scale workloads, in financial services to meet government regulations, for research by leading universities, and at technology vendors globally.
Intel recently published its findings on the diverse range of big data storage challenges that Alluxio can address.
"Big data analytics is driving new requirements for distributed memory across clusters for real-time streaming, interactive queries, analytics and graph processing," said Michael Greene, Intel vice president, Software and Services Group and general manager of System Technologies and Optimization. "We are excited to work with developer communities on Alluxio and to optimize Alluxio solutions on Intel platforms. Ultimately, this helps our customers create more innovative and high performance cloud and big data solutions."
In financial services, Alluxio brings many advantages. It helps banks make faster and better trading decisions through dramatic performance improvements and also helps satisfy regulatory requirements. Barclays, the global financial services firm with 48 million customers and clients, recently published a report about how it uses Alluxio to boost big data analytics performance without duplicating confidential customer information to disk.
Last summer, IBM Research published a study about using Tachyon for "ultra-fast big data processing" to overcome "critical bottlenecks for system workloads."
For some of the world's cloud computing giants, Alluxio is allowing business analysts to discover insights interactively by analyzing petabytes of data in near real-time to improve customer experience.
"As one of the largest Internet companies in the world, Baidu constantly faces the challenges of managing data at multi-petabyte scale. By adopting innovative technologies like Alluxio we are able to help our users extract meaningful and useful data almost instantly," said James Peng, Chief Architect at Baidu. "Our deployment of an Alluxio cluster has already reached 1,000 workers, which is one of the largest Alluxio clusters in the world. The tiered storage of Alluxio has provided us great flexibility in managing data in large-scale. We are seeing an average 10-fold, and up to 30-fold performance improvement in supporting interactive query system and other types of workloads. This greatly improved the speed in making important business decisions."
"As the cloud computing business for Alibaba Group, the world's leading e-commerce business, Alibaba manages many of the world's largest data centers, including the largest big data cluster ever built in China," said Wensong Zhang, CTO and Senior Research Fellow of AliCloud, founder of Linux Virtual Server. "With Alluxio combined with AliCloud OSS as well as other AliCloud cloud service products, our customers can leverage the technology trends of hardware to run important jobs at the fastest performance. We have been contributing to the Alluxio open source community and believe that Alluxio will play a critical role in the future of big data infrastructure."
As a PhD candidate at UC Berkeley, Haoyuan Li saw Spark adoption driving the requirements for more developer-friendly methods for how big data frameworks access persistent data at in-memory speeds. Formerly known as Tachyon, the Alluxio system quickly gained prominence in use cases that required in-memory storage speeds for Spark computation and received early backing from enterprise software and storage leaders, including EMC and Pivotal. Where storage and file systems have historically required high customization and tuning, Alluxio brings a unified interface that's intuitive for developers, easy for operators, and delivers unprecedented speeds for data access to support the broadest range of big data use cases such as machine learning, real-time analytics and streaming data.
"As a layer that abstracts away the differences of existing storage systems from the cluster computing frameworks such as Apache Spark and Hadoop MapReduce, Alluxio can enable the rapid evolution of the big data storage, similarly to the way the Internet Protocol (IP) has enabled the evolution of the Internet," said Prof. Ion Stoica, co-author of Spark, co-founder and executive chairman of DataBricks, co-director of UC Berkeley AMPLab and Ph.D. co-advisor to Haoyuan Li.
"AMPLab has created some of the most important open source technologies in the new big data stack, including Apache Spark," said Michael Franklin, Professor of Computer Science and Director of the AMPLab at UC Berkeley. "Alluxio is the next project with roots in the AMPLab to have major impact. We see it playing a huge disruptive role in the evolution of the storage layer to handle the expanding range of big data use cases."
To protect the project from potential trademark litigation and to preserve the intellectual property of the open source software community contributions internationally, the community changed the project name from Tachyon to Alluxio. A newly-created non-profit organization, Alluxio Open Foundation, will host the project.
In 2015, Andreessen Horowitz invested $7.5M in Alluxio Inc., which has since assembled a team consisting of the world's leading distributed computing experts from Carnegie Mellon University, Google, Palantir, UC Berkeley AMPLab and VMWare to continue to innovate and realize the vision for Alluxio.
ABOUT ALLUXIO, INC. Alluxio, Inc. was founded by the creators and top contributors of the open source Alluxio project -- the first memory-centric virtual distributed storage system. Alluxio's memory-centric architecture provides orders of magnitudes performance gains and superior manageability by allowing developers to interact with a single storage layer API. Alluxio, Inc. is venture-backed by Andreessen Horowitz. For more information, contact email@example.com.
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