This document discusses the rise of scale-out NAS storage to address growing unstructured file data needs. Scale-out NAS uses clustering and a global namespace to scale capacity and performance linearly by adding commodity servers and disks. Isilon IQ is highlighted as an example scale-out NAS system that uses a shared memory architecture for high performance and efficiency. The growth of unstructured file data like videos and documents is outpacing structured data, driving more organizations to adopt scale-out NAS that can easily expand to handle petabytes of file storage requirements.
This document discusses integrating Supermicro, Greenplum, and SAS to enable big data analytics platforms and infrastructure. It provides an agenda that includes discussing big data analytics platforms and infrastructure as well as a 1,000 node Hadoop cluster using EMC and Supermicro.
Sustainable IT for Energy Management: Approaches, Challenges, and TrendsEdward Curry
An invited talk to the Galway-Mayo Institute of Technology on the current state of the art in Sustainable IT for energy management, the challenges, and the emerging trends.
Enterprise Energy Management using a Linked Dataspace for Energy IntelligenceEdward Curry
Energy Intelligence platforms can help organizations manage power consumption more efficiently by providing a functional view of the entire organization so that the energy consumption of business activities can be understood, changed, and reinvented to better support sustainable practices. Significant technical challenges exist in terms of information management, cross-domain data integration, leveraging real-time data, and assisting users to interpret the information to optimize energy usage. This paper presents an architectural approach to overcome these challenges using a Dataspace, Linked Data, and Complex Event Processing. The paper describes the fundamentals of the approach and demonstrates it within an Enterprise Energy Observatory.
E. Curry, S. Hasan, and S. O’Riáin, “Enterprise Energy Management using a Linked Dataspace for Energy Intelligence,” in The Second IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT 2012), 2012.
Developing an Sustainable IT Capability: Lessons From Intel's JourneyEdward Curry
Intel Corporation set itself a goal to reduce its global-warming greenhouse gas footprint by 20% by 2012 from 2007 levels. Through the use of sustainable IT, the Intel IT organization is recognized as a significant contributor to the company’s sustainability strategy by transforming its IT operations and overall Intel operations. This article describes how Intel has achieved IT sustainability benefits thus far by developing four key capabilities. These capabilities have been incorporated into the Sustainable ICT Capability Maturity Framework (SICT-CMF), a model developed by an industry consortium in which the authors were key participants. The article ends with lessons learned from Intel’s experiences that can be applied by business and IT executives in other enterprises.
Intel Developer Forum: Taming the Big Data Tsunami
using Intel® Architecture by Clive D’Souza, Solutions Architect, Intel Corporation and
Dhruv Bansal, Chief Science Officer, Infochimps
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
The white paper discusses how enterprises are facing exponentially growing amounts of data that is breaking down traditional storage architectures. It outlines NetApp's approach to addressing big data challenges through what it calls the "Big Data ABCs" - analytics, bandwidth, and content. This allows customers to gain insights from massive data sets, move data quickly for high-performance applications, and store large amounts of content for long periods without increasing complexity. NetApp provides solutions to help enterprises take advantage of big data and turn it into business value.
This document discusses integrating Supermicro, Greenplum, and SAS to enable big data analytics platforms and infrastructure. It provides an agenda that includes discussing big data analytics platforms and infrastructure as well as a 1,000 node Hadoop cluster using EMC and Supermicro.
Sustainable IT for Energy Management: Approaches, Challenges, and TrendsEdward Curry
An invited talk to the Galway-Mayo Institute of Technology on the current state of the art in Sustainable IT for energy management, the challenges, and the emerging trends.
Enterprise Energy Management using a Linked Dataspace for Energy IntelligenceEdward Curry
Energy Intelligence platforms can help organizations manage power consumption more efficiently by providing a functional view of the entire organization so that the energy consumption of business activities can be understood, changed, and reinvented to better support sustainable practices. Significant technical challenges exist in terms of information management, cross-domain data integration, leveraging real-time data, and assisting users to interpret the information to optimize energy usage. This paper presents an architectural approach to overcome these challenges using a Dataspace, Linked Data, and Complex Event Processing. The paper describes the fundamentals of the approach and demonstrates it within an Enterprise Energy Observatory.
E. Curry, S. Hasan, and S. O’Riáin, “Enterprise Energy Management using a Linked Dataspace for Energy Intelligence,” in The Second IFIP Conference on Sustainable Internet and ICT for Sustainability (SustainIT 2012), 2012.
Developing an Sustainable IT Capability: Lessons From Intel's JourneyEdward Curry
Intel Corporation set itself a goal to reduce its global-warming greenhouse gas footprint by 20% by 2012 from 2007 levels. Through the use of sustainable IT, the Intel IT organization is recognized as a significant contributor to the company’s sustainability strategy by transforming its IT operations and overall Intel operations. This article describes how Intel has achieved IT sustainability benefits thus far by developing four key capabilities. These capabilities have been incorporated into the Sustainable ICT Capability Maturity Framework (SICT-CMF), a model developed by an industry consortium in which the authors were key participants. The article ends with lessons learned from Intel’s experiences that can be applied by business and IT executives in other enterprises.
Intel Developer Forum: Taming the Big Data Tsunami
using Intel® Architecture by Clive D’Souza, Solutions Architect, Intel Corporation and
Dhruv Bansal, Chief Science Officer, Infochimps
EMC Isilon: A Scalable Storage Platform for Big DataEMC
This white paper provides insights into EMC Isilon's shared storage approach, covering a wide range of desired characteristics including increased efficiency and reduced total cost.
The white paper discusses how enterprises are facing exponentially growing amounts of data that is breaking down traditional storage architectures. It outlines NetApp's approach to addressing big data challenges through what it calls the "Big Data ABCs" - analytics, bandwidth, and content. This allows customers to gain insights from massive data sets, move data quickly for high-performance applications, and store large amounts of content for long periods without increasing complexity. NetApp provides solutions to help enterprises take advantage of big data and turn it into business value.
The document summarizes Ocarina Storage's data reduction technology which can reduce storage needs by up to 10x through content-aware compression and deduplication. It works with existing storage systems and processes, has no performance impact, and allows lossless data retrieval. The company aims to partner with large storage vendors and help customers in industries like media and life sciences to significantly reduce storage costs. Key customers like Kodak and Disney are excited about the potential for major storage savings.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
The document discusses seven trends in data storage and networking for 2020. It predicts that NVMe will be widely adopted for performance-intensive workloads. It also notes that networking is taking on more aspects of storage, such as caching data. Growing data from IoT and AI is increasing demand for cost-effective long-term storage solutions like tape archives. Cloud strategies are also shifting to be more proactive in optimizing costs.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
Challenges and Best Practices for Storing/ Challenges and Best Practices for ...NetApp
This white paper discusses challenges with storing and archiving data in the petroleum and gas exploration industry and presents solutions from NetApp and Interica. The challenges include rapidly growing storage needs, high costs of periodically remastering tape archives due to data deterioration and system obsolescence, risks of data corruption with tape, and slow data recovery from tape. New disk-based storage technologies like data compression, deduplication, and RAID can help address these challenges by providing better data management, protection, and faster access compared to tape-based solutions. NetApp and Interica provide integrated data protection, management and archiving solutions leveraging these disk technologies.
This document discusses future trends in big data. It notes that the amount of data produced grows enormously every year due to new technologies and devices. Big data provides businesses with better sources of analysis and insights. Key trends discussed include the growth of open source tools like Hadoop and Spark, increased use of machine learning and predictive analytics, edge computing and analytics to process IoT data more efficiently, integration of big data and cloud computing, use of big data for cybersecurity, and growing demand for data science jobs. The conclusion states that big data will significantly impact businesses and 15% of IT organizations will move services to the cloud by 2021.
This document discusses big data, including its definition, challenges, sources, types (volume, velocity, variety), and applications in various domains like engineering design, ecommerce, and product lifecycle management. It notes that big data is growing exponentially due to increased data collection and requires new technologies and architectures to process. The document outlines advantages of big data like improved innovation, customer satisfaction, and risk analysis.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
The capabilities DataCore delivers have recently been significantly uplifted and streamlined further for virtualized server environments in its latest SANsymphony-V release. For brevity, it’s hard to beat DataCore’s press release which points out that “SANsymphony offers a flexible, open software platform from which to provision, share, reconfigure, migrate, replicate, expand and upgrade storage without slowdowns or downtime.” The product is agnostic with regard to the underlying storage hardware and can essentially breathe life and operational value into whatever is on a user’s floor. It is robust, flexible, and responsive and it can deliver value in terms of, for instance, better economics, improved response times, high availability (HA), and easy management administration.
This white paper will examine SANsymphony-V's place in the Software-Defined Storage marketplace and review it's core features and capabilities.
The Genpact Intelligent Process Insights Engine (IPIE) provides a platform for developing purpose-built analytics applications to drive business outcomes. It uses a Systems of Engagement approach, applying process expertise to map information supply chains and pull only relevant data from various systems into the IPIE. Applications are then built on the IPIE to deliver consistent analytics and insights across the enterprise. This approach organically embeds data governance and avoids issues of traditional analytics methods. The Genpact IPIE and associated applications can provide a "single version of the truth" that enterprises seek to improve performance through intelligent operations.
This newsletter highlights innovative concepts that can help manufacturers tackle important issues. Articles discuss how data analysis can lower costs and increase profits, how connectivity enables smart manufacturing, and how collaboration tools can support remote work. The newsletter provides updates on industry events and recognizes Cisco for enhancing customer value in manufacturing.
This document discusses the concept of a "Predictive Enterprise" and Intel's role in enabling it. A Predictive Enterprise uses real-time analytics and automated decision-making to sense emerging trends, predict outcomes, and proactively respond. The document outlines technology barriers like data explosion and security threats. It argues Intel can help overcome these through solutions that optimize resources, improve collaboration and mobility, and make data centers more efficient. Partnering with Intel and its ecosystem can help companies transform IT strategies and gain competitive advantages in the emerging business environment.
CASE STUDY ON METHODS AND TOOLS FOR THE BIG DATA ANALYSISIRJET Journal
This document discusses big data analysis tools and methods. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data from various sources that cannot be processed with traditional computing approaches due to its size and complexity. It then discusses some of the major challenges in big data such as capturing, storing, searching, sharing, and analyzing large amounts of diverse data. The document provides an overview of different big data tools and methods for processing large datasets and addresses their limitations. It focuses on using cloud technologies and improving data management to better handle big data challenges.
This document discusses big data, including what it is, its characteristics, advantages, and challenges. Big data refers to extremely large data sets that cannot be processed with traditional data processing tools. It is characterized by its volume, variety, velocity, variability, and veracity. Big data has advantages in fields like predicting diseases and improving transportation safety. However, challenges include storing large amounts of data from various sources and processing it quickly. The document outlines tools used for big data like Hadoop and MongoDB and concludes that big data plays a vital role in today's world.
The July 2023 edition of the Enterprise Architecture Professional Journal.
This issue of the Enterprise Architecture Professional Journal brings complementary views from opposite sides of the world, speaking to the evolution and future of the Enterprise and Business Architecture disciplines.
The first article comes to us from Paul Taylor and Inji Wijegunaratne, participants in an industry engagement forum run by the University of Melbourne in Victoria, Australia. Entitled “Enterprise Architecture in the Digital Age”, it highlights some historical perspectives on the evolution of the EA discipline, and speaks to how EA must continue to change to provide value in four very different contemporary business models.
The second feature article comes to us from the highly experienced Whynde Kuehn, co-founder of the Business Architecture Guild, expert in Digital Transformation, and author of the recent book Strategy to Reality. In this article, entitled “The Evolution of Business Architecture and the Opportunity for Enterprise Architecture”, Whynde writes about the enormous benefits available to organizations from the appropriate application of both Business and Enterprise Architecture.
Mis on andmekeskuse uus standard - hüperkonvergents?
Kui kõiki kesksüsteeme ei ole võimalik pilve viia ja serverikeskuse kasv suurendab halduse keerukust, on väljapääs serverikeskuse konvergents. Simplivity Omnicube on konvergentsi uus tase. Millised on serverikeskuse kasvuga seotud põhiprobleemid ja kuidas neid lahendada? Kuidas korraldada Disaster Recovery ja Backup?
This White Paper provides an introduction to the EMC Isilon scale-out data lake as the key enabler to store, manage, and protect unstructured data for traditional and emerging workloads.
1) EMC invests heavily in R&D, spending 12% of revenue ($1.5 billion) annually to develop innovative products and acquire technology companies to provide customers with best-in-breed solutions for their information infrastructure needs.
2) Information growth is relentless, increasing approximately 60% annually, with over 988 exabytes of new information generated in 2007 alone.
3) EMC's strategy is to provide an information infrastructure through networked storage, security, virtualization, intelligent information management, enterprise content management, and managed services to store, protect, optimize and leverage information for customers.
The Evolution of the Leonardo DiCaprio Haircut: A Journey Through Style and C...greendigital
Leonardo DiCaprio, a name synonymous with Hollywood stardom and acting excellence. has captivated audiences for decades with his talent and charisma. But, the Leonardo DiCaprio haircut is one aspect of his public persona that has garnered attention. From his early days as a teenage heartthrob to his current status as a seasoned actor and environmental activist. DiCaprio's hairstyles have evolved. reflecting both his personal growth and the changing trends in fashion. This article delves into the many phases of the Leonardo DiCaprio haircut. exploring its significance and impact on pop culture.
Modern Radio Frequency Access Control Systems: The Key to Efficiency and SafetyAITIX LLC
Today's fast-paced environment worries companies of all sizes about efficiency and security. Businesses are constantly looking for new and better solutions to solve their problems, whether it's data security or facility access. RFID for access control technologies have revolutionized this.
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The document summarizes Ocarina Storage's data reduction technology which can reduce storage needs by up to 10x through content-aware compression and deduplication. It works with existing storage systems and processes, has no performance impact, and allows lossless data retrieval. The company aims to partner with large storage vendors and help customers in industries like media and life sciences to significantly reduce storage costs. Key customers like Kodak and Disney are excited about the potential for major storage savings.
Enterprises are facing exponentially increasing amounts of data that is breaking down traditional storage architectures. NetApp addresses this "big data challenge" through their "Big Data ABCs" approach - focusing on analytics, bandwidth, and content. This enables customers to gain insights from massive datasets, move data quickly for high-speed applications, and securely store unlimited amounts of content for long periods without increasing complexity. NetApp's solutions provide a foundation for enterprises to innovate with data and drive business value.
The document discusses seven trends in data storage and networking for 2020. It predicts that NVMe will be widely adopted for performance-intensive workloads. It also notes that networking is taking on more aspects of storage, such as caching data. Growing data from IoT and AI is increasing demand for cost-effective long-term storage solutions like tape archives. Cloud strategies are also shifting to be more proactive in optimizing costs.
Big Data 101 - Creating Real Value from the Data Lifecycle - Happiest Mindshappiestmindstech
The big impact of Big Data in the post-modern world is
unquestionable, un-ignorable and unstoppable today.
While there are certain discussions around Big Data being
really big, here to stay or just an over hyped fad; there are
facts as shared in the following sections of this whitepaper
that validate one thing - there is no knowing of the limits
and dimensions that data in the digital world can assume.
IRJET - Big Data Analysis its ChallengesIRJET Journal
This document discusses big data analysis and its challenges. It begins by defining big data and business analytics, noting that large amounts of data are now being generated daily that require new techniques to analyze. It describes some of the key challenges in handling big data, including issues around storage, analysis, and reporting on large, complex datasets. The document then discusses the four Vs of big data - volume, variety, velocity, and veracity. It concludes by noting limitations in current research and opportunities for future work to better understand the impacts of big data and business analytics on competitive advantages.
Challenges and Best Practices for Storing/ Challenges and Best Practices for ...NetApp
This white paper discusses challenges with storing and archiving data in the petroleum and gas exploration industry and presents solutions from NetApp and Interica. The challenges include rapidly growing storage needs, high costs of periodically remastering tape archives due to data deterioration and system obsolescence, risks of data corruption with tape, and slow data recovery from tape. New disk-based storage technologies like data compression, deduplication, and RAID can help address these challenges by providing better data management, protection, and faster access compared to tape-based solutions. NetApp and Interica provide integrated data protection, management and archiving solutions leveraging these disk technologies.
This document discusses future trends in big data. It notes that the amount of data produced grows enormously every year due to new technologies and devices. Big data provides businesses with better sources of analysis and insights. Key trends discussed include the growth of open source tools like Hadoop and Spark, increased use of machine learning and predictive analytics, edge computing and analytics to process IoT data more efficiently, integration of big data and cloud computing, use of big data for cybersecurity, and growing demand for data science jobs. The conclusion states that big data will significantly impact businesses and 15% of IT organizations will move services to the cloud by 2021.
This document discusses big data, including its definition, challenges, sources, types (volume, velocity, variety), and applications in various domains like engineering design, ecommerce, and product lifecycle management. It notes that big data is growing exponentially due to increased data collection and requires new technologies and architectures to process. The document outlines advantages of big data like improved innovation, customer satisfaction, and risk analysis.
This document discusses big data characteristics, issues, challenges, and technologies. It describes the key characteristics of big data as volume, velocity, variety, value, and complexity. It outlines issues related to these characteristics like data volume and velocity. Challenges of big data include privacy and security, data access and sharing, analytical challenges, human resources, and technical challenges around fault tolerance, scalability, data quality, and heterogeneous data. The document also discusses technologies used for big data like Hadoop, HDFS, and cloud computing and provides examples of big data projects.
The capabilities DataCore delivers have recently been significantly uplifted and streamlined further for virtualized server environments in its latest SANsymphony-V release. For brevity, it’s hard to beat DataCore’s press release which points out that “SANsymphony offers a flexible, open software platform from which to provision, share, reconfigure, migrate, replicate, expand and upgrade storage without slowdowns or downtime.” The product is agnostic with regard to the underlying storage hardware and can essentially breathe life and operational value into whatever is on a user’s floor. It is robust, flexible, and responsive and it can deliver value in terms of, for instance, better economics, improved response times, high availability (HA), and easy management administration.
This white paper will examine SANsymphony-V's place in the Software-Defined Storage marketplace and review it's core features and capabilities.
The Genpact Intelligent Process Insights Engine (IPIE) provides a platform for developing purpose-built analytics applications to drive business outcomes. It uses a Systems of Engagement approach, applying process expertise to map information supply chains and pull only relevant data from various systems into the IPIE. Applications are then built on the IPIE to deliver consistent analytics and insights across the enterprise. This approach organically embeds data governance and avoids issues of traditional analytics methods. The Genpact IPIE and associated applications can provide a "single version of the truth" that enterprises seek to improve performance through intelligent operations.
This newsletter highlights innovative concepts that can help manufacturers tackle important issues. Articles discuss how data analysis can lower costs and increase profits, how connectivity enables smart manufacturing, and how collaboration tools can support remote work. The newsletter provides updates on industry events and recognizes Cisco for enhancing customer value in manufacturing.
This document discusses the concept of a "Predictive Enterprise" and Intel's role in enabling it. A Predictive Enterprise uses real-time analytics and automated decision-making to sense emerging trends, predict outcomes, and proactively respond. The document outlines technology barriers like data explosion and security threats. It argues Intel can help overcome these through solutions that optimize resources, improve collaboration and mobility, and make data centers more efficient. Partnering with Intel and its ecosystem can help companies transform IT strategies and gain competitive advantages in the emerging business environment.
CASE STUDY ON METHODS AND TOOLS FOR THE BIG DATA ANALYSISIRJET Journal
This document discusses big data analysis tools and methods. It begins by defining big data as large volumes of structured, semi-structured, and unstructured data from various sources that cannot be processed with traditional computing approaches due to its size and complexity. It then discusses some of the major challenges in big data such as capturing, storing, searching, sharing, and analyzing large amounts of diverse data. The document provides an overview of different big data tools and methods for processing large datasets and addresses their limitations. It focuses on using cloud technologies and improving data management to better handle big data challenges.
This document discusses big data, including what it is, its characteristics, advantages, and challenges. Big data refers to extremely large data sets that cannot be processed with traditional data processing tools. It is characterized by its volume, variety, velocity, variability, and veracity. Big data has advantages in fields like predicting diseases and improving transportation safety. However, challenges include storing large amounts of data from various sources and processing it quickly. The document outlines tools used for big data like Hadoop and MongoDB and concludes that big data plays a vital role in today's world.
The July 2023 edition of the Enterprise Architecture Professional Journal.
This issue of the Enterprise Architecture Professional Journal brings complementary views from opposite sides of the world, speaking to the evolution and future of the Enterprise and Business Architecture disciplines.
The first article comes to us from Paul Taylor and Inji Wijegunaratne, participants in an industry engagement forum run by the University of Melbourne in Victoria, Australia. Entitled “Enterprise Architecture in the Digital Age”, it highlights some historical perspectives on the evolution of the EA discipline, and speaks to how EA must continue to change to provide value in four very different contemporary business models.
The second feature article comes to us from the highly experienced Whynde Kuehn, co-founder of the Business Architecture Guild, expert in Digital Transformation, and author of the recent book Strategy to Reality. In this article, entitled “The Evolution of Business Architecture and the Opportunity for Enterprise Architecture”, Whynde writes about the enormous benefits available to organizations from the appropriate application of both Business and Enterprise Architecture.
Mis on andmekeskuse uus standard - hüperkonvergents?
Kui kõiki kesksüsteeme ei ole võimalik pilve viia ja serverikeskuse kasv suurendab halduse keerukust, on väljapääs serverikeskuse konvergents. Simplivity Omnicube on konvergentsi uus tase. Millised on serverikeskuse kasvuga seotud põhiprobleemid ja kuidas neid lahendada? Kuidas korraldada Disaster Recovery ja Backup?
This White Paper provides an introduction to the EMC Isilon scale-out data lake as the key enabler to store, manage, and protect unstructured data for traditional and emerging workloads.
1) EMC invests heavily in R&D, spending 12% of revenue ($1.5 billion) annually to develop innovative products and acquire technology companies to provide customers with best-in-breed solutions for their information infrastructure needs.
2) Information growth is relentless, increasing approximately 60% annually, with over 988 exabytes of new information generated in 2007 alone.
3) EMC's strategy is to provide an information infrastructure through networked storage, security, virtualization, intelligent information management, enterprise content management, and managed services to store, protect, optimize and leverage information for customers.
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The Unbelievable Tale of Dwayne Johnson Kidnapping: A Riveting Sagagreendigital
Introduction
The notion of Dwayne Johnson kidnapping seems straight out of a Hollywood thriller. Dwayne "The Rock" Johnson, known for his larger-than-life persona, immense popularity. and action-packed filmography, is the last person anyone would envision being a victim of kidnapping. Yet, the bizarre and riveting tale of such an incident, filled with twists and turns. has captured the imagination of many. In this article, we delve into the intricate details of this astonishing event. exploring every aspect, from the dramatic rescue operation to the aftermath and the lessons learned.
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The Origins of the Dwayne Johnson Kidnapping Saga
Dwayne Johnson: A Brief Background
Before discussing the specifics of the kidnapping. it is crucial to understand who Dwayne Johnson is and why his kidnapping would be so significant. Born May 2, 1972, Dwayne Douglas Johnson is an American actor, producer, businessman. and former professional wrestler. Known by his ring name, "The Rock," he gained fame in the World Wrestling Federation (WWF, now WWE) before transitioning to a successful career in Hollywood.
Johnson's filmography includes blockbuster hits such as "The Fast and the Furious" series, "Jumanji," "Moana," and "San Andreas." His charismatic personality, impressive physique. and action-star status have made him a beloved figure worldwide. Thus, the news of his kidnapping would send shockwaves across the globe.
Setting the Scene: The Day of the Kidnapping
The incident of Dwayne Johnson's kidnapping began on an ordinary day. Johnson was filming his latest high-octane action film set to break box office records. The location was a remote yet scenic area. chosen for its rugged terrain and breathtaking vistas. perfect for the film's climactic scenes.
But, beneath the veneer of normalcy, a sinister plot was unfolding. Unbeknownst to Johnson and his team, a group of criminals had planned his abduction. hoping to leverage his celebrity status for a hefty ransom. The stage was set for an event that would soon dominate worldwide headlines and social media feeds.
The Abduction: Unfolding the Dwayne Johnson Kidnapping
The Moment of Capture
On the day of the kidnapping, everything seemed to be proceeding as usual on set. Johnson and his co-stars and crew were engrossed in shooting a particularly demanding scene. As the day wore on, the production team took a short break. providing the kidnappers with the perfect opportunity to strike.
The abduction was executed with military precision. A group of masked men, armed and organized, infiltrated the set. They created chaos, taking advantage of the confusion to isolate Johnson. Johnson was outnumbered and caught off guard despite his formidable strength and fighting skills. The kidnappers overpowered him, bundled him into a waiting vehicle. and sped away, leaving everyone on set in a state of shock and disbelief.
The Immediate Aftermath
The immediate aftermath of the Dwayne Johnson kidnappin
Orpah Winfrey Dwayne Johnson: Titans of Influence and Inspirationgreendigital
Introduction
In the realm of entertainment, few names resonate as Orpah Winfrey Dwayne Johnson. Both figures have carved unique paths in the industry. achieving unparalleled success and becoming iconic symbols of perseverance, resilience, and inspiration. This article delves into the lives, careers. and enduring legacies of Orpah Winfrey Dwayne Johnson. exploring how their journeys intersect and what we can learn from their remarkable stories.
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Early Life and Backgrounds
Orpah Winfrey: From Humble Beginnings to Media Mogul
Orpah Winfrey, often known as Oprah due to a misspelling on her birth certificate. was born on January 29, 1954, in Kosciusko, Mississippi. Raised in poverty by her grandmother, Winfrey's early life was marked by hardship and adversity. Despite these challenges. she demonstrated a keen intellect and an early talent for public speaking.
Winfrey's journey to success began with a scholarship to Tennessee State University. where she studied communication. Her first job in media was as a co-anchor for the local evening news in Nashville. This role paved the way for her eventual transition to talk show hosting. where she found her true calling.
Dwayne Johnson: From Wrestling Royalty to Hollywood Superstar
Dwayne Johnson, also known by his ring name "The Rock," was born on May 2, 1972, in Hayward, California. He comes from a family of professional wrestlers, with both his father, Rocky Johnson. and his grandfather, Peter Maivia, being notable figures in the wrestling world. Johnson's early life was spent moving between New Zealand and the United States. experiencing a variety of cultural influences.
Before entering the world of professional wrestling. Johnson had aspirations of becoming a professional football player. He played college football at the University of Miami. where he was part of a national championship team. But, injuries curtailed his football career, leading him to follow in his family's footsteps and enter the wrestling ring.
Career Milestones
Orpah Winfrey: The Queen of All Media
Winfrey's career breakthrough came in 1986 when she launched "The Oprah Winfrey Show." The show became a cultural phenomenon. drawing millions of viewers daily and earning many awards. Winfrey's empathetic and candid interviewing style resonated with audiences. helping her tackle diverse and often challenging topics.
Beyond her talk show, Winfrey expanded her empire to include the creation of Harpo Productions. a multimedia production company. She also launched "O, The Oprah Magazine" and OWN: Oprah Winfrey Network, further solidifying her status as a media mogul.
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Esg Wp Isilon Scale Out Nas Comes Of Age Sep 08
1. ESG
WHITEPAPER
Isilon IQ
Scale-Out NAS Comes of Age
By Terri McClure
With John McKnight and Steve Duplessie
September, 2008
Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
2. ESG REPORT
Scale-Out NAS Comes of Age
Table of Contents
Table of Contents..................................................................................................................................................... i
Scale-Out NAS Comes of Age ............................................................................................................................... 1
New Market Dynamics .......................................................................................................................................... 1
Addressing the Challenge: .................................................................................................................................... 2
Scale-Out File Storage ........................................................................................................................................... 2
Scale-Up versus Scale-Out ................................................................................................................................... 2
Scale-Up ............................................................................................................................................................... 2
Scale-Out .............................................................................................................................................................. 3
Scale-Out File Storage Attributes ......................................................................................................................... 4
Clustering .............................................................................................................................................................. 4
N-Way Clustering Approaches .............................................................................................................................. 4
Global Namespace-enabled ................................................................................................................................. 5
Power, Cooling and Space Efficiency (PCSE) ...................................................................................................... 5
Self-Managing and Self-Healing ........................................................................................................................... 5
Advanced Scale-Out Features ............................................................................................................................... 5
Transparent Data Mobility ..................................................................................................................................... 5
Tiered Storage Support ......................................................................................................................................... 6
Scale-Out NAS Comes of Age: .............................................................................................................................. 6
Isilon IQ .................................................................................................................................................................... 6
Isilon Advantage: SMP Architecture ..................................................................................................................... 7
Summary .................................................................................................................................................................. 8
All trademark names are property of their respective companies. Information contained in this publication has been obtained by sources The
Enterprise Strategy Group (ESG) considers to be reliable but is not warranted by ESG. This publication may contain opinions of ESG, which
are subject to change from time to time. This publication is copyrighted by The Enterprise Strategy Group, Inc. Any reproduction or
redistribution of this publication, in whole or in part, whether in hard-copy format, electronically, or otherwise to persons not authorized to
receive it, without the express consent of the Enterprise Strategy Group, Inc., is in violation of U.S. copyright law and will be subject to an
action for civil damages and, if applicable, criminal prosecution. Should you have any questions, please contact ESG Client Relations at
(508) 482-0188. This ESG White Paper was developed with the assistance and funding of Isilon Systems.
-i-
Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
3. ESG REPORT
Scale-Out NAS Comes of Age
Scale-Out NAS Comes of Age
New Market Dynamics
The IT market is changing. The Internet Era of computing is upon us and commercial enterprises are going to
get dragged in—whether they like it or not. Web 2.0, cloud computing, and SOA content/data will coexist in
commercial enterprises, along with transactional and distributed content—requiring a mix of
price/performance/functionality that differs from both. Most traditional storage players can’t (yet) support the
performance requirements of high bandwidth file-based data as they are optimized to support small block/file
transaction processing, which requires very different architectural performance characteristics. Just as there
became a separate—but additive—type of data to contend with during the arrival of the Distributed Era of
computing (file versus pure block-based transactional data), data generated in the Internet Era will also exhibit
brand new characteristics. Just as file-optimized storage devices found their way to the mainstream commercial
markets to co-exist with traditional core system devices, next-generation scale-out NAS arrays capable of
addressing the specific attributes and requirements of today’s Web 2.0 generated data will also be required.
Within a relatively short time, the majority of capacity under management in the commercial sector will be born as
file-based rich digital content. Just as small random access file data generated in the distributed computing era
dwarfed small random access block data from the transactional era, in short order, the large-file, collaborative
data of the Internet Era will do the same within organizations. Further, where large orders are still measured in
TBs in transactional or distributed environments, they are measured in multiple PBs in Internet computing
environments.
File storage encompasses a wide range of documents, including Word, Excel, PDFs, PowerPoint, scanned
images, CAD/CAM, source code, check images, x-rays, as well as Internet Era rich digital content such as video,
audio, blogs, and wikis. These types of files are often referred to as unstructured data and current ESG research
indicates that data growth in this area is exceeding that of other data types—estimating 62 Exabytes of archived
file data by 2012, dwarfing database- and e-mail-based archive data (see Figure 1).
FIGURE 1. PROJECTED ARCHIVE DATA GROWTH BY TYPE
Total Archived Capacity, by Content Type - Worldwide (TB)
100,000,000
90,000,000
80,000,000
70,000,000
60,000,000
50,000,000
40,000,000
30,000,000
20,000,000
10,000,000
0
2008 2009 2010 2011 2012
Unstructured 10,443,868 15,808,970 24,242,857 39,364,875 62,749,188
Database 1,837,780 2,991,043 4,823,578 8,110,447 13,639,302
E-mail 1,442,346 2,557,446 4,380,761 7,745,201 13,484,097
Source: ESG Research Report: Digital Archiving: End-User Survey & Market Forecast 2006-2010, January, 2006
-1-
Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
4. ESG REPORT
Scale-Out NAS Comes of Age
Not surprisingly, the massive growth of file data is driving growth in the Network Attached Storage (NAS) market.
Vendors have been adapting technology to help users cope with managing file data growth; introducing denser
NAS arrays, data reduction technologies, storage management, and storage optimization solutions. Like most
technologies, the solutions were brought to market to solve an existing problem—and most look backward rather
than to the future. Today, the challenge with most enterprises is that file data growth is already out of control;
this pattern of file data growth outpacing e-mail and database-driven growth has been going on for quite a while!
Now, commercial enterprises are struggling with the new file characteristics of Internet Era data, further
exacerbating the problem.
It is no surprise that we often hear data center managers say that they love their first NAS appliance—and curse
their tenth, or worse yet, their hundredth! The growth of file-based data has left enterprise data centers bursting
at the seams. The growth of file data, as well as the shifting nature of the files themselves to richer formats, is
leading data center managers to consider taking a new approach to storing and managing file-based data and
NAS vendors to introduce entirely new architectures. For managing growth and meeting the performance
characteristics required by richer file data, “scale-out” is the buzzword of the day: the next step in NAS’s rich
history of evolving to solve file storage and management challenges.
Addressing the Challenge:
Scale-Out File Storage
Scale-Up versus Scale-Out
Multi-dimensional scale has already appeared on the market and it is a core requirement of this new generation
of file-based storage architectures. Scale-out, the ability to independently scale and tune bandwidth, processing,
and storage capacity on the fly—all while managing the file system and single global namespace—will be the
new backbone of file-based storage solutions.
Scale-out storage architectures are significantly different than the monolithic, scale-up storage architectures (e.g.
traditional NAS or SAN systems) that developed in the Distributed Computing Era. Scale-out has been around
for a while, but it has been tucked away in a corner, mostly used in HPC and scientific computing environments,
as well as media and entertainment. But the advent of new computing use models such as Web 2.0, SaaS, and
SOA introduces the requirement for scale-out in commercial enterprises.
Scale-Up
Scale-up storage is just what it sounds like; it is designed to be monolithic, where lots of storage sits behind one
or two file server heads and is designed to scale into the multi-TB range behind those file server heads. Once
the limit on storage is hit, a new monolithic system is installed with a new file system to manage. There is no
way to share the workload between the systems, and migrating directories or files between systems means
remapping and remounting for each and every client with access. Those that have been through it know the pain
of the process; it can be excruciating in a large enterprise environment with lots of clients and zero tolerance for
downtime.
Scale-up systems have no economical way to scale performance without some significant price penalty.
Performance in today’s monolithic systems is often scaled by adding a storage rack and more spindles to
increase throughput and reduce latency (and, as a byproduct, reduce storage utilization). This is an expensive
proposition for serving large sequential files. Adding processing power independently, as can be done with scale-
out systems, not only saves floor and rack space. In addition to getting better performance, it would significantly
reduce power consumption since processors typically use 95% less power than an additional disk shelf would
consume.
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
5. ESG REPORT
Scale-Out NAS Comes of Age
Scale-Out
Scale-out file storage utilizing standard NAS protocols (NFS, CIFS) meets the need for independent scale of
storage capacity, processors, and bandwidth. Adding capacity and bandwidth, as well as file system expansion,
is done online with minimal system performance impact. This granular scaling capability provides a
price/performance advantage as it allows users to start small and scale where needed.
TABLE 1. SCALE-OUT VERSUS SCALE-UP NAS
Scale-out NAS meets a real market requirement for efficiently dealing with the large files typical of Internet Era
file-based unstructured data. Recent ESG research indicates that scale-out NAS will be the fastest-growing
segment of the file storage market (in both revenue and capacity) between 2007 and 2012, reaching 6.7
Exabytes in 2012 (see Figure 2).
FIGURE 2. SCALE-OUT NAS SHIPMENT FORECAST THROUGH 2012
Source: ESG Research, September 2008
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
6. ESG REPORT
Scale-Out NAS Comes of Age
Scale-Out File Storage Attributes
The next-generation file market is just beginning to go mainstream. Though vendors have been offering products
since 2000, most of the focus has been in niche markets. Enterprise-class features required today, such as
remote mirroring, snapshots, and redundant components for high availability and disaster recovery will be a core
component of mainstream scale-out NAS systems. But time has taught many lessons regarding manageability,
scalability, and efficiency. As a result, combined with the enormous quantity of file-based data that exists and will
continue to grow like mad, requirements for scale-out file storage systems will need to incorporate most, if not all,
of the following traits:
Clustering, to be managed as a single entity
Global namespace capability
Independent scalability of bandwidth, processors, and storage
Power efficiency
Self-managing
Self-healing
Clustering
A clustered file system runs concurrently on multiple physical storage nodes and is managed as a single entity.
Essentially, a cluster removes the limitations of individual devices, thereby removing the boundaries of the boxes
and enabling efficient management of multiple file servers. There are a number of approaches to clustering on
the market. One approach is to employ clustering on a traditional scale-up architecture using a dual-node
system. Commonly referred to as “two-way clustering,” dual node systems are primarily deployed for failover
and to maintain high availability. Typically, these solutions enable one controller head to assume the identity of
the failing controller head, and allow the failed controller’s data volumes to continue to be accessed or written to
by the new controller head. This inherently limits performance and scalability, as processing power is halved
when one controller head fails. Management complexity and relative high cost to achieve the high availability
are the main limiting factors with this approach.
Unlike scale-up’s two-way clustering implementation, scale-out systems employ n-way clustering that can start
with as few as three nodes, but scales well beyond. The advantages of scale-out clustered systems are scale
and ease of use. I/O loads are handled in parallel, leveraging distributed lock management and distributed
metadata so any processing node is able to handle any request. Another advantage to clustered and
independently scaled systems is the cost. Users can start out small and then grow into a massively parallel
system. The performance ceiling is raised by adding more processors, the capacity by adding more storage for
“just-in-time” scalability. And they can be easily managed because the entire cluster is handled as a single
entity. IT managers simply cannot afford to manage hundreds of file systems individually—people don’t scale.
N-Way Clustering Approaches
Clustered storage with a Distributed File System (DFS): Distributed clustered storage is a networked
storage system that allows users to combine and add storage nodes, all of which access the same pool
of data. These solutions reside directly on the storage layer with fully distributed file systems across any
number of nodes/storage controllers. Since the software resides at the storage layer itself, it can fully
control layout of data (data striping) across all the storage nodes that make up the cluster. The cluster
works together as an intelligent unified team, with each node capable of running on its own and
communicating with other nodes to deliver files in response to user needs. Each node in the cluster is a
coherent peer, meaning each node knows everything about the other. Distributed clustered storage
provides much higher levels of availability, reliability, scalability, aggregate throughput, and ease of
management when compared to 2-way clustering.
Symmetric Clustered Architecture: Symmetric clustered architectures share many attributes of DFS
clusters: symmetric clustered architectures grow resources seamlessly and enable the modular growth,
or “pay-as-you-grow,” benefits of the storage system. When more memory, bandwidth, capacity, or drive
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
7. ESG REPORT
Scale-Out NAS Comes of Age
actuators are needed, the cluster can be grown by simply adding additional nodes to the cluster. And
symmetric clustered architectures provide extremely high levels of availability. But rather than leveraging
a peer design, as with a DFS cluster, in symmetric cluster architectures as more nodes are added to the
cluster, it still has one logical brain, regardless of the number of nodes in the solution. It maintains its
coherency as one logical, dynamically expandable system. .
Global Namespace-enabled
This is a simple concept that is extremely difficult to achieve. In layman’s terms, a global namespace is a virtual
representation of a group of disparate physical file systems. It sits between clients and the assorted file servers
in a given environment and adds a layer of abstraction that divorces what the client sees as mount points from
the physical server mount points. It is a map that takes care of translating the virtual mount points to physical file
servers and presents users with one consolidated view of the file server ecosystem. It is the secret sauce that
enables a single point of management and advanced features, such as non-disruptive data migration and load
balancing.
It is important to differentiate native global namespace support from namespace aggregation. Namespace
aggregation solutions essentially present a single pane of glass for administering storage management for
multiple NAS systems. These solutions create gateways (either software-only or switch-based software) through
which data from several different file systems is redirected to be accessed from a common point. Namespace
aggregation solutions can typically control laying out a file (striping data) across disk volumes to a specific silo—
but not across the silos that make up the cluster—while still allowing data movement between tiers of storage
with limited or no client interruption. While this architecture approach can be attractive on the surface, the IT
administrator is still managing, growing, and configuring “islands of storage” (heterogeneous silos of storage)—
but now with an additional virtualization layer. Ultimately, this solution approach can create higher complexity,
higher management burden, and higher long term operational costs.
Power, Cooling and Space Efficiency (PCSE)
Scale-out file storage is inherently more power efficient due to granular scalability—adding what you need versus
the gross overprovisioning that normally accompanies monolithic infrastructure silos. As discussed previously,
adding processing power independently, as can be done with scale-out systems, not only saves floor and rack
space versus monolithic scale-up systems, in addition to getting better performance, it would significantly reduce
power consumption since processors typically use 95% less power than adding another disk shelf would
consume.
Self-Managing and Self-Healing
Scale-out file storage systems will need to support deeper levels of policy-based self management and healing.
The infrastructure will need to withstand failures and automatically adjust and heal itself. The file storage
infrastructure will absorb new processor, bandwidth, and storage capacity, then automatically re-balance and
optimize across the newly added resources—with little or no human intervention. Again, there are some of these
capabilities already on the market and those that don’t already offer robust policy-based management have it on
the roadmap.
Advanced Scale-Out Features
Transparent Data Mobility
Transparent data mobility is an important feature; first and foremost to consolidate file-based storage without
suffering enterprise-wide downtime, but also to help load balance between processors and disks. Consolidation
and migration to appropriate storage tiers can significantly increase disk utilization while reducing management
costs, capital outlay, and services costs through the reduction in the total number of file servers and the
-5-
Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
8. ESG REPORT
Scale-Out NAS Comes of Age
associated storage. This is another feature that some scale-out NAS vendors have implemented and are ahead
of the curve on—and is on the roadmaps of the rest.
Tiered Storage Support
Tiered storage support is an advanced feature that will become prevalent in scale-out systems as the market and
systems continue to mature. All data is not created equal. There are two primary data life forms: dynamic and
persistent. Dynamic data is in a state of change and fluidity, typically something recently created. Persistent data
is non-changing and static. It could be recently created objects or older data in a reference state. But eventually,
all data becomes persistent. There are four simple stages of life for any kind of information, regardless of its
format or the application that generated it: Dynamic, Active Online Data, Persistent Active Online Data,
Persistent Inactive Online/Nearline Data and Persistent Inactive Offline Data. To run a cost effective IT
1
organization, data needs to be managed and stored according to what stage it is in.
Each tier requires a different infrastructure with different response times and different economics, and you need a
simple way to migrate (non-disruptively) between tiers. Scale-out file storage will account for data lifecycle
stages and support policy-based storage tiering based on file attributes: age, type, origin, size, and more. Scale-
out storage systems will incorporate high-end storage devices, such as fibre-channel disk, and slower, but more
affordable, high capacity SATA drives—allowing IT shops to cost-effectively manage data based on lifecycle
stage.
Scale-Out NAS Comes of Age:
Isilon IQ
Isilon, formed in 2001, was one of the first vendors to recognize the market shift to scale-out file-based storage
systems, which they call “Clustered Storage.” Since then, it has become a leader in scale-out NAS storage
systems. Its products are designed from the ground up to address the unpredictable requirements of large file-
based data. Unlike traditional scale-up NAS systems optimized for smaller data sets and different workflows,
Isilon’s systems are designed to handle massive amounts of file-based data, including digital files such as audio,
video, images, and other rich digital information; often requiring fast concurrent access by hundreds, if not
thousands, of users. The performance requirements for this type of digital information are intense, with users
requiring near instant large file access—something traditional scale up systems typically do not have the
processing power or bandwidth to deliver.
Isilon meets ESG’s core criteria as a scale-out NAS provider and offers advanced scale-out features such as
transparent data mobility, load balancing, and tiered storage support.
Clustered, managed as a single entity
Global namespace-enabled
Ability to scale bandwidth, processors, and storage independently
Power-efficiency
Self-managing
Self-healing
Transparent data mobility
Tiered storage support
Clustered, with a shared global namespace: Isilon systems are clustered file servers, managed within
a single, shared global namespace. The Isilon IQ X-Series Clustered Storage Systems can scale up to
96 nodes, 2.3 PB and up to 20GB/s of aggregate throughput while still being managed as a single entity
under a global shared namespace.
1
For more information see ESG Brief: A Methodology for Driving Total IT Efficiency Using Four Simple Data Lifecycle Stages, June 2008
-6-
Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
9. ESG REPORT
Scale-Out NAS Comes of Age
Scale performance, bandwidth, and capacity independently: Isilon IQ provides granular scalability
through its modular design. Performance is scaled by adding Isilon IQ Accelerator nodes, which add
processing power, memory, bandwidth, and parallel read and write access to a single file system. Users
can choose to scale single stream throughput and aggregate throughput or IO/s simply by adding more
nodes. Isilon now ships 10 GbE support with the Accelerator-x product. Capacity can also be scaled by
adding Isilon IQ-X storage nodes or Isilon EX storage expansion nodes.
Self managing/transparent data mobility: Isilon IQ comes with a web-based management interface for
single level management across the cluster. When nodes are added to the cluster, one click of the
mouse (or front panel LCD) is required. The rest is automated. Isilon’s AutoBalance absorbs new
storage into the cluster and grows the file system, rebalancing loads across cluster utilizing new nodes.
And SmartConnect provides a single virtual host name for client mounts, then manages the distribution
of client connections across the cluster based on defined policies.
Power, Cooling, and Space Efficiency (PCSE): As stated previously, scale-out NAS systems are
inherently power efficient because of their granularity of scale and “right sizing” scale. In other words,
there is no need to add more spindles and consume energy on spinning rust to boost performance when
a processor node can be added and use 95% less power. Isilon is also incorporating more power
efficient components into the design, leveraging the power consumption efficiencies gained with the
next-generation hardware advancements from Intel processors and power supplies. Isilon's X-Series
achieves 20% greater power efficiency over Isilon’s previous architecture.
Self-healing: Isilon’s FlexProtect data protection technology allows users to set data protection policies
on the fly at an extremely granular level: cluster, directory, or file. Policies can be based on the desired
level of data protection. Isilon’s N+4 protection also allows for up to four simultaneous failures without
ever losing data—no other storage system can with stand four failures like this in a single file
system/volume. FlexProtect also delivers fast data rebuilds in the event of a drive or even full node
failure—data can be rebuilt across any free space within the cluster, so space is not lost to spare
recovery drives and recovery is extremely quick. Because Isilon OneFS can leverage all the nodes and
spindles in a cluster to rebuild in the background, thus achieving massively parallel operations—a failed
drive can typically be rebuilt as a background process in less than an hour.
Tiered storage support: SyncIQ is a disk to disk replication product, but also supports simple, policy-
based file migration between storage tiers based on a number of characteristics, such as last access
time, file name, or age. Entire directories or sub-directories can be included or excluded from migration
jobs. This ensures only specific portions of the quot;sourcequot; file system—OneFS—are migrated from online
to nearline storage.
Isilon also has all the features users have come to expect from traditional NAS systems, such as support for
industry standards like NFS, CIFS, HTTP, FTP, NDMP, SNMP, LDAP, ADS and NIS; quota management; thin
provisioning; and snapshots, and has an added layer of protection with its FlexProtect RAID support (N+1
through N+4).
Isilon Advantage: SMP Architecture
On top of its scale-out NAS features, Isilon’s latest OneFS release (5.0) brings symmetric multiprocessing (SMP)
clustered storage architecture to the table. This enables Isilon to take advantage of multi-core processors by
evenly distributing workloads within and across available processors and cores. Because the SMP architecture
means multiple processors (and/or cores) can share a common main memory, any processor (or core) can work
on any task—memory is not dedicated to a specific processor node. Isilon has designed its OneFS operating
system to work in concert with its SMP design so that the system can move tasks between processors for
extremely efficient workload balancing. In conjunction with OneFS’ wide striping ability to stripe data across
nodes in a cluster, Isilon achieves the high aggregate performance and bandwidth required for large file-based
workflows.
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
10. ESG REPORT
Scale-Out NAS Comes of Age
SMP is hard. Isilon was one of the first to market with an SMP-based clustered NAS solution. Isilon introduced
quad core processors in the X-Series platform in January of 2008, but users were not able to realize the full
impact until OneFS 5.0 was released as Isilon was only leveraging a single core. The newest release of OneFS
unlocks the other three cores, and the SMP-based architecture automatically incorporates the cores into the
workload sharing algorithms. But it’s not only X-Series customers that can realize the performance boost.
Isilon’s clustered architecture is designed for both backward and forward compatibility—previous versions of its
processor and storage nodes can co-exist in a cluster with current versions. For users, that means aggregate
cluster performance can be boosted by introducing new processor nodes into the cluster. The cluster absorbs
the new capacity and automatically balances the load across the new cores. This is an important point and a
clear advantage—in a scale-up world, this kind of upgrade would require a forklift and a whole new system.
Summary
Isilon is in the sweet spot for new rich media and file-based data opportunities as the market is moving in its
direction. Many traditional NAS vendors were late to recognize the shift and are just entering the scale-out
market, while Isilon is already on its fifth generation product, giving it valuable experience. Isilon products are
road tested and in use at leading companies like NBC Sports—which stored video of the Beijing Olympic Games
on Isilon systems for proxy and broadband content—providing NBC producers with reliable access to critical
content for rapid review, identification, and selection operations necessary to quickly produce and deliver
groundbreaking coverage of the Beijing Olympics in the United States. If a company’s success is measured in
customer retention, NBC Sports’ use of Isilon speaks volumes; this is the third Olympics event where NBC has
teamed with Isilon (Athens and Torino being the previous two). For a company few people know of, the client list
reads like a who’s who for rich digital content names: UCLA Laboratory of Neurological Imaging (LONI), The U.S.
Geological Survey (USGS), Kodak EasyShare Gallery, NASA, NPR, Sony Music, ABC, Facebook, Second Life,
MySpace, Paramount Digital Entertainment… the list goes on and includes entertainment, oil and gas, Web 2.0,
medical, and life sciences companies.
Isilon’s focus has been to leverage its foot-hold in the HPC, Internet, media and entertainment markets into
enterprise environments. The company is realizing success with this strategy. Isilon sits at the intersection
where Web 2.0 meets business. Web 2.0 shares many HPC attributes: large files, scale-out being more
important than scale-up, and a need for granular scale at the processor, bandwidth, file system, and storage
capacity levels—online and independently. As with other mission-critical systems, there is typically zero
tolerance for downtime. While specialty file serving appliances have clear benefits in transactional IT
environments, rich media is a whole new game where parallel file systems, clustering, and global namespace
capabilities are of increased importance. Isilon’s big challenge is to draw the parallels and get commercial
enterprises to realize the similarities.
Don’t take this to mean the incumbent players are ignoring the Internet-fueled market opportunity and
challenges. Large incumbent vendors will not ignore this opportunity, but face the challenge of addressing these
new requirements while maintaining their positions in other markets. This is the window of opportunity for Isilon
and others to make a name for themselves.
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.
11. ESG REPORT
Scale-Out NAS Comes of Age
20 Asylum Street
Milford, MA 01757
Tel: 508-482-0188
Fax: 508-482-0218
www.enterprisestrategygroup.com
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Copyright 2008, The Enterprise Strategy Group, Inc. All Rights Reserved.