Welcome!

@DXWorldExpo Authors: Zakia Bouachraoui, Yeshim Deniz, Liz McMillan, Elizabeth White, Pat Romanski

Related Topics: @DXWorldExpo, Java IoT, Microservices Expo, Containers Expo Blog, @CloudExpo, SDN Journal

@DXWorldExpo: Article

Consolidating Big Data

How to make your data center more cost-effective while improving performance

Cloud computing has opened the doors to a vast array of online services. With the emergence of new cloud technologies, both public and private companies are seeing increases in performance gains, elasticity and convenience. However, maintaining a competitive advantage has become increasingly difficult. Service providers are taking a closer look at their data storage infrastructure for ways to improve performance and cut costs.

If the status quo remains, maintaining low-cost cloud services will become increasingly difficult. Service providers will incur higher costs, while consumers become burdened with storage capacity restrictions. Such obstacles are influencing service providers to find new ways to scale cost-effectively and increase performance in the data center.

Cost-Benefit Analysis
In response to the increase of online account activity, service providers are consolidating their data centers to a centralized environment. By doing so, they are able to cut costs while increasing efficiency, allowing data to be accessible from any location. Centralizing equipment enables providers the ability to deliver enhanced Internet connections, performance and reliability.

However, with these added benefits also come disadvantages. For instance, scalability becomes more expensive and difficult to achieve. Improving efficiency within a centralized data center requires the purchase of additional high-performance, specialized equipment, which increases costs and energy consumption, challenging endeavors to control at scale. In an economy where cost-cutting is becoming a necessity for large and small enterprises alike, these added expenses are unacceptable.

Characteristics of the Cloud
Solving performance problems, like data bottlenecks, is a growing concern for cloud providers who must oversee significantly more users and accompanying performance demands, than do enterprises. Although the average user of an enterprise system requires elevated performance, these systems generally manage fewer users who are able to access their data directly through the network. Moreover, enterprise system users are accessing, saving and sending comparatively relatively small files that require less storage capacity and performance.

Outside the internal enterprise network, however, it's a different story. Cloud systems are simultaneously being accessed by a multitude of users across the Internet, which itself becomes a performance bottleneck. The average cloud user stores relatively larger files than the average enterprise user placing greater strains on data center resources. The cloud provider's storage system not only has to scale to each user, but must also sustain performance across all users as well.

Best Practices
In response to growing storage demands, cloud providers are faced with profound business implications. Service providers need to scale quickly in order to meet the booming demand for more data storage. The following best practices can help optimize data center ROI in a period of significant IT cutbacks:

  • Opt for commodity components when possible: Low-energy hardware makes good business sense. Commodity hardware is not only cost-effective, but also energy-efficient, which significantly reduces both setup and operating costs in one move.
  • Seek out a distributed storage system: Distributed storage presents the best way to build at scale even though the data center trend has been moving toward centralization. Increased performance at the software level counterbalances the performance advantage of a centralized data storage approach.
  • Avoid bottlenecks: A single point of entry can easily lead to a performance bottleneck. Adding caches to relieve the bottleneck, as most data center infrastructures currently do, quickly adds cost and complexity to a system. On the other hand, a horizontally scalable system that distributes data among all nodes delivers a high level of redundancy.

Moving Forward
Currently, Big Data storage consists mainly of high performance, vertically scaled storage systems. Since these infrastructures can only scale to a single petabyte and are costly, they are not a sustainable solution. Moving to a horizontally scaled data storage model that distributes data evenly onto energy-efficient hardware can reduce costs and increase performance in the cloud. With these insights, cloud service providers can take the necessary steps to improve the efficiency, scalability and performance of their data storage centers.

More Stories By Stefan Bernbo

Stefan Bernbo is the founder and CEO of Compuverde. For 20 years, he has designed and built numerous enterprise scale data storage solutions designed to be cost effective for storing huge data sets. From 2004 to 2010 Stefan worked within this field for Storegate, the wide-reaching Internet based storage solution for consumer and business markets, with the highest possible availability and scalability requirements. Previously, Stefan has worked with system and software architecture on several projects with Swedish giant Ericsson, the world-leading provider of telecommunications equipment and services to mobile and fixed network operators.

Comments (0)

Share your thoughts on this story.

Add your comment
You must be signed in to add a comment. Sign-in | Register

In accordance with our Comment Policy, we encourage comments that are on topic, relevant and to-the-point. We will remove comments that include profanity, personal attacks, racial slurs, threats of violence, or other inappropriate material that violates our Terms and Conditions, and will block users who make repeated violations. We ask all readers to expect diversity of opinion and to treat one another with dignity and respect.


DXWorldEXPO Digital Transformation Stories
The deluge of IoT sensor data collected from connected devices and the powerful AI required to make that data actionable are giving rise to a hybrid ecosystem in which cloud, on-prem and edge processes become interweaved. Attendees will learn how emerging composable infrastructure solutions deliver the adaptive architecture needed to manage this new data reality. Machine learning algorithms can better anticipate data storms and automate resources to support surges, including fully scalable GPU-c...
Machine learning has taken residence at our cities' cores and now we can finally have "smart cities." Cities are a collection of buildings made to provide the structure and safety necessary for people to function, create and survive. Buildings are a pool of ever-changing performance data from large automated systems such as heating and cooling to the people that live and work within them. Through machine learning, buildings can optimize performance, reduce costs, and improve occupant comfort by ...
As Cybric's Chief Technology Officer, Mike D. Kail is responsible for the strategic vision and technical direction of the platform. Prior to founding Cybric, Mike was Yahoo's CIO and SVP of Infrastructure, where he led the IT and Data Center functions for the company. He has more than 24 years of IT Operations experience with a focus on highly-scalable architectures.
The explosion of new web/cloud/IoT-based applications and the data they generate are transforming our world right before our eyes. In this rush to adopt these new technologies, organizations are often ignoring fundamental questions concerning who owns the data and failing to ask for permission to conduct invasive surveillance of their customers. Organizations that are not transparent about how their systems gather data telemetry without offering shared data ownership risk product rejection, regu...
René Bostic is the Technical VP of the IBM Cloud Unit in North America. Enjoying her career with IBM during the modern millennial technological era, she is an expert in cloud computing, DevOps and emerging cloud technologies such as Blockchain. Her strengths and core competencies include a proven record of accomplishments in consensus building at all levels to assess, plan, and implement enterprise and cloud computing solutions. René is a member of the Society of Women Engineers (SWE) and a m...
Enterprises are striving to become digital businesses for differentiated innovation and customer-centricity. Traditionally, they focused on digitizing processes and paper workflow. To be a disruptor and compete against new players, they need to gain insight into business data and innovate at scale. Cloud and cognitive technologies can help them leverage hidden data in SAP/ERP systems to fuel their businesses to accelerate digital transformation success.
Poor data quality and analytics drive down business value. In fact, Gartner estimated that the average financial impact of poor data quality on organizations is $9.7 million per year. But bad data is much more than a cost center. By eroding trust in information, analytics and the business decisions based on these, it is a serious impediment to digital transformation.
Digital Transformation: Preparing Cloud & IoT Security for the Age of Artificial Intelligence. As automation and artificial intelligence (AI) power solution development and delivery, many businesses need to build backend cloud capabilities. Well-poised organizations, marketing smart devices with AI and BlockChain capabilities prepare to refine compliance and regulatory capabilities in 2018. Volumes of health, financial, technical and privacy data, along with tightening compliance requirements by...
Predicting the future has never been more challenging - not because of the lack of data but because of the flood of ungoverned and risk laden information. Microsoft states that 2.5 exabytes of data are created every day. Expectations and reliance on data are being pushed to the limits, as demands around hybrid options continue to grow.
Digital Transformation and Disruption, Amazon Style - What You Can Learn. Chris Kocher is a co-founder of Grey Heron, a management and strategic marketing consulting firm. He has 25+ years in both strategic and hands-on operating experience helping executives and investors build revenues and shareholder value. He has consulted with over 130 companies on innovating with new business models, product strategies and monetization. Chris has held management positions at HP and Symantec in addition to ...