How AI might assist enterprises to scale back information storage prices

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The quantity of information managed by the world’s enterprises is rising. According to 1 supply, the full quantity of information created, captured, copied and consumed globally was about 64.2 zettabytes in 2020 — equal to a trillion gigabytes. Unsurprisingly, corporations report that the price of storing their information can be climbing. In a 2018 Enterprise Storage Forum survey, enterprise leaders stated that the excessive prices of operation, an absence of storage capability, and growing old gear had been amongst their high considerations.

The rising prices of storage have pushed many corporations to undertake cloud choices, which supply the benefit of low entry prices. But with prices inching up as extra companies transfer on-line — a Pepperdata report discovered that greater than one-third of corporations have cloud service funds overruns of as much as 40% — IT leaders are exploring options.

On the cloud facet, a nascent crop of startups are making use of AI to the issue of managing cloud spend. Vendors like Densify and Cast AI declare that their AI-powered platforms can suggest the very best storage configuration for a corporations’ workloads by taking into numerous necessities. Other know-how suppliers have turned their consideration to on-premises methods, creating algorithms that they declare can scale back storage prices both with {hardware} solutions or novel file compression methods.

“Data storage today suffers from several challenges: Storage deployments are often made up of a variety of different storage media such as memory, flash, disk drives and tapes. In addition, organizations run multiple storage arrays based on access protocols … or based on criticality of the workloads,” Gartner analysis VP Arun Chandrasekaran instructed VentureBeat through e-mail. “The usage of AI has the potential to streamline data lifecycle management based on criticality, performance, security and costs requirements of data.”

Cloud optimization

During the pandemic, the strain to digitize operations led a report variety of corporations to maneuver to the cloud. According to a latest survey from O’Reilly, 90% of organizations had been utilizing cloud computing of some variety in 2021, whereas Flexera’s State of the Cloud Report reveals that 35% of corporations spent greater than $12 million on cloud operations in 2021.

The adoption pattern gave rise to startups creating AI-powered platforms designed to regulate utilization to reign in expenditures. One is Densify, which analyzes workloads throughout non-public information facilities, Amazon Web Services, Microsoft Azure, Google Cloud Platform and IBM’s cloud choices to find out how a lot CPU, RAM and storage they want — then suggests methods to save lots of. Densify can use already-available log information to start optimizing immediately. After that, the platform will proceed to overview cloud suppliers’ pricing modifications, purposes’ wants and new merchandise to seek out the place prospects can scale back bills additional.

“Usually within two to four weeks, you’ve got 50% of the savings,” CEO Gerry Smith instructed VentureBeat in a earlier interview. “Depending on where the savings are, within another two to four months, [you’ll get] 100% of the savings.”

Cast AI, a Densify competitor, equally leverages AI to optimize cloud spend. Supporting main cloud service suppliers, the platform connects to current clouds and generates a report back to determine cost-saving alternatives.

“We have other models that use global datasets for market characteristic predictions,” CEO Yuri Frayman instructed VentureBeat in October 2021. “For example, we train a global model to predict instance preemptions by machine type, region, availability zone and seasonality. This model is shared autonomously across all customers, and all the data is used to retrain the model continuously.”

On-premises and compression

For corporations that haven’t made the transfer to the cloud — or who’ve their information unfold throughout cloud and on-premises environments — there are answers like Accenture’s Storage Optimization Analytics, which mixes search and AI to grasp enterprise content material and automate information classification.

Accenture claims that it reduces storage prices by detecting duplicate or near-duplicate content material, serving to prospects transfer or archive the suitable information on the proper time. Storage Optimization Analytics additionally automates migration to lower-cost storage and tracks storage financial savings, computing the general return on funding (ROI).

IT supplier Rahi Systems gives an identical service known as Pure1 Meta, which makes use of AI fashions to foretell capability and efficiency and supply recommendation on workload deployment and optimization. Pure1 Meta can run simulations for particular workloads, producing solutions to capability planning questions whereas ostensibly serving to to extend useful resource utilization.

An Nvidia AI mannequin compressing movies.

AI can be more and more enjoying a task in file compression. For movies, music, and pictures, AI-based compression can present the identical — or near the identical — degree of visible high quality with fewer bits. Another profit is that it’s simpler to improve, standardize, and deploy new AI codecs versus commonplace codecs, for the reason that fashions will be educated in a comparatively brief period of time and — importantly — don’t require special-purpose {hardware}.

Websites like and VanceAI leverage fashions to compress photos with out compromising on high quality or decision. Qualcomm and Google have experimented with AI-driven codecs for each audio and video. And Alphabet-owned DeepMind has created an AI system to compress movies on YouTube, decreasing the typical quantity of information that YouTube must stream to customers by 4% with no noticeable loss in video high quality.

Looking to the long run

Gartner’s Chandrasekaran notes that the adoption of AI applied sciences for information administration, which fall beneath the class of “AIops,” stays fairly low. (AIops platforms goal to boost IT by leveraging AI to research information in a corporation’s from instruments and units). But he provides that the pandemic has been a catalyst for adoption as organizations try to automate sooner to reply to “rapidly changing” circumstances.

Recent surveys agree. According to Emergn, 87% of corporations anticipate their investments in automation abilities to extend over the subsequent 12 to 26 months. And in a 2020 K2 ballot, 92% of enterprise leaders stated that they contemplate course of automation very important to success within the trendy office.

“There is a lot of ‘AI washing’ in the industry today. Hence, vetting vendor claims and deploying a solution that delivers ROI can be frustrating. AIops requires a lot of integration,” Chandrasekaran stated. “For teams that aren’t skilled in architecting and maintaining complex data environments, a robust AIops deployment may become a pipe dream. There also needs to be a cultural change, where organizations are willing to make data-driven decisions.”

Looking forward, Chandrasekaran expects to see extra “versatile” AI-powered storage administration options past the merchandise already in the marketplace. These options might allow better clever automation and remediation workflows by using AI, he believes.

“AI techniques can help optimize placement of data on the right storage tiers — balancing performance and costs. In addition, AI can help with better availability of data infrastructure, enabling businesses to access data faster and create a reliable infrastructure,” Chandrasekaran added.

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