Machine Learning (ML), Artificial Intelligence (AI) and automation are composed to change the world. They are changing the way customers engage with companies completely. Both technologies have altered the way businesses operates and thinks regarding business intelligence and data storage.
Broader adoption of artificial intelligence and machine learning has some system and storage managers very excited. For instance, machine learning algorithms can be incorporated into the control layer to allow administrators to diagnose the causes of traffic congestion more easily. This empowers them to predict potentially exposed network sectors.
However, it goes way beyond being just a traffic cop. Machine learning and artificial intelligence are influencing data storage in a vast range of ways. Let us have a look at some of the top AI and ML data storage trends and predictions:
With businesses moving towards cloud storage and some few dedicated storage arrays, dynamic storage software with an integrated deep learning algorithm can be of great help for organizations to gain additional storage capacity, at 60 -70 percent cost reduction.
More Software-Defined Storage
This has been hyped as a trend for many years now. Machine learning and AI are acting as accelerators. Software-defined storage has many potential benefits, helping businesses overcome their reluctance to being told that there is hitherto another new technology they need to adopt. Machine learning and AI will enhance faster acceptance of software-defined storage.
The early computing days saw sufficient instrumentation being added to systems. Actually, there are entire association and conferences devoted to the instrumentation and measurement and of PCs.
With Windows servers thriving from the mid-1990s, this side of the business has faded gradually. However, this appears to be taking a new bearing and machine learning and AI open up new horizons. As a result, we predict another trend of greater instrumentation in the coming years.
The introduction of software-defined storage is a major influence in the development of AI and machine learning in data storage environments. Adding a diverse software control layer on top of the hardware permits the software to monitor more tasks. This helps in freeing up the storage manager to create space for more strategic duties.
Artificial intelligence supports facilitation that adopts an active and flexible architecture. It can dynamically re-route data center, automatically regulate cooling in data centers, and intelligently control access rights.
Enhanced Security and Reliability
Loss of data and security are key concerns for modern businesses. Certain data storage vendors are starting to harness machine learning and AI to speed up turnaround time during downtime, increase availability, and prevent data loss via systematic backup and data recovery strategies. This can be achieved through data recovery nyc.
AI enhances smart security features to detect packet/data loss within data centers or during transit.
Hybrid Storage Clouds
The argument regarding private versus public cloud storage appears to be debatable in the face of whether software-defined storage, AI and machine learning architectures should be in a position to transition data from one type of cloud to another seamlessly. Also, organizations can manage their data as one pool, irrespective of where the data physically resides. Therefore, the purists who seek all private and public clouds may not carry the day. It is the hybrid cloud that is highly likely to flourish.
The use of machine learning and AI will hasten fluid hybrid cloud solutions deployment as a repository since after data is analyzed and the logic maps developed, they must flow visibly to local analytics engines at the verge in a cycle of continuous development.
Everyone seems to predict more flash, so what’s new? AI and machine learning will just add yet more stimulus to this unstoppable trend that is sweeping almost all forms of storage. This will drive the use of flash and memory as the primary storage medium since you cannot process edge resolutions fast enough otherwise.
Perhaps the major driver that will eventually provide the use case for integrating machine learning and AI into storage will be the car drivers. Today’s high-end cars (cars without autonomous features) posses between 64 and 200GB storage space, mostly for infotainment functions and maps. We might have more than 1TB of storage in tomorrow’s autonomous vehicles, and this will not be for the drive function alone.
The driving factors why more storage will be necessary locally include advanced gesture and voice recognition, intelligent assistant in the car, buffering infotainment to reduce network, bandwidth utilization of the peak network, and caching software updates.
Parallel File Systems
Storage systems will be required to deliver performance at scale in order, to support machine learning and AI. This simply means that they must be in a position to work well at a projected scale with technologies such as flash and parallel file systems.
For purposes of future-proofed infrastructure, the parallel file system should also be able to cost-effectively and simply handle data that is colder or older and support a flawless path to future technologies such as flash-narrative and flash formats tools that maximize the performance of the flash while avoiding flash-specific longevity-hurdles and flash-specific performance.
This is where the storage is capable of recognizing and responding to opportunities and problems without human intervention. When technology takes hold, you need to expect a change in productivity. Arriving at the neural stage will not happen overnight. Actualization of the neutral storage network is laid out in three phases, manifesting gradually, with one phase leading to the other. Phase 1 is where storage is instrumented with telemetry in order to collect data from non-traditional sources. For instance, networking flows, data flows, user-level access patterns, and data about software and hardware failures. This phase will establish comparatively in the software-defined data storage at early stages. Phase 2 is what is referred to as self-driving.
Once the storage is software-defined, algorithms are needed to become far-reaching enough and integrated to solve multifaceted storage management problems. This is a crucial step on the journey to building the tuning, monitoring, and healing service chains required for self-driving. Neural storage networks can only take root after these two phases have been fully attained.
The year 2019 looks very promising for technological innovations. We look forward to more accurate and faster Artificial Intelligence and Machine Learning applications coupled with other new exciting developments.Big data storage requirements will continue to expand. We are set to witness faster and more accurate machine learning (ML) and Artificial Intelligence (AI) applications and some new exciting developments. The exponential advancement of technologies is expected to give the ability for the Internet of Things, self-teaching AI, and NLP to change not only the business industry but also our daily life operations.
Author: Robin Jago is editor in chief at TTR Data Recovery. He is a data scientist, and has decades of experience in writing posts on big data management and analytics.