Johannesburg, 21 Nov 2024
In southern Africa, more and more enterprises are introducing AI (artificial intelligence) for IT operations (AIOps) to handle huge data volumes, improve efficiency and facilitate automated O&M. Today, AI technologies in the storage field are no longer limited to the monitoring and O&M of devices; instead, they are integrated into data storage products.
There are three major monitoring and O&M trends of southern Africa enterprise data storage environments:
- Enterprises are using AI to improve O&M automation of storage systems.
- Enterprises and storage vendors are jointly developing three-layer AI architecture (cloud-centre-device AI).
- Storage vendors are building intelligent storage products to optimise device efficiency and reliability.
The explosive growth of data volumes in data centres have created new challenges for storage management, such as fault location and risk identification. This means that existing O&M methods are no longer sufficient. According to Huawei’s market insight team, by the year 2023, 40% of IT operation teams in southern Africa enterprises will use AI-augmented automation. Enterprises are expected to invest more in AI tech to automate storage O&M in data centres, improving resource management and O&M efficiency.
To produce high accuracy and reliability, AI training requires a large amount of data for accumulation and model optimisation. To meet this demand, enterprises are using storage vendors’ AI management tools to build three-layer (device-centre-cloud) AI architecture to centrally manage storage devices, simplify infrastructure O&M and improve efficiency.
In traditional storage, algorithms and data are coupled and multiple fixed algorithms are distributed at the cache, in the scheduling layers, and in the storage pools of storage devices. However, algorithm parameters need to be manually adjusted to ensure the access efficiency of different types of data. In contrast, intelligent storage incorporates architectural innovations by decoupling algorithms from data. A self-learning and adaptive algorithm library enable autonomous decisions on the layout, scheduling and reduction of different data types, ensuring efficient and flexible access in diverse data applications.
Enterprise CIOs should look to deploy new evaluation elements for storage AI management software.
To accelerate enterprise digital transformation, both storage vendors and enterprises must consider how to integrate AI management software into enterprise production and management services. It is recommended that enterprises establish clear evaluation factors and standards for the AI management software provided by storage vendors. This will drive storage vendors to upgrade AI management software based on the core values enterprises care most about. The evaluation elements should cover the following dimensions:
Responsibility scope: AI is not developed to replace humans, but to assist and strengthen human abilities and contributions by learning and transcending how human beings perceive and respond to the world. It is recommended that enterprises develop the responsibility scope of AI within which storage vendors can upgrade and expand AI capabilities to guarantee that storage AI management is under enterprise control.
Technical specifications: AI algorithms depend on learning and training. Model understanding and training data volumes determine the error rate of AI inference results. It is recommended that enterprises develop service-specific, quantifiable AI technical specifications that can be proven by storage vendors, while storage vendors that do not meet AI specification requirements will not be adopted by enterprises.
Capability extension: The evaluation criteria for AI management software should extend from independent capabilities to end-to-end closed-loop designs. For example, storage disk fault prediction should focus on the closed-loop capabilities of storage management software such as identification, pre-warning, proactive isolation, replacement and data rebalancing.
Equally, internal infrastructure teams can use intelligent management software for storage resources to fit service requirements, and ensure service agility through standardised and service-oriented resource management. Meanwhile, they need to stay up to date with AI trends; explore management intelligence in intelligent storage infrastructure; and use AI to mine data value and inform business decisions.
Finally, due to the large-scale deployment of AI in storage devices and management software, enterprise infrastructure management teams need to systematically plan data-centric AI capabilities. Enterprise digital transformation starts with retraining internal staff to ensure tech stacks transform from solely storage management to end-to-end automation and enterprise AI capability building.
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