KSManage provides a wide range of power control strategies integrating the energy consumption data of various devices and IT equipment.
KAYTUS, an IT infrastructure provider, has announced the launch of KSManage, a data centre management platform that significantly boosts the intelligence and efficiency of data centre operations through its comprehensive full lifecycle maintenance, precise fault diagnosis, and advanced energy management tailored for the dynamic environments of modern data centres. This platform is adept at effectively tackling the operational and management challenges faced by data centres, particularly those arising from the deployment of AIGC-based applications, such as the Large Language Model.
According to the Head of Operations at the data centre of a financial institution, the AIGC applications such as Large Language Models have been demanding the computing power of data centres, leading to their sustained expansion and presenting operation engineers with ever-growing challenges. Usually, the construction of data centres is phased, while operation and maintenance represent the most time-consuming aspects of the entire lifecycle. As data centres deploy an increasing number of devices and applications, it becomes necessary for multiple departments to collaborate on tasks such as influx, installation, inventory, and maintenance of a large number of IT equipment. In addition, the data silos between departments have introduced new challenges, making the workflow extremely complex. The complexity and scale of the data centre continue to increase, intensifying the challenges in data centre operations and IT asset management.
The AIOps (Artificial Intelligence for IT Operations) is a key highlight of KSManage, which was built by KAYTUS for the intelligent operation of infrastructure. Through intelligent response processes and data-driven decision-making support, KSManage is capable of uniformly analysing 100,000+ and over 100 million monitoring indicators. It swiftly and accurately pinpoints faults, with a fault diagnosis rate exceeding 98%. It also effectively addresses the problem of system fault tolerance in the event of occasional hardware failures. The drive fault prediction can detect risks 15 days in advance, and the accuracy of memory fault prediction has increased by 30%.
In addition, it integrates AI algorithms for performance and capacity prediction. The platform employs the innovative ETF threshold-free alarm algorithm, enabling alarms for server cluster performance and capacity without set thresholds, achieving an alarm accuracy rate of 95.26%. This ensures a precise allocation of computing power. AIOps not only significantly improves the stability and reliability of core business operations but also offer capabilities like proactive intelligent stop-loss and fault location. This evolution in data centre operations transitions from passive response to proactive prevention, and further to intelligent prevention, unlocking new value in data centre operations.
KSManage provides a wide range of power control strategies integrating the energy consumption data of various devices and IT equipment, including air conditioning, lighting, power supply, fire protection, access control, as well as server equipment, switches, storage, and other devices. The solution digitalises the data centre, providing a visual display of the server rooms and racks throughout the facility. By managing the energy consumption of the data centre and analysing its carbon footprint in real time, it can detect carbon emissions from specific server rooms and racks, thereby enabling the evaluation, tracking, and management of carbon emissions from particular business sections. Utilising these results, the system can analyse overall space utilisation within the data centre and offer data support for the installation and removal of servers, thereby optimising rack density. Moreover, real-time analysis of the data centre’s carbon footprint, based on energy consumption, aids in improving space utilisation and reducing energy consumption by 15-20%. This reduction significantly lowers the Power Usage Effectiveness (PUE) of the data centre.
