Price: 0
Number of applications: 2
12.01.26 (inclusive)
It is discussed depending on the result: idea, model, implementation in production
Idea
ICT tasks
Food industry
Intelligent control systems
Purpose and description of task (project)
Implementation of the cloud management module using the intelligent Workload Placement decision module (AI/ML-based Workload Placement Advisor), which analyzes the characteristics of each new or existing workload and automatically recommends the optimal deployment environment — public cloud (AWS/Azure/GCP), private cloud or on-premise data center — taking into account cost, performance, security, and compliance requirements.
Software/ IS
Companies that have deployed hybrid and multi-cloud infrastructures (public + private cloud, multiple providers) face critical difficulties in making decisions about the deployment of new workloads (workload placement). Each load (VM, container, database, AI/ML model) can be run in a public cloud (AWS, Azure, GCP), a private cloud (based on VMware, OpenStack) or an on-premise data center, but choosing the optimal location requires simultaneous consideration of dozens of parameters: requirements security and compliance (GDPR, HIPAA, data localization), projected costs (public cloud pricing, reserved instances, private cloud with predictable costs), performance characteristics (latency, CPU/GPU requirements, I/O), redundancy and disaster recovery policies, as well as availability of computing resources and quotas.
The expected effect of the introduction of such systems: reduction of IaaS costs by 27-40%
Nikolay
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