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
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
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.
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