Hardware and software complex (HSC)

What is a HSC

It is an integrated system consisting of hardware (equipment) and software that work together to perform one or more specialized tasks. Such a complex is a complete technical solution, tested and validated by manufacturers, and its functional and technical characteristics are determined solely by the combination of software and hardware. Service support is also provided through a "single window".

Main components of the HSC

• Hardware: devices for collecting, processing and storing information (e.g. computers, servers, specialized controllers, sensors, network equipment).
• Software: specialized software that controls the hardware and implements the necessary algorithms of data processing, control and interaction.

Key features

• Integration: hardware and software components are optimized to work together.
• Specialization: each HSC is designed to solve specific tasks, which ensures high efficiency and reliability.
• Ready-to-use: delivered as a ready-to-use solution, minimizing the need for additional configuration.
• Technical support and updates: vendors provide support and updates to maintain security and functionality.
• Scalable: expandable and adaptable to changing tasks and workloads.

Implementation Features

Developing and implementing a PAC requires significant resources and skilled professionals, as well as compliance with industry standards and security requirements. However, integrating hardware and software can automate processes, increase productivity, and reduce the likelihood of errors.

Examples of HSCs

Virtualization and hyperconverged solutions

Such HSCs are based on a bundle of a server, storage and virtualization platform. The hyperconverged approach takes a special place among domestic solutions:

Hyperconverged platforms are a scalable solution where servers with local storage are combined into a single cluster (10+ nodes). Each node contributes both compute resources and shared storage space. An example of such implementation is a platform based on domestic vStack HCP software and ITPOD servers.

The typical architecture of such solutions usually includes:

• Compute layer - servers that provide high performance for virtual machines and applications;
• Dedicated storage - storage that solves specialized problems:
  • Creating file globes (shared network folders) for document storage and collaboration
  • Organization of object S3-stores (distributed data storage)
  • Dedicated high-performance storage for demanding DBMSs
  • Isolation of critical data from the general cluster load

Control and analysis of network traffic

The Russian market offers hardware and software complexes for the telecom industry that combine server platforms and storage systems with deep traffic analysis (DPI) tools. As an example of such a solution we can mention complexes based on "SKAT DPI" software from VAS Experts - they are used in networks of large telecom operators to analyze and filter traffic. A characteristic feature is a multilevel architecture with storage resources separation.

Let's take a closer look at the HSC from ITPOD and VAS Experts, which was implemented in one of the telecom companies. The architecture includes:

• A server layer with DPI and QoE (Quality of Experience) services deployed;
• High-performance All-Flash storage for operational data;
• Cost-effective storage based on hybrid storage for long-term statistics.

This division of storage resources provides a balance between access speed and cost when working with large volumes of data. Operational analytics for the last month are generated quickly by being placed on SSD drives, while historical data for years is stored on more cost-effective HDD disks.

AI solutions for business processes

Applying artificial intelligence in business requires specialized infrastructure to run the models. As an example, let us consider HSCs based on ITPOD AI/ML Computing servers with AI BPA platform Ainergy - they allow to automate processes from processing accounting documents to intelligent analysis of customer requests.

1. The architecture of such PACs takes into account the requirements of AI/ML systems:

• Server platforms with support for 2 to 8 GPU gas pedals to scale from simple inference tasks (inferencing) to training large models;
• Storage for centralized storage of datasets and model outputs;
• An integration layer that links AI systems to enterprise business applications.