Data centers are becoming more interdependent and complex. Moreover, they’re handling enormous amounts of data. The rise in cloud computing and edge computing adds to the complexities of managing modern data centers. Data center infrastructure management (DCIM) is a conglomerate of tools with integrated processes designed to ease modern data center management challenges.
This post gives an overview of what DCIM is, why it’s important, and how it’s been traditionally handled. It also explores the challenges modern data center infrastructure networks pose for DCIM and provides strategies and tools for handling DCIM successfully and overcoming the complexities of modern networks.
What Is DCIM?
DCIM tracks the entire data center infrastructure and gives you visibility into the critical assets and their connectivity. These include air conditioners, uninterruptible power supply (UPS) devices, and IT equipment such as network switches, storage area network [SAN] switches, and servers. With DCIM, data center operators can manage all data center network, thermal, and electrical infrastructures from within a central system.
Why Is DCIM Important?
DCIM is an essential solution for data center teams to reduce the complexity of managing data center infrastructure. It helps them understand the power consumption of data centers and their components and create a sustainable environment. With this data, you can switch to renewable energies, thus supporting corporate sustainability initiatives.
When designing infrastructure, you need information on network, power, and cooling at the rack level to help you decide where to place new servers for optimal performance. DCIM provides the information data center managers need to determine how much equipment to place into a rack.
The cooling, power, and environmental data that DCIM provides helps ensure data center availability and reduces time to repair. Environmental data can, for example, alert data center managers when there’s too much humidity, high temperature, or reduced airflow.
An Overview of Traditional DCIM
Historically, many data centers were sufficiently small. Data center operators could manage most of the infrastructure using legacy tools and processes. Here are some of the common operations in traditional DCIM:
- Data operators would use hometown databases and spreadsheets to record the location of assets, maintenance history, and specifications. If you wanted to know the available capacity in terms of space, you needed to send someone on-site.
- The first-generation DCIM software required high deployment time due to the complexity of the software. Also, it takes time to procure, install, and host a DCIM server on the premises.
- The capacity of DCIM to integrate with other tools deployed in data centers was limited.
- Data was held in multiple systems, and there was no way to integrate or share it across different systems.
How Is DCIM Currently Managed?
Modern DCIM consists of a conglomerate of tools integrated across the whole data center ecosystem to allow data center managers to handle all infrastructure management needs from a single interface. By embracing automation, DCIM removes repetitive manual tasks common in traditionally managed data center infrastructure.
Various components of DCIM are currently handled in the following ways:
Asset management: With modern DCIM, you do not need to use spreadsheets to manage inventories. Modern DCIM provides instantaneous information on the assets in the data centers, such as servers, routers, and networking. Data center managers can search the assets and know their physical locations without sending someone to the site.
Capacity planning: Modern DCIM uses predictive analytics to help you plan future expansions and upgrades. Data center managers can allocate space for new servers and manage network and power connectivity from a central location.
Connectivity management: Modern DCIM software allows you to manage logical and physical connections within the data center. It provides an overview of port cable paths and port capacity. This enables you to implement port connections and ensures port compatibility with deployed devices.
Network planning: Modern DCIM allows you to create network topology diagrams, giving you a detailed view of the structure and layout of your network. With these diagrams, you can develop plans for fixing issues and running troubleshooting.
Resource deployment: With the DCIM server hosted on cloud premises, it is faster to deploy modern DCIM. You do not need to procure and install a new server like with traditional DCIM. Besides, modern DCIM provides automated tools and workflows to support resource deployments.
How DCIM Fits Into the Current Tech Landscape
In the current landscape, businesses have embraced hybrid digital infrastructure for data centers. Cloud infrastructures, virtualization technology, and IoT devices are common recent tech advancements. Also, there’s an increase in server and storage densities as well as power and cooling demands as data centers try to process large-scale data faster. Businesses have adopted colocation facilities that allow them to outsource their data center while retaining control of IT operations.
With all these rapid changes happening, DCIM has evolved to meet the demands of hybrid digital infrastructure. DCIM brings effective centralized capacity planning and asset management to colocation data centers. Market analysis by Grand View Research forecasts that the global DCIM market size will grow from $2.38 billion in 2022 to $7.79 billion by 2030, at a compound annual growth rate of 15.9% during the analysis period. Edge computing, cloud computing, and high growth in data center traffic are some of the top reasons for this expansion.
Particularly, the move toward edge computing has necessitated changes to how modern data center infrastructure is managed. DCIM has evolved to help manage distributed environments, including providing visibility into the performance of edge data centers.
Modern data centers are dealing with large amounts of data. Real-time data analytics is more important now than ever before. Modern DCIM can process data in real time to identify inefficiencies early. This allows data center operators to decide on resource allocation and capacity planning quickly.
Modern DCIM is built based on deep learning, digital twin, and machine learning technologies to match the recent artificial intelligence (AI) developments.
Challenges That Modern Networks Pose for DCIM
Modern networks have grown in complexity and size. Consequently, the number of networked devices is expanding. Modern network environments consist of many interdependent components. This is unlike traditional networks that consist of two or more computers linked together to allow sharing of files and electronic communication. Servers, virtual machines, and IoT devices are major components of today’s networks. Managing this large number of devices, network components, and connections within a data center is challenging.
Further, modern data center infrastructure moved to virtual networks. While the agility of virtual networks is a plus to data centers, it can complicate infrastructure management. These environments experience frequent changes in configurations, additions, and removal of devices. DCIM tools quickly adapt to the changes and provide real-time visibility over the network infrastructure.
The IT team can always secure computers and servers in a data center against cybersecurity threats. However, attackers can target other devices for modern data centers, such as UPS and HVAC systems. Specifically, cybercriminals aim to compromise the communication stack that helps these devices to perform their functions. With the growing complexity of networks, DCIM solutions need to incorporate advanced analytics capabilities to identify trends, detect anomalies, and enable predictive maintenance.
Tools and Strategies for Handling DCIM for Modern Networks
Below are some of the top strategies and tools to help you navigate the challenges that modern, complex networks pose to DCIM.
- The number of networked devices in modern data centers is high. Thus, you should keep an inventory of the physical components connected to the networks within the data center. These include servers, routers, power distribution units, and their interconnections.
- It’s important to quickly discover devices newly connected to these networks and determine their relationship and interdependencies for easier data center network management. Tools that automatically discover new device additions to your network infrastructure save you time and help update your inventory instantly.
- Ensure the DCIM solution you’re using is integrated with proper security tools. These security platforms correct security events and network data. They make detecting threats, responding to incidents, and managing vulnerability effective and quicker.
- Integrate DCIM with other IT management systems, such as network planning systems, to help you automatically scan the entire network and understand the overall health of the data center network and the status of network resources and devices. Through this integration, you can, for example, monitor sudden network traffic spikes, errors on routers and switches, and server overload.
- Ensure the DCIM solution is highly scalable to accommodate the growing size of modern data center networks. The tool should be flexible enough to adapt to evolving network architectures and technologies such as virtual networks.
Conclusion
Data centers have evolved to become highly complex environments with numerous interconnected systems and components. DCIM is a comprehensive approach that aims to simplify and streamline data center operations management. Modern DCIM integrates comprehensive tools for asset tracking, capacity planning, energy planning, environmental sensing, and power management.
To learn more about the DCIM capabilities of NetBox, the leading network source of truth trusted by thousands of enterprises, visit netboxlabs.com/dcim.
This post was written by Caroline Wanjiru. Caroline is a software developer and a technical writer. In her work, she has developed interests and worked on many machine learning and artificial intelligence projects.