Cloud Asset Management (CAM), as the name suggests, refers to the practice of tracking and managing any resource that can or does contribute to the delivery of cloud services. Examples of assets include: virtual or physical storage, virtual or physical servers, software licenses, and staff knowledge that may not yet be documented.
Cloud asset management is a nontechnical aspect of cloud service delivery that stems from traditional asset management and aligns with popular IT management frameworks, such as the ITIL service lifecycle. Asset management is an essential aspect of any effective business management plan, that aims at systematically understanding how assets are procured, maintained, upgraded, and disposed of cost-effectively. For native cloud companies, tracking cloud assets is a must, as many of the assets are non-tangible.
While the basic tenets of tracking assets can be done by spreadsheet, this approach is only helpful for the smaller companies, as it is error-prone and can quickly become cumbersome. At the enterprise level, with the sheer number of devices connected to their vast networks, using specialized asset tracking software is essential for managing asset life cycles. A variety of software are available for asset management.
Asset management is highly adaptable for cloud environments. The major cloud providers also provide native frameworks and tools for their platforms.
Google’s Cloud Asset Inventory is designed to give real-time information, pulled from Google Cloud resources and policies, on the current state of your cloud assets throughout the organization. Integrated automation tools can then use this information, or snapshot, to monitor for any security or policy violations and take corrective action if directed. For further analysis, the asset inventory’s metadata history can be exported. Google also plays well with others, by integrating with other Security Information and Event Management (SIEM) tools organizations can create a unified, comprehensive view of all their resources throughout all environments.
AWS offers a similar set of tools, the AWS Systems Manager Inventory. The AWS SMI collects metadata from assets, and if connected can save it to an Amazon S3 bucket where analysis can reveal their state. AWS promotes this process as a one-click procedure. AWS is customizable, allowing the collection of custom parameters, as well as scheduling collecting items.
As well, and beyond simply creating an inventory, IBM offers their IBM Multicloud Management Platform (MCMP) Cost and Asset Management (CAM) that analyzes costing and performance, continuously informing leadership of wastage and cost savings opportunities, effectively answering, what does it cost to run the business, to provide IT services? What resources and how much are being consumed? Where are the best areas to make trade-offs, or shed unused services? And how can IT align more closely with future goals.
Asset management boils down to tracking and logging company assets, like counting inventory on shelves. But in the cloud, it’s made complicated by the number of physical as well as virtual assets that are being created and utilized in cloud configurations. To alleviate these pains cloud asset management software provides the following benefits.
Luckily, asset management software for the cloud is effective. The following are asset management best practices that can further set organizations up for success.
Mentioned above are several types of asset management software, including EAM, ITAM, and SAM software for tracking assets. The features these packages provide for Cloud Asset Management Software can include:
Container orchestration platforms can be found for every major cloud provider. However, many of them are based on the popular open-source container orchestration software Kubernetes. The following are some of the most familiar names in container cloud services.
Kubernetes is open-source, and largely considered the gold standard for container orchestration, though, as stated above, and because it is highly portable, there are many vendors to choose from that can accommodate it. Kubernetes is highly flexible and used in the delivery of complex applications. Docker container orchestration, or Docker Swarm, is Docker’s flavor of orchestration software that is included with Docker. Both are solid and effective solutions for massively scaling deployments, as well as implementation and management.
The following table highlights several comparisons between the two.
Docker Swarm |
Kubernetes |
|
---|---|---|
App Definition & Deployment |
Desired state definition in YAML file |
Desired State definition |
Autoscaling |
No autoscaling possible |
Cluster autoscaling, horizontal pod autoscaling |
Availability |
Service replication at Swarm Node level |
Stacked Control Plane node with load balancing either inside or outside the cluster |
Cloud Support |
Azure |
AWS, Azure, Google |
Graphic User Interface (Gui) |
GUI not available; must use 3rd party tools |
GUI is available; web interface |
Load Balancing |
No auto load balancing, but port exposure for external load balance services |
Horizontal scaling & load balancing |
Networking |
Multi-layered overlay network with peer-to-peer distribution among hosts |
Flat peer-to-peer connections between pods and nodes |
Storage Volume Sharing |
Shares storage with other containers |
Shares storage within the same Pod |
Updates & Rollbacks |
Rolling updates and service health monitoring |
Automated rollouts & rollbacks |
Companies securing their part of cloud operations need to consider four areas of concern, how the cloud security approach is designed, how security will be implemented and governed, how to protect the property and data, and how to respond when attacks are successful.