Log Analysis for Software Defined Data Center (SDDC)

Modern infrastructure is generating log data at a rate far faster than humans can analyze it. And now that data centers can be built and torn down under scripted control, the amount of activity and data is growing exponentially.

The traditional data analysis method is to review log files according to the list every week or every day. This method can no longer meet the requirements of data review in the software-defined data center (SDDC). The modern architecture of SDDC has highly automated dynamic deployment capabilities for multi-layer applications, which requires real-time log analysis to be in place. Analysis is the key to complex troubleshooting, dynamic configuration, high performance and excellent security.

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In a software-defined data center, you look at variables beyond just a bunch of servers. You want to see the amount of provisioning and provisioning time, you want to know the performance and IOPS of the bare metal, you want to understand how the data center network and all the individual virtual networks are running, how secure they are, and what the possible weak spots are. And with IaaS companies and managed service providers, you can manage multiple of these virtual data centers at the same time.

Only with a comprehensive log management solution can it be possible to identify root performance bottlenecks, security vulnerabilities, and optimal configuration of SDDC resources. This solution requires extracting data from various components and processing it into a composite view of infrastructure system log data. The resulting operational intelligence enables deep, enterprise-wide visibility to ensure optimal use of SDDC resources, as well as advanced alert systems that can be called upon to handle relevant emergency events.

Without these capabilities, IT administrators are left to rely entirely on system metrics, which greatly limits their ability to independently make overall performance decisions, most likely at a datacenter level. Things like memory consumption, CPU utilization, and storage can easily overlook valuable diagnostic information in log files.

Here is the type of information that SDDC log analysis can provide:

Machine provisioning, deprovisioning, and movement: In modern data centers, virtual machines are often moved from one physical machine to another, and even synchronized during the move using technologies such as v-motion. To optimize the virtual machine movement process, historical reports on workloads for virtual machine movement, provisioning, and deprovisioning are provided to help the team understand where the process can be optimized or/and assist in removing bare metal machines.

Data input to bare metal utilization: Use the performance, consistency and predictability of bare metal servers to further enhance the elasticity, on-demand availability and flexibility of cloud technology. Log analysis allows IT decision makers to integrate accurate information about machine efficiency into the overall deployment, expansion and application planning of the SDDC environment.

Intrusion monitoring and management: Log data can be used to identify abnormal activities and create real-time automatic alerts for areas of concern. IT administrators cannot use traditional form log analysis methods to draw conclusions from log data that point to potential performance or security issues. Log analysis based on management solutions automatically runs these processes, freeing IT administrators from tedious form log analysis tasks, while enhancing the transparency of infrastructure operations and effectively preventing data leaks.

Forensic analysis and compliance auditing: Correlate log data to track suspected intrusions or data loss and enforce compliance with security regulations.

Incident Control: Real-time alarm configuration identifies and isolates damaged or poorly performing components to prevent infrastructure-wide losses. Users can also analyze log data to determine the causal relationship between independent failures and performance issues to nip them in the bud.

Infrastructure optimization: With active network log management solutions, IT decision makers can build infrastructure based on diverse and evolving business needs. DevOps can also use log data from integrated test environments to correlate test results with log data generated by SDDC infrastructure and applications.

Cost savings: Fewer tools and IT expertise are required to manage and maintain complex SDDC infrastructure.

It's also very simple to implement. Just like pulling the server monitoring logs out of the server data, the process is as easy as installing an agent. In the case of SDDC, the agent must already be part of the scripts or production VMs for all configurations. But in addition to the VMs, the agent needs to be installed on all machines that are bare metal, such as each VMware ESX server. It only takes one more step than straight server logs - making sure the boundaries between VMs are clear and that their respective configurations are clear.

To extend log analysis from monitoring a single component to managing the entire SDDC, users need to establish a cloud-based log analysis solution that is completely independent of the uncertain SDDC infrastructure. While IT professionals are accustomed to the traditional practice of monitoring log data errors, DevOps running SDDC must find the underlying network components that cause system behavior shifts. With advanced machine learning-based log management solutions, DevOps can solve problems faster and more effectively and optimize performance.