deep observability

Containers and Kubernetes Observability Tools and Best Practices

Containers and Kubernetes are popular technologies for developing and deploying cloud-native applications. Containers are lightweight and portable units of software that can run on any platform. Kubernetes is an open-source platform that orchestrates and manages containerized workloads and services.

Containers and Kubernetes offer many benefits, such as scalability, performance, portability, and agility. However, they also introduce new challenges for observability. Observability is the ability to measure and understand the internal state of a system based on the external outputs. Observability helps developers and operators troubleshoot issues, optimize performance, ensure reliability, and improve user experience.

Observability in containers and Kubernetes involves collecting, analyzing, and alerting on various types of data and events that reflect the state and activity of the containerized applications and the Kubernetes clusters. These data and events include metrics, logs, traces, events, alerts, dashboards, and reports.

In this article, we will explore some of the tools and best practices for observability in containers and Kubernetes.

Tools for Observability in Containers and Kubernetes

There are many tools available for observability in containers and Kubernetes. Some of them are native to Kubernetes or specific container platforms, while others are third-party or open-source solutions. Some of them are specialized for certain aspects or layers of observability, while others are comprehensive or integrated solutions. Some of them are:

  • Kubernetes Dashboard: Kubernetes Dashboard is a web-based user interface that allows users to manage and monitor Kubernetes clusters and resources. It provides information such as cluster status, node health, pod logs, resource usage, network policies, and service discovery. It also allows users to create, update, delete, or scale Kubernetes resources using graphical or YAML editors.
  • Prometheus: Prometheus is an open-source monitoring system that collects and stores metrics from various sources using a pull model. It supports multi-dimensional data model, flexible query language, alerting rules, and visualization tools. Prometheus is widely used for monitoring Kubernetes clusters and applications, as it can scrape metrics from Kubernetes endpoints, pods, services, and nodes. It can also integrate with other tools such as Grafana, Alertmanager, Thanos, and others.
  • Grafana: Grafana is an open-source visualization and analytics platform that allows users to create dashboards and panels using data from various sources. Grafana can connect to Prometheus and other data sources to display metrics in various formats such as graphs, charts, tables, maps, and more. Grafana can also support alerting, annotations, variables, templates, and other advanced features. Grafana is commonly used for visualizing Kubernetes metrics and performance
  • EFK Stack: EFK Stack is a combination of three open-source tools: Elasticsearch, Fluentd, and Kibana. Elasticsearch is a distributed search and analytics engine that stores and indexes logs and other data. Fluentd is a data collector that collects
    and transforms logs and other data from various sources and sends them to Elasticsearch or other destinations. Kibana is a web-based user interface that allows users to explore and visualize data stored in Elasticsearch. EFK Stack is widely used for logging and observability in containers and Kubernetes as it can collect and analyze logs from containers pods, nodes, services, and other software.
  • Loki: Loki is an open-source logging system that is designed to be cost-effective and easy to operate. Loki is inspired by Prometheus and uses a similar data model and query language. Loki collects logs from various sources using Prometheus service discovery and labels. Loki stores logs in a compressed and indexed format that enables fast and efficient querying. Loki can integrate with Grafana to display logs alongside metrics

Best Practices for Observability in Containers and Kubernetes

Observability in containers and Kubernetes requires following some best practices to ensure effective, efficient, and secure observability Here are some of them:

  • Define observability goals and requirements: Before choosing or implementing any observability tools or solutions, it is important to define the observability goals and requirements for the containerized applications and the Kubernetes clusters These goals and requirements should align with the business objectives, the user expectations, the service level agreements (SLAs), and the compliance standards. They should also specify what data and events to collect, how to analyze them, how to alert on them, and how to visualize them.
  • Use standard formats and protocols: To ensure interoperability and compatibility among different observability tools and solutions, it is recommended to use standard formats and protocols for collecting, storing, and exchanging data and events. For example, use OpenMetrics for metrics, JSON for logs, OpenTelemetry for traces, CloudEvents for events. Containers and Kubernetes Observability Tools and Best Practices. These standards can help reduce complexity, overhead, and vendor lock-in in observability.
  • Leverage native Kubernetes features: Kubernetes provides some native features that can help with observability For example, use labels and annotations to add metadata to Kubernetes resources that can be used for filtering, grouping, or querying. Use readiness probes and liveness probes to check the health status of containers. Use resource requests and limits to specify the resource requirements of containers. Use horizontal pod autoscaler (HPA) or vertical pod autoscaler (VPA) to scale pods based on metrics. Use custom resource definitions (CRDs) or operators to extend the functionality of Kubernetes resources These features can help improve the visibility, control, and optimization of containers and Kubernetes clusters.

Machine Learning for Network Security, Detection and Response

Cybersecurity is the defense mechanism used to prevent malicious attacks on computers and electronic devices. As technology becomes more advanced, it will require more complex skills to detect malicious activities and computer networks’ flaws. This is where machine learning can help.

Machine learning is a subset of artificial intelligence that uses algorithms and statistical analysis to make assumptions about a computer’s behavior. It can help organizations address new security challenges, such as scaling up security solutions, detecting unknown and advanced attacks, and identifying trends and anomalies. Machine learning can also help defenders more accurately detect and triage potential attacks, but it may bring new attack surfaces of its own.

Machine learning can be used to detect malware in encrypted traffic, find insider threat, predict “bad neighborhoods” online, and protect data in the cloud by uncovering suspicious user behavior. However, machine learning is not a silver bullet for cybersecurity. It depends on the quality and quantity of the data used to train the models, as well as the robustness and adaptability of the algorithms.

A common challenge faced by machine learning in cybersecurity is dealing with false positives, which are benign events that are mistakenly flagged as malicious. False positives can overwhelm analysts and reduce their trust in the system. To overcome this challenge, machine learning models need to be constantly updated and validated with new data and feedback.

Another challenge is detecting unknown or zero-day attacks, which are exploits that take advantage of vulnerabilities that have not been discovered or patched yet. Traditional security solutions based on signatures or rules may not be able to detect these attacks, as they rely on prior knowledge of the threat. Machine learning can help to discover new attack patterns or adversary behaviors by using techniques such as anomaly detection, clustering, or reinforcement learning.

Anomaly detection is the process of identifying events or observations that deviate from the normal or expected behavior of the system. For example, machine learning can detect unusual network traffic, login attempts, or file modifications that may indicate a breach.

Clustering is the process of grouping data points based on their similarity or proximity. For example, machine learning can cluster malicious domains or IP addresses based on their features or activities, and flag them as “bad neighborhoods” online.

Reinforcement learning is the process of learning by trial and error, aiming to maximize a cumulative reward. For example, machine learning can learn to optimize the defense strategy of a system by observing the outcomes of different actions and adjusting accordingly.

Machine learning can also leverage statistics, time, and correlation-based detections to enhance its performance. These indicators can help to reduce false positives, identify causal relationships, and provide context for the events. For example, machine learning can use statistical methods to calculate the probability of an event being malicious based on its frequency or distribution. It can also use temporal methods to analyze the sequence or duration of events and detect anomalies or patterns. Furthermore, it can use correlation methods to link events across different sources or domains and reveal hidden connections or dependencies.

Machine learning is a powerful tool for cybersecurity, but it also requires careful design, implementation, and evaluation. It is not a one-size-fits-all solution, but rather a complementary approach that can augment human intelligence and expertise. Machine learning can help to properly navigate the digital ocean of incoming security events, particularly where 90% of them are false positives. The need for real-time security stream processing is now bigger than ever.

Strategies to Combat Emerging Gaps in Cloud Security

As cloud clients input 2023 with a hybrid presence in multiple clouds, they work on prioritizing techniques to fight rising gaps in cloud security.

Most big agencies are getting access to cloud offerings in numerous public clouds, whilst preserving organization structures and personal clouds of their company’s facts centers.

One of the ways of closing these gaps in security could be adopting deep observability. We have already reviewed a few Deep Observability providers such as Gigamon. While Gigamon probably can be considered a current market leader in this relatively new and small market with under $2B annual market size, they still should watch out for the newcomers who come with shiny new products and great technologies under the hood.

CtrlStack is one of these startups and they recently got a second round of funding from Lightspeed VC, led by Kearny Jackson and Webb Investment Network.

The delivery of features and applications by today’s digital-first companies and developers is accelerating. Teams from information technology operations and software development must collaborate closely to do this, forming a practice known as DevOps. When events occur, they may involve any number of digital environment systems, including operations, infrastructure, code, or any combination of modifications made to any of them.

The CtrlStack platform connects cause and effect to make troubleshooting easier and incident root cause analysis faster by tracking relationships between components in a customer’s systems. Developers and engineers can solve problems quickly by giving DevOps teams the tools they need.

By forming an understanding graph of all of the infrastructure, interconnected offerings, and impact, CtrlStack can supply the full picture while capturing the devices’ modifications and relationships throughout the whole device stack. Using CtrlStack product DevOps groups can view dependencies, measure the impact of modifications and examine occasions in actual time.

Key capabilities of the platform encompass an occasion timeline that permits groups to browse and clear out out extrade occasions, without having to sift via log documents or survey users, and a visual representation that offers insights into operational data. Both of those capabilities additionally force dashboards for builders and DevOps groups.

Developers can also access their dashboards that give visibility for any modifications to code commits, configuration documents, or function flags, – all in one click. DevOps groups get a dashboard for root reason evaluation that permits them to seize all of the context for the time being they came about with a searchable timeline of dependencies displaying the whole impacted topology and impacted metrics.

Deep Observability and Zero Trust

Zero trust architecture has established itself as a highly recognized method of safeguarding both on-premises systems and the cloud in response to the exponential rise in ransomware and other cyber threats. In example, although only 51% of EMEA IT and security professionals said they were confident implementing zero trust in 2019, that percentage increased noticeably to 83% in 2022.

The implicit trust that is placed in internal network traffic, people, or devices is eliminated by a zero trust architecture, to put it simply. Businesses can increase both productivity and security with this defense / defense in depth approach to security.

For businesses, implicit confidence in the technology stack can be a major problem. IT teams frequently struggle to put the right trust controls in place because they typically assume that the company owns the system, that all users are employees, or that the network was previously safe. These trust indicators, however, are insufficient. Organizations are becoming more exposed to risk as a result of trust built on assumptions. These careless measurements of trust can be utilized by threat actors against a company to facilitate network intrusion and data breaches.

A zero trust framework gets rid of any implicit trust and instead determines whether a company should grant access in each specific situation. It is more crucial now that bring-your-own-device (BYOD) initiatives have become so popular due to the rise of remote and hybrid working.

To increase the effectiveness of metric, event, log, and trace-based monitoring and observability tools and reduce risk, deep observability is the addition of real-time network-level intelligence. With it comes more insight to strengthen a company’s security posture since deep observability enables security professionals to examine the metadata that threat actors leave behind after evading endpoint detection and response systems or SIEMs. Therefore, it is essential to support a thorough zero trust strategy.

In the end, zero trust’s primary objective is to identify and categorize all network-connected devices, not only those that have endpoint agents installed and functioning, and to tightly enforce a least-privilege access policy based on a detailed analysis of the device. This cannot be done for devices or users that you can not access.

Growth in Deep Observability Services

Gigamon, a deep observability company has recently reported a 100% YoY increase in Deep Observability Pipeline ARR. Some market researches estimate the Deep Observability market size as $2B in 2026.  According to Gigamon, the company leads this market with 68% market share. Cybersecurity solutions should adequately protect the entire system while eliminating blind spots. This becomes particularly challenging in multi-cloud and hybrid environments. SOC teams need historical data and insights into attackers’ strategies, and most of all they need time to properly prepare and respond. With customers like US Department of Defense and Lockheed Martin, Gigamon looks well-packed to deliver high-quality Deep Observability network solutions and AI-based threat detection to the US and international markets.