December 2022 Summaries

Implementing service-level objectives (SLOs) is essential for organizations aiming to enhance software development quality and application performance, especially in the context of microservices-based architectures. SLOs serve as a crucial component of service-level agreements (SLAs) between providers and customers, helping to manage customer satisfaction by setting clear performance benchmarks for services. By defining and monitoring SLOs, organizations can streamline the collection of metrics, facilitate decision-making, promote automation, and minimize downtime, ultimately improving software quality. Creating effective SLOs involves defining critical service paths, establishing reliability measurements, setting realistic targets, and leveraging telemetry data generated by applications. Tools like Dynatrace aid in this process by providing centralized observability, topology mapping, and AI-driven analytics, which help teams develop error budgets and make data-driven decisions. Overall, SLOs focus on aligning service performance with user needs rather than just technical metrics, thereby supporting greater automation and efficiency in DevOps practices.

Successful cloud modernization involves key elements such as leveraging partners and embracing the role of cloud architects to navigate the increasingly complex landscape. The process requires collaboration since modernization and digitization cannot be achieved alone, with partners being essential for faster and more scalable outcomes. A recent Dynatrace webinar featuring Forrester Principal Analyst Lee Sustar highlighted that cloud modernization entails updating legacy applications to optimize them for cloud environments, requiring rapid, secure, and reliable transitions. Modernization champions, encompassing developers, site reliability engineers (SREs), and cloud architects, are critical in ideating future architectures, optimizing operations, and creating application delivery pipelines. The initial steps of cloud modernization should focus on low-hanging fruit to build confidence and real-world results, often with the guidance of experienced partners. For organizations lacking in-house cloud architect skills, partners provide crucial support and insight from previous projects, while cloud providers offer resources to cultivate architectural practices over time. As cloud modernization accelerates, organizations are advised to prioritize agility and resilience, learning from past experiences to continually evolve and succeed.

Dynatrace has been recognized as a leader in the ISG 2022 Provider Lens report for its cloud-native observability and security capabilities in both the U.S. and Europe. The report by ISG, a global technology research and advisory firm, highlights the need for a new approach to cloud complexity, emphasizing unified observability and AI-driven solutions. Dynatrace distinguishes itself with its comprehensive platform that extends beyond traditional observability methods to include topology mapping, user behavior, and application security, all supported by the latest open-source standards like OpenTelemetry. The platform's AI, particularly the Davis AI, is praised for its ability to manage the complexities of distributed systems by reducing alert noise and providing real-time, automatic root-cause analysis. Dynatrace's cloud-native platform is designed to tackle modern challenges such as cloud migration and microservices refactoring, offering end-to-end observability and security in dynamic environments. Additionally, the launch of Grail, a cloud-native data lakehouse, enhances Dynatrace's capabilities by enabling the ingestion and analysis of large data volumes for improved observability, security, and business analytics.

AIOps, or Artificial Intelligence for IT operations, integrates big data and machine learning to enhance automation and transform digital operations by providing actionable insights for IT teams. It aims to automate processes across the enterprise, particularly in complex environments driven by cloud-native technologies like microservices and serverless platforms. AIOps is applied in CloudOps for incident and event management, in DevOps to streamline and accelerate software delivery, and in SecOps to enhance application security. The technology processes data through four stages: collection, aggregation, analysis, and execution, with the analysis stage being the most challenging due to its complexity in identifying root causes. Deterministic AI, an alternative to traditional machine learning, employs fault-tree analysis for precise root-cause identification by modeling the topology of data centers or applications, allowing for quick and repeatable detection of technical and foundational issues. AIOps supports human operators by filtering data to reduce noise and alert fatigue, thereby enabling IT professionals to focus on critical tasks. The evolution of AIOps, especially with a deterministic approach, promises greater automation and efficiency in managing modern multicloud environments, enhancing the ability of DevOps teams to innovate rather than just maintain existing systems.

In today's rapidly evolving business landscape, organizations face significant challenges, including the aftermath of COVID-19, economic volatility, and technological upheaval, which necessitate a shift towards flexibility, adaptability, and customer-centric approaches. An open, extensible software intelligence platform offers a solution by facilitating interoperability and innovation, contrasting with legacy systems that often cause inertia and hinder growth. As companies transition to cloud operations, open technologies are crucial for enhancing enterprise agility and innovation, while minimizing resource use. According to the 2022 Dynatrace CIO Report, nearly 60% of CIOs are concerned about the increasing complexity of their technology stacks, which underscores the need for more automated IT operations. Open platforms, unlike proprietary systems, prevent vendor lock-in and promote seamless data integration, enabling organizations to focus on core growth areas and achieve competitive advantage. The debate between building or buying such platforms leans towards purchasing ready-made solutions to avoid reinventing the wheel and focus resources on strategic initiatives. Platforms like Dynatrace exemplify this approach by offering a unified platform for cloud transformation, leveraging AI and automation to streamline processes and enhance operational efficiency.

In the Forrester Wave™: Artificial Intelligence for IT Operations (AIOps) Q4 2022 report, Dynatrace was named a leader, receiving the highest scores in the Current Offering and Strategy categories for its innovative approach to AIOps. The recognition highlights Dynatrace's superior vision, which leverages a unified model, AI, and automation to address complex IT operations. Key features contributing to its high scores include Smartscape topology and dependency mapping, digital experience monitoring, and application and infrastructure monitoring, all powered by the Davis AI engine. Dynatrace's Smartscape provides a dynamic and interactive view of the full stack, while the Davis AI engine delivers precise, instant answers across applications, infrastructure, and digital experience, enhancing business insights. The company’s recent introduction of Grail, a causational data lakehouse, further supports its commitment to multicloud complexity management and digital transformation, offering contextual analytics on observability, security, and business data.

In 2023, IT leaders will focus on several key observability trends to navigate the challenges posed by rising energy costs and inflation while maintaining innovation and growth. Trustworthy AI will become essential for automating complex digital ecosystems, as bias in AI poses a barrier to widespread automation. Organizations will aim to merge observability, security, and business analytics to handle the data explosion from multicloud environments and complex technology stacks, using AI-powered platforms to break down data silos and drive intelligent automation. The evolution from DevSecOps to SecDevBizOps will emphasize shared responsibility in cybersecurity, driven by the demands of cyber-insurance, requiring every innovator to manage risk. Additionally, data context-driven automation will emerge as a priority, enabling more precise AISecOps by unifying data and its context to enhance decision-making and automation across software delivery, security, and operations.

Organizations grappling with expanding and complex cloud environments face challenges in obtaining precise insights from log monitoring platforms. The article highlights three log ingestion strategies for scaling log management in Dynatrace without relying on the OneAgent. These strategies include using Fluentd for syslog data, leveraging Cribl for existing log shippers, and utilizing Fluent Bit for AWS Fargate logs. Fluentd serves as a flexible data collector that decouples data sources, while Cribl offers a unified platform for data operations, and Fluent Bit is a lightweight log processor ideal for resource-constrained environments. Each strategy involves setting up configurations to channel log data into Dynatrace, enhancing observability, monitoring, and troubleshooting capabilities. The article also hints at upcoming methods for log ingestion, such as OpenTelemetry logs acquisition, and encourages readers to engage with the Dynatrace Community for further discussions on log management best practices.

Network errors, such as dropped packets or retransmissions, can be mitigated by protocols like TCP/IP, which has remained foundational in networking due to its error-compensating capabilities. Despite this, understanding network errors is crucial for maintaining service performance and connectivity. Common network issues include network collisions, checksum errors, full queues, time-to-live exceeded errors, and packet retransmissions. Tools like ifconfig, netstat, ethtool, tcpretrans, tcpdump, and Wireshark are essential for diagnosing these problems. Effective network monitoring involves tools that automatically track performance baselines, alert users to anomalies, and provide insights into service availability and responsiveness. Dynatrace is highlighted as a comprehensive solution for network monitoring, offering insights into service health, user impact, and root causes of connectivity issues. The complexity of modern network infrastructures, including virtual environments, adds additional challenges to monitoring efforts, underscoring the importance of robust analytical tools.

The OneAgent version 1.255 release notes, published on December 12, 2022, detail various enhancements, new features, and resolved issues across multiple platforms and components. Key updates include added support for Microsoft SQL Server, GraphQL, and NGINX, along with new LPAR and region metrics for z/OS. The release also introduces changes such as the default installation of the Npcap driver on Windows and the removal of support for certain operating systems by 2026. Resolved issues span a range of components, including improvements in .NET code stability, bug fixes in JavaScript and Android modules, and performance enhancements for Oracle process grouping. The release addresses vulnerabilities and improves input validation for Real User Monitoring (RUM), while also providing various cumulative updates that compile previous fixes and enhancements.

Dynatrace Managed release version 1.256, initiated on December 12, 2022, introduces several changes and improvements, particularly regarding cluster resilience and operating system support. Notable updates include the discontinuation of support for Suse LES 12.3 and Debian 9 for new cluster nodes, with future support changes planned for Red Hat Enterprise Linux and Amazon Linux. Enhancements focus on preventing data loss by disabling OneAgent traffic on undersized disks and updating data replication for Premium High Availability deployments. The release also fixes numerous issues across application security, cluster management, and cluster management console components, addressing problems like broken security problem handling, missing mobile key user actions, and improved cluster metrics performance. Additionally, support for Red Hat Enterprise Linux versions 8.7 and 9.1 has been added, while previous release updates have resolved various other issues, including enhancements in monitoring, log display formatting, and management zone assignments.

Dynatrace for Government has achieved StateRAMP authorization at the Moderate impact level, becoming the first observability platform to do so, signifying its compliance with rigorous security and compliance standards essential for supporting digital transformations in state and local governments and public education institutions. StateRAMP streamlines the authorization process for cloud service providers by providing a consistent security framework, allowing agencies to reuse security packages and reduce redundancies, thereby accelerating the adoption of cloud-based technologies. The Dynatrace Software Intelligence Platform enhances public sector digital transformation by offering real-time observability, AI-driven automation, and advanced application security, allowing agencies to deliver innovative cloud-native applications, resolve issues proactively, and improve IT resource efficiency. Additionally, Dynatrace's platform supports cross-functional collaboration and security enhancement by automating vulnerability detection and enabling comprehensive insights across hybrid, multicloud environments. With StateRAMP and FedRAMP authorizations and adherence to standards like FIPS 140-2 and NIST 800-53, Dynatrace ensures a secure environment for state and local governments and educational institutions, offering a seamless free trial and extensive user support to facilitate their digital transformation efforts.

Dynatrace cloud automation is designed to enhance progressive delivery in cloud environments by providing end-to-end orchestration, which supports DevOps platform engineers in managing complex cloud-native architectures. As these distributed systems become more prevalent, the role of DevOps platform engineers, akin to site reliability engineers, is crucial in ensuring the availability, performance, and efficiency of cloud platforms. The term "DevOps" was popularized by Patrick Debois in 2009 to encourage collaboration between development and operations teams through practices like continuous integration and delivery. DevOps platform engineers focus on creating stable and efficient software development pipelines using tools and platforms such as GitHub, Jenkins, and Kubernetes. Automation plays a vital role in this context, allowing for streamlined workflows and faster deployment without manual intervention. Cloud automation, supported by platforms like Dynatrace, simplifies the integration of various DevOps tools and processes, fostering better software quality and faster development cycles.

Kubernetes out-of-memory (OOMKilled) issues can be challenging to troubleshoot, as demonstrated by Robert Wunderer, founder of CapriSys GmbH, who encountered such a problem after deploying a new software version in a multi-tenant e-commerce system. Despite having adequate resource settings in place, the rollout led to high HTTP failure rates across tenants due to the report service pods being killed for memory shortages. Using Dynatrace, Robert identified an increased memory requirement in the new version, which was causing the pods to hit their memory limits and restart. By increasing the memory limit from 400MB to 600MB, the issue was resolved, highlighting the need for proper resource allocation and testing in Kubernetes environments. The case underscores the importance of observability tools like Dynatrace in quickly identifying root causes and ensuring smooth operations, while also emphasizing the value of comprehensive testing to prevent future issues.

Dynatrace hosts its SaaS clusters on Amazon Web Services (AWS), where they operate 51 clusters on 1100 EC2 instances across six AWS regions, achieving an availability of 99.95% since moving to AWS in 2014. This success is attributed to Dynatrace's use of its own platform for monitoring and its reliance on the Dynatrace Davis® AI for alerting on issues like the disk write latency problem encountered in AWS's Sydney data center. When Davis detected disk latency issues across multiple instances, Dynatrace's Autonomous Cloud Enablement (ACE) team collaborated with AWS to confirm and resolve a known hardware issue, demonstrating the importance of proactive monitoring in hybrid-multicloud environments. This incident underscores the benefits of using advanced monitoring tools that detect anomalies and provide timely alerts, allowing for quick remediation and maintaining service level agreements.