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市场调查报告书
商品编码
1476417
全球 Docker 监控市场预测(~2030 年):按组件、部署类型、组织规模、监控、使用情况和区域进行分析Docker Monitoring Market Forecasts to 2030 - Global Analysis By Component (Solution, Service and Other Components), Deployment Type, Organization Size, Monitoring, Application and By Geography |
2023年,全球Docker监控市场规模为6.469亿美元,预计预测期内复合年增长率为19.8%,到2030年将达到22.911亿美元。 Docker监控是指监控容器化应用程式的效能、资源使用情况和健康状况。
它包括追踪 CPU 使用率、记忆体消费量、网路活动和容器状态等指标。 Prometheus、Grafana 和 Docker 的内建监控功能等工具可以收集和分析这些指标,从而提供对容器效能的深入了解并协助进行故障排除。监控 Docker 容器使操作员能够确保最佳效能、及早发现潜在问题并维护容器化环境的整体健康和稳定性。
即时可见性和效能优化的需求
即时可见性和效能最佳化在微服务和分散式架构中非常重要,其中许多互连组件动态互动。 Docker 监控解决方案透过提供对各个容器、服务和相依性的精细可见性来解决这些环境问题,使企业能够找出效能瓶颈并指导资源分配即时最佳化。此外,这些解决方案透过提供仪表板、警报和分析来推动市场成长,使管理员能够即时监控 Docker 效能指标,从而促进主动故障排除和最佳化。
分散式系统监控的复杂性
容器化环境的动态特性为维护和管理监控解决方案带来了挑战。当容器扩展或缩小、在主机之间移动或状态变更时,必须经常更新监控配置以确保准确可靠的监控。这增加了组织的维护开销,并从成本和资源角度降低了 Docker 监控解决方案的吸引力。因此,组织可能会犹豫是否实施会带来巨大效能开销的监控解决方案,尤其是在高吞吐量或延迟敏感的环境中。
机器学习和分析集成
机器学习演算法可以自动侦测Docker监控资料中的异常情况,例如异常的资源使用模式、异常的容器行为和安全威胁。透过持续分析大量监控资料,基于 ML 的 Docker 监控解决方案可以快速识别与预期规范的偏差,并触发警报或自动补救措施,以防止事件,从而缩短回应时间并降低服务中断的风险。透过动态调整资源分配以回应不断变化的需求,基于 ML 的 Docker 监控解决方案可协助组织最大限度地提高资源利用率、最大限度地降低成本并提高整个容器化环境的效能,从而确保最佳效能。
监控工具的安全问题
Docker 监控工具中的安全漏洞可能会使组织面临资料外洩和外洩的风险。攻击者可以利用监控工具中的漏洞来未授权存取敏感资讯、破坏容器化应用程式并窃取有价值的资料。这种违规行为对组织的声誉、客户信任和财务稳定性的潜在影响是巨大的,使得组织对于采用 Docker 监控解决方案犹豫不决。
COVID-19 的影响
Docker 使用的激增导致 Docker 监控解决方案的需求增加,以确保效能、安全性和可靠性。随着团队变得更加分散并且更加依赖云端基础设施,监控工具对于维持营运连续性和优化资源利用率变得至关重要。此外,疫情凸显了可扩展性和弹性的重要性,而 Docker 监控市场也越来越重视可扩展性监控,因为组织在面对不确定和不断变化的业务条件时优先考虑适应性。
服务业务预计将在预测期内成为最大的业务
随着 Docker 和容器化在软体开发和配置中的普及,服务领域预计将出现良好的成长,增加对有效监控解决方案的需求。支援 Docker 监控的服务透过提供针对容器化环境客製化的专业知识和工具来推动此需求。这推动了具有不同需求的各种组织采用 Docker 监控工具。
可扩展性监控领域预计在预测期内复合年增长率最高
由于可扩展性监控领域的快速扩展和处理动态工作负载的能力,预计在预测期内将以最高的复合年增长率成长。 Docker 生态系统中的可扩展性、监控和监控解决方案专注于追踪容器执行个体的效能指标、资源利用率和整体系统运作状况,因为它们会根据我提出的不断变化的需求进行扩展或缩减。这种对弹性的重视与 Docker 的核心原则是一致的,也是利用容器化的组织的关键要求。
预计亚太地区在预测期内将占据最大的市场占有率。这是由于亚太地区对云端运算的接受度不断提高,组织越来越多地容器编排管理来跨多重云端和混合云端环境部署和管理容器化工作负载,而Docker 容器在利用该平台时正在获得动力。随着对资料隐私和安全性的日益关注,亚太地区的企业正在寻找能够提供强大安全功能(例如漏洞扫描、存取控制和敏感资料加密)的 Docker 监控解决方案。
由于 Docker 容器在企业环境中的广泛采用,预计北美在预测期内将出现最高的复合年增长率。 Docker 容器在开发人员和 IT 专业人员中越来越受欢迎,因为它们提供了一种轻量级且高效的方式来包装、部署和管理应用程式。然而,随着 Docker 容器的采用不断增加,对强大的监控解决方案以确保容器化应用程式的最佳效能、可用性和安全性的需求变得至关重要。
According to Stratistics MRC, the Global Docker Monitoring Market is accounted for $646.9 million in 2023 and is expected to reach $2291.1 million by 2030 growing at a CAGR of 19.8% during the forecast period. Docker monitoring is the practice of overseeing containerized applications for performance, resource utilization, and health. It involves tracking metrics like CPU usage, memory consumption, network activity, and container status. Tools such as Prometheus, Grafana, and Docker's built-in monitoring capabilities collect and analyze these metrics, providing insights into container performance and aiding in troubleshooting. By monitoring Docker containers, operators can ensure optimal performance, identify potential issues early, and maintain the overall health and stability of their containerized environments.
Need for real-time visibility and performance optimization
Real-time visibility and performance optimization are critical in microservices and distributed architectures where numerous interconnected components interact dynamically. Docker monitoring solutions cater to these environments by providing granular visibility into individual containers, services, and dependencies, enabling organizations to pinpoint performance bottlenecks and optimize resource allocation in real-time. Moreover these solutions offer dashboards, alerts, and analytics that enable administrators to monitor Docker performance metrics in real-time, facilitating proactive troubleshooting and optimization drives the growth of the market.
Complexity of monitoring distributed systems
The dynamic nature of containerized environments introduces challenges in maintaining and managing monitoring solutions. As containers scale up or down, move between hosts, or change states, monitoring configurations may need frequent updates to ensure accurate and reliable monitoring. This can increase maintenance overheads for organizations, making Docker monitoring solutions less appealing from a cost and resource perspective. Thus organizations may hesitate to deploy monitoring solutions that introduce significant performance overheads, especially in high-throughput or latency-sensitive environments.
Integration of machine learning and analytics
Machine learning algorithms can automatically detect anomalies in Docker monitoring data, such as unusual resource utilization patterns, abnormal container behavior, or security threats. By continuously analyzing vast amounts of monitoring data, ML-powered Docker monitoring solutions can rapidly identify deviations from expected norms and trigger alerts or automated remediation actions, improving incident response times and reducing the risk of service disruptions. By dynamically adjusting resource allocation in response to changing demand, ML-powered Docker monitoring solutions help organizations maximize resource utilization, minimize costs, and ensure optimal performance across containerized environments.
Security concerns around monitoring tools
Security vulnerabilities in Docker monitoring tools can expose organizations to the risk of data breaches or leakage. Attackers may exploit vulnerabilities in monitoring tools to gain unauthorized access to sensitive information, compromise containerized applications, or exfiltrate valuable data. The potential impact of such breaches on organizational reputation, customer trust, and financial stability can be significant, leading organizations to hesitate in adopting Docker monitoring solutions.
Covid-19 Impact
Surge in Docker usage consequently drove increased demand for Docker monitoring solutions to ensure performance, security, and reliability. With teams distributed and reliance on cloud infrastructure heightened, monitoring tools became essential for maintaining operational continuity and optimizing resource utilization. Moreover, the pandemic underscored the importance of scalability and resilience, leading to a greater emphasis on scalability monitoring within the Docker monitoring market as organizations prioritized adaptability in the face of uncertain and evolving business conditions.
The service segment is expected to be the largest during the forecast period
The service segment is estimated to have a lucrative growth, as Docker and containerization become more prevalent in software development and deployment, the need for effective monitoring solutions has grown. Services catering to Docker monitoring have fueled this demand by offering specialized expertise and tools tailored to containerized environments. This has facilitated the adoption of Docker monitoring tools by a broader range of organizations with diverse requirements.
The scalability monitoring segment is expected to have the highest CAGR during the forecast period
The scalability monitoring segment is anticipated to witness the highest CAGR growth during the forecast period, owing to their ability to scale rapidly and handle dynamic workloads. Scalability monitoring solutions in the Docker ecosystem focus on tracking container instances' performance metrics, resource utilization, and overall system health as they scale up or down in response to changing demand. This emphasis on elasticity aligns with Docker's core principles and has become a critical requirement for organizations leveraging containerization.
Asia Pacific is projected to hold the largest market share during the forecast period owing the increasing acceptance of cloud computing in the region is fueling the adoption of Docker containers, as organizations leverage container orchestration platforms like Kubernetes for deploying and managing containerized workloads across multi-cloud and hybrid cloud environments. With growing concerns about data privacy and security, organizations in Asia Pacific are looking for Docker monitoring solutions that offer robust security features, including vulnerability scanning, access controls, and encryption of sensitive data.
North America is projected to have the highest CAGR over the forecast period, due to the widespread adoption of docker containers in enterprise environments. Docker containers provide a lightweight and efficient way to package, deploy, and manage applications, making them increasingly popular among developers and IT professionals. However, as the adoption of Docker containers continues to rise, the need for robust monitoring solutions becomes paramount to ensure optimal performance, availability, and security of containerized applications.
Key players in the market
Some of the key players in the Docker Monitoring Market include IBM, AppDynamics, BMC Software, Broadcom, Datadog, Dynatrace, Instana Centreon, Manage Engine, Micro Focus, Microsoft, Oracle, ScienceLogic, Solarwinds, Splunk, Sumo Logic, Sysdig, Wavefront and Riverbed Technology
In April 2024, IBM Offers Meta's Llama 3 Open Models on Watsonx, Expands Portfolio of Next Generation Enterprise-Ready Models. This expands IBM's watsonx.ai model library to help enterprises innovate with its in-house Granite series of models, as well as those from leading model providers like Meta.
In January 2024, Fortinet and IBM Cloud Collaborate to Help Clients Protect Their Workloads from Increased Cybersecurity Threats. With clients reporting that they are facing new network and application threats, it's critical to have the right ecosystem of technology partners that can unite around a shared mission of protecting their clients from risk.
In January 2024, Cisco launches the Smart Agent for Cisco AppDynamics. It enables agent lifecycle management, significantly simplifies application instrumentation for full-stack observability through intelligent agent automation and management, and helps customers introduce new applications faster.
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.