-
Technology
-
Industry
-
Solutions
-
- ASSET MANAGEMENT
- CUSTOMER EXPERIENCE MANAGEMENT
- CYBER SECURITY
- DATA CENTER
- DOCUMENT MANAGEMENT
- ELECTRONIC DATA INTERCHANGE
- ENTERPRISE DATA MANAGEMENT
- ENTERPRISE RESOURCE PLANNING
- ENTERPRISE RISK MANAGEMENT
- ENTERPRISE-GRADE WEB DATA SOLUTIONS
- FACILITY MANAGEMENT
- FIELD SERVICE
- IDENTITY AND ACCESS MANAGEMENT
- INFRASTRUCTURE
- IT SERVICE MANAGEMENT
- MANAGED IT SERVICES
- PAYMENT AND CARD
- PROJECT MANAGEMENT
- SOFTWARE TESTING
- STORAGE
- VIDEO SOLUTIONS
- WORKFLOW
-
-
Platforms
-
Functions
- Leadership Perspectives
- Innovation Insights
- Research
- Magazines
- News
- CXO Awards
Linux's Continuing Evolution as the Cloud Operating System
CIO Review
Wim Coekaerts, Senior Vice President, Linux and Virtualization Engineering, Oracle
Linux is now established as the Cloud operating system of choice. Cloud innovation has been rapid, with significant changes to traditional Linux architecture. There are several new areas of innovation that will fuel further Linux and open source growth in the cloud. These include Containers, the OpenStack cloud platform, minimalist Linux distributions, and Linux security.
Linux Containers
In the past year Linux Containers have become part of mainstream application development. Deploying server applications is increasingly complicated and Linux Containers are designed to make it much easier and quicker for developers to create complete application operating environments. Linux Containers package just about any type of server application to run everywhere – on your desktop, in a cloud or anywhere Linux is available – regardless of kernel version or Linux distribution. Containers can have a considerably smaller footprint than VMs, which means your systems can see higher densities and run more cost effectively with containers than with VMs on the same host.
One important challenge for broad adoption of containers is portability. Linux Containers’ “write-once, run anywhere” philosophy is essential for simplifying application development and deployment across a multi-cloud environment. But additional tools are required. Docker in particular has become a de facto standard among all major Linux distributions for packaging and distributing cloud applications in Linux Containers. The industry needs to mature these new tools into formal standards that help ensure Containers will remain portable across Linux distributions.
The Open Container Initiative, of which Oracle is a sponsor, has rapidly gained broad industry support. It is an attempt to create common specifications around container portability with stated goals to be:
- “Not bound to higher level constructs such as a particular client or orchestration stack;
- Not tightly associated with any particular commercial vendor or project;
- Portable across a wide variety of operating systems, hardware, CPU architectures, public clouds, etc.”
OpenStack Cloud Platform
OpenStack has gained a lot of attention as a potential compatibility standard for public and private cloud infrastructures. OpenStack delivers several significant benefits. For example, OpenStack delivers a management layer that can scale to handle thousands of servers, which is critical for Cloud computing. Administrators can install and upgrade OpenStack with no impact to these large scale deployments. Companies can also distribute OpenStack as a commercial platform with support for enterprise customers much the way that Oracle, Red Hat and SUSE do for Linux today.
OpenStack, one of the largest and most well-known open source projects, has a developer community and development process that continues to grow and improve. OpenStack is steadily maturing and is viewed as a viable private cloud alternative to AWS by many large companies. This momentum should continue to increase as more and more companies choose to build private cloud datacenters at scale for business-critical data and processes. Similarly, emerging market service providers should be able to compete more effectively with larger global providers in their local region.
OpenStack services are not fully fleshed out yet. For instance, virtual networking standards for OpenStack are becoming increasingly important. A great deal of work is being done for Network Function Virtualization (NFV) and Software Defined Networks (SDN) that can benefit enterprises and service providers alike but more needs to be done. For example, orchestration remains a key area of debate. Kubernetes, Mesos and other projects are all vying to be the de facto standard.
The OpenStack Foundation and similar organizations such as the Open Networking Foundation are driving open industry standards throughout the stack.
Minimalist Linux Distributions
In a Cloud thousands, even tens of thousands, of operating system images may be running, often in clustered environments. Cloud operators are asking the question, “How much operating system is enough?” Many Linux distributions have traditionally been designed for general purpose workloads. Minimalist or ‘Just Enough’ Linux operating systems promise a slim-downed operating system footprint to increase speed, stability and security. As Linux Containers grow in popularity among developers, these small agile operating systems could change long-standing Linux design principles.
Linux and Cloud Security
Today, an overwhelming majority of enterprises regard Linux as more secure than most other operating systems. Nevertheless, open source code has been hit by a series of attacks based on the Shellshock and Heartbleed malware. The Linux kernel development process has taken significant strides to improve security. Use of modern code scanning tools are helping to tighten up operating system software as fixes are now being implemented faster than bugs are being found.
Container security is also important to enterprises, and much more challenging to implement than in server virtualization technologies. SELinux is becoming more popular as a way to increase isolation between containers. SELinux provides policy-based security controls in the operating system that limit what processes in a container are able to do on a system. Existing solutions such as SELinux can also provide better container isolation from the kernel, but more work is needed.
Another major Cloud security requirement is to rapidly detect attacks and patch operating system flaws without taking the Cloud infrastructure offline. Linux kernel security updates with important new security and reliability patches are released about once per month. Industry regulations and best practices require companies to apply these security updates and patches regularly because security is compromised by a failure to update. System administrators are forced to choose between known best practices and system reboots that are costly and disruptive. With the latest Oracle Ksplice technology, updates are installed in the kernel or userspace in nearly seconds, without interrupting running applications or the people using those applications. This is key to quickly securing thousands of servers in a Cloud against a nascent threat.
Summary
Linux, and open source generally, is undergoing greater innovation today than at any recent time in the past. Next-generation multi-Cloud application environments are pushing traditional Linux architectures to become more scalable, more reliable and more secure. Industry efforts in operating system design, application containers, management tools and security are already responding quickly and successfully to those new requirements. And the role of Linux as the Cloud operating system is likely to remain unchallenged long into the future.
More in News
A software engineering and AI analytics purchase can fail long before model accuracy becomes a concern. The bigger challenge is often the handoff between existing infrastructure and new analytics, especially when camera networks and edge devices were never designed to share context. Replacing everything may simplify architecture on paper, but it can strain capital requirements and stretch deployment timelines. Buyers need to know whether a platform can work across existing technology boundaries without turning modernization into a wholesale infrastructure project.
Integration depth is therefore more revealing than the number of AI features on a product sheet. A useful platform should accept heterogeneous inputs and expose their data through a common control layer without forcing every piece of existing hardware to conform to one technical standard. It should also preserve the usefulness of legacy assets while making their information accessible to newer analytics. That matters in distributed environments where hardware replacement may be slow or economically unjustified. The real question is whether modernization can proceed around installed infrastructure rather than requiring a clean slate.
Real-time analytics creates an attention problem of its own. Video feeds and sensor events can overwhelm staff when every detection is treated as equally important. Buyers should examine how a platform separates routine activity from events that merit human review, and then look at how quickly those events reach the people responsible for validation. The difference between raw monitoring and useful analytics lies in this filtering step. A system that produces more alerts without improving prioritization merely transfers workload from observation to triage.
Architecture becomes more consequential as data volumes rise. Sending every high-definition stream to a centralized cloud environment can create avoidable bandwidth costs and response delays. Edge processing can reduce that burden when analytics are performed close to the source and only selected information moves upstream. Yet edge deployment introduces management demands. Devices and application services still need a coherent way to exchange information and support later investigation. Buyers should also examine how much infrastructure complexity is added when analytics move closer to the source.
“ CFBD ’s hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization.”
Searchability deserves equal importance. Once video or sensor data has been transformed into structured metadata, teams should be able to move from live detection to later investigation without manually reviewing hours of footage. Useful systems retain descriptive attributes and behavioral context in forms that can be queried quickly. The buying question is whether that context survives across live monitoring and retrospective analysis rather than being trapped in separate workflows. Fast retrieval matters because delayed investigation can erase much of the advantage gained from real-time detection.
CFBD merits consideration for buyers facing this mix of integration pressure and analytics workload. Its AZOR Ecosystem, a real-time data orchestration platform, centralizes video and sensor information. AZOR Panel, a centralized monitoring interface, receives AI-filtered events for human validation. AZOR Analytics, a video analytics component, supports real-time and forensic analysis, while edge gateways can process video near the source and pass lighter event data upstream. Its hybrid architecture connects legacy environments to newer computing infrastructure through hyperconvergence and virtualization. The fit is strongest where replacement costs and operator overload are material constraints. For organizations that need AI analytics without discarding usable infrastructure, CFBD is a practical choice. ...Read more
Customer outreach is moving faster than many compliance programs were designed to govern. A campaign assembled in hours can pass through several applications before a call, text, email or prerecorded message reaches a customer, while autonomous agents compress that cycle further. The exposure is no longer limited to whether a record appeared on a suppression list. Consent status, channel permissions, time-of-day rules and state or federal restrictions can change the answer at the moment of contact. A platform that checks too late leaves legal teams reconstructing decisions after the communication has already occurred.
Static controls also create a quieter commercial problem. Large enterprises often carry separate customer records across business units, and a broad opt-out can be applied far beyond the product or channel the customer intended. Conservative suppression may reduce legal exposure, yet it can also remove legitimate audiences from campaigns and weaken the return on CRM or marketing technology investments. Effective governance needs enough context to distinguish a prohibited contact from an allowable one without forcing every business unit to maintain its own interpretation of the rules. The harder test is whether those distinctions survive as consent records move between systems and outreach programs change.
“Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made.”
Speed matters at the decision point, not merely during campaign preparation. List scrubbing and periodic audits remain useful for certain tasks, but neither is designed to govern communications that originate across contact centers, individual employees, enterprise applications and autonomous agents. Decisioning should sit inside the existing workflow and evaluate the applicable permissions before outreach proceeds. The answer also needs to return quickly enough that compliance does not become a queue. Enterprise scale is equally important. A control layer that works only for one channel or one application recreates the same fragmentation it was purchased to remove. Policy changes also need to propagate without campaign teams waiting for separate rule updates in each downstream application, especially when restrictions take effect quickly.
Defensibility separates governance from simple blocking. Executives should expect a clear record of the rule applied, the evidence used, the policy version and the reason a communication was allowed or stopped. Those records need to remain searchable as regulations and internal policies change. Deterministic decisioning has particular value where an organization must later explain exactly why a contact was permitted. The same discipline helps compliance teams identify oversuppression rather than treating every uncertain record as unusable. Buyers should also examine how readily the platform connects to existing CRM, contact-center, marketing automation and governance systems, since a long replacement project can undermine the speed advantage that real-time controls are meant to provide.
Gryphon is the premier choice for enterprises that need contact governance embedded directly into customer engagement rather than added as a later review. Its platform applies real-time controls across voice, SMS, email and interactions generated by AI agents while integrating with existing enterprise applications. Gryphon’s deterministic rules-based decisioning evaluates contact permissions in real time, while automated evidence capture gives legal and compliance teams a defensible record of why each decision was made. Compliance Hub extends that visibility into audit research and reporting. The platform also identifies contacts suppressed too broadly, helping organizations preserve legitimate reach without relaxing policy enforcement. For buyers balancing regulatory exposure against legitimate customer contact, point-of-contact enforcement paired with documented decision logic makes Gryphon a practical recommendation. ...Read more
Disconnected data work rarely begins at the pipeline itself. The delay often appears earlier, when a proposed data product moves from a business idea into requirements, architecture decisions, access controls and a development environment. Each handoff can introduce another tool or approval path, while product context becomes harder to preserve. By the time engineering begins, teams may already be reconciling mismatched project names, duplicated documentation, fragmented ownership and inconsistent setup across systems.
Portfolio-level visibility also matters before engineering starts. A platform that preserves business cases alongside product definitions can help leadership compare proposed work, assign teams and select technology stacks without separating prioritization from the delivery path that eventually executes those decisions.
That fragmentation becomes expensive when orchestration is purchased as another isolated layer. Data teams commonly work across cloud infrastructure, code repositories, ticketing systems and specialist data platforms, while product managers and architects need continuity across the same work. Replacing that estate is rarely the practical objective. A stronger platform coordinates existing environments while preserving product identity and approved technology choices throughout delivery. Integration depth matters less as a feature count than as a way to remove repeated setup and cross-tool reconciliation.
“Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments.”
Self-service also needs boundaries. Provisioning development environments, granting access, creating repositories and triggering infrastructure changes can remove substantial waiting time, but only when those actions follow established controls. The useful distinction is whether routine requests can execute from approved templates and policies rather than pass through manual service tickets. That changes the role of platform and architecture teams. Instead of completing repetitive setup on demand, they can establish guardrails that engineering teams use independently.
Traceability becomes harder once a project leaves experimentation and enters controlled delivery. Changes to requirements can alter pipeline work, while release movement creates dependencies across development, test, staging and production. Executives need a clear line from the original business case to the technical work that follows, particularly when multiple data products compete for budget or shared engineering capacity. Visibility into status, resource use, dependencies and release progress helps management identify where work is waiting without rebuilding the picture from separate tools. It can also expose queueing between teams before delayed approvals become late-stage release problems.
Release control should be treated as part of orchestration rather than an adjacent DevOps concern. Creating a pipeline is only part of the purchase decision. The harder question is whether code and data products can move through governed environments without custom coordination each time. Automated CI/CD setup, reusable templates, policy-based promotion and dependency visibility can make that movement repeatable. This becomes more important for AI-related data work, where experiments can appear quickly but production use depends on controlled access, governed data movement, documented lineage and consistent release practices.
Calibo merits recommendation for enterprises that want data orchestration tied directly to the broader delivery lifecycle. Its Data Fabric Studio supports reusable data pipelines. The wider platform carries product context into the development toolchain while automating environment setup. Calibo can establish access to selected technology stacks and generate CI/CD pathways for controlled movement between development and production environments. Its Release Orchestration capability extends that model into deployment governance and dependency management. This gives data teams a self-service framework that reduces manual handoffs while keeping technical work connected to the product context and enterprise controls that initiated it. ...Read more
As software development becomes more reliant on AI, companies have started to focus more on the process of coding, changing, and attributing. Code attribution systems for AI are becoming common for helping engineering professionals know the source of code, differentiate human contributions from that done by AI, and remain visible in the development environment.
Their importance goes beyond that of mere tracking since attribution can affect intellectual property management, security assessments, compliance procedures, and engineering performance. However, the implementation of these platforms poses some problems that organizations need to solve before they can effectively use attribution.
How Can Organizations Maintain Accurate Code Attribution?
An important issue here is the question of establishing accurate attribution in a complicated process of software development. Nowadays, software development includes many repositories, development environments, libraries, automated systems, and cooperation models. The suggestions generated by the artificial intelligence can be accepted, modified, mixed with the existing code, or completely rewritten by the developer. As a result, it becomes difficult to distinguish what part was done with the help of AI and what was developed independently by humans.
Data quality also poses a challenge. Attributing authorship requires the availability of development history, history of code changes, prompts, suggestions, and modification patterns. Poor data quality can lead to erroneous findings, especially where organizations employ different methods for software development or use a different set of tools for development. It is imperative for businesses to come up with data standards for the consistency of findings.
Issues related to privacy and intellectual property rights make the scenario even more complicated. The source code may include business logic, proprietary processes, and customer data. Companies that choose to implement the attribution platforms need to pay special attention to the way the development data will be gathered, analyzed, stored, and made available.
What Makes AI Attribution Difficult Across Enterprise Development?
The other challenge is that of incorporating the attribution process into the current engineering process. Companies typically have heterogeneous technology stacks and development processes. This implies that any attribution solution has to be compatible with the source code repositories, issue tracking platforms, code reviews, security mechanisms and so forth. If not, fragmentation and extra manual processes arise.
Interpretation is just as crucial. Metrics related to attribution should not be automatically assumed to reflect developer productivity or the quality of code written. While AI can alter how engineers spend their time, more generated code does not imply a better result. Business context regarding maintainability, reliability, reviews, security, and business needs should also be factored in, along with attribution.
Attributing AI code is going to be contingent upon transparency, interoperability, and good governance. Enterprises are going to require established processes for attributing contributions made by AI and also explaining how the information is to be utilized. Software systems capable of creating traceable evidence, easy integration, and clear reporting can enable enterprises to create more trust when it comes to AI-enabled development efforts. Taking a good approach towards these considerations will enable enterprises to get visibility regarding the use of AI in coding without having to risk their intellectual property rights and engineering accountability. ...Read more
