How to Choose Open Source SDN
CIOREVIEW >> OpenSource >> NEWS

How to Choose Open Source SDN

CIO Review

Software-defined Networking (SDN) community has disrupted the network industries for decades where big-named vendors ventured into engineering of their networks. The need of enterprises to innovate the existing SDN framework to suite necessity, fostered development within the SDN community, giving rise to many open source projects. This openness in SDN software projects carves its way towards gaining high efficiency and with reduced cost. It is important for C-level executives to evaluate how these open source products can help them meet their organization’s objectives. 

Attributes of Open SDN:

Stay ahead of the industry with exclusive feature stories on the top companies, expert insights and the latest news delivered straight to your inbox. Subscribe today.

Open SDN is an umbrella which encompasses various network technologies, making networks agile and flexible. These network technologies are empowered with standards, software, application interfaces/ software development kits, and hardware.             

Open Standards:

Generally, Open Standards are specifications for hardware and software, developed and maintained through a shared process. These standards are freely and publicly available, facilitating interoperability between various products and services. These open standards or protocols are non-proprietary and is applicable in heterogeneous environments. For instance, Cisco’s open standard proprietary protocol solution ‘onePK’ enables customers to develop SDN-type centralized applications onto its solution. Open Standards are included in the areas of Packet Processing where it is seen that non-traditional associations—Institute of Electrical and Electronics Engineers (IEEE), Metro Ethernet Forum (MEF) and many more continue to operate interoperability between new networking software and traditional networks.

Open Source Software:

Open source software allows individual to leverage source code and modify the existing software. From the SDN central perspective, a SDN Open Source Software embeds data plane software like ‘Open vSwitch’, control plane software like ‘OpenDaylight’ and ‘OpenStack’. These open source software’s are released under Free and Open Source Software (FOSS) licensing. These softwares promote public collaboration and sharing, as software copyright holders license the right to study, change, and distribute the source code.

Some of these open source SDN projects are controlled by a single company, which extracts more tax from the entire ecosystem that leverages their open source code. Exemplating Oracle, which drove its product ‘MySQL’ to dual license that compelled independent software vendors to purchase commercial license to embed MySQL into their product. For CIO’s who does not want to make a false move while choosing a product can opt for vendor-specific plug-ins or OpenStack, which offers licensed software for free.

Open Application Programming Interface (API) and Software Development Kit (SDK):

An open SDN solution provides API for programmability, and is intended for the development of SDN applications by the customers and device vendors. API is a set of protocols, routines, and tools for building software applications, while, SDKs are packages of pre-written code. Some of the SDKs are published APIs, available for anyone to write into their own applications. Open APIs are provided by every SDN and SDN controller solution—OpenFlow, OpenDaylight, OpenContrail and Cisco’s onePK. Additionally, it is important to know that anyone can read/write software applications to these APIs, but the proprietary version is dedicated to the vendor’s devices.

Open Hardware:

Open hardware has ‘open’ references for both compute and networking products, in which the design specifications of hardware are licensed, allowing anyone to study, modify, create and distribute. Individuals can add features and fix bugs in the software; they can even update and improve the source code that underlies in the open hardware. Both Copyright Law and Patent Law are applicable to open hardware; Trademark Law is pertinent to branding name and logos of the open hardware.

What to look for in ‘Open SDN’?

Tons of networking vendors out there are stacking OpenDaylight or OpenFlow for their networks, but following the crowd might be a big mistake while looking for Open SDN. The open source software should better serve business—establishing problem-free network, and add value to the growth of enterprise. The evaluation of openness of SDN is a business-critical factor and needs to be assessed on the following:

• It is important to use an open source software and API that are managed and developed by foundations—Linux, Apache and many more.

• Analyze the vendors who contribute the most for open source project ecosystem, this will help CIOs to gain trust on that vendor.

• Probe suppliers (networking or cloud vendors) to participate in a foundation-managed project and open their APIs to foundations for standardization.

• Get confirmation from suppliers about indemnification from threat of lawsuit for writing/adopting an API.

• Encourage companies that control open source projects like VMware to drive projects—Open vSwitch to foundations.

CIO’s Midas touch

Buying a product is a jeopardizing work for enterprise. CIO needs to focus on the benefits that the selected vendor’s product offers as the purchase should be worth a dime. As ROI is constantly weighing on the CIO’s mind, mentioned are some of the benefits Open SDN software possesses:

Reliability: Reliability is one of the important attribute to be considered before investing. As softwares include bugs, these are failures to meet the specifications. On perspective of bugs, severe defects in an open source SDN software can be fixed within no time as the source code are open to developers. After these bugs are fixed by developers, users can get their current version updated by unofficial fix or they can wait for an official fix. Whereas in closed-source software, the users are dependent upon the vendors' internal processes to get update for the bugs.

Stability: Software has been an almighty tool for every business environment which keeps a stable flow of profits into the organization. This is the spot where the software vendors play their turn by persuading their customers for an upgrade. Even open source software is not exempted from upgrades, but prevents daunting of pressures from software vendors.                   

Auditability: While searching for Open Source SDN software, CIOs tend to forget about the software’s auditability. Coming to Closed Source software framework, users envisaging the future changes trust vendors for claims made for different qualities—security, freedom from backdoors, adherence to standards and flexibility. But, as the Closed Source software does not have an open source code and those claims form the users remain incoherent. With ‘Openness’ flourishing in the market, inspection or certification needs from third party auditors for business-critical processes will increase in the near future.       

Cost: When it comes to the costs incurred in a business tool, CIOs should not focus on what is the purchase cost of software, but should check the Total Cost of Ownership (TCO) of the software. An Open Source software offers low TCO by enabling users to upgrade at low fees, and management tool cost. Maintaining is very simple as the Open Source software reduces downtime and data loss. Also the existing hardware life is increased without disrupting performance.  Similarly, in Closed Source software framework the whole proprietary of the software is affiliated to its vendors including the cost of upgrades and maintenance, leaving no option for the users to choose other vendors.    

Flexibility and Freedom: Open source SDN Software will eliminate the dependency of the CIOs to a confined SDN Software Vendor when compared to the Closed Source Vendors. With this freedom an Open Source offers flexibility, as there are many developers out there who provide source code which are available; at architectural level flexibility comes when users leverage trusted standards for interworking. 

More in News

AI agents are exposing a problem that conventional workflow software has rarely solved. Many enterprises run essential work across SaaS platforms, integration tools, local scripts and shared spreadsheets. Agents are then expected to work across all of them, gather enough context and make safe decisions. The difficulty lies in the gap between what an agent can infer and what the business can actually control. Point-to-point integrations move data but do not preserve the history of a process. iPaaS platforms connect systems, yet long-running work can still end up scattered across queues, callbacks, approvals and exceptions. For buyers, introducing agents is only part of the challenge. They also need a process that can show exactly what happened. Workflow orchestration can provide that structure when it carries context along with the work instead of simply routing it from one system to another. Agents still need room to exercise judgment, but that judgment needs boundaries. A model might classify an email, interpret intent, retrieve missing context and recommend what should happen next. It should not have to work out the refund procedure or customer verification process from scratch every time a request comes in. Repeatable steps are less expensive to execute through deterministic logic and easier to audit. The agent can then handle the parts that require interpretation while established actions remain within versioned process logic. “Agents can make decisions where judgment is required while the workflow handles repeatable actions.” That separation is useful only if the business can see what happened in each workflow. Executives need a way to inspect the process template, runtime history, agent decision and failure path in one place. Once APIs, agents, human reviewers and external events are involved, ordinary system logs do not provide the whole picture. Buyers need to know which action ran, what data moved, what decision was made and what happened when a step timed out or had to be retried. Keeping that information with the process also makes automation easier to improve because performance data remains connected to the work that produced it. The amount of engineering required to get there matters too. An orchestration platform has limited practical value if a company needs to build a large specialist team before it can put a useful process into production. Existing services and SaaS APIs should be composable into business logic that people can understand and change without rebuilding the entire integration map. A code-first approach is useful when software teams get version control, business reviewers can see the workflow as a visual graph, auditors can trace what happened and agents have a stable process map to work within. The larger issue is ownership of the process, not simply how many tasks can be automated. Long-running workflows need to retain state, and agent decisions need to remain visible without requiring a model call at every step. Once the process is running, event-driven feedback can show where it needs improvement. The platform also has to work for organizations with different levels of software maturity. One team may be coordinating a large collection of microservices, while another needs custom workflow logic around ERP, CRM, field-service and workforce systems without having to wait for a vendor to add the functionality to its roadmap. LittleHorse takes this approach with Saddle Command Center and its Business-as-Code model for building workflows across microservices, SaaS platforms, agents and human-in-the-loop steps. Agents can make decisions where judgment is required while the workflow handles repeatable actions. Individual instances remain traceable, and workflow event data can be published to Apache Kafka for analysis. Support for Java, Python, Go and C# also allows engineering teams to maintain the business logic without having to adopt a specialist workflow language. For enterprises working across disconnected SaaS environments or complex microservice estates, LittleHorse provides a practical way to give AI agents room to make decisions while keeping the surrounding process visible and controlled. ...Read more
Sage migration decisions often begin with a contradiction. Finance and IT teams want the subscription feel of SaaS, yet the applications they rely on still carry custom workflows, connected databases, reporting routines and partner-managed changes. A generic cloud host can move the server, but it may leave the business managing every handoff when access breaks or latency appears during a critical task. Month-end close, warehouse workflows, payroll access and reporting cycles leave little room for cloud experiments that behave well only under ideal conditions. The weak point is usually not migration itself. It is the support chain that follows. Servers sit somewhere, a hosting provider manages the platform, the software publisher owns the application, a Sage consultant handles business logic and the internal team is left to coordinate the room. A single interruption then becomes a routing problem. Executives should favor a hosting model that reduces escalation layers without stripping away control over the ERP. Control matters because Sage environments rarely behave like standard SaaS tenants. Updates, integrations, VPN links, reporting tools and adjacent applications may need business-specific treatment. Shared resources can look efficient until they limit troubleshooting or change windows. Dedicated virtual environments, network isolation, clear backup design and documented availability standards give leadership a firmer basis for risk decisions. The point is not more infrastructure for its own sake. It is a service model that keeps customization possible while making ownership clearer. Ransomware risk and phishing exposure have changed the due diligence standard for hosted ERP. Sage access cannot be separated from identity controls, recovery routines, monitoring practices and response authority. A provider that only hosts the application may still leave security teams stitching together evidence after an incident. Before renewal terms are signed, buyers should test how backup frequency, network segmentation, disaster recovery design and incident escalation work in practice. Cloud economics create a second trap. Public cloud flexibility can turn into variable outlay when workloads are poorly matched to the platform. Licensing shifts and Microsoft choices make architecture a finance issue as much as an IT issue. Lowest monthly price can be misleading when internal staff must manage exceptions or pull multiple suppliers into every problem. A stronger decision weighs contract predictability, application performance, recovery posture and the cost of internal coordination. Sage projects also require a provider that can work alongside ERP partners rather than displace them. Against that buying logic, Cloud at Work is a premier choice for Sage cloud hosting. It model is built around Sage end users and fewer support handoffs, then extended that base into Azure and managed technology services where the customer environment demands it. Its portfolio spans Virtual Private Cloud, Infrastructure as a Service, Desktop as a Service, Managed Services and Managed Cybersecurity, giving buyers a path from hosted Sage to broader cloud management without changing accountability every time the environment expands. Dedicated resources, virtual firewalls, backup design and Sage-aware support match the pressures that matter most. For leaders who want Sage to feel closer to a managed service while preserving customization, Cloud at Work warrants serious consideration. ...Read more
Digital transformation remains a priority for organizations across Canada, but for many leaders, the challenge is no longer deciding whether to modernize. It is figuring out how to do it without disrupting the systems the business relies on every day. Many organizations are operating in a mixed environment where old and new technologies must work side by side. Core applications that were implemented years ago still support critical operations. ERP and commercial off-the-shelf platforms have been customized over time to fit unique business processes. Data often lives in multiple systems and cybersecurity concerns continue to grow as organizations expand their use of cloud services, mobile applications and external partners. The result is a level of complexity that can make modernization feel risky, even when change is clearly needed. This is why successful digital transformation rarely starts with technology. It starts with understanding the business. Leaders need a clear picture of which systems continue to deliver value, where inefficiencies exist and which investments will have the greatest impact. Organizations often spend too much money replacing systems that still serve an important purpose or implementing new solutions before fully understanding the long-term costs. The most effective transformation partners help organizations make informed decisions rather than pushing change for its own sake. The same practical approach applies to emerging technologies such as artificial intelligence. While AI continues to attract attention, its success depends heavily on the quality of the data behind it. Organizations that struggle with fragmented information, inconsistent processes or weak governance often find it difficult to unlock meaningful value from AI investments. Data modernization, cybersecurity and system modernization are closely connected. Progress in one area often depends on getting the others right. Security has become another defining factor in successful transformation initiatives. Whether operating in healthcare, education, municipal government or the private sector, Canadian organizations face increasing expectations around privacy, access management and accountability. Security cannot be treated as a separate project that follows modernization efforts. It needs to be built into planning and decision-making from the beginning. Strong governance, clear documentation and defined responsibilities help organizations reduce risk while giving leadership teams confidence that projects remain on track. Execution is equally important. Many transformation initiatives struggle not because the strategy is wrong but because employees are left behind during the process. New systems, workflows and technologies only create value when people understand how to use them and why the changes matter. Clear communication, realistic timelines and strong change management are often the difference between a successful implementation and an expensive disappointment. For organizations operating across different regions of Canada, bilingual communication and local stakeholder engagement can further influence outcomes. For organizations looking to modernize in a practical and manageable way, IPSG Technology offers an approach grounded in business realities rather than technology trends. The company combines custom application development, website modernization, cloud services, cybersecurity, data optimization and change enablement to help organizations navigate complex transformation initiatives with confidence. Its strength lies in helping clients modernize ERP and COTS environments without unnecessary replacement, align AI initiatives with data readiness and incorporate security from the outset. By focusing on clarity, governance and measurable outcomes, IPSG Technology helps organizations move forward without losing sight of operational continuity, budget control and long-term business value. ...Read more
Mid-sized companies often reach a point where data volume has outgrown the reporting habits built around it. Sales systems, finance platforms, customer records and workforce tools accumulate information, yet decision-makers still wait for manually assembled reports or rely on partial views. The buying problem is rarely a shortage of software. It is the cost and coordination burden of connecting systems, preparing reliable data and turning it into useful action without building a large specialist team. Platform selection should begin with the data foundation. Dashboards and AI models cannot compensate for inconsistent definitions, missing records or poorly governed pipelines. Executives need to know how a platform profiles and cleans data while preserving traceability from source to output. Integration also matters beyond the initial connection. A workable platform must support existing databases and business applications while reducing the amount of custom code required to keep those links current. Migration demands, refresh frequency and access controls deserve scrutiny before implementation begins. The next pressure is time to proof. Many firms cannot justify a large upfront investment in engineers and data scientists before a use case has shown credible returns. A platform should let a business test a narrow problem and measure model accuracy before committing to broader deployment. Low-code workflow design can shorten that cycle, but ease of configuration must not remove oversight. Buyers should examine how knowledge bases and semantic layers are managed when model outputs affect staff decisions or customer-facing processes. Access to insight presents a separate test. Static reports remain useful for recurring review, yet business leaders increasingly need answers that were not anticipated when a dashboard was built. Natural-language querying can reduce dependence on report backlogs, provided the platform grounds responses in governed company data and shows enough context for users to judge the result. Predictive functions should be assessed in the same manner. Forecasts are valuable only when teams can understand the inputs and monitor performance before connecting a prediction to a defined next step. The final buying concern is service depth. Mid-sized firms may adopt a capable platform and still lack the people to design data models or maintain AI workflows. A provider should be able to supply targeted support without turning every change into a consulting project. Subscription or usage-based pricing can lower the entry barrier, though buyers should compare consumption controls and support terms carefully. The strongest fit will combine self-service tools with practical help around implementation and model tuning, backed by ongoing maintenance when internal capacity is limited. Aidas Technologies  is a premier choice for firms that need this combination without assembling separate platforms and specialist teams. Its AI-powered data and analytics platform brings data preparation, reporting, predictive modeling and workflow automation into one environment through low-code tools. The company also offers professional services for setup and custom development, plus model support and continued maintenance, allowing buyers to test focused use cases before scaling. A usage-based subscription model further suits mid-sized organizations that need tighter control over upfront cost. For executives prioritizing faster proof and guided adoption, Aidas Technologies merits serious consideration. ...Read more