Switching On the White Box Switches
About UsConferencePartner With Us
CIOREVIEW >> Software >> NEWS

Switching On the White Box Switches

CIO Review

White box switches refers to the ability to use ‘generic’ off-the-shelf switching and routing hardware in the forwarding plane of a SDN (Software Defined Network). White box switches are really just ‘blank’ standard hardware. They constitute the basic element of the commodity networking ecosystem required to help organizations to choose the elements they need to realize their SDN objectives.

Adorable to Facebook, Amazon, and Google and inherently disliked by networking vendors considering them a threat, the notion of White Box switches surrogates a lot of pre-built notions. The first wave of white box was on PCs and servers, and we already know that white box servers running Linux have won that battle in leading web services and cloud service providers such as Google, Facebook, and Amazon, and also some communication service providers and enterprise sectors. Google and Facebook are working with ODMs to build their own servers and switches, because they not only want to differentiate at the software level but also differentiate at the hardware level. The matter of the fact is that they have become very important in this age of SDN (Software Defined Networking) due to their “Open Platform” nature. CIOs today require extensive expertise on the basic fundamentals of a White Box switch; position it against the traditional Ethernet switch and know about its use cases to ascertain if they are an option for us today.

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.

White box switches are finally emerging from the shadows and offering an alternative in the network OS as well as flexibility. The phrase “white box” has long been in use. The very same ODMs (Original Design Manufacturers) that produced them are getting into this arena of manufacturing white-box switches. White-box switches look just like any ordinary switch—and their manufacturers will be familiar to those who have purchased white-box servers: Celestica, Accton, and so on.  The most common operating system for white box switches is Linux-based, due to its high availability of free and open tools.

What Makes it Different?

White box switches are different from traditional switches as they lack intelligence; whereas traditional switches and routers have their own forwarding and routing tables. In SDN environments, applications on top of the SDN Controller offer this higher-level intelligence by using OpenFlow to dictate connection routes and tasks. A Bare Metal switch has got no operating system installed on it. It is a structure with multiple ports. When a company buys it, it loads its own operating system to make it functional. At bare minimum, it has a boot loader installed-Open Network Install Environment (ORIE), which enables Operating System (OS) installation. Bare Metal switches are mainly manufactured by Taiwanese companies like Quanta QCT, Accton, and Alpha networks. These companies are also known as ODMs (Original Device Manufacturers). A White Box switch can run any traditional networking protocols or it can run any SDN based southbound protocols like Open flow. Usually, white box switches are offered by small and medium size start-up companies like Cumulus, Big Switch, and others, but now traditional networking vendors have also introduced their version of white box switches, like Juniper has unveiled its own, named OCX1100 which runs Juniper’s own operating system JUNOS .

Future of White Box Switches

Data centers are the initial target for white box switches. Especially the large web scale companies like Facebook, Amazon, and Google have an affinity towards them because of the following reasons:

1. Because of the need of massive deployment of switches in their large data centers, the number of ports required in each switch is high. As white box switches are cheaper and the ports density is high, they are a viable option for data center deployment by these companies

2. The big companies—besides CAPEX savings—are looking at the openness and flexibility of the switch platform. They don’t have to adhere to the traditional networking box. They have their own band of developers to develop and support any SDN based networking that offer them much broader flexibility. A White Box is just a perfect platform to offer such flexibility.

Now the question arises: Are these large web scale companies the only target market for the White Box switch? The answer to this question is ‘No’. Any small or medium cloud based provider that require Ethernet switches in data centers can opt for White Box because of the cost savings associated compared to proprietary switches and because of the familiar IT tools/ commands they are used to.

However, white box switches are not meant for everyone, at least not at this present moment. The reasons behind this observation are:

The hardware is not as reliable as the traditional box. In fact this is not a shortcoming. A typical data center is designed in a way that switches take on collective risk and thus failure of a single switch or even multiple switches do not affect traffic. (It is just like commodity servers failing and other servers taking the load)

Are they good and suitable enough to be deployed in other such typical large enterprises? Perhaps, not yet!

The SLAs, support and reliability are the factors that cannot be ignored. These risks can be extenuated, to some extent, by finding a middle way like dealing with branded Brite Boxes from known vendors like HP and Dell. They have a large support network which can help maintain a product installed in stringent SLA environment. Also, White Boxes are not yet ready to offer all services that a service provider needs to offer like for example, Carrier Ethernet based services.

Having said that, White Box switches are meeting their target market requirements successfully and that is the main reason behind their ever increasing sales figures. Thus they can be seriously considered for their typical use case, which is data center application. While the advantages of current white-box platforms may not be enough to tip the scales of your purchasing decision in its favor, it may open your eyes to the possibilities available today and give you a roadmap to your next big decision.

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