How Big Data And SDN Support Each Other

The desire of many companies to at least try out data-centric business models or introduce them on a project-related basis and thus use big data in any form requires an IT infrastructure that can also meet the associated requirements. The contribution of an SDN (software-defined network) proves to be particularly valuable: it makes it possible to handle the traffic more flexibly, to constantly redesign it and to avoid some trouble by always the best possible network performance is guaranteed. Conversely, Big Data in combination with Analytics contributes to the functioning and success of SDN and “on demand” offerings of telecommunications companies.

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The use of big data in businesses and public institutions, including universities and research institutes, is steadily increasing. IDC forecasts over $ 200 billion in revenue for Big Data & Business Analytics (BDA) in 2020 . In 2016, market researchers estimated that worldwide sales in the segment were well over $ 130.1 billion.

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Cisco has found that while more and more companies are becoming aware of the benefits of analyzing their data, they also point out that they rely heavily on and are highly dependent on various applications in the cloud. In order to recognize the resulting problem, you do not have to be a network expert: The public Internet threatens to quickly reach its limits.

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Using big data concepts, such as in the form of an Apache Hadoop platform, brings enormous demands on the network. As a rule, the processing of a task in Hadoop has a direct impact on an application that is important for the business. In other words, the analysis of extensive databases is often worthwhile only if it serves to be able to use the applicable findings directly or at least after a very short time.

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Nobody wants to ask for the cheapest flight from A to B via C, the question whether the behavior of a mobile client in the network is just the normal use or suspicious, or the question of whether it is possible, of a campaign advertised with radio advertising to have the expected number in stores, wait until tomorrow.

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The answer should be available immediately, but at least within a minute.Because, after all, it does not appear to be more complicated for the user than the request for a web search. Unlike in the case of the list, which is not created in advance, complex queries and calculations, which may only ever be required, play in the background the users do not matter.

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In the case of Hadoop, for example, it is imperative to have tools that enable performance analysis and troubleshooting, for example, to resolve data conflicts or congestion or to troubleshoot problems with the virtualized infrastructure, such as the virtual ports or the names to manage the network node and its role.

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In addition, comprehensive control over the traffic on the primary and secondary connections is required for the HDTV (Hadoop Distributed File System) to work.For this to be economically feasible, a dynamic and intelligent allocation of the bandwidth is required. And again, it must be possible to identify and correct the bottlenecks that may be encountered in bringing together the results of the analysis process.

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So, if you want to tackle a big data project based on an SDN , you have to be sure that you have a reliable network infrastructure with high bandwidths. In addition, as an additional requirement, problems can be resolved immediately, allowing administrators to optimize the data flow at any time and adapt to changing requirements.

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One of them is Colt Technology Services with its IQ Network. It can scale up to 100 Gbps bandwidth, minimizing the risk of bandwidth shortages. In addition to companies in the financial sector, especially those that have to demonstrate compliance with regulations such as MiFID II or FRTB , companies that are already well advanced in using the data of distributed objects in the context of IoT projects with big data components also benefit from this , or who have ambitious plans in this regard.

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However, network operators are also working to apply the benefits of SDN to less-complex and demanding infrastructures. These include Ethernet connections between data centers and public cloud services at Azure ( Microsoft ) and AWS ( Amazon ), where the processing of big data is rapidly increasing. Again, network services that can be retrieved or reconfigured “on demand” offer great flexibility and tremendous benefits

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Conversely, the design of the backbone for telecommunications services is playing an increasingly important role in predictive analytics and the exploitation of metadata obtained through big data. They help operators to scale and control their services to an unprecedented level.

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In particular, big data helps SDN control traffic, streamline communication between services and layers of services. But even for cyber security or the networking of data centers – be it within an SDN or across multiple SDN – big data is indispensable as an auxiliary tool. All SDN platforms are based on NFV (Network Functions Virtualization). They are intended to automate and speed up decisions made by people so far. For this to function as desired, APIs – programmable interfaces – are widely used.

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The idea behind this is to use big data first to collect comprehensive data on decisions made and their implications, in order to develop feedback loops to improve the platform. This also includes information about which customers have the connectivity requirements and how they can best be met.

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This is no longer a dream of the future: customers can already log on to the service portal and adapt their own capacities to the requirements of unexpected events. Whereby this is not just an order in the classical sense, but the provision of the desired capacities can take place within minutes.

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In an SDN, the use of big data and data analytics can also lead to new business and usage models. Businesses are looking for more flexible and less expensive WAN services. You do not necessarily want to book a specific capacity for one or even two years, for example if you only need it once a month or for a few days a year.

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In SDN environments, services that meet this need are almost self-evolving. The network operators can access data that they receive about the use of the infrastructure during operation. This may include data about the bandwidth, performance and compliance of SLAs or characteristics of the applications and the assessment of the quality of service by the user. Thus, the way is clear, away from a more or less extensive but fixed price list to adaptively designed.

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Building such networks on your own is expensive, time-consuming and tedious. In addition, most companies are likely to wonder if they should deal with it at all, if network operation is not their core business. Then it makes sense to rely on a service provider who deliberately only takes care of it.