Technology Trends 2015

Here are the five trends that I believe all of us in ICT should keep an eye on in the coming year. They represent the primary driving forces behind new business opportunities. In some cases, they will cause discontinuities, and elsewhere they will present challenges to overcome. But together, they will set the direction for technology development.

From single-service to multi-application platform, the communication network becomes a massively distributed compute, storage, and networking infrastructure.

Just how much impact mobile communication, the network infrastructure that carries it, and the applications that make it interesting and useful have had on the world is not news. Every industry on the planet is undergoing a transformation, adopting digital and virtual processes, products, and ways of working – even the mobile communication industry itself. And each individual and organization is adapting to make the most of it. Virtualization and programmability are at the core of this transformation. The network resources that make it all possible are becoming virtual, more flexible, and more usable, to form a versatile and global platform.

The rising dependence on network services and broadband capacity has triggered substantial growth in the number of compute and storage resources deployed in networks globally. These resources make it possible to deliver mobile-broadband services that are capable of meeting modern subscriber demands, to support features like prioritization and buffering, and offer traffic management.

As the number of network resources becomes significant, virtualization is paving the way for repurposing: using resources for applications other than those originally intended.

Virtualization, its related technologies – including containers and hypervisors – and the ability to create network slices enable resources provisioned for network operations to be used by other applications. These applications may not even have existed when the resource was initially deployed.

Through virtualization, networks can transform into programmable distributed platforms that can provide sophisticated applications with the compute, storage, and networking capabilities they need. They will also be able to provide support for services that have been mashed together, taking multiple components delivered by different service providers.

Just as virtualization is transforming IT, software-defined networking (SDN) is transforming the architecture of telecom networks. The increased level of abstraction that these two complementary technologies afford provides the network agility and programmability needed to run networks dynamically and efficiently, and ensuring they are capable of supporting applications yet to be invented. Virtualization and SDN enable operators to divide the same infrastructure into network slices – each with the just the right characteristics required for the application at hand.

At the forefront of compute, storage, and network technology, Ericsson and a number of operators have been researching how to develop resources in the context of broadband-networking equipment, with a focus on design for smart and programmable networks. This research has to some degree shaped our concept for service provider SDN and how we work with Network Functions Virtualization (NFV).

But what does all this mean in practice?

In effect, networks will become massively distributed compute/storage/networking platforms, programmable by third parties for intelligent network operation, as well as by other applications.

And these large distributed platforms will support a wide variety of innovative applications, and provide characteristics like extremely low latency or wide-scale parallel processing possible across large geographical areas. Many applications will benefit from this. It’s like transportation networks that support self-driven vehicles, or smart grids that deliver sustainable power efficiently. Both examples require millisecond latency, and both have regional scale.

Competitive advantage will be created through the ability to recognize these opportunities and virtualize them as services on a distributed platform, instead of continuing on the traditional path, with centralized servers providing compute and storage resources.

As dependency on networks rises, focus on security and privacy increases.

As networks transform from being closed, protected environments into open, programmable, and distributed platforms, the significance of security and privacy is gearing up a notch. The technology challenge lies in utilizing the openness and global reach of the network platform, while protecting assets and user privacy, so that society as a whole can reap the benefits of new network capabilities without being subject to attack or breaches of security.

The demand for enhancements in security and cryptography is primarily being driven by the need to protect society and individuals from theft and intrusion as our dependence on digital assets and services increases. In the context of 4G and 5G mobile networks, security is becoming more than just authentication and firewalls; it is developing into an end-to-end protection scheme, encapsulated with data integrity. But, if it is possible to breach security, it is also possible to ascertain when and where data has been accessed.

Keyless Signature Infrastructure (KSI) is an interesting technology, as it affords integrity through analysis of data, providing information on when and where it has been accessed. By comparing audit trails of authorized users with data integrity logs, unauthorized security breaches can be detected and analyzed.

Technologies such as adequate cryptographic solutions for cloud are of interest, as they will enable encrypted data to be processed. In the near term, research into new algorithms and protocol improvements for TLS 1.3 and QUIC, and key management concepts like Blockchain – used in Bitcoin – and KSI, are the technologies to look out for.

Standards and protocols are critical to defining and implementing a secure network, especially in an open, distributed context. Ericsson has developed a security reliability model (SRM) to ensure that our products and related services meet security requirements as they adapt in response to the changing threat scenarios. By using SRM to build secure components, the resulting networks can support trusted services for the public sector, enterprises, and consumers.

In practice, collaboration and standardization are the key elements that ensure networks remain secure and privacy protected. As each partner in the global ICT ecosystem focuses on security in their specific technologies, working together to integrate solutions and produce standards is essential to keep malice at bay, and to provide society, businesses, and individuals with the confidence that their assets and information are protected.

Increased capabilities in analytics and machine learning will unlock new ways of doing business.

Modern networks carry massive amounts of data, and the growth trend shows no signs of leveling off. This volume of data is a highly valuable resource, as it provides insight into customers, improves traffic pattern predictions, highlights potential business opportunities, and can help identify the services that are being used and those that aren’t. The key to delivering these benefits is real-time analysis of network metadata.

The amount of data traffic carried by mobile networks first surpassed voice in 2009, and it has been rising steadily ever since (Ericsson Mobility Report). It’s not just the amount of data that has increased; its shape, speed, and trustworthiness have also shifted. And so, the benefit gained from analyzing network metadata has become far more interesting for business. In the pre-smartphone era, metadata primarily included routing, signaling, and alarms – fundamental information for good network management. Today, network metadata is much richer and includes location, user experience, app usage, and congestion – fundamental information for developing smart services.

As technology provides the ability to analyze information in real time, the value of metadata is soaring. The benefit for networks is more efficient operations – knowing where congestion is likely to occur enables intelligent prevention; the ability to predict traffic and mobility patterns enables more efficient use of resources. For advertisers, the benefit is improved ability to target consumers. For developers, service usage insights provide an understanding of what users like and want, and where and when they want it, enabling precise service adaptation and the creation of user-centric solutions. Ultimately, enterprises get a smart business platform, and subscribers get more personalized and more entertaining services over networks that are more automated.

Today’s networks were not originally designed for extracting and acting upon network metadata. Consequently, capabilities for extracting useful data and making intelligent observations and predictions need to be added, and security must be in place to assure that such capabilities uphold personal integrity.

Doing this represents a significant hurdle that needs to be overcome: data analysis tools need to keep up with the rising volume, variety, velocity, and veracity of data. Analytics must develop as quickly as data is growing. Machine learning is one way of ensuring that tools keep pace with the data. This particular branch of data analysis technology – with its roots in statistics, computer science, mathematics, and artificial intelligence – is gaining traction. The era of the smart machine is here.

Smart machines such as autonomous vehicles, advanced robots, virtual personal assistants, and smart advisors are emerging in all industries, and they all depend on the network as a platform to connect to the expanding set of data sources.

Smart machines use technologies like deep learning to deliver the kind of insights that modern applications require. And these technologies are changing the very nature of computing machines. By applying advanced analytics and machine learning to understand context, smart machines are capable of learning from feedback and acting autonomously. And this is where real-time analytics comes into play, enabling the level of automation needed to keep smart machines apace with data evolution.

For the Networked Society, the challenge of analytics and machine learning lies in the power to rapidly develop capabilities in parallel with the rapid expansion of data sources, so that businesses and networks can respond to data in real time and reap the benefits of big-data analysis.

Customized network slices to support upwards of 26 billion devices (beyond 2020) of all shapes and sizes to suit all needs.

In our most recent Mobility Report, Ericsson estimated that the global number of connected devices is set to top 26 billion by 2020. Estimates from other ICT players are similar. Some predict slightly more, some predict slightly fewer, but whatever the exact figure, that’s a lot of devices to provision and a lot of data to manage. And so, networks need to gear up, becoming more flexible and rapidly scalable to cope with widely varying use cases.

For the Networked Society, the challenge of analytics and machine learning lies in the power to rapidly develop capabilities in parallel with the rapid expansion of data sources, so that businesses and networks can respond to data in real time and reap the benefits of big-data analysis.

Based on an average of about two per individual, 15 billion devices (an evolution of today’s smartphones, tablets, and wearables) designed for human interaction with input and viewing interfaces will be connected to the network using standard over-the-air protocols. In addition to these devices, networks will support billions of things. Things are more likely to interact with each other, and with other central systems, than with human beings. The technology industry calls the networks of these things the Internet of Things (IoT), and the networking industry calls this IoT class of devices machine-type-communication (MTC).

MTC can be further divided into two broad categories: massive MTC and critical MTC. Massive MTC includes the many small, low-cost devices (typically sensor-like machines, such as traffic monitors on roadways, environment readers in critical habitats, or activators) that enable machines to react to instructions. The devices that fall into this category tend to have very low power requirements, and support applications where battery charging may not be feasible owing to their location.

Ericsson is working on designing networks that could support millions, even billions, of such devices that would last for years in outdoor environments. Future networks will maximize sleep cycles, request data on a need-to-know basis (lean transmission), use streamlined protocols that minimize communication overhead, and be built for the use case at hand with different feature sets, transmission modes, and power models rather than taking a one-size-fits-all approach.

Critical MTC is the polar opposite to massive MTC. Network slices for critical MTC are typically extreme in one or two dimensions, such as very low latency or extremely high reliability. They suit industries like mining or logging, to support applications such as the remote operation of heavy machinery, which uses video, telemetry, and automatic pilot software to operate vehicles at a safe distance from a hazardous environment. A safe distance could be anything from a few meters away from a protective barrier to thousands of kilometers away, in a more convenient location.

Ericsson has developed a prototype remote digger operated over a critical MTC connection, showing that it is possible for a single human to operate multiple machines in a safer, more sustainable way. The networks may be local (completely contained within a single mineshaft) or spread over a wide area as they control mobile monitoring devices on drones or other moving platforms.

So, the question is what will these new devices require from the network? To answer this, Ericsson has developed several use cases for massive MTC and critical MTC to help our researchers and engineers design and build networks with the right level of support for the given application. We have discovered that the IoT requires networks with many different characteristics – some with low latency, some with low power, and some with high reliability, while some networks will allow devices to communicate only when it is convenient; such as during non-busy hours.

Efficient use of power is key, and one suitable way to reduce consumption is through streamlined protocols that minimize communication overhead. Other ways include different types of sleep mode and discontinuous reception and transmission.

Networks need to be able to provide a connection that is tuned to the particular IoT/MTC task. They need to be able to support the growth in human-based devices as well as MTC. So we have developed the concept of network slices to accommodate these different requirements.

Implementing network slices is as much an OSS/BSS management challenge as it is a network challenge. With distributed compute/storage/networking resources and virtualization, the network will be able to implement slices.

Just as people and society have become connected over the past decade, the next 10 years will be about things becoming connected – providing us all with a benefit, be it a safer working environment, greater efficiency, or a reduced impact on our environment. The IoT needs an infrastructure to grow, and that will be brought about through concepts like network slices that provide a massive degree of customization.

As industries shift to provide virtual products and services.

Two major transformations – digitalization and mobilization – are changing the way people and society function, and the media industry is leading the way. Media has undergone several transformation cycles, from broadcasting and the sale of physical products (like CDs and DVDs) through actual stores, to selling digital products (downloads, pay-per-view, and on-demand TV) through user portals, to selling services (like streaming) on a subscription basis. This transformation has taken place at the same time as the dual shift in the consumption of content (from the single fixed screen to multiple mobile devices) and the creation of content (from enterprise to everyone).

Network operators, enterprises, manufacturing, government… just about every organization on the planet is affected to some degree by mobile and digital transformation. The amount of data created by a massive range of sources has given rise to big data analytics. And mobile values – such as personalization, immediate activation, and the ability to act on the move – are redefining enterprise and consumer demands.

The speed and depth of this transformation varies from industry to industry; in some cases, it is taking longer to come about, and in others, it appears less explicit. The motor industry, for example, is in an advanced stage of transformation, as modern vehicles come equipped with infotainment services, streaming media, navigation, and vehicle service updates – much of which is delivered over the mobile network. This infrastructure enables insurance companies to offer more attractive user-based insurance (UBI) derived from usage patterns provided by the vehicle’s internal systems – again over the mobile network. This is the classic pattern we have observed in the Networked Society: in other words, transformation in one industry leads to the creation of innovative services in another.

This shift to digital and mobile-first is not limited to industries where components are actually on the move. Heavy industries and utilities have digitalized and moved to mobile communication, even though their vital components are fixed. Remote meter reading over the mobile network circumvents the need for physical connections to billions of devices. Large, unmovable power-generating equipment produces data as well as power. Units may also “personalize” their power output, adjusting it for external data (smart grid) and internal data (operating parameter status).

To facilitate industry transformation, every single supporting technology needs to enable network agility. From a telecoms perspective, this need for adaptation applies to every layer of the network, to keep communication fast, smart, affordable, sustainable, secure, available, and useful.

Digital and mobile transformation has led to the merger and collaboration of different industries. This is evident as the IT and telecom worlds converge, where developments on one side require corresponding innovation on the other. Collaboration is evident in the world of applications, where seemingly unconnected industries are coming together to create innovative solutions glued together by connectivity.

So what will this mean for you and me, as the borders between one industry and the next become fuzzier and the connections between industries no longer fit into traditional business models? Two things: innovation and collaboration – in other words, smart technology development. An improvement in the speed of a microwave link cannot be made at the cost of inefficiencies elsewhere. An improvement in network architecture cannot come at the cost of complexity for application builders, and an improvement in billing systems cannot be paid for by network infrastructure investment.