Trend No. 6: The Empowered Edge
Edge computing describes a computing topology in which information processing and content collection and delivery are placed closer to the sources, repositories and consumers of this information. Edge computing draws from the concepts of distributed processing. It tries to keep the traffic and processing local to reduce latency, exploit the capabilities of the edge and enable greater autonomy at the edge.
Much of the current focus on edge computing comes from the need for IoT systems to deliver disconnected or distributed capabilities into the embedded IoT world for specific industries such as manufacturing or retail. However, edge computing will come to be a dominant factor across virtually all industries and use cases as the edge is empowered with increasingly more sophisticated and specialized compute resources and more data storage. Increasingly complex edge devices including robots, drones, autonomous vehicles and operational systems are accelerating this shift in focus.
The evolution of edge-oriented IoT architectures, with intelligence migrating toward endpoints, gateways and similar devices, is underway. However, today’s edge architectures are still somewhat hierarchic, with information flowing through well-defined layers of endpoints to the near edge, sometimes the far edge and, eventually, centralized cloud and enterprise systems.
Over the long term, this neat set of layers will dissolve to create a more unstructured architecture consisting of a wide range of “things” and services connected in a dynamic flexible mesh linked by a set of distributed cloud services. In this scenario, a smart “thing,” such as a drone, might communicate with an enterprise IoT platform, a government drone tracking service, local sensors and city-level local cloud services, and then conduct peer-to-peer exchanges with nearby drones for navigational purposes.
The edge, near edge and far edge connect to centralized data centers and cloud services. Edge computing solves many pressing issues, such as high bandwidth costs and unacceptable latency. The edge computing topology will enable the specifics of digital business and IT solutions uniquely well in the near future.
Communicating to the Edge — The Role of 5G
Connecting edge devices with one another and with back-end services is a fundamental aspect of IoT and an enabler of smart spaces. 5G is the next-generation cellular standard after 4G Long Term Evolution (LTE; LTE Advanced [LTE-A] and LTE Advanced Pro [LTE-A Pro]). Several global standards bodies have defined it — International Telecommunication Union (ITU), 3rd Generation Partnership Project (3GPP) and ETSI. Successive iterations of the 5G standard also will incorporate support for NarrowBand Internet of Things (NB-IoT) aimed at devices with low-power and low-throughput requirements. New system architectures include core network slicing as well as edge computing.
5G addresses three key technology communication aspects, each of which supports distinct new services, and possibly new business models (such as latency as a service): Enhanced mobile broadband (eMBB), which most providers will probably implement first.
Ultrareliable and low-latency communications (URLLC), which addresses many existing industrial, medical, drone and transportation requirements where reliability and latency requirements surpass bandwidth needs.
Massive machine-type communications (mMTC), which addresses the scale requirements of IoT edge computing.
Use of higher cellular frequencies and massive capacity will require very dense deployments with higher frequency reuse. As a result, we expect that most public 5G deployments will initially focus on islands of deployment, without continuous national coverage. We expect that, by 2020, 4% of
network-based mobile communications service providers globally will launch the 5G network commercially. Many CSPs are uncertain about the nature of the use cases and business models that may drive 5G. We expect that, through 2022, organizations will use 5G mainly to support IoT communications, high-definition video and fixed wireless access. The release of unlicensed radio spectrum (Citizens Broadband Radio Service [CBRS] in the U.S., and similar initiatives in the U.K. and Germany) will facilitate the deployment of private 5G (and LTE) networks. This will enable enterprises to exploit the advantages of 5G technology without waiting for public networks to build out coverage.
Trend No. 7: Distributed Cloud
A distributed cloud refers to the distribution of public cloud services to different locations outside the cloud providers’ data centers, while the originating public cloud provider assumes responsibility for the operation, governance, maintenance and updates. This represents a significant shift from the centralized model of most public cloud services and will lead to a new era in cloud computing.
Cloud computing is a style of computing in which elastically scalable IT-enabled capabilities are delivered as a service using internet technologies. Cloud computing has long been viewed as synonymous with a “centralized” service running in the provider’s data center; although, private and hybrid cloud options emerged to complement this public cloud model. Private cloud refers to the creation of cloud-style services dedicated to individual companies often running in their own data centers. Hybrid cloud refers to the integration of private and public cloud services to support parallel, integrated or complementary tasks. The aim of the hybrid cloud was to blend external services from a provider and internal services running on-premises in an optimized, efficient and cost-effective manner.
Implementing a private cloud is hard. Most private cloud projects do not deliver the cloud outcomes and benefits organizations seek. Also, most of the conversations Gartner has with clinetes about
- Shifting the responsibility and work of running hardware and software infrastructure to cloud providers.
- Exploiting the economics of cloud elasticity (scaling up and down) from a large pool of shared resources
- Benefiting from the pace of innovation in sync with the public cloud provider.
- Using the cost economics of global hyperscale services.
- Using the skills of large cloud providers in securing and operating world-class services
Distributed Cloud Delivers on the Hybrid Cloud Promise
The location of the cloud services is a critical component of the distributed cloud computing model. Historically, location has not been relevant to cloud definitions, although issues related to it are important in many situations. With the arrival of the distributed cloud, location formally enters the definition of a style of cloud services. Location may be important for a variety of reasons, including data sovereignty and latency-sensitive use cases. In these scenarios, the distributed cloud service provides organizations with the capabilities of a public cloud service delivered in a location that meets their requirements.
Trend No. 8: Autonomous Things
Autonomous things are physical devices that use AI to automate functions previously performed by humans. The most recognizable forms of autonomous things are robots, drones, autonomous vehicles/ships and appliances. AI-powered IoT elements, such as industrial equipment and consumer appliances, are also a type of autonomous thing. Each physical device has a focus for its operation as it relates to humans. Their automation goes beyond the automation provided by rigid programming models, and they exploit AI to deliver advanced behaviors that interact more naturally with their surroundings and with people. Autonomous things operate across many environments (land, sea and air) with varying levels of control. Autonomous things have been deployed successfully in highly controlled environments such as mines. As the technology capability improves, regulation permits and social acceptance grows, autonomous things will increasingly be deployed in uncontrolled public spaces.
Common Technology Capabilities
Autonomous things are developing very rapidly, partly because they share some common technology capabilities. Once the challenges to developing a capability have been overcome for one type of autonomous thing, the innovation can be applied to other types of autonomous things. The following common capabilities and technologies were inspired by the 2020 NASA Technology. Taxonomy:38
Perception: The ability to understand the physical space in which the machine is operating. This includes the need to understand the surfaces in the space, recognize objects and their trajectories, and interpret dynamic events in the environment.
Interaction: The ability to interact with humans and other things in the physical world using a variety of channels (such as screens and speakers) and sensory outputs (such as light, sound and haptics).
Mobility: The ability to safely navigate and physically move from one point to another in the space through some form of propulsion (such as walking, cruising/diving, flying and driving).
Manipulation: The ability to manipulate objects in the space (such as lifting, moving, placing and adjusting) and to modify objects (for example, by cutting, welding, painting and cooking).
Collaboration: The ability to coordinate actions through cooperation with different things and to combine actions to complete tasks such as multiagent assembly, lane merges and swarm movements.
Autonomy: The ability to complete tasks with a minimum of external input, and to respond to a dynamically changing space without recourse from cloud-based processing or other external resources.
When exploring particular use cases for autonomous things, start with an understanding of the space or spaces in which the thing will operate and the people, obstacles, terrain and other autonomous objects it will need to interact with. For example, navigating a street is much easier than a sidewalk because streets have lines, stoplights, signs and rules to follow. Next, consider the outcomes you are trying to achieve with the autonomous thing. Finally, consider which technical capabilities will be needed to address this defined scenario.
Trend No. 9: Practical Blockchain
A blockchain is an expanding list of cryptographically signed, irrevocable transactional records. Each record contains a time stamp and reference links to previous transactions. With this information, anyone with access rights can trace back a transactional event, at any point in its history, belonging to any participant. A blockchain is one architectural design of the broader concept of distributed ledgers. Blockchain and other distributed ledger technologies provide trust in untrusted environments, eliminating the need for a trusted central authority. Blockchain has become the common shorthand for a diverse collection of distributed ledger products.
Blockchain potentially drives value in a number of different ways:
Blockchain removes business and technical friction by making the ledger independent of individual applications and participants and replicating the ledger across a distributed network to create an authoritative record of significant events. Everyone with permissioned access sees the same information, and integration is simplified by having a single shared blockchain model.
Blockchain also enables a distributed trust architecture that allows parties that do not know or inherently trust one another to create and exchange value using a diverse range of assets.
With the use of smart contracts as part of the blockchain, actions can be codified such that changes in the blockchain trigger other actions.
Blockchain has the potential to reshape industries by enabling trust, providing transparency and enabling value exchange across business ecosystems — potentially lowering costs, reducing transaction settlement times and improving cash flow. Assets can be traced to their origin, significantly reducing the opportunities for substitutions with counterfeit goods. Asset tracking also has value in other areas, such as tracing food across a supply chain to more easily identify the origin of contamination or tracking individual parts to assist in product recalls. Another area in which blockchain has potential is identity management. Smart contracts can be programmed into the blockchain where events can trigger actions; for example, payment is released when goods are received.
Blockchain Will Be Scalable by 2023
Blockchain remains immature for enterprise deployments due to a range of technical issues including poor scalability and interoperability. Blockchain’s key revolutionary innovation is that it eliminates all need for trust in any central or “permissioned” authority. It achieves that largely through decentralized public consensus, which is not yet used in enterprise blockchain, where organizations and consortia govern membership and participation. But enterprise blockchain is proving to be a key pillar in digital transformation that supports evolutionary and incremental improvements in trust and transparency across business ecosystems (see “Blockchain Unraveled: Determining Its Suitability for Your Organization”).
By 2023, blockchain will be scalable technically, and will support trusted private transactions with the necessary data confidentiality. These developments are being introduced in public blockchains first. Over time, permissioned blockchains will integrate with public blockchains. They will start to take advantage of these technology improvements, while supporting the membership, governance and operating model requirements of permissioned blockchains (see “Hype Cycle for Blockchain Technologies, 2019”).
Trend No. 10: AI Security
Over the next five years AI, and especially ML, will be applied to augment human decision making across a broad set of use cases. At the same time, there will be a massive increase in potential points of attack with IoT, cloud computing, microservices and highly connected systems in smart spaces. While this creates great opportunities to enable hyperautomation and leverage autonomous things to deliver business transformation, it creates significant new challenges for the security team and risk leaders. There are three key perspective to explore when considering how AI is impacting the security space:
- Protecting AI-powered systems. This requires securing AI training data, training pipelines and ML models.
- Leveraging AI to enhance security defense. This uses ML to understand patterns, uncover attacks and automate aspects of the cybersecurity processes while augmenting the actions of human security analysts.
- Anticipating nefarious use of AI by attackers. Identifying these attacks and defending against them will be an important addition to the cybersecurity role.
Protecting AI-Powered Systems
AI presents new attack surfaces and thus increases security risks. Just as security and risk management leaders would scan their assets for vulnerabilities and apply patches to fix them, application leaders must monitor ML algorithms and the data they ingest to determine whether there are extant or potential corruption (“poisoning”) issues. If infected, data manipulation could be used to compromise data-driven decisions that demand data quality, integrity, confidentiality and privacy.