The role of the Mobile Edge Computing is to coordinate user devices, edge hosts and carrier network infrastructure. It is anticipated that mobile edge computing platforms will run virtual machines at a given host site. The different layers (infrastructure, platform, application,and services) of mobile edge computing must be considered when designing and implementing service orchestration, programmability and access.
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The API market is generally speaking communication-enable virtually app with carrier functionality such as text, voice, identity, location, and other capabilities. Mind Commerce can see this type of marketplace expanding to include mobile edge computing specific capabilities.
One of the key aspects of evolving Mobile Edge Computing (MEC) market is that it will be by nature a distributed application/service/content marketplace. Accordingly, application and content developers will need to have access to thousands of distributed edge computing platforms for purposes of provisioning, administering and managing services. Developers will need to access both carrier owned/controlled MEC platforms as well as those owned or on the premise of enterprise, industrial, and government customers.
One of the challenges for managing MEC based Apps is the great difference that will be found between apps in terms of parameters and requirements such as caching, security and key performance indicators. This is because the mobile edge computing market aligns with vertical markets that have very different service requirements.
New methods of accounting for computational usage including data access & processing will need to be developed and implemented to support various third parties such as OTT service providers.
Mobile Edge Computing supported 5G networks will generate massive amounts of data. Data may be passed as a real-time stream to enterprise organizations for real-time decision making. In addition to conventional data analytics software, systems may be augmented with artificial intelligence to provide further data management efficiencies as well as improved decision making effectiveness.
In many cases, the data itself and actionable information will be the product, often delivered in a Data as a Service (DaaS) market model. As the industrial IoT market in particular evolves, there will an increasingly large amount of unstructured machine data. This rapidly growing amount of machine generated industrial data will drive substantial opportunities for AI support of unstructured data analytics solutions.
Service providers must balance the need to determine what data may be processed at the edge (with potential real-time business implications) versus data that may be simply transmitted to a centralized cloud for storage and post-processing. The use of Artificial Intelligence (AI) for decision making in data analytics will be crucial for efficient and effective decision making, especially in the area of streaming data and real-time analytics associated with MEC.
Data by itself is useless. Data needs to be managed and presented in a manner that is useful as information. DaaS represents a service model in which data is transformed into useful information.
A surprising number of enterprises entities, both current and prospective DaaS customers, do not realize they have options for combinations of data including (1) their own data, (2) other companies’ data (3) public data, or a combination of all three. Accordingly, it was not surprising to find confusion even for many of those already considering Data as a Service or already with DaaS in place.
One of the key opportunities for DaaS is enterprise data syndication which is the opportunity for companies of various sizes to syndicate (e.g. share and monetize) their data. This is one of the biggest opportunities for MEC infrastructure and service providers and the DaaS market as whole.
However, there remain challenges above and beyond the core adoption barriers, which include specific security, privacy and care of custody concerns.