CROSS-REFERENCE TO PRIOR APPLICATIONS

This application is a U.S. National Phase application under 35 U.S.C. § 371 of International Application No. PCT/EP2020/053712, filed on Feb. 13, 2020, and claims benefit to European Patent Application No. EP 19156914.4, filed on Feb. 13, 2019. The International Application was published in English on Aug. 20, 2020 as WO 2020/165316 A1 under PCT Article 21(2).

FIELD

The disclosure relates to computer networks, particularly to wireless networks, particularly to mobile networks and, more specifically, to latency critical applications provided within those networks.

BACKGROUND

A network services exchange provider may employ a communication facility, such as a data center, in which multiple customers of the provider locate network, server, and storage gear and interconnect to a variety of telecommunications and other network service provider(s). Data centers may be shared by the multiple tenants having their networking equipment located within the data centers.

With Information Technology (IT) and communications facilities in safe, secure hands, telecommunications, Internet, application service providers, cloud service providers, content providers, and other providers, as well as enterprises, enjoy less latency and the freedom to focus on their core business. Additionally, customers may reduce their traffic back-haul costs and free up their internal networks for other uses.

However, there are some scenarios being realized by server based latency critical applications which require data processing near a respective end user.

Autonomous vehicles, for example, will generate a lot of data. Much of this data will be unstructured and will need to be run through powerful analytics programs to produce actionable data with any value to businesses. Edge computing architectures will help to prioritize what data needs to remain on the edge to be processed by the vehicle's onboard computing power or by any computing device nearby the vehicle and what data should be relayed back to data centers for analysis. Edge data centers will serve a critical role in this network, functioning as a relay station and providing extra computing power for mission critical analytics that need to remain near end users.

In an autonomous vehicle, even a few milliseconds of delay can result in an accident and catastrophic loss of life. The stakes are simply too high to allow the vehicles' networks to be plagued by lag. Self-driving cars need to react immediately to changing road conditions; they can't simply come to a stop while waiting for instructions or recommendations from a distant cloud server analyzing data.

Edge computing can offer a solution to this problem. In fact, the heavy investment in autonomous vehicle research has been one of the reasons so many tech companies are pushing to improve and expand their edge computing architectures. By co-locating servers and computing resources in versatile edge facilities located in both high traffic areas and more far-flung areas with limited bandwidth access, companies can ensure that their autonomous vehicles are able to access the data they need with minimal latency to make decisions quickly. As Internet of Things (IoT) devices, self-driving cars also have the ability to make their own decisions without relying on guidance from servers located in distant data centers.

A further example scenario is given by computer games which experience lag when connecting to a central server and latency could mean the difference between victory and defeat.

Edge computing is not a new concept, but several trends have come together to create an opportunity to turn massive amounts of machine-based data into actionable intelligence closer to the source of the data. Typical edge computing devices typically reside away from a centralize computing available in a cloud.

Edge computing enables analytics and data gathering to occur at or nearby the source of the data. The role of edge computing to date has mostly been used to ingest, store, filter, and send data to cloud systems. Meanwhile, it is also desirable, that edge computing systems are packing more compute, storage, and analytic power to consume and act on the data nearby the end user location.

There are some scenarios where it is useful to bring server based applications closer to terminals, i.e. user equipment (UE) and, thus, to acquire a reduced latency with respect to roundtrip-time (RTT). Specifically, MNOs (Mobile Network Operators) undertake efforts to integrate edge computing in their networks.

However, it is still quite difficult or even impossible to provide latency critical applications via a wireless interface, i.e. a radio interface of a base station of a wireless network as currently used schedulers associated with such a base station operate according to a best effort and fair schedule approach when allocating the available resources on the wireless interface which leads to an inacceptable variance in latency and jitter for any latency critical application.

SUMMARY

In an exemplary embodiment, the present invention provides a method. The method includes: a) provisioning at least one latency critical application which is to be provided to at least one end user device in a cell via a base station serving the cell; b) determining for each latency critical application of the at least one latency critical application mean latency and mean throughput which are required by the respective latency critical application, and assigning the determined mean latency LAVand mean throughput DAVto a reference point value, wherein the reference point value is calculated via a reference point value function PWR(L, D), wherein the reference point value function PWR(L, D) assigns the tuple (LAV, DAV) consisting of mean latency LAVand mean throughput DAVto the reference point value PWR(LAV, DAV), and wherein the reference point value represents an allocation of resource utilization of the respective latency critical application; c) allocating the reference point value to the respective latency critical application; d) calculating via a time variable point value function PW(L, D, T) an actual point value PW for the respective latency critical application based on latency and throughput currently required by the respective latency critical application at a point in time T_N−u within a time interval [T_N−u,T_N], wherein the point value function PW(L, D, T) assigns at a point in time T each tuple (L, D) consisting of latency L and throughput D to a distinct point value PW(L, D), and wherein each point value represents an actual allocation of resource utilization of the respective latency critical application; e) comparing the reference point value with the actual point value; f) determining a difference value between the reference point value and the actual point value; g) repeating steps d) to f) for a subset of points in time T_v within the time interval [T_N−u,T_N], wherein N, u, v are positive integer values, and u≤N and N−u≤v≤N; h) summing up all difference values determined in step f) for all points in time T_v of the subset to form a sum SDP; and i) continuously optimizing current use of resources in the cell by considering the sum SDP of step h) for adapting the respective latency critical application in real time to current transmission conditions in a network.

