  Edge computing can be defined as an emerging technology that uses cloud
computing to leverage edge data centers to process, store, and analyze data
close to the source. Traditional cloud computing architectures are not designed
for latency-critical applications such as AI (Artificial Intelligence) and IoT
(Internet Of Things) because they rely on low data volumes generated by
applications running near highly-populated areas. When volume grows beyond 50
miles from the population center, networks experience higher latency and packet
loss rates which impacts application performance. Since everyone's life is
equipped with more and more IoT devices by the day, decisions should be made at
a split second in edge computing. It is really crucial to perform at optimum
level, some devices specially the medical wearables deal with patient life and
any delay in decision making will result in disaster. Similarly, modern day
autonomous self driving vehicles where late decisions can endup in accidents
and really there is no room for any errors.
  This paper provides a new approach to improve performance of the edge
computing by having two identical computing systems in which one system will
act as primary and another as reserved or secondary, this system will be
available at the local environment of the IoT device and not in cloud. The
secondary system will be reserved for mission critical requests and whenever
the primary system breaches latency threshold for response only then the
request will be re-routed to the secondary system. Both the systems will sync
data on background and can also serve as backup computing systems in case of
any failure to one of the systems.
