The Computing Continuum refers to an integrated, seamless range of computing resources and environments that span from edge devices and local area networks to centralized cloud data centers. It represents an integrated framework that combines edge, fog, and cloud computing to provide a flexible, scalable, and efficient computing environment suitable for a wide range of applications and services.


CLAP: Cost and Latency-Aware Placement of Microservices on the Computing Continuum

For microservices-based real-time stream processing applications, computing at the edge delivers fast responses for low workloads, but as workload increases, the response time starts to slow down due to limited compute capacity. Abundant compute capacity in the cloud delivers fast responses even for higher workloads but incurs very high cost of operation. For applications which can tolerate latencies up to a certain limit, using either of them has one or the other drawback and for different applications and edge infrastructures, it is non-trivial to decide when to use only edge resources and when to leverage cloud resources. In this paper, we propose CLAP, which dynamically understands the relationship between workload and application latency, and automatically adjusts placement of microservices across edge and cloud computing continuum, with the goal of jointly reducing latency as well as cost of running microservices based streaming applications. CLAP leverages Reinforcement Learning (RL) technique to learn the optimal placement for a given workload and based on the learnings, adjusts placement of microservices as the application workload changes. We conduct experiments with real-world video analytics applications and show that CLAP adapts placement of microservices in response to varying workloads and achieves low latency for applications in a cost-efficient manner. Particularly, we show that for two real world video analytics applications i.e. human attributes and face recognition, CLAP is able to reduce average cost (across 4 days at different locations) by 47% and 58% for human attributes detection and face recognition application, respectively, while consistently maintaining latency below the tolerable limit.