Matching-based Scheduling of Asynchronous Data Processing Workflows on the Computing Continuum

Abstract

Today's distributed computing infrastructures encompass complex workflows for real-time data gathering, transferring, storage, and processing, quickly overwhelming centralized cloud centers. Recently, the computing continuum that federates the Cloud services with emerging Fog and Edge devices represents a relevant alternative for supporting the next-generation data processing workflows. However, eminent challenges in automating data processing across the computing continuum still exist, such as scheduling heterogeneous devices across the Cloud, Fog, and Edge layers.We propose a new scheduling algorithm called C 3 -MATCH, based on matching theory principles, involving two sets of players negotiating different utility functions: 1) workflow microservices prefering computing devices with lower data processing and queuing times; 2) computing continuum devices prefering microservices with corresponding resource requirements and less data transmission time. We evaluate C 3 -MATCH using realworld road sign inspection and sentiment analysis workflows on a federated computing continuum across four Cloud, Fog, and Edge providers. Our combined simulation and real execution results reveal that C 3 -MATCH achieves up to 67% lower completion time than three state-of-the-art methods with 10 ms-1000 ms higher transmission time.

Incremental Multilayer Resource Partitioning for Application Placement in Dynamic Fog

“…We use the Gn_Graph function from the NetworkX package to generate workflows compliant with the model in Section 3.5. Each request has a size in the range 0.002 MB -50 MB, which generates a service workload of 100 MI -500 MI according to a representative data stream workflow [1]. Each service has a deadline of 20 ms -200 ms based on the analysis of five types of time-critical applications (see Table 5).…”

“…Service deadline [ms] E-commerce [2] 0.01 -1 50 -100 Video processing [1] 0.01 -10 100 -300 Face detection [4] 0.2 -14 80 -100 Virtual reality [3] 0.002 -50 20 -50 Natural language processing [5] 3 -5 100 -200 25 devices with the highest betweenness centrality [35] as Fog gateways. The device with the lowest betweenness centrality represents the Cloud data center.…”

“…Video stream processing is a traffic sign classification application following road safety inspection concerns [1]. Encoding (En) service receives and encodes the highresolution raw video stream.…”

“…H IGHLY distributed applications, such as video processing [1], e-commerce [2], virtual reality [3], object recognition, face detection [4], or natural language processing [5], raise significant time-critical and low-latency challenges impossible to fulfill using centralized Cloud data center technologies [6]. To mitigate this problem, Fog computing [7] has emerged as a distributed paradigm encompassing highly heterogeneous and dynamic devices, hierarchically split between the high-end Cloud and the low-end user devices.…”

CNN-assisted Road Sign Inspection on the Computing Continuum

“…2) Data queues: implement asynchronous workflow tasks in which containerized tasks concurrently process the data to reduce the workflow completion time. We implemented asynchronous communication based on 1) Kubernetes KubeMQ [17] for the Edge devices, and 2) ZeroMQ for the Cloud instances.…”