  Collaborative edge computing has become a popular paradigm where edge devices
collaborate by sharing resources. Data dissemination is a fundamental problem
in CEC to decide what data is transmitted from which device and how. Existing
works on data dissemination have not focused on coflow scheduling in CEC, which
involves deciding the order of flows within and across coflows at network
links. Coflow implies a set of parallel flows with a shared objective. The
existing works on coflow scheduling in data centers usually assume a
non-blocking switch and do not consider congestion at different links in the
multi-hop path in CEC, leading to increased coflow completion time (CCT).
Furthermore, existing works do not consider multiple flow sources that cannot
be ignored, as data can have duplicate copies at different edge devices. This
work formulates the multi-source coflow scheduling problem in CEC, which
includes jointly deciding the source and flow ordering for multiple coflows to
minimize the sum of CCT. This problem is shown to be NP-hard and challenging as
each flow can have multiple dependent conflicts at multiple links. We propose a
source and coflow-aware search and adjust (SCASA) heuristic that first provides
an initial solution considering the coflow characteristics. SCASA further
improves the initial solution using the source search and adjust heuristic by
leveraging the knowledge of both coflows and network congestion at links.
Evaluation done using simulation experiments shows that SCASA leads to up to
83% reduction in the sum of CCT compared to benchmarks without a joint
solution.
