Cloud-edge　hybrid　systems　are　known　to　support　delay-sensitive　applications　of　contemporary　industrial　Internet　of　Things　(IoT).　While　edge　nodes　(ENs)　provide　IoT　users　with　real-time　computing/network　services　in　a　pay-as-you-go　manner,　their　resources　incur　cost.　Thus,　their　profit　maximization　remains　a　core　objective.　With　the　rapid　development　of　5G　network　technologies,　an　enormous　number　of　mobile　devices　(MDs)　have　been　connected　to　ENs.　As　a　result,　how　to　maximize　the　profit　of　ENs　has　become　increasingly　more　challenging　since　it　involves　massive　heterogeneous　decision　variables　about　task　allocation　among　MDs,　ENs,　and　a　cloud　data　center　(CDC),　as　well　as　associations　of　MDs　to　proper　ENs　dynamically.　To　tackle　such　a　challenge,　this　work　adopts　a　divide-and-conquer　strategy　that　models　applications　as　multiple　subtasks,　each　of　which　can　be　independently　completed　in　MDs,　ENs,　and　a　CDC.　A　joint　optimization　problem　is　formulated　on　task　offloading,　task　partitioning,　and　associations　of　users　to　ENs　to　maximize　the　profit　of　ENs.　To　solve　this　high-dimensional　mixed-integer　nonlinear　program,　a　novel　deep-learning　algorithm　is　developed　and　named　as　a　Genetic　Simulated-annealing-based　Particle-swarm-optimizer　with　Stacked　Autoencoders　(GSPSA).　Real-life　data-based　experimental　results　demonstrate　that　GSPSA　offers　higher　profit　of　ENs　while　strictly　meeting　latency　needs　of　user　tasks　than　state-of-the-art　algorithms.