Fog　computing　is　an　emerging　paradigm　that　supplies　storage,　computation,　and　networking　resources　between　traditional　cloud　data　centers　and　end　devices.　This　article　focuses　on　the　resource　provisioning　problem　in　collaborative　fog　computing　for　multiple　delay-sensitive　users.　Our　goal　is　to　implement　a　resource　provisioning　strategy　for　network　operators　to　minimize　the　total　monetary　cost　by　considering　the　deadline　and　capacity　constraints.　Two　scenarios　are　considered:　unlimited-processor　fog　nodes　(UPFN)　and　limited-processor　fog　nodes　(LPFN).　In　either　scenario,　we　prove　that　the　resource　provisioning　problem　is　NP-hard.　First,　we　consider　the　UPFN　scenario　that　the　processors　of　fog　nodes　are　unlimited　and　users＇　requests　can　be　ideally　processed　in　parallel.　Two　algorithms　are　proposed　which　greedily　delete　fog　nodes　based　on　the　local　or　global　collaborative　influences　until　there　is　no　feasible　provisioning　to　guarantee　the　deadline　of　users.　Then　we　extend　the　resource　provisioning　problem　to　a　more　realistic　and　complicated　scenario　LPFN　in　which　the　scheduling　delay　cannot　be　ignored.　Two　types　of　tasks　are　considered.　One　is　the　arbitrarily　divided　tasks,　and　a　near-optimal　solution　bounded　by　83OPT+epsilon　28m　alpha　has　been　found.　m　is　the　number　of　fog　nodes,　and　alpha　is　the　upper　bound　on　the　Lipschitz　constant　of　the　delay　function.　Another　one　is　the　application-driven　tasks,　and　we　propose　a　heuristic　algorithm.　Extensive　experiments　validate　the　efficiency　of　the　proposed　algorithms.