Service　computing　is　an　emerging　and　distributed　computing　mode　in　cloud　service　systems,　and　has　become　an　interesting　research　direction　for　both　academia　and　industry.　Note　that　the　cloud　service　systems　always　display　new　characteristics,　such　as　stochasticity,　large　scale,　loose　coupling,　concurrency,　non-homogeneity　and　heterogeneity,　thus　their　load　balancing　investigation　has　been　more　interesting,　difficult　and　challenging　until　now.　By　using　resource　management　and　job　scheduling,　this　paper　proposes　an　integrated,　real-time　and　dynamic　control　mechanism　for　large-scale　cloud　service　systems　and　their　load　balancing　through　combining　supermarket　models　with　not　only　work　stealing　models　but　also　scheduling　of　public　reserved　resource.　To　this　end,　this　paper　provides　a　novel　stochastic　model　with　weak　interactions　by　means　of　nonlinear　Markov　processes.　To　overcome　theoretical　difficulties　growing　out　of　the　state　explosion　in　high-dimensional　stochastic　systems,　this　paper　applies　the　mean-field　theory　to　develop　a　macro　computational　technique　in　terms　of　an　infinite-dimensional　system　of　mean-field　equations.　Furthermore,　this　paper　proves　the　asymptotic　independence　of　the　large-scale　cloud　service　system,　and　show　how　to　compute　the　fixed　point　by　virtue　of　an　infinite-dimensional　system　of　nonlinear　equations.　Based　on　the　fixed　point,　this　paper　provides　effective　numerical　computation　for　performance　analysis　of　this　system　under　a　high　approximate　precision.　Therefore,　we　hope　that　the　methodology　and　results　given　in　this　paper　can　be　applicable　to　the　study　of　more　general　large-scale　cloud　service　systems.