Mobile　cloud　computing,　which　comes　up　in　recent　years,　is　a　new　computing　paradigm.　It　enables　people　to　access　remote　clouds　by　mobile　device,　even　to　build　mobile　micro-cloud(MuCloud)　with　mobile　device　to　provide　lightweight　service.　Despite　extensive　studies　of　task　scheduling　in　wired　cloud,　effective　scheduling　in　mobile　cloud　still　remains　challenges:1)　Unreliable　wireless　connection　and　dynamic　join　and　quit　of　MuCloud　often　result　in　decreased　reliability　of　scheduling;　2)　As　the　process　capacities　of　wired　clouds　and　MuClouds　vary　greatly,　it　is　hard　to　achieve　load　balancing;　3)　During　moving,　tasks,　such　as　traffic　navigation,　may　be　scheduled　consecutively　by　mobile　users　as　space-time　changes.　Such　application　scenarios　often　incur　makespan　accumulation　which　impairs　user　experience,　even　causes　system　crash.　Our　work　aims　at　such　problems.　We　firstly　illustrate　the　reason　for　selection　of　makespan　and　load　balancing　as　two　key　performance　indicators　for　task　scheduling　in　the　proposed　architecture　of　mobile　cloud　which　integrates　MuClouds.　Then　after　introduction　to　Monte　Carlo　method,　degenerated　Monte　Carlo　estimate　is　defined　and　a　scheduling　algorithm　based　on　degenerated　Monte　Carlo　estimate　(DMCE)　is　presented.　With　extensive　simulation　experiments,　the　two　above-mentioned　indicators　of　task　scheduling　using　different　algorithms　including　DMCE,　Max-Min,　Min-Min　and　IGA　are　compared　and　evaluated.　Accumulative　effect　and　relative　load　are　introduced　to　measure　scheduling　performance.　The　experimental　results　show　that:　1)Compared　with　other　algorithms,　DMCE　achieves　smallest　makespan　on　average　when　scheduled　respectively;　2)　DMCE　has　least　accumulative　effect　when　task　sets　scheduled　consecutively,　which　makes　makespan　of　a　task　set　hardly　relevant　to　the　order　of　scheduling;　3)Among　these　algorithms,　DMCE　outperforms　others　in　keeping　relative　load　balancing　by　assigning　tasks　to　clouds　in　proportion　to　each　cloud＇s　process　capacity.　©　2014　SERSC.