Cloud　computing,　which　is　advocated　as　an　economic　platform　for　daily　computing,　has　become　a　hot　topic　for　both　industrial　and　academic　communities　in　the　last　couple　of　years.　The　basic　idea　behind　cloud　computing　is　that　resource　providers,　which　own　the　cloud　platform,　offer　elastic　resources　to　end　users.　In　this　paper,　we　intend　to　answer　one　key　question　to　the　success　of　cloud　computing:　in　cloud,　do　many　task　computing　(MTC)　or　high　throughput　computing　(HTC)　service　providers,　which　offer　the　corresponding　computing　service　to　end　users,　benefit　from　the　economies　of　scale?　To　the　best　of　our　knowledge,　no　previous　work　designs　and　implements　the　enabling　system　to　consolidate　MTC　and　HTC　workloads　on　the　cloud　platform　and　no　one　answers　the　above　question.　Our　research　contributions　are　threefold:　first,　we　propose　an　innovative　usage　model,　called　dynamic　service　provision　(DSP)　model,　for　MTC　or　HTC　service　providers.　In　the　DSP　model,　the　resource　provider　provides　the　service　of　creating　and　managing　runtime　environments　for　MTC　or　HTC　service　providers,　and　consolidates　heterogeneous　MTC　or　HTC　workloads　on　the　cloud　platform;　second,　based　on　the　DSP　model,　we　design　and　implement　Dawningcloud,　which　provides　automatic　management　for　heterogeneous　workloads;　third,　a　comprehensive　evaluation　of　Dawningcloud　has　been　performed　in　an　emulatation　experiment.　We　found　that　for　typical　workloads,　in　comparison　with　the　previous　two　cloud　solutions,　Dawningcloud　saves　the　resource　consumption　maximally　by　46.4%　(HTC)　and　74.9%　(MTC)　for　the　service　providers,　and　saves　the　total　resource　consumption　maximally　by　29.7%　for　the　resource　provider.　At　the　same　time,　comparing　with　the　traditional　solution　that　provides　MTC　or　HTC　services　with　dedicated　systems,　Dawningcloud　is　more　cost-effective.　To　this　end,　we　conclude　that　for　typical　MTC　and　HTC　workloads,　on　the　cloud　platform,　MTC　and　HTC　service　providers　and　the　resource　service　provider　can　benefit　from　the　economies　of　scale.　Copyright　©　2009　ACM.