Emerging　cloud-based　software　services　have　proposed　request　for　self-adaptive　resource　allocation　that　provides　to　dynamically　adjust　resources　on　demand.　Traditional　self-adaptive　resource　allocation　methods　are　rule-driven　that　they　combine　expert　knowledge　with　a　separate　set　of　management　rules　for　each　software,　leading　to　high　implementation　complexity　and　administrative　cost.　Recent　researches　on　self-adaptive　resource　allocation　mainly　focus　on　machine　learning　and　control　theory.　However,　machine　learning　techniques　require　plenty　training　data,　which　in　many　cases　is　insufficient　and　leads　to　low　accuracy　of　QoS　prediction　model　as　well　as　the　ineffectiveness　of　resource　allocation,　while　resource　allocation　based　on　control　theory　needs　a　large　number　of　iterations,　resulting　in　a　high　overhead　such　as　frequent　virtual　machine　switches.　This　paper　proposes　a　self-adaptive　resource　allocation　method　that　is　actually　a　framework　composed　of　feedback　loops,　each　of　which　goes　through　our　designed　iterative　QoS　prediction　model　and　PSO-based　runtime　decision　algorithm.　In　contrast　to　previous　QoS　prediction　models　which　predict　a　QoS　value　once　and　for　all,　ours　improves　the　predicted　QoS　value　in　iterations　towards　the　best　one.　In　the　prediction,　we　first　use　the　workload,　the　allocated　resource,　the　real　QoS　value　and　an　operation　of　resource　allocation　to　produce　a　QoS　value.　Then　we　employ　PSO-based　runtime　decision　algorithm　together　with　the　predicted　QoS　value　to　determine　future　resource　allocation　operations.　The　loops　iterate　until　the　PSO-based　algorithm　suggests　no　further　improvement　over　the　current　resource　allocation.　We　evaluate　our　approach　on　RUBiS　benchmark,　illustrating　that　based　on　the　same　historical　data,　our　method　can　achieve　a　better　QoS　prediction　accuracy　that　is　15%　higher　than　the　current　state　of　the　art.　Moreover,　it　is　also　shown　the　effectiveness　of　cloud　application　resource　allocation　is　improved　by　roughly　5%–6%.　©　2019