Cloud-based　software　services　necessitate　adaptive　resource　allocation　with　the　promise　of　dynamic　resource　adjustment　for　guaranteeing　the　Quality-of-Service　(QoS)　and　reducing　resource　costs.　However,　it　is　challenging　to　achieve　adaptive　resource　allocation　for　software　services　in　complex　cloud　environments　with　dynamic　workloads.　To　address　this　essential　problem,　we　propose　an　adaptive　resource　allocation　strategy　for　cloud-based　software　services　with　workload-time　windows.　Based　on　the　QoS　prediction,　the　proposed　strategy　first　brings　the　current　and　future　workloads　into　the　process　of　calculating　resource　allocation　plans.　Next,　the　particle　swarm　optimization　and　genetic　algorithm　(PSO-GA)　is　proposed　to　make　runtime　decisions　for　exploring　the　objective　resource　allocation　plan.　Using　the　RUBiS　benchmark,　the　extensive　simulation　experiments　are　conducted　to　validate　the　effectiveness　of　the　proposed　strategy　on　improving　the　performance　of　resource　allocation　for　cloud-based　software　services.　The　simulation　results　show　that　the　proposed　strategy　can　obtain　a　better　trade-off　between　the　QoS　and　resource　costs　than　two　classic　resource　allocation　methods.　©　2013　IEEE.