1. A method for automatically maintaining an autonomic computing system in a steady state, the autonomic computing system having a plurality of parameters, each parameter having one or more thresholds, the autonomic computing system having a plurality of influencers, adjustment of the influencers affecting values of the parameters, the method comprising: in response to determining that one or more of the parameters are each reaching one of the thresholds of the parameter, the one or more of the parameters referred to as to-be-affected parameters, identifying each to-be-affected parameter and the thresholds of the to-be-affected parameter; for each influencer, determining a correlation value between the influencer and each to-be-affected parameter; and, adjusting one or more of the influencers so that the to-be-affected parameters return to more-normal values, based on the correlation values determined, wherein the influencers that are distinctly associated with the parameters are referred to as direct influencers, such that adjustment of each influencer affects the value of only one of the parameters, wherein the influencers that are indirectly associated with the parameters are referred to as meta influencers, the meta influencers organized over at least two levels including a first level and a last level, adjustment of each meta influencer at a first level directly affects one of the direct influencers, which in turn affects the value of one of the parameters, adjustment of each meta influencer at each level other than the first level directly affects one of the meta influencers at an immediately lower level; wherein, for each influencer, determining the correlation value between the influencer and each to-be-affected parameter comprises: for each direct influencer, determining the correlation value between the direct influencer and the parameter affected by the direct influencer; for each meta influencer at the first level, determining the correlation value between the meta influencer and the direct influencer affected by the meta influencer; for each meta influencer at each level other than the first level, determining the correlation value between the meta influencer and the meta influencer at an immediately lower level; and wherein adjusting one or more of the influencers so that the to-be-affected parameters return to more-normal values comprises: for each to-be-affected parameter, selecting a particular meta influencer at the last level for which a product of the correlation values along a path between the particular meta influencer at the last level and the direct influencer affecting the to-be-affected parameter is high and all the correlation values between meta influencers along the path are positive; adjusting the particular meta influencers at the last level so that the to-be-affected parameters return to more-normal values.