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The increasing popularity of the cloud computing paradigm and the emerging concept of federated cloud computing have motivated research efforts towards intelligent cloud service selection aimed at developing techniques for enabling the cloud users to gain maximum benefit from cloud computing by selecting services which provide optimal performance at lowest possible cost. Given the intricate and heterogeneous nature of current clouds, the cloud service selection process is, in effect, a multi criteria optimization or decision-making problem. The possible criteria for this process are related to both functional and nonfunctional attributes of cloud services. In this context, the two major issues are: (1) choice of a criteria-set and (2) mechanisms for the assessment of cloud services against each criterion for thorough continuous cloud service monitoring. In this paper, we focus on the issue of cloud service monitoring wherein the existing monitoring and assessment mechanisms are entirely dependent on various benchmark tests which, however, are unable to accurately determine or reliably predict the performance of actual cloud applications under a real workload. We discuss the recent research aimed at achieving this objective and propose a novel user-feedback-based approach which can monitor cloud performance more reliably and accurately as compared with the existing mechanisms.