Recent years have seen the massive migration of enterprise applications to the cloud. One of the challenges posed by cloud applications is Quality-of-Service (QoS) management, which is the problem of allocating resources to the application to guarantee a service level along dimensions such as performance, availability and reliability. This paper aims at supporting research in this area by providing a survey of the state of the art of QoS modeling approaches suitable for cloud systems. We also review and classify their early application to some decision-making problems arising in cloud QoS management.

This paper contains a complete and updated review of the literature for multiobjective flowshop problems, which are among the most studied environments in the scheduling research area. No previous comprehensive reviews exist in the literature. Papers about lexicographical, goal programming, objective weighting, and Pareto approaches have been reviewed. Exact, heuristic, and metaheuristic methods have been surveyed. Furthermore, a complete computational evaluation is also carried out. A total of 23 different algorithms including both flowshop-specific methods as well as general multiobjective optimization approaches have been tested under three different two-criteria combinations with a comprehensive benchmark. All methods have been studied under recent state-of-the-art quality measures. Parametric and nonparametric statistical testing is profusely employed to support the observed performance of the compared methods. As a result, we have identified the best-performing methods from the literature, which along with the review, constitutes a reference work for further research.

The adoption of cloud technologies is steadily increasing. In such systems, applications can benefit from nearly infinite virtual resources on a pay-per-use basis. However, being the cloud massively multi-tenant and characterized by highly variable workloads the development of more and more effective provisioning policies assumes paramount importance. Boosted by the success of the cloud, the application of Game Theory models and methodologies has also become popular, since they have been demonstrated to suit perfectly to cloud social, economic, and strategic structures. This paper aims to study, model and efficiently solve the cost minimization problem associated with the service provisioning of SaaS virtual machines in multiple IaaSs. We propose a game-theoretic approach for the runtime management of resources from multiple IaaS providers to be allocated to multiple competing SaaSs, along with a cost model including revenues and penalties for requests execution failures. A distributed algorithm for identifying Generalized Nash Equilibria has been developed and analysed in detail. The effectiveness of our approach has been assessed by performing a wide set of analyses under multiple workload conditions. Results show that our algorithm is scalable and provides significant cost savings with respect to alternative methods (80 percent on average). Furthermore, increasing the number of IaaS providers SaaSs can achieve 9-15 percent cost savings from the workload distribution on multiple IaaSs.