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The rise in popularity of mobile applications creates a growing demand to deliver richer functionality to users executing on mobile devices with limited resources. The availability of cloud computing platforms has made available unlimited and scalable resource pools of computation and storage that can be used to enhance service quality for mobile applications. This paper exploits the observation that using local resources in close proximity to the user, i.e. local clouds, can increase the quality and performance of mobile applications. In contrast, public cloud offerings (e.g. Amazon Web Services) offer scalability at the cost of higher delays, higher power consumption and higher price on the mobile device. In this paper we introduce MAP Cloud, a hybrid, tiered cloud architecture consisting of local and public clouds and show how it can be leveraged to increase both performance and scalability of mobile applications. We model the mobile application as a workflow of tasks and aim to optimally decompose the set of tasks to execute on the mobile client and 2-tier cloud architecture considering multiple QoS factors such as power, price, and delay. Such an optimization is shown to be NP-Hard, we propose an efficient simulated annealing based heuristic, called CRAM that is able to achieve about84% of optimal solutions when the number of users is high. We evaluate CRAM and the 2-tier approach via implementation(on Android G2 devices and Amazon EC2, S3 and Cloud Front)and extensive simulation using two rich mobile applications(Video-Content Augmented Reality and Image processing). Our results indicate that MAP Cloud provides improved scalability as compared to local clouds, improved efficiency (power/delay)(about 32% lower delays and power) and about 40% decrease in price in comparison to only using public cloud.