Mobile Cloud Computing (MCC) aims at leveraging remote resources to boost application performance on mobile devices while conserving resources such as battery, memory, and storage. Offloading computations and outsourcing tasks are, however, associated with numerous challenges known from distributed systems. Typical mobile applications have a monolithic design and are not laid out for a distributed deployment and execution. In this work, we present how to design and partition such applications and how these partitions stay synchronized in a cost-efficient manner at runtime. We introduce our comprehensive and extendable framework MOCCAA (MObile Cloud Computing AdaptAble) that supports developers along this path. Its performance gain is mainly achieved through a new graph partitioning heuristic that is searching for the maximally beneficial cut, through minimized monitoring efforts for resource consumption prediction, through scalable and location-aware resource discovery and management, and through our graph-based delta synchronization of local and remote object states. In particular, the graph partitioning heuristic and the delta synchronization allow us to reduce synchronization costs and improve quality dimensions such as latency and bandwidth consumption.

中文翻译：

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通过MOCCAA优化移动云计算的应用程序

移动云计算（MCC）旨在利用远程资源来提高移动设备上的应用程序性能，同时节省诸如电池，内存和存储之类的资源。但是，卸载计算和外包任务与分布式系统中已知的众多挑战相关。典型的移动应用程序具有整体设计，并且没有被布局用于分布式部署和执行。在这项工作中，我们将介绍如何设计和分区此类应用程序，以及这些分区如何在运行时以经济高效的方式保持同步。我们介绍了全面且可扩展的框架MOCCAA（移动云计算AdaptAble），该框架可为开发人员提供支持。它的性能提升主要是通过一种新的图分区启发式算法来实现的，该启发式算法正在寻求最大程度的收益削减，通过最小化对资源消耗预测的监视工作，可伸缩的和可感知位置的资源发现和管理，以及通过基于图的本地和远程对象状态的增量同步。特别地，图分区启发式和增量同步使我们能够减少同步成本并改善质量维度，例如延迟和带宽消耗。