Optimizing Applications for Mobile Cloud Computing Through MOCCAA
Optimizing Applications for Mobile Cloud Computing Through MOCCAA
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DOI：
10.1007/s10723-019-09492-0
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发表时间：
2019-11
影响因子：
5.5
通讯作者：
Harun Baraki;Alexander Jahl;Stefan Jakob;Corvin Schwarzbach;Malte Fax;K. Geihs
中科院分区：
文献类型：
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作者：
Harun Baraki;Alexander Jahl;Stefan Jakob;Corvin Schwarzbach;Malte Fax;K. Geihs
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，同时节省电池、内存和存储等资源。然而，卸载计算和外包任务与分布式系统中已知的许多挑战相关联。典型的移动应用程序都是单片设计，并不是为分布式部署和执行而设计的。在本文中，我们将介绍如何设计和划分这类应用程序，以及这些分区如何在运行时以经济高效的方式保持同步。我们推出了全面的可扩展框架MOCCAA (MObile Cloud Computing adaptive)，支持开发人员沿着这条道路发展。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，这种启发式算法正在寻找最有利的切割，通过最小化资源消耗预测的监控工作，通过可扩展和位置感知的资源发现和管理，以及通过我们基于图的本地和远程对象状态的增量同步。特别是，图分区启发式和增量同步使我们能够降低同步成本并提高延迟和带宽消耗等质量维度。
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.