A framework for partitioning and execution of data stream applications in mobile cloud computing

Abstract

The contribution of cloud computing and mobile computing technologies lead to the newly emerging mobile cloud computing paradigm. Three major approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study how to optimize the computation partitioning of a data stream application between mobile and cloud to achieve maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the makespan of executions as in other applications. We first propose a framework to provide runtime support for the dynamic computation partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm for optimal computation partition. Both numerical evaluation and real world experiment have been performed, and the results show that the partitioned application can achieve at least two times better performance in terms of throughput than the application without partitioning.

Application optimization in mobile cloud computing: Motivation, taxonomies, and open challenges

“…(b) Data stream application partitioning framework: A framework for optimal partitioning of data stream application to maximize the speed/throughput in processing is proposed in Yang et al (2012). The partitioning problem is modeled as the problem of assigning a set of components of dataflow graph to the resources with an objective to maximize the data stream application throughput.…”

Seamless application execution in mobile cloud computing: Motivation, taxonomy, and open challenges

“…Yang et al propose a data stream application partitioning framework (Yang et al, 2012) that aims to maximize the speed and throughput. The framework consists of modules running on both (mobile device and the cloud server) sides.…”

A Survey of Mobile Cloud Computing Applications: Perspectives and Challenges

“…In the literature, there are two definitions for MCC based on the same line of observations discussed above extending MC to MCC. In the first definition [15,16], MCC is defined as a computing model combining mobile computing and the cloud, where the cloud can handle large storage and processing for mobile devices remotely. In the second definition [17,18], the cloud does not have to be a remote powerful server, but one that advances mobile devices cooperating for storage and processing.…”