Towards an Elastic Application Model for Augmenting the Computing Capabilities of Mobile Devices with Cloud Computing

We propose a new elastic application model that enables seamless and transparent use of cloud resources to augment the capability of resource-constrained mobile devices. The salient features of this model include the partition of a single application into multiple components called weblets, and a dynamic adaptation of weblet execution configuration. While a weblet can be platform independent (e.g., Java or .Net bytecode or Python script) or platform dependent (native code), its execution location is transparent—it can be run on a mobile device or migrated to the cloud, i.e., run on one or more nodes offered by an IaaS provider. Thus, an elastic application can augment the capabilities of a mobile device including computation power, storage, and network bandwidth, with the light of dynamic execution configuration according to device’s status including CPU load, memory, battery level, network connection quality, and user preferences. This paper presents the motivation behind developing elastic applications and their architecture including typical elasticity patterns and cost models that are applied to determine the elasticity patterns. We implement a reference architecture and develop a set of elastic applications to validate the augmentation capabilities for smartphone devices. We demonstrate promising results of the proposed application model using data collected from one of our example elastic applications.

[1]  David Garlan,et al.  User Guidance of Resource-Adaptive Systems , 2008, ICSOFT.

[2]  Randy H. Katz,et al.  Above the Clouds: A Berkeley View of Cloud Computing , 2009 .

[3]  Mary Shaw,et al.  Dynamic configuration of resource-aware services , 2004, Proceedings. 26th International Conference on Software Engineering.

[4]  Mahmut T. Kandemir,et al.  Energy-driven integrated hardware-software optimizations using SimplePower , 2000, Proceedings of 27th International Symposium on Computer Architecture (IEEE Cat. No.RS00201).

[5]  Galen C. Hunt,et al.  The Coign automatic distributed partitioning system , 1999, OSDI '99.

[6]  Xinwen Zhang,et al.  Securing elastic applications on mobile devices for cloud computing , 2009, CCSW '09.

[7]  Chandra Krintz,et al.  NWSLite: a light-weight prediction utility for mobile devices , 2004, MobiSys '04.

[8]  Haifeng Chen,et al.  Intelligent Workload Factoring for a Hybrid Cloud Computing Model , 2009, 2009 Congress on Services - I.

[9]  Mahadev Satyanarayanan,et al.  The case for cyber foraging , 2002, EW 10.

[10]  Leon Gommans,et al.  Seamless live migration of virtual machines over the MAN/WAN , 2006, Future Gener. Comput. Syst..

[11]  David Garlan,et al.  Aura: an Architectural Framework for User Mobility in Ubiquitous Computing Environments , 2002, WICSA.

[12]  Alan Messer,et al.  Adaptive offloading for pervasive computing , 2004, IEEE Pervasive Computing.

[13]  Zhiyuan Li,et al.  Adaptive computation offloading for energy conservation on battery-powered systems , 2007, 2007 International Conference on Parallel and Distributed Systems.

[14]  Mahadev Satyanarayanan,et al.  Using history to improve mobile application adaptation , 2000, Proceedings Third IEEE Workshop on Mobile Computing Systems and Applications.

[15]  Franck Cappello,et al.  Cost-benefit analysis of Cloud Computing versus desktop grids , 2009, 2009 IEEE International Symposium on Parallel & Distributed Processing.

[16]  Mahadev Satyanarayanan,et al.  Tactics-based remote execution for mobile computing , 2003, MobiSys '03.

[17]  Edward Walker,et al.  The Real Cost of a CPU Hour , 2009, Computer.

[18]  Gustavo Alonso,et al.  R-OSGi: Distributed Applications Through Software Modularization , 2007, Middleware.

[19]  Jie Qiu,et al.  The Method and Tool of Cost Analysis for Cloud Computing , 2009, 2009 IEEE International Conference on Cloud Computing.

[20]  Alan Messer,et al.  Adaptive offloading inference for delivering applications in pervasive computing environments , 2003, Proceedings of the First IEEE International Conference on Pervasive Computing and Communications, 2003. (PerCom 2003)..

[21]  Mads Darø Kristensen,et al.  Dynamic Resource Management and Cyber Foraging , 2009, Middleware for Network Eccentric and Mobile Applications.

[22]  Byung-Gon Chun,et al.  Augmented Smartphone Applications Through Clone Cloud Execution , 2009, HotOS.

[23]  Mahadev Satyanarayanan,et al.  Balancing performance, energy, and quality in pervasive computing , 2002, Proceedings 22nd International Conference on Distributed Computing Systems.

[24]  Chunyan Miao,et al.  ELM-Based Intelligent Resource Selection for Grid Scheduling , 2009, 2009 International Conference on Machine Learning and Applications.

[25]  Krishna P. Gummadi,et al.  Towards Trusted Cloud Computing , 2009, HotCloud.

[26]  Paramvir Bahl,et al.  The Case for VM-Based Cloudlets in Mobile Computing , 2009, IEEE Pervasive Computing.

[27]  Mahadev Satyanarayanan,et al.  Internet suspend/resume , 2002, Proceedings Fourth IEEE Workshop on Mobile Computing Systems and Applications.