Mobile Cloud Offloading Models

Mobile cloud offloading models are usually modeled as an application graph mapped to a surrogate network. An application is a directed acyclic graph, where vertexes are computation tasks and edges are data channels connecting tasks. The sites that run offloaded codes may be one surrogate or a network of devices. To offload the computation tasks to the surrogate network is to map the vertexes and edges from application graph to the surrogate network. In general, offloading objectives usually target saving mobile device's energy and decreasing application's execution time, and an optimal mapping may exist at a given time point. For a longer time span, a series of offloading mappings may exist to optimize an offloading objective function. In this chapter, we present a set of mobile cloud computing offloading decision models to demonstrate the mathematical construction to build one-to-one, one-to-many, and many-to-many offloading decision models. A preliminary set of evaluation results also presented to demonstrate the trade-offs of using offloading approaches with different system parameters' setup.

[1]  Dijiang Huang,et al.  Mobile cloud computing service models: a user-centric approach , 2013, IEEE Network.

[2]  Jimmy J. Lin,et al.  Book Reviews: Data-Intensive Text Processing with MapReduce by Jimmy Lin and Chris Dyer , 2010, CL.

[3]  Dijiang Huang,et al.  Making offloading decisions resistant to network unavailability for mobile cloud collaboration , 2013, 9th IEEE International Conference on Collaborative Computing: Networking, Applications and Worksharing.

[4]  Marin Litoiu,et al.  Partitioning applications for hybrid and federated clouds , 2012, CASCON.

[5]  Ranveer Chandra,et al.  Empowering developers to estimate app energy consumption , 2012, Mobicom '12.

[6]  Gustavo Alonso,et al.  Calling the Cloud: Enabling Mobile Phones as Interfaces to Cloud Applications , 2009, Middleware.

[7]  Gustavo Alonso,et al.  Dynamic Software Deployment from Clouds to Mobile Devices , 2012, Middleware.

[8]  Björn Butzin,et al.  Microservices approach for the internet of things , 2016, 2016 IEEE 21st International Conference on Emerging Technologies and Factory Automation (ETFA).

[9]  Dervis Karaboga,et al.  A comparative study of Artificial Bee Colony algorithm , 2009, Appl. Math. Comput..

[10]  Ellen W. Zegura,et al.  Computing in cirrus clouds: the challenge of intermittent connectivity , 2012, MCC '12.

[11]  Ermyas Abebe,et al.  Adaptive application offloading using distributed abstract class graphs in mobile environments , 2012, J. Syst. Softw..

[12]  T. Pham-Gia,et al.  System availability in a gamma alternating renewal process , 1999 .

[13]  Sam Newman,et al.  Building Microservices , 2015 .

[14]  Dijiang Huang,et al.  Modeling multi-factor multi-site risk-based offloading for mobile cloud computing , 2014, 10th International Conference on Network and Service Management (CNSM) and Workshop.

[15]  Ralf Klamma,et al.  Framework for Computation Offloading in Mobile Cloud Computing , 2012, Int. J. Interact. Multim. Artif. Intell..

[16]  Fabrizio Montesi,et al.  Microservices: Yesterday, Today, and Tomorrow , 2017, Present and Ulterior Software Engineering.

[17]  Mario Gerla,et al.  Vehicular Cloud Computing , 2012, 2012 The 11th Annual Mediterranean Ad Hoc Networking Workshop (Med-Hoc-Net).

[18]  Khaled A. Harras,et al.  Towards resource sharing in mobile device clouds: power balancing across mobile devices , 2013, MCC '13.

[19]  Rajesh Gupta,et al.  Evaluating the effectiveness of model-based power characterization , 2011 .

[20]  Antonio Corradi,et al.  VM consolidation: A real case based on OpenStack Cloud , 2014, Future Gener. Comput. Syst..

[21]  Milind Kulkarni,et al.  Techniques for Fine-Grained, Multi-site Computation Offloading , 2011, 2011 11th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[22]  Jacob Chakareski.  Adaptive multiview video streaming: challenges and opportunities , 2013, IEEE Communications Magazine.

[23]  Marco Jahn,et al.  Designing a Smart City Internet of Things Platform with Microservice Architecture , 2015, 2015 3rd International Conference on Future Internet of Things and Cloud.

[24]  Hojung Cha,et al.  AppScope: Application Energy Metering Framework for Android Smartphone Using Kernel Activity Monitoring , 2012, USENIX Annual Technical Conference.

[25]  Victor C. M. Leung,et al.  EMC: Emotion-aware mobile cloud computing in 5G , 2015, IEEE Network.

[26]  Tao Li,et al.  A Framework for Partitioning and Execution of Data Stream Applications in Mobile Cloud Computing , 2012, 2012 IEEE Fifth International Conference on Cloud Computing.

[27]  Naehyuck Chang,et al.  Online estimation of the remaining energy capacity in mobile systems considering system-wide power consumption and battery characteristics , 2013, 2013 18th Asia and South Pacific Design Automation Conference (ASP-DAC).

[28]  Chandra Krintz,et al.  Using bandwidth data to make computation offloading decisions , 2008, 2008 IEEE International Symposium on Parallel and Distributed Processing.

[29]  J. Wenny Rahayu,et al.  Mobile cloud computing: A survey , 2013, Future Gener. Comput. Syst..

[30]  Filip De Turck,et al.  Graph partitioning algorithms for optimizing software deployment in mobile cloud computing , 2013, Future Gener. Comput. Syst..

[31]  Henri E. Bal,et al.  Cuckoo: A Computation Offloading Framework for Smartphones , 2010, MobiCASE.

[32]  Mostafa Ammar,et al.  IC-Cloud: Computation Offloading to an Intermittently-Connected Cloud , 2013 .

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

[34]  Dijiang Huang,et al.  MoSeC: Mobile-Cloud Service Composition , 2015, 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering.

[35]  Hojung Cha,et al.  DevScope: a nonintrusive and online power analysis tool for smartphone hardware components , 2012, CODES+ISSS.

[36]  Massoud Pedram,et al.  A Nested Two Stage Game-Based Optimization Framework in Mobile Cloud Computing System , 2013, 2013 IEEE Seventh International Symposium on Service-Oriented System Engineering.

[37]  Khaled A. Harras,et al.  Towards Mobile Opportunistic Computing , 2015, 2015 IEEE 8th International Conference on Cloud Computing.

[38]  Kun Yang,et al.  Performance Analysis of Fault-Tolerant Offloading Systems for Pervasive Services in Mobile Wireless Environments , 2008, 2008 IEEE International Conference on Communications.

[39]  Jie Liu,et al.  Improving energy efficiency of personal sensing applications with heterogeneous multi-processors , 2012, UbiComp '12.

[40]  Lei Yang,et al.  Accurate online power estimation and automatic battery behavior based power model generation for smartphones , 2010, 2010 IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS).

[41]  Pei-Chann Chang,et al.  Development of a cloud-based service framework for energy conservation in a sustainable intelligent transportation system , 2015 .

[42]  Yong Liu,et al.  An Energy-Efficient Multisite Offloading Algorithm for Mobile Devices , 2013, Int. J. Distributed Sens. Networks.

[43]  Mario Gerla,et al.  Vehicular cloud networking: architecture and design principles , 2014, IEEE Communications Magazine.

[44]  Mohammed Atiquzzaman,et al.  Bandwidth-adaptive partitioning for distributed execution optimization of mobile applications , 2014, J. Netw. Comput. Appl..

[45]  Richard S. Hall,et al.  OSGI in Action, Creating Modular Applications in Java , 2011 .

[46]  Ermyas Abebe,et al.  A Hybrid Granularity Graph for Improving Adaptive Application Partitioning Efficacy in Mobile Computing Environments , 2011, 2011 IEEE 10th International Symposium on Network Computing and Applications.