MIN MIN Algorithm for the Smartphone

: As popularity of mobile computing is rapidly growing and many more mobile providers are emerging, cost efficiency and resource cost maximization become two major concerns of mobile providers to remain competitive while making battery life. In the first part of the thesis, we investigated the battery life maximization problem in federated mobile environments where mobile providers cooperate to increase the degree of multiplexing. In this part, outline novel economics-inspired Application scheduling mechanisms to tackle the battery life maximization problem from the perspective of a mobile provider acting solely propose admission control mechanisms tailored within a battery  life management framework to maximize resource cost  where various proposed plans in multiple marketplaces are supported by the provider we propose an auction-based dynamic proposed mechanism suitable for selling the spare capacity of the data center. In the subsequent chapter, present a realization of the proposed dynamic proposed mechanism within a proposed as a service framework.

[1]  Felix Büsching,et al.  DroidCluster: Towards Smartphone Cluster Computing -- The Streets are Paved with Potential Computer Clusters , 2012, 2012 32nd International Conference on Distributed Computing Systems Workshops.

[2]  Earl Oliver,et al.  The challenges in large-scale smartphone user studies , 2010, HotPlanet '10.

[3]  Lars C. Wolf,et al.  CANDIS: Heterogenous Mobile Cloud Framework and Energy Cost-Aware Scheduling , 2013, 2013 IEEE International Conference on Green Computing and Communications and IEEE Internet of Things and IEEE Cyber, Physical and Social Computing.

[4]  Ramachandran Ramjee,et al.  PRISM: platform for remote sensing using smartphones , 2010, MobiSys '10.

[5]  Mateo Valero,et al.  Supercomputing with commodity CPUs: Are mobile SoCs ready for HPC? , 2013, 2013 SC - International Conference for High Performance Computing, Networking, Storage and Analysis (SC).

[6]  Srikanth V. Krishnamurthy,et al.  Computing while charging: building a distributed computing infrastructure using smartphones , 2012, CoNEXT '12.

[7]  Jeffrey H. Reed,et al.  Wireless distributed computing: a survey of research challenges , 2012, IEEE Communications Magazine.

[8]  Jeffrey H. Reed,et al.  Task allocation and scheduling in wireless distributed computing networks , 2011 .

[9]  Marco Conti,et al.  From opportunistic networks to opportunistic computing , 2010, IEEE Communications Magazine.

[10]  Shivakant Mishra,et al.  MapReduce System over Heterogeneous Mobile Devices , 2009, SEUS.

[11]  Lu Fang,et al.  REPC: Reliable and efficient participatory computing for mobile devices , 2014, 2014 Eleventh Annual IEEE International Conference on Sensing, Communication, and Networking (SECON).

[12]  Deborah Estrin,et al.  Participatory sensing: applications and architecture , 2010, MobiSys '10.

[13]  Sanjay Ghemawat,et al.  MapReduce: Simplified Data Processing on Large Clusters , 2004, OSDI.

[14]  Haris Volos,et al.  Wireless distributed computing in cognitive radio networks , 2012, Ad Hoc Networks.