Enabling actionable analytics for mobile devices: performance issues of distributed analytics on Hadoop mobile clusters

Significant innovations in mobile technologies are enabling mobile users to make real-time actionable decisions based on balancing opportunities and risks to take coordinated actions with other users in their workplace. This requires a new distributed analytic framework that collects relevant information from internal and external sources, performs real-time distributed analytics, and delivers a critical analysis to any user at any place in a given time frame through the use of mobile devices such as smartphones and tablets. This paper discusses the advantages and challenges of utilizing mobile devices for distributed analytics by showing its feasibility with Hadoop analytic framework.

[1]  Injong Rhee,et al.  CUBIC: a new TCP-friendly high-speed TCP variant , 2008, OPSR.

[2]  Tom White,et al.  Hadoop: The Definitive Guide , 2009 .

[3]  Raouf Boutaba,et al.  Cloud computing: state-of-the-art and research challenges , 2010, Journal of Internet Services and Applications.

[4]  Domenico Talia,et al.  A Distributed Allocation Strategy for Data Mining Tasks in Mobile Environments , 2012, IDC.

[5]  Chye Liang,et al.  Hyrax: Crowdsourcing Mobile Devices to Develop Proximity-Based Mobile Clouds , 2012 .

[6]  van der Arjan Schaft,et al.  50th IEEE Conference on Decision and Control and European Control Conference, 2011 , 2011 .

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

[8]  Rajkumar Buyya,et al.  Article in Press Future Generation Computer Systems ( ) – Future Generation Computer Systems Cloud Computing and Emerging It Platforms: Vision, Hype, and Reality for Delivering Computing as the 5th Utility , 2022 .

[9]  Eugene Marinelli,et al.  Hyrax: Cloud Computing on Mobile Devices using MapReduce , 2009 .

[10]  Randy H. Katz,et al.  A view of cloud computing , 2010, CACM.

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

[12]  T. H. Tse,et al.  A Tale of Clouds: Paradigm Comparisons and Some Thoughts on Research Issues , 2008, 2008 IEEE Asia-Pacific Services Computing Conference.

[13]  Xi He,et al.  Cloud Computing: a Perspective Study , 2010, New Generation Computing.

[14]  Chonho Lee,et al.  A survey of mobile cloud computing: architecture, applications, and approaches , 2013, Wirel. Commun. Mob. Comput..

[15]  Dongman Lee,et al.  A virtual cloud computing provider for mobile devices , 2010, MCS '10.

[16]  Ellen W. Zegura,et al.  Serendipity: enabling remote computing among intermittently connected mobile devices , 2012, MobiHoc '12.

[17]  Christos G. Cassandras,et al.  Timeout control in distributed systems using Perturbation Analysis , 2011, IEEE Conference on Decision and Control and European Control Conference.

[18]  Chris Rose,et al.  A Break in the Clouds: Towards a Cloud Definition , 2011 .

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

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

[21]  Cecilia Mascolo,et al.  Satin: A Component Model for Mobile Self Organisation , 2004, CoopIS/DOA/ODBASE.

[22]  GhemawatSanjay,et al.  The Google file system , 2003 .

[23]  Stein Gjessing,et al.  Experimental evaluation of TCP performance in multi-rate 802.11 WLANs , 2012, 2012 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks (WoWMoM).

[24]  Din J. Wasem,et al.  Mining of Massive Datasets , 2014 .

[25]  Alexander Afanasyev,et al.  Host-to-Host Congestion Control for TCP , 2010, IEEE Communications Surveys & Tutorials.

[26]  Shruti Sanadhya,et al.  Adaptive flow control for TCP on mobile phones , 2011, 2011 Proceedings IEEE INFOCOM.