Energy efficient middleware: Design and development for mobile applications

Over the recent years, the popularity of smartphones has increased dramatically. The advanced integrated technology in smartphones like GPS, high-speed CPU, a real world coloured display, Wi-Fi and Bluetooth etc. All these within small size light weight device attracts people a lot to obtain them. The stated capacities of those components motivate developers to create millions of useful applications. However, smartphone devices are energy constraint as they rely on limited battery power supply that has not been increased at the same pace to support the power demands. As both the hardware and the software tend to drain the battery power, the demand for energy efficient applications has increased to keep the mobile devices useful. Optimization related to memory data access create significant difference to performance and power consumption of broad range of dataintensive application. Memory Data layout transformation represents a very interesting class of optimizations. Transform Array of Structure (AOS) to Structure of Array (SOA) is one of the commonly applied and recognized transformation. The transformation reduces the memory access count and subsequently reduces the memory access energy. Thereby, we introduce data layout transformation service as solution to minimize the power consumed by application. The Service will convert the data layout in memory from AOS to SOA. The conversion will reduce the power consumed by memory and processor. Eventually, result in efficient and extended battery life.

[1]  Nuno Faria,et al.  Impact of Data Structure Layout on Performance , 2013, 2013 21st Euromicro International Conference on Parallel, Distributed, and Network-Based Processing.

[2]  C. Bonnet,et al.  Android power management: Current and future trends , 2012, 2012 The First IEEE Workshop on Enabling Technologies for Smartphone and Internet of Things (ETSIoT).

[3]  Hojung Cha,et al.  Reducing Energy Consumption of Alarm-induced Wake-ups on Android Smartphones , 2015, HotMobile.

[4]  Qiang Zheng,et al.  Energy-Aware Web Browsing on Smartphones , 2015, IEEE Transactions on Parallel and Distributed Systems.

[5]  Xiao Ma,et al.  eDoctor : Automatically Diagnosing Abnormal Battery Drain Issues on Smartphones , 2013 .

[6]  Bo-Cheng Lai,et al.  Automatic Data Layout Transformation for Heterogeneous Many-Core Systems , 2014, NPC.

[7]  Christian Bonnet,et al.  Self-adaptive battery and context aware mobile application development , 2014, 2014 International Wireless Communications and Mobile Computing Conference (IWCMC).

[8]  William G. Griswold,et al.  Managing the Energy-Delay Tradeoff in Mobile Applications with Tempus , 2015, Middleware.

[9]  Takeshi Yamada,et al.  A Case Study of User-Defined Code Transformations for Data Layout Optimizations , 2015, 2015 Third International Symposium on Computing and Networking (CANDAR).

[10]  Robert Strzodka Abstraction for AoS and SoA layout in C , 2011 .

[11]  Ding Li,et al.  An investigation into energy-saving programming practices for Android smartphone app development , 2014, GREENS 2014.

[12]  Gernot Heiser,et al.  An Analysis of Power Consumption in a Smartphone , 2010, USENIX Annual Technical Conference.

[13]  Avinash Sodani,et al.  Intel Xeon Phi Processor High Performance Programming: Knights Landing Edition 2nd Edition , 2016 .

[14]  Indrajit Bhattacharya,et al.  TrackMe - a low power location tracking system using smart phone sensors , 2015, 2015 International Conference on Computing and Network Communications (CoCoNet).

[15]  Gang Mei,et al.  Impact of data layouts on the efficiency of GPU-accelerated IDW interpolation , 2016, SpringerPlus.

[16]  William G. Griswold,et al.  APE: an annotation language and middleware for energy-efficient mobile application development , 2014, ICSE.

[17]  Thomas Fahringer,et al.  Automatic Data Layout Optimizations for GPUs , 2015, Euro-Par.

[18]  Rong-cai Zhao,et al.  Data layout transformation for structure vectorization on SIMD architectures , 2015, 2015 IEEE/ACIS 16th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD).

[19]  Preeti Ranjan Panda,et al.  Energy optimization in Android applications through wakelock placement , 2014, 2014 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[20]  Yingjun Lyu,et al.  Automated Energy Optimization of HTTP Requests for Mobile Applications , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[21]  Masuma Akter Rumi,et al.  CPU power consumption reduction in android smartphone , 2015, 2015 3rd International Conference on Green Energy and Technology (ICGET).

[22]  Hiroaki Kobayashi,et al.  Xevolver: An XML-based code translation framework for supporting HPC application migration , 2014, 2014 21st International Conference on High Performance Computing (HiPC).

[23]  Balaji A. Naik,et al.  Optimization in Power Usage of Smartphones , 2015 .

[24]  Denis Barthou,et al.  Exploring and Evaluating Array Layout Restructuring for SIMDization , 2014, LCPC.

[25]  Basem Shihada,et al.  Green smartphone GPUs: Optimizing energy consumption using GPUFreq scaling governors , 2015, 2015 IEEE 11th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob).

[26]  Liwen Chang,et al.  Optimization and architecture effects on GPU computing workload performance , 2012, 2012 Innovative Parallel Computing (InPar).

[27]  Matti Siekkinen,et al.  Energy consumption anatomy of live video streaming from a smartphone , 2014, 2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC).

[28]  Sijing Zhang,et al.  Energy efficiency in smartphones: A survey on modern tools and techniques , 2015, 2015 21st International Conference on Automation and Computing (ICAC).

[29]  Geng Liu,et al.  Algorithm and Data Optimization Techniques for Scaling to Massively Threaded Systems , 2012, Computer.