Misco: a MapReduce framework for mobile systems

  title={Misco: a MapReduce framework for mobile systems},
  author={Adam Ji Dou and Vana Kalogeraki and Dimitrios Gunopulos and Taneli Mielik{\"a}inen and Ville H. Tuulos},
  booktitle={PETRA '10},
The proliferation of increasingly powerful, ubiquitous mobile devices has created a new and powerful sensing and computational environment. Software development and application deployment in such distributed mobile settings is especially challenging due to issues of failures, concurrency, and lack of easy programming models. We present a framework which provides a powerful software abstraction that hides many of such complexities from the application developer. We design and implement a mobile… 

Figures and Tables from this paper

A mapreduce framework for heterogeneous mobile devices

  • R. HuangChin-Hsien Wu
  • Computer Science
    2014 International Conference on Intelligent Green Building and Smart Grid (IGBSG)
  • 2014
The mapreduce framework can combine a multi-thread parallel computing with a load balance method to improve the performance and can demonstrate the feasibility and efficiency of the mapred reduce framework for heterogeneous mobile devices.

Scheduling for real-time mobile MapReduce systems

This paper proposes a two level scheduling scheme, designed for the MapReduce programming model, that effectively predicts application execution times and dynamically schedules application tasks and demonstrates that the scheduling system is efficient, has low overhead and performs up to 32% faster than its competitors.

A broadband embedded computing system for MapReduce utilizing Hadoop

A heterogeneous computing system for MapReduce applications that couples cloud computing with distributed embedded computing that combines a central cluster of Linux servers with a broadband network of embedded set-top box (STB) devices.

Developing a mobile recommender system

A mobile recommender system that exploits the individual data collected by multiple users on their mobile phones to provide personalized and better services to the end users is presented.

Runtimes and optimizations for MapReduce

A comprehensive taxonomy of Map Reduce runtimes for different computing environments ranging from traditional homogeneous PC cluster server to dynamic, virtual, heterogeneous, and mobile computing platform is described, aiming at a good understanding of wide applicability of this model.

Misco: A System for Data Analysis Applications on Networks of Smartphones Using MapReduce

This work demonstrates a novel system that provides a principled approach to developing distributed data clustering applications on networks of smartphones and other mobile devices and demonstrates how MISCO can efficiently identify anomalies in the road surface conditions and has low energy and resource overhead.

Toward Ubiquitous MapReduce Processing

  • I. Satoh
  • Computer Science
    2016 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS)
  • 2016
The framework proposed in this paper deploys programs for data processing at the nodes that contain the target data as a map step and executes the programs with the local data and aggregates the results of the programs to certain nodes as a reduction step.

Implementing MapReduce Applications in Dynamic Cloud Environments

This chapter describes P2P-MapReduce, a framework that exploits a peer-to-peer (P2P) model to manage intermittent node participation, master failures, and MapReduce job recovery in a decentralized but effective way and provides an evaluation of its performance.

MobSched: Customizable scheduler for mobile cloud computing

Simulation results show that the proposed scheduler, MobSched, which is based on a linear programming formulation, can efficiently optimize multiple objectives such as power and throughput, while being constrained with requirements such as minimum quality of service, and maximum bandwidth usage that has to be met by the system.

Enabling collaborative MapReduce on the Cloud with a single-sign-on mechanism

A software framework for individual virtual machines to execute a MapReduce application in a parallel/collaborative way without the necessity of installing a middleware or specific software package for system management is developed.



MapReduce System over Heterogeneous Mobile Devices

The feasibility of using smart mobile devices in a MapReduce system is examined by exploring several areas, including quantifying the contribution they make to computation throughput, end-user participation, power consumption, and security.

MapReduce: simplified data processing on large clusters

This presentation explains how the underlying runtime system automatically parallelizes the computation across large-scale clusters of machines, handles machine failures, and schedules inter-machine communication to make efficient use of the network and disks.

Lightweight Middleware Architecture for Mobile Phones

A lightweight middleware architecture is implemented to support the development of ubiquitous applications for mobile phones and is validated by developing several application prototypes on top of it and using the applications in a ubiquitous test environment.

Evaluating MapReduce for Multi-core and Multiprocessor Systems

It is established that, given a careful implementation, MapReduce is a promising model for scalable performance on shared-memory systems with simple parallel code.

Mars: A MapReduce Framework on graphics processors

Mars hides the programming complexity of the GPU behind the simple and familiar MapReduce interface, and is up to 16 times faster than its CPU-based counterpart for six common web applications on a quad-core machine.

The nesC language: A holistic approach to networked embedded systems

nesC has been used to implement TinyOS, a small operating system for sensor networks, as well as several significant sensor applications, and its experience and evaluation shows that it is effective at supporting the complex, concurrent programming style demanded by this new class of deeply networked systems.

Olympus: A High-Level Programming Model for Pervasive Computing Environments

A new high-level programming model for pervasive computing environments, Olympus, which provides developers with operators for commonly used functions and ensures that developers do not have to worry about how various tasks are performed in the space in which their program is to be deployed.

System architecture directions for networked sensors

Key requirements are identified, a small device is developed that is representative of the class, a tiny event-driven operating system is designed, and it is shown that it provides support for efficient modularity and concurrency-intensive operation.

A Robust Decentralized Job Scheduling Approach for Mobile Peers in Ad-hoc Grids

  • K. HummelGerda Jelleschitz
  • Computer Science
    Seventh IEEE International Symposium on Cluster Computing and the Grid (CCGrid '07)
  • 2007
This work proposes and evaluates a robust decentralized job scheduling approach for mobile peers forming an ad-hoc grid based on a first come first serve strategy executed locally by each peer and demonstrates the feasibility of the approach.

Integrating polling, interrupts, and thread management

This paper describes a general-purpose, multithreaded, communication system that uses both polling and interrupts to receive messages and shows that the integrated system achieves robust performance: in most cases, it performs as well as or better than systems that rely exclusively on interrupts or polling.