 Energy Results: Memory Assistant Arcade Game  Performance Results:  Response Time ▪ Memory assistant: 17.3 sec -> 1.5 sec ▪ Arcade game: 6 FPS -> 13.

Slides:



Advertisements
Similar presentations
Context-awareness, cloudlets and the case for AP-embedded, anonymous computing Anthony LaMarca Associate Director Intel Labs Seattle.
Advertisements

Eduardo Cuervo - Duke Aruna Balasubramanian - U Mass Amherst Dae-ki Cho - UCLA Alec Wolman, Stefan Saroiu, Ranveer Chandra, Paramvir Bahl – Microsoft Research.
Mobile Cloud Computing: A Comparison of Application Models Group #6 Chandra Shekhar Jammi( ) Venkata Sri Krishnakanth Pulla( ) Prashant Tiwari.
The case for VM based Cloudlets in Mobile Computing
Source: IEEE Pervasive Computing, Vol. 8, Issue.4, Oct.2009, pp. 14 – 23 Author: Satyanarayanan, M., Bahl, P., Caceres, R., Davies, N. Adviser: Chia-Nian.
SCENARIO Suppose the presenter wants the students to access a file Supply Credenti -als Grant Access Is it efficient? How can we make this negotiation.
Electrical & Computer Engineering Department Ryerson University EDP Topics of Xavier Fernando
Smartphones as distributed system with extreme heterogeneity Lin Zhong Rice Efficient Computing Group (recg.org) Dept. of Electrical & Computer Engineering.
Slide Courtesy: Prof. Pradipta De, SUNY Korea Mobile Cloud Computing.
1/30/2015 Just-in-Time Virtual Machine Provisioning for Cloud Offload Kiryong Ha Carnegie Mellon University.
Architectures and Systems for Mobile-Cloud Computing: A Workload-Driven Perspective Prashant Nair Adviser: Moin Qureshi ECE Georgia Tech Xin Zhang Adviser:
Notes to the presenter. I would like to thank Jim Waldo, Jon Bostrom, and Dennis Govoni. They helped me put this presentation together for the field.
DEPARTMENT OF COMPUTER ENGINEERING
ANDROID OPERATING SYSTEM Guided By,Presented By, Ajay B.N Somashekar B.T Asst Professor MTech 2 nd Sem (CE)Dept of CS & E.
Reducing the Energy Usage of Office Applications Jason Flinn M. Satyanarayanan Carnegie Mellon University Eyal de Lara Dan S. Wallach Willy Zwaenepoel.
Eyal de Lara Department of Computer Science University of Toronto.
Alec Wolman, Stefan Saroiu, Ranveer Chandra, Victor Bahl – Microsoft Research Eduardo Cuervo – Duke Aruna Balasubramanian – U Mass Amherst Dae-ki Cho -
Mobile Assistance Using the Internet The MAUI Project Victor Bahl, Microsoft Research Joint work with Aruna Balasubramanian (Intern, UMASS), Ranveer Chandra,
Slides modified and presented by Brandon Wilson.
Eyal de Lara Department of Computer Science University of Toronto.
Operating Systems Concepts 1. A Computer Model An operating system has to deal with the fact that a computer is made up of a CPU, random access memory.
Virtual Machine Monitors CSE451 Andrew Whitaker. Hardware Virtualization Running multiple operating systems on a single physical machine Examples:  VMWare,
ThinkAir: Dynamic Resource Allocation and Parallel Execution in Cloud for Mobile Code Offloading Sokol Kosta, Pan Hui Deutsche Telekom Labs, Berlin, Germany.
Presenter : Miresh Shukla EEL 6788 ADVANCED TOPICS IN COMPUTER NETWORKS Dr. DAMLA TURGUT.
Slingshot: Deploying Stateful Services in Wireless Hotspots Ya-Yunn Su Jason Flinn University of Michigan.
CS 153 Design of Operating Systems Spring 2015 Lecture 24: Android OS.
P2P Systems Meet Mobile Computing A Community-Oriented Software Infrastructure for Mobile Social Applications Cristian Borcea *, Adriana Iamnitchi + *
Accelerating Mobile Applications through Flip-Flop Replication
Virtualization Concept. Virtualization  Real: it exists, you can see it.  Transparent: it exists, you cannot see it  Virtual: it does not exist, you.
MOBILE CLOUD COMPUTING
Smart Phone Laboratory ECEN 489 Srinivas Shakkottai.
Copyright© Jeffrey Jongko, Ateneo de Manila University Android.
Small Devices on DBGlobe System George Samaras Chara Skouteli.
Moving the RFID Value Chain Value Proposition Cost and Complexity What is it? (passive RFID) Where is it? (active RFID) How is it? (Sensors) Adapt to it.
Improving Network I/O Virtualization for Cloud Computing.
Clone-Cloud. Motivation With the increasing use of mobile devices, mobile applications with richer functionalities are becoming ubiquitous But mobile.
Dynamic VM Synthesis for Cloudlet -ISTC Retreat Poster- Kiryong Ha, Padmanabhan S Pillai, Mahadev Satyanarayanan.
Providing User Context for Mobile and Social Networking Applications A. C. Santos et al., Pervasive and Mobile Computing, vol. 6, no. 1, pp , 2010.
Eduardo Cuervo – Duke University Aruna Balasubramanian - University of Massachusetts Amherst Dae-ki Cho - UCLA Alec Wolman, Stefan Saroiu, Ranveer Chandra,
IBM Bluemix Ecosystem Development Hands on Workshop Section 1 - Overview.
Power Guru: Implementing Smart Power Management on the Android Platform Written by Raef Mchaymech.
Cloud Computing – UNIT - II. VIRTUALIZATION Virtualization Hiding the reality The mantra of smart computing is to intelligently hide the reality Binary->
Data-Centric Systems Lab. A Virtual Cloud Computing Provider for Mobile Devices Gonzalo Huerta-Canepa presenter 김영진.
Nguyen Thi Thanh Nha HMCL by Roelof Kemp, Nicholas Palmer, Thilo Kielmann, and Henri Bal MOBICASE 2010, LNICST 2012 Cuckoo: A Computation Offloading Framework.
Internet of Things. Creating Our Future Together.
WHAT'S THE DIFFERENCE BETWEEN A WEB APPLICATION STREAMING NETWORK AND A CDN? INSTART LOGIC.
A Software Energy Analysis Method using Executable UML for Smartphones Kenji Hisazumi System LSI Research Center Kyushu University.
Nguyen Thi Thanh Nha HMCL by Ying Zhang, Gang Huang, Xuanzhe Liu, Wei Zhang, Hong Mei, and Shunxiang Yang Refactoring Android Java Code for On-Demand Computation.
A method for using cloud computing for Android By: Collin Molnar.
ANDROID AS A SERVER PLATFORM ON CLOUD COMPUTING SONA COLLEGE OF TECHNOLOGY SUBMITTED BY: NAGADEVI PRIYA.G DIVYA PURNIMA.S.S
Application development process Part 1. Overview State of the mobile industry Size of the market Popularity of platforms How users use their devices Internationalisation.
Dynamic Mobile Cloud Computing: Ad Hoc and Opportunistic Job Sharing.
Mary Ganesan and Lora Strother Campus Tours Using a Mobile Device.
Edge Computing ——vision, challenges and promise. 物联网云计算.
Virtual Machine Monitors
Computer Information Systems
Implementing Remote Procedure Call
Quantifying the Impact of Edge Computing on Mobile Applications
Slingshot: Deploying Stateful Services in Wireless Hotspots
MOBILE DEVICE OPERATING SYSTEM
Introduction to Edge Computing
Collaborative Offloading for Distributed Mobile-Cloud Apps
Sentio: Distributed Sensor Virtualization for Mobile Apps
Virtualization Techniques
Remote Procedure Call Landon Cox February 7, 2018.
Cloud computing mechanisms
Windows Virtual PC / Hyper-V
CSE 451: Operating Systems Autumn Module 24 Virtual Machine Monitors
Presentation transcript:

 Energy Results: Memory Assistant Arcade Game  Performance Results:  Response Time ▪ Memory assistant: 17.3 sec -> 1.5 sec ▪ Arcade game: 6 FPS -> 13 FPS Motivation Overview Mobile Assistance Using Infrastructure (MAUI) Victor Bahl, Ranveer Chandra, Stefan Saroiu, Alec Wolman, Ming Zhang – Networking Research Group, MSR Redmond Kris Tolle – External Research University Partners: Duke, UCLA, CMU, Purdue Results Enable smartphone applications to overcome the severe resource limitations (Battery, CPU, Memory, 3G Wireless) of today’s handheld devices Problem : MAUI for.NET Apps Handheld Device Trends  Device technology keeps improving  Faster CPU, larger screen, more RAM, faster WLAN, lots of useful sensors (e.g. camera, GPS, accelerometer, compass)  Battery technology is not keeping up  A resource-intensive application can drain a fully charged phone in 1 hr 20 mins  A major breakthrough is required – seems unlikely 3G Network Issues  Bandwidth  3G networks are already congested in cities  Comparison of US carriers :  Latency  Round trip time (in ms) for 3G & Wi-Fi  3G: 150 to 350 ms  Wi-Fi: 20 ms Solution Mobile Assistance Using Infrastructure (MAUI)  Enables Next Generation Apps: Resource-Intensive  Offload computation to nearby infrastructure ▪ Interactive applications require fast response times ▪ Lets push the cloud closer to mobile devices  Use WLAN as primary network, 3G as fallback  Enables new interactive resource intensive apps:  Augmented Reality  Corrective Human Behavior  Mobile 3D Gaming Architecture  Cloudlets: collaboration w/CMU on VM- based offload  Proteus: Profiling and Offload for Legacy Apps  Energy-Aware Program Partitioning for.NET Applications  Security: Improving Guest Security in Virtualized Environments MAUI Server RPC Smartphone Application Client Proxy Profiler Solver MAUI Runtime Hypervisor Root Partition (VM) MAUI Controller Server Proxy Solver Application Guest Partition (VM) Legacy Apps  Finding Execution Zones For Offload  Classify each system call as local or non-local  Uses CeLog event tracking to record syscalls, CPU, Memory, interrupts, Disk, Network  Implement transparent offload with process suspend & resume (using Debug API) Applications Time Offload State Transfer Z1Z1  Dynamic Energy-Aware Offload for.NET Apps  Partitioning.NET applications into: ▪ Must run on the mobile (GUI, Sensors) ▪ Must run on infrastructure node ▪ Can run at either location  “Semi-Automatic” Partitioning ▪ User classifies methods with.NET attributes ▪ Granularity of partitioning at method level ▪ MAUI runtime handles control and state transfer  Solver: Optimize battery usage subject to latency constraints ▪ Analyzes annotated call graph to determine which portions of the application to offload  Voice-based translator  Too resource-intensive to run on WinMo phones  Interactive Arcade Game  More than doubled the frame-rate by offloading the enemy strategy routines  Memory Assistant  Built a simple UI around XCG’s face-recognizer, ported to use the MAUI runtime  Obtained an order of magnitude improvement in energy consumed  Attached hardware power meter to smartphone battery to collect energy measurements