TerraSwarm. Energy is Conservable: Temporal Modeling for Resource Alloca;on in Swarm Devices. David Jun, Long Le, Douglas L. Jones

Transcription

1 TerraSwarm Energy is Conservable: Temporal Modeling for Resource Alloca;on in Swarm Devices David Jun, Long Le, Douglas L. Jones University of Illinois at Urbana-Champaign SEC 13 Workshop on the Swarm at the Edge of the Cloud, September 24, 2013 Theme / Task:

2 Why is energy not just another resource? TerraSwarm Research Center David Jun / 2

3 Why is energy not just another resource? It is conservable over ;me. TerraSwarm Research Center David Jun / 3

4 A Food Bank Analogy Perishable goods CPU uilizaion, network bandwidth Should distribute all goods, since they would otherwise go to waste TerraSwarm Research Center Non- perishable goods Device energy How much should I distribute now vs. save for later? David Jun / 4

5 When is Energy Just Another Resource? Many applica;ons can be modeled as iid, and do not require extra planning over ;me Ex: data logging Ac;vity monitoring is a growing class of mobile compu;ng applica;ons that do not sa;sfy this assump;on Ex: Always on speech recogni;on, ac;vity tracking, context awareness Physical phenomena have iner;a (exploited to save energy when nothing interes;ng is happening) TerraSwarm Research Center David Jun / 5

6 An;cipa;ng Demand Wildlife acous;c monitoring applica;on Dynamic op;miza;on to schedule sensing ac;ons balancing inference performance and device energy consump;on Absent Present 45 minutes 30 minutes Calling Resting 1.5 seconds 10 seconds TerraSwarm Research Center David Jun / 6

7 An;cipa;ng Demand Wildlife acous;c monitoring applica;on Dynamic op;miza;on to schedule sensing ac;ons balancing inference performance and device energy consump;on [6] Greedy, Without proposed sensor Optimal, Without proposed sensor Optimal, With proposed sensor Average Error x Average Energy Consumption (ma) [6] D. Jun, L. Long, D.L. Jones, Cheap Noisy Sensors Can Improve Ac;vity Monitoring Under Stringent Energy Constraints, IEEE GlobalSIP, Dec 2013 TerraSwarm Research Center David Jun / 7

8 Resource Alloca;on in Swarm Devices Two- level framework in Swarm OS 1. Resource alloca;on broker (RAB) Allocates resources to cells / applica;ons at a coarse ;me- scale [1] 2. Applica;on- specific task scheduler Given resource constraints, exploit fine- scale temporal model to schedule tasks and u;lize resources Ex: sensor management in acous;c monitoring [1] J.A. Colmenares, et. al., Tessella;on: Refactoring the OS around Explicit Resource Containers with Con;nuous Adapta;on, 2013 TerraSwarm Research Center David Jun / 8

9 Example Ac;vity Profile Energy Consumed Energy consumed correspond to monitoring app responding to physical ac;vity ;me TerraSwarm Research Center David Jun / 9

10 Global Resource Alloca;on Applica;on 1 Applica;on 2 Energy consumed correspond to monitoring app responding to physical ac;vity Periodic resource alloca;ons ;me TerraSwarm Research Center David Jun / 10

11 The Alloca;on Problem Applica;on 1 Applica;on 2 Energy consumed correspond to monitoring app responding to physical ac;vity Periodic resource alloca;ons ;me 1. How much energy should the en;re system use in each interval? 2. How much energy does each applica;on get? TerraSwarm Research Center David Jun / 11

12 Just Another Resource Applica;on 1 Applica;on 2 Each epoch gets the average discharge rate: total badery capacity / desired life;me Solve op;miza;on problem (QoS, u;lity) to allocate among processes ECOSystem [2], Nemesis [3] Not well- matched for energy usage profile of monitoring applica;ons ;me [2] H. Zeng, et. al., Currentcy: A Unifying Abstrac;on for Expressing Energy Management Policies, 2003 [3] R. Neugebauer, D. McAuley, Energy is just another resource: Energy accoun;ng and energy pricing in the Nemesis OS, 2001 TerraSwarm Research Center David Jun / 12

13 An;cipated Demand Applica;on 1 Applica;on 2 Developing a theory based on GRACE [4]: Maximize expected u;lity subject to expected resource constraints Automa;cally adjust total energy used by system at each interval while sa;sfying desired run;me Achieve op;mal alloca;on At each alloca;on epoch, applica;ons need to communicate an;cipated demand Accomplished with condiional expected u;lity vs. energy func;on [4] D.G. Sachs, et. al., GRACE: A Cross- layer Adapta;on Framework for Saving Energy, 2003 TerraSwarm Research Center David Jun / 13 ;me

14 Challenges Coordina;on between applica;ons and RAB: Requires accurate repor;ng of u;lity vs. energy curves Profiling and learning energy usage over ;me Dynamic op;miza;on based scheduling algorithms that avoid need to know arrival rates, ;me- varying channel parameters, etc. Performance bounded using Lyapunov drih technique [5] [5] M.J. Neely, "Low Power Dynamic Scheduling for Compu;ng Systems." In Green Communica;ons and Networking, 2012 TerraSwarm Research Center David Jun / 14

15 Conclusions Monitoring applica;ons in mobile compu;ng raise new challenges for energy management Energy alloca;ons should be demand- based to ensure that sufficient energy is available at the right ;mes to maximize expected u;lity Tessella;on s two- layer scheduling framework can support this Communica;on between applica;ons and RAB will be cri;cal for robust scheduling TerraSwarm Research Center David Jun / 15