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Eric Jung, Yichuan Wang, Iuri Prilepov, Frank Maker,
Xin Liu, & Venkatesh AkellaUniversity of California, Davis
User-Profile-Driven Collaborative Bandwidth Sharing for Mobile Phones
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OutlineIntroductionResource Aware Collaborative Execution
(RACE)RACE Modeling and PoliciesEvaluationConclusion
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Mobile Apps are Cloud Apps
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Social Networking
Location
Synchronization
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Network OverloadingSmartphones have changed the game for
providersMobile data usage will double annually through
2014 (Cisco) Driven by smartphone ascent – for AT&T, 3% of
smart-phone customers take up 40% of data usage(WSJ)
Smartphones were ~ 15% of device sales in 2009, 41% in 2013 (Telecom Industry Association)
Expected long-term solutionsNew Infrastructure Rollout (LTE, WiMAX, Femto)Tiered servicing models 4
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New Problems for Providers/UsersPhones - battery life performance,
crowded spectrumService providers - Network loads
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OutlineIntroductionResource Aware Collaborative
Execution (RACE)RACE Modeling and PoliciesEvaluationConclusion
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Resource Aware Collaborative Execution (RACE)A relay scheme – Phones act as data relay
nodes to augment network connectivity
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Benefits of RACENetwork performance
Improve network coverage Possibly offload data traffic onto femtocell or
non-cellular networks
Energy efficiencyEnergy for WiFi is significantly less than
3G/EDGEUsers with heavy usage profiles can leverage
resources of less constrained users
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User-Profile-Driven ManagementBandwidth Sharing/Tethering
Microsoft – COMBINE UMich- TCP-level Inverse Multiplexing
(PRISM)UCSB/Microsoft - Cool-Tether
What’s new?User ProtectionDynamic and User-Profile-Driven Decisions
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Design IssuesUser Protection
Helpers reserve resources for their own useReject requests if it endangers future activityIn the current work, voice is considered
primary phone activity to be protected
Dynamic User-Profile-Driven DecisionsDecision based on state of phone (Battery,
Signal Strength, Queue)User Profile used as input to decision process
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User Profiles – can people afford to help?
Normalized histogram of 53-day call history for 3 users
User profiles are widely varyingUser 3 likely has significant extra energy over time
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OutlineIntroductionResource Aware Collaborative Execution
(RACE)RACE Modeling and PoliciesEvaluationConclusion
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System Modeling1 requester, 2 helpers assumedSystem state consists of
Time to recharge – 16 hour discharge time assumedBattery levelsSignal StrengthDownload queue
Actions: Self-serve, request, serve/reject request
Rewards: successful call minutes, downloads, service
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RACE Decision Policy
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RACE FormulationPolicy 1 – Altruistic RACE
Central server with global knowledge of all phone states makes collaborative execution decisions
Policy 2 – RACE with helper protectionHelper phones with self protection
Policy 3 - Decentralized HeuristicHeuristic policy based on “energy-threshold”
Note: 1 requester, 2 helpers assumed
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Policy 1-Altruistic RACE Cloud Server
determines decision for ALL phones
Objective to maximize the global reward
“Altruistic” : helper phones may sacrifice their protection for greater global performance
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Policy 2-RACE with Helper Protection
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Cloud Server controls requester decisions
Helper phone has its own MDP Reward for helping,
call minutesRewards determine
protection for call time
Helper MDP calculated on phone
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Energy ThresholdPredicts energy required to handle all future voice
activity with certain probabilityCalculated from call historyCan be calculated on phones
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Heavy Energy Threshold
Light Call Profile Light Energy Threshold
Heavy Call Profile
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Policy 3-Energy Threshold Heuristic
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DecentralizedSimple Heuristic
PolicyRequester: Self-Serve if
Energy>threshold
Helper:Serve request if
Energy>threshold
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OutlineIntroductionResource Aware Collaborative Execution
(RACE)RACE Modeling and PoliciesEvaluationConclusion
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EvaluationPower measurementSimulations
Real call tracesControlled data traffic
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Power Measurement SetupPower measured through DC power
supply, PyVISA Python Package
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Power ProfilingPower discharge downloading 1MB file
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EDGE: ~55 J WiFi: ~5.3J
Ad-Hoc WiFi Connection Setup: ~6.5JFor requester, potential reduction in energy of almost
10x for 1MB
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Energy Transitions
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Legendec: call cost (1 min)eoi: WiFi wakeupef/eg : forwarding/
receiving cost (WiFi)
edl: download cost (1MB file)
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SimulationsConstant download arrival probability pd
Simulate over multiple download arrival probabilities
Constant download size of 1MBUser 1 is requester, user 2 & 3 are helpersSimulated over 10 days for each phoneMetrics
Average throughputDownloads servedAverage phone lifetime
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User Profiles
User 1 (requester) has heavy profile: will likely make requests
User 2 (helper) has moderate profile: may or may not accept request
User 3 (helper) has sparse profile: should have extra energy to serve
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Throughput, Number ServedThroughput higher for
any RACE type policy than without
Policy 1 achieves highest throughput
Energy threshold achieves lowest out of RACE
Phone 3 (light user) serves much more than phone 2
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Phone Lifetime (16 hour max)
Well protected helper: lasts (close to) 16 hrsPolicy 1 helpers: lower lifetimes because of global
rewardHelper phones > 920 min for all energy-threshold
policiesTradeoff: Protection and Requester Throughput
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ConclusionRACE exploits smart phone technology, user diversity to
improve energy efficiency/network connectivityRACE is dynamic decision process based on energy
costs, user profiles, system states3 policy types: centralized, helper protected, heuristic
Centralized, helper-protected formed as MDPHeuristic is decentralized, based on energy threshold concept
Policy trends:MDP policies favor throughput over helper phone protectionHeuristic protects better with lower throughput to requester
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Future WorkNetwork-side improvement
Amount of data offloadedStudy energy/bit reductionsIncentive possibilities
IncentiveUse social networking sites to implement incentive
structure
More extensive profiling, thresholding improvements
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Cloud ServicesCloud services can be used to mitigate
many overhead issuesLocating Peers
BrightKite, Loopt Service provider-enabled solutions
Incentive Social Networking Sites/Groups for Participation Service Provider Tracker/Incentives
Policy Determination Policies for sharing can be calculated in the cloud
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IncentivesService provider oriented
Better connectivity/coverageHigher data rateExtend coverage of WiFi/femto cells
Social network orientedCar-pool groupSocial groups
Current practiceSeveral Phones already with WiFi hotspot capability