Balancing Interactive Performance and Budgeted Resources in Mobile

Balancing Interactive Performance and Budgeted Resources in Mobile Applications Brett Higgins

Brett Higgins 2

Brett Higgins 3

The Most Precious Computational Resource “The most precious resource in a computer system is no longer its processor, memory, disk, or network, but rather human attention. ” – Gollum Garlan et al. (12 years ago) Brett Higgins 4

User attention vs. energy/data 3 GB Brett Higgins $$$ 5

Balancing these tradeoffs is difficult! • Mobile applications must: • Select the right network for the right task • Understand complex interactions between: • • • Type of network technology Bandwidth/latency Timing/scheduling of network activity Performance Energy/data resource usage Opportunity: system support • Cope with uncertainty in predictions Brett Higgins 6

Spending Principles • Spend resources effectively. • Live within your means. • Use it or lose it. Brett Higgins 7

Thesis Statement By: • Providing abstractions to simplify multinetworking, • Tailoring network use to application needs, and • Spending resources to purchase reductions in delay, Mobile systems can help applications significantly improve user-visible performance without exhausting limited energy & data resources. Brett Higgins 8

Roadmap • Introduction • Application-aware multinetworking • Intentional Networking • Purchasing performance with limited resources • Informed Mobile Prefetching • Coping with cloudy predictions • Meatballs • Conclusion Brett Higgins 9

The Challenge Diversity of networks Diversity of behavior Email • Fetch messages You. Tube • Upload video Match traffic to available networks Brett Higgins 10

Current approaches: two extremes All details hidden All details exposed Please insert packets Result: mismatched traffic Result: hard for applications Brett Higgins 11

Solution: Intentional Networking • System measures available networks • Applications describe their traffic • System matches traffic to networks Brett Higgins 12

Abstraction: Multi-socket • Multi-socket: virtual connection • Analogue: task scheduler • Measures performance of each alternative • Encapsulates transient network failure Client Server Brett Higgins 13

Abstraction: Label • Qualitative description of network traffic • Interactivity: foreground vs. background • Analogue: task priority Client Server Brett Higgins 14

Evaluation Results: Email • Vehicular network trace - Ypsilanti, MI On-demand fetch time BG fetch time 70 Best bandwidth 50 Best latency 40 Aggregate 30 10 7 x Intentional Networking 0 350 300 Time (seconds) 60 20 400 250 3% 200 150 100 50 0 Brett Higgins 15

Intentional Networking: Summary • Multinetworking state of the art • All details hidden: far from optimal • All details exposed: far too complex • Solution: application-aware system support • Small amount of information from application • Significant reduction in user-visible delay • Only minimal background throughput overhead • Can build additional services atop this Brett Higgins 16

Roadmap • Introduction • Application-aware multinetworking • Intentional Networking • Purchasing performance with limited resources • Informed Mobile Prefetching • Coping with cloudy predictions • Meatballs • Conclusion Brett Higgins 17

Mobile networks can be slow Without prefetching With prefetching $#&*!! No need for profanity, Brett. Data fetching is slow User: angry Fetch time hidden User: happy Brett Higgins 18

Mobile prefetching is complex • Lots of challenges to overcome • • • How do I balance performance, energy, and cellular data? Should I prefetch now or later? Am I prefetching data that the user actually wants? Does my prefetching interfere with interactive traffic? How do I use cellular networks efficiently? • But the potential benefits are large! Brett Higgins 19

Who should deal with the complexity? Users? Developers? Brett Higgins 20

What apps end up doing The Data Hog The Data Miser Brett Higgins 21

Informed Mobile Prefetching • Prefetching as a system service • Handles complexity on behalf of users/apps • Apps specify what and how to prefetch • System decides when to prefetch Application prefetch() IMP Brett Higgins 22

Informed Mobile Prefetching • Tackles the challenges of mobile prefetching • • • Balances multiple resources via cost-benefit analysis Estimates future cost, decides whether to defer Tracks accuracy of prefetch hints Keeps prefetching from interfering with interactive traffic Considers batching prefetches on cellular networks Brett Higgins 23

Balancing multiple resources Performance Energy Cellular data 3 GB Benefit $$$ Cost Brett Higgins 24

Balancing multiple resources • Performance (user time saved) • Future demand fetch time • Network bandwidth/latency • Battery energy (spend or save) • Energy spent sending/receiving data • Network bandwidth/latency • Wireless radio power models (powertutor. org) • Cellular data (spend or save) • Monthly allotment • Straightforward to track Brett Higgins 3 GB $$$ 25

Weighing benefit and cost • IMP maintains exchange rates Joules Bytes Seconds • One value for each resource • Expresses importance of resource • Combine costs in common currency • Meaningful comparison to benefit • Adjust over time via feedback • Goal-directed adaptation Brett Higgins 26

Users don’t always want what apps ask for • Some messages may not be read • Low-priority • Spam • Should consider the accuracy of hints • Don’t require the app to specify it • Just learn it through the API • App tells IMP when it uses data • (or decides not to use the data) • IMP tracks accuracy over time Brett Higgins 27

Evaluation Results: Email 500 7 5 4 3 2 1 0 Optimal (100% hits) Energy (J) Time (seconds) 6 2 -8 x 5 3 G data usage 400 4 300 3 IMP meets all resource goals 2 200 ~300 ms 2 x Energy usage 100 Less energy than all others (including Wi. Fi-only!) 0 3 G data (MB) Average email fetch time 8 1 0 Brett Higgins Only Wi. Fi-only used less 3 GBudget data marker (but…) 28

IMP: Summary • Mobile prefetching is a complex decision • Applications choose simple, suboptimal strategies • Powerful mechanism: purchase performance w/ resources • Prefetching as a system service • • Application provides needed information System manages tradeoffs (spending vs. performance) Significant reduction in user-visible delay Meets specified resource budgets • What about other performance/resource tradeoffs? Brett Higgins 29

Roadmap • • Introduction Application-aware multinetworking Purchasing performance with limited resources Coping with cloudy predictions • Motivation • Uncertainty-aware decision-making (Meatballs) • Decision methods • Reevaluation from new information • Evaluation • Conclusion Brett Higgins 30

Mobile Apps & Prediction • Mobile apps rely on predictions of: • Networks (bandwidth, latency, availability) • Computation time • These predictions are often wrong • Networks are highly variable • Load changes quickly • Wrong prediction causes user-visible delay • But mobile apps treat them as oracles! Brett Higgins 31

Example: code offload Response time 0. 1% 99. 9% 10 sec 100 sec 0. 1% Likelihood of 100 sec response time Brett Higgins 0. 0001% 32

Example: code offload Response time 0. 1% 99. 9% 10 sec 100 sec Elapsed time 0 sec Expected response time 10. 09 sec Expected response time (redundant) 10. 00009 sec Brett Higgins 33

Example: code offload Response time 0. 1% 99. 9% 10 sec 100 sec Elapsed time 9 sec Expected response time 1. 09 sec Expected response time (redundant) 1. 00909 sec Brett Higgins 34

Example: code offload Response time 0. 1% 99. 9% 10 sec 100 sec Elapsed time 11 sec Expected response time 89 sec Expected response time (redundant) 10. 09 sec Brett Higgins Conditional expectation 35

Spending for a good cause • It’s okay to spend extra resources! • …if we think it will benefit the user. • Spend resources to cope with uncertainty • …via redundant operation ✖ • Quantify uncertainty, use it to make decisions • Benefit (time saved) • Cost (energy + data) Brett Higgins 36

Meatballs • A library for uncertainty-aware decision-making • Application specifies its strategies • Different means of accomplishing a single task • Functions to estimate time, energy, cellular data usage • Application provides predictions, measurements • Allows library to capture error & quantify uncertainty • Library helps application choose the best strategy • Hides complexity of decision mechanism • Balances cost & benefit of redundancy Brett Higgins 37

Roadmap • • Introduction Application-aware multinetworking Purchasing performance with limited resources Coping with cloudy predictions • Motivation • Uncertainty-aware decision-making (Meatballs) • Decision methods • Reevaluation from new information • Evaluation • Conclusion Brett Higgins 38

Deciding to operate redundantly • Benefit of redundancy: time savings • Cost of redundancy: additional resources • Benefit and cost are expectations • Consider predictions as distributions, not spot values • Approaches: • Empirical error tracking • Error bounds • Bayesian estimation Brett Higgins 39

Empirical error tracking • Compute error upon new measurement • Weighted sum over joint error distribution • For multiple networks: • Time: min across all networks • Cost: sum across all networks ε(BP 1) ε(BP 2) observed error = predicted. 5. 6. 7. 8. 9 1 1. 2 1. 3 1. 4 Brett Higgins 40

Error bounds • Range + p(next value is in range) • Student’s-t prediction interval • Calculate bound on time savings of redundancy 9 8 7 6 5 4 3 2 1 0 Time to send 10 Mb Time (seconds) Bandwidth (Mbps) Network bandwidth BP 1 9 8 7 6 5 4 3 2 1 0 BP 2 Predictor says: Use network 2 Max time savings from redundancy T 1 Brett Higgins T 2 41

Error bounds • Calculate bound on net gain of redundancy max(benefit) – min(cost) = max(net gain) • Use redundancy if max(net gain) > 0 9 8 7 6 5 4 3 2 1 0 Time to send 10 Mb Time (seconds) Bandwidth (Mbps) Network bandwidth BP 1 9 8 7 6 5 4 3 2 1 0 BP 2 Predictor says: Use network 2 Max time savings from redundancy T 1 Brett Higgins T 2 42

Bayesian Estimation • Basic idea: • Given a prior belief about the world, • and some new evidence, • update our beliefs to account for the evidence, • AKA obtaining posterior distribution • using the likelihood of the evidence • Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor; ensures posterior sums to 1 Brett Higgins 43

Bayesian Estimation • Example: “will it rain tomorrow? ” • • Prior: historical rain frequency Evidence: weather forecast (simple yes/no) Posterior: believed rain probability given forecast Likelihood: • When it will rain, how often has the forecast agreed? • When it won’t rain, how often has the forecast agreed? • Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor; ensures posterior sums to 1 Brett Higgins 44

Bayesian Estimation • Applied to Intentional Networking: • • Prior: bandwidth measurements Evidence: bandwidth prediction + implied decision Posterior: new belief about bandwidths Likelihood: • When network 1 wins, how often has the prediction agreed? • When network 2 wins, how often has the prediction agreed? • Via Bayes’ Theorem: posterior = likelihood * prior p(evidence) Normalization factor; ensures posterior sums to 1 Brett Higgins 45

Decision methods: summary • Empirical error tracking • Captures error distribution accurately • Computationally intensive • Error bounds • Computationally cheap • Prone to overestimating uncertainty • Bayesian • Computationally cheap(er than brute-force) • Appears more reliant on history Brett Higgins 46

Reevaluation: conditional distributions Response time 0. 1% 99. 9% 10 sec 100 sec Decision Elapsed Time One server 0 Two servers 10 … Brett Higgins 47

Evaluation: methodology • Network trace replay, energy & data measurement • Same as IMP • Metric: weighted cost function • time + cenergy * energy + cdata * data Low-cost Mid-cost High-cost cenergy 0. 00001 0. 001 Energy per 1 second delay reduction 100 J 1 J 36 sec 3. 6 sec Battery life reduction under 6 min average use (normally 20 hours) Brett Higgins 48

Evaluation: Int. NW, walking trace Weighted cost (norm. ) Simple 2 1. 8 1. 6 1. 4 1. 2 1 24% 0. 8 0. 6 0. 4 0. 2 0 Meatballs Low-resource strategies improve 2 x No cost Low cost Brett Higgins Error bounds leans towards redundancy Meatballs matches the best strategy Mid cost High cost 49

Evaluation: Pocket. Sphinx, server load Simple Meatballs Error bounds leans towards redundancy Weighted cost (norm. ) 1. 4 1. 2 1 23% 0. 8 0. 6 0. 4 Meatballs matches the best strategy 0. 2 0 No cost Low cost Brett Higgins Mid cost High cost 50

Meatballs: Summary • Uncertainty in predictions causes user-visible delay • Impact of uncertainty is significant • Considering uncertainty can improve performance • Library can help applications consider uncertainty • • Small amount of information from application Library hides complex decision process Redundant operation mitigates uncertainty’s impact Sufficient resources are commonly available • Spend resources to purchase delay reduction Brett Higgins 51

Conclusion • Human attention is the most precious resource • Tradeoffs between performance, energy, cellular data • Difficult for applications to get right • Necessitates system support – proper abstractions • We provide abstractions for: • Using multiple networks effectively • Budgeted resource spending • Hedging against uncertainty with redundancy • Overall results • Improved performance without overspending resources Brett Higgins 52

Brett Higgins 53

Future work • Intentional Networking • Automatic label inference • Avoid modifying the server side (generic proxies) • Predictor uncertainty • Better handling of idle periods • Increase uncertainty estimate as measurements age • Add periodic active measurements? Brett Higgins 54

Abstraction: Atomicity & Ordering Constraints • App specifies boundaries, partial ordering • Receiving end enforces delivery atomicity, order Server Chunk 1 Chunk 2 use_data() Chunk 3 Brett Higgins 55

Evaluation Results: Vehicular Sensing • Trace #2: Ypsilanti, MI Urgent update time BG throughput Best bandwidth 7 8 7 Best latency 5 4 Aggregate 3 2 1 0 48% Intentional Networking Brett Higgins Throughput (KB/sec) Time (seconds) 6 6 5 4 6% 3 2 1 0 56

Evaluation Results: Vehicular Sensing • Trace #2: Ypsilanti, MI 7 Time (seconds) 6 5 4 3 2 1 0 Best bandwidth BG throughput 8 Best latency Aggregate Intentional Networking, two-process Brett Higgins 7 Throughput (KB/sec) Urgent update time 6 5 4 3 2 1 0 57

IMP Interface • Data fetching is app-specific Application prefetch() IMP • App passes a fetch callback • Receives a handle for data • App provides needed info • When prefetch is desired • When demand fetch occurs • When data is no longer wanted Brett Higgins 58

How to estimate the future? • Current cost: straightforward • Future cost: trickier • Not just predicting network conditions • When will the user request the data? • Simplify: use average network conditions • Average bandwidth/latency of each network • Average availability of Wi. Fi • Future benefit • Same method as future cost Brett Higgins 59

Balancing multiple resources • Cost/benefit analysis • How much value can the resources buy? • Used in disk prefetching (TIP; SOSP ‘ 95) • Prefetch benefit: user time saved • Prefetch cost: energy and cellular data spent • Prefetch if benefit > cost • How to meaningfully weigh benefit and cost? Brett Higgins 60

Error bounds • How to calculate bounds? • Chebyshev’s inequality? • Simple, used before; provides loose bounds • Bounds turned out to be too loose • Confidence interval? • Bounds true value of underlying distribution • Quantities (e. g. , bandwidth) neither known nor fixed • Prediction interval • Range containing the next value (with some probability) • Student’s-t distribution, alpha=0. 05 Brett Higgins 61

How to decide which network(s) to use? • First, compute the time on the “best” network T 1 = Σ s/B 1 * posterior(B 1| BP 1 > BP 2) • Next, compute the time using multiple networks Tall = Σ min(s/B 1, s/B 2) * posterior(B 1, B 2 | BP 1 > BP 2) • Finally, do the same with resource costs. • If cost < benefit, use redundancy Brett Higgins 62

Tall = Σ min(s/B 1, s/B 2) * posterior(B 1, B 2 | BP 1 > BP 2) L(BP 1 > BP 2|B 1, B 2) * prior(B 1, B 2) = Σ s/max(B 1, B 2) * p(BP 1 > BP 2) prior(B 1) Here is where each component comes from. 1 2 3 4 5 6 7 8 9 10 prior(B 2) likelihood(BP 1 > BP 2|B 1, B 2) B 2 = 4 B 2 = 5 B 2 = 6 1 2 3 4 5 6 7 8 9 10 B 1 = 1 B 1 = 8 B 1 = 9 p(BP 1 > BP 2) p(BP 1 < BP 2)

The weight of history • A measurement’s age decreases its usefulness • Uncertainty may have increased or decreased • Meatballs gives greater weight to new observations • Brute-force, error bounds: decay weight on old samples • Bayesian: decay weight in prior distributions Brett Higgins 64

Reevaluating redundancy decisions • Confidence in decision changes with new information • Suppose we decided not to transmit redundantly • Lack of response is new information • Consider conditional distributions of predictions • Meatballs provides interface for: • Deferred re-evaluation • “Tipping point” calculation: when will decision change? Brett Higgins 65

Applications • Intentional Networking • Bandwidth, latency of multiple networks • Energy consumption of network usage • Reevaluation (network delay/change) • Speech recognition (Pocket. Sphinx) • • Local/remote execution time Local CPU energy model For remote execution: network effects Reevaluation (network delay/change) Brett Higgins 66

Evaluation: Int. NW, driving trace Simple Meatballs Weighted cost (norm. ) 2 Not much benefit from using Wi. Fi 1. 5 1 0. 5 0 No cost Low cost Brett Higgins Mid cost High cost 67

Evaluation: Pocket. Sphinx, walking trace Simple Meatballs Weighted cost (norm. ) 1. 4 1. 2 Benefit of redundancy persists more 1 0. 8 >2 x 0. 6 2335% 0. 4 0. 2 0 No cost Low cost Brett Higgins Mid cost Meatballs matches the best strategy High cost 68