Measuring and Managing Answer Quality for Online Data-Intensive Services

Fully autonomous aerial systems (FAAS) fly complex missions guided wholly by software. If users choose software, compute hardware and aircraft well, FAAS can complete missions faster and safer than unmanned aerial systems piloted by humans. On the other hand, poorly managed edge resources slow down missions, waste energy and inflate costs. This paper presents a model-driven approach to manage FAAS. We fly real FAAS missions, profile compute and aircraft resource usage and model expected demands. Naive profiling approaches use traces from previous flights to infer resource usage. However, edge resources can affect where FAAS fly and which data they sense. Usage profiles can diverge greatly across edge management policies. Instead of using traces, we characterize whole flight areas to accurately model resource usage for any flight path. We combine expected resource demands to model mission throughput, i.e., missions completed per fully charged battery. We validated our model by creating FAAS, measuring mission throughput across many system settings. Our FAAS benchmarks, released through our open source FAAS suite SoftwarePilot, execute realistic missions: autonomous photography, search and rescue, and agricultural scouting using well-known software. Our model predicted throughput with 4% error across mission, software and hardware settings. Competing approaches yielded 10-24% error. We used our SoftwarePilot benchmarks to study (1) GPU acceleration, scale up, and scale out, (2) onboard, edge and cloud computing, (3) energy and monetary budgets, and (4) software driven GPU management. We found that model-driven management can boost mission throughput by 10X and reduce costs by 87%.

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“…• Ubora: Mimicks adaptive quality management in recent research [24,25]. Each mission is treated as a query.…”

Section: Adaptive Hardware-workload Co-designmentioning

confidence: 99%

Managing edge resources for fully autonomous aerial systems

Boubin

Babu

Stewart

et al. 2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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“…Bridging the gap between approximations for offline and online processing, Kelley et al presented Ubora, a system that learns which components respond slowly and transparently circumvents them from slowing down the system as a whole [26]. This is done by duplicating some incoming requests: the first elides data from slow responses, the second is allowed to take a long time, and is in return expected to provide a highquality response.…”

Section: Related Workmentioning

confidence: 99%

“…This is done by duplicating some incoming requests: the first elides data from slow responses, the second is allowed to take a long time, and is in return expected to provide a highquality response. This response is memoized, and can be used later to provide overall higher quality responses while still keeping throughput high, processing 37% more queries than a competing controller guided by the rate of timeouts [26]. The approach of Kelley et al works on queries that yield "mature answers", i.e.…”

Section: Related Workmentioning

confidence: 99%

Quality-Elasticity: Improved Resource Utilization, Throughput, and Response Times Via Adjusting Output Quality to Current Operating Conditions

Larsson

Tärneberg

Klein

et al. 2019

2019 IEEE International Conference on Autonomic Computing (ICAC)

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This work addresses two related problems for online services, namely poor resource utilization during regular operating conditions, and low throughput, long response times, or poor performance under periods of high system load. To address these problems, we introduce our notion of qualityelasticity as a manner of dynamically adapting response qualities from software services along a fine-grained spectrum. When resources are abundant, response quality can be increased, and when resources are scarce, responses are delivered at a lower quality to prioritize throughput and response times. We present an example of how a complex online shopping site can be made quality-elastic. Experiments show that, compared to state of the art, improvements in throughput (57% more served queries), lowered response times (8 time reduction for 95 th percentile responses), and an estimated 40% profitability increase can be made using our quality-elastic approach. When resources are abundant, our approach may achieve upwards of twice as high resource utilization as prior work in this field.

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“…However, delaying updates degrades accuracy. Prior work has shown that staleness corresponds to answer quality [7,9,10]. Hard limits on staleness prevent gross degradation on quality.…”

Section: Motivationmentioning

confidence: 99%

Revisiting online scheduling for AI-driven internet of things

Babu

Stewart

2019

Proceedings of the 4th ACM/IEEE Symposium on Edge Computing

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AI-driven Internet of Things (IoT) use AI Inference to characterize data processed from various sensors. Together, AI and IoT support smart buildings, cities, cars and drones. However, AI Inference requires updates when executed in new contexts. Frequent updates consume more energy and drain Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for thirdparty components of this work must be honored. For all other uses, contact the owner/author(s).

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Measuring and Managing Answer Quality for Online Data-Intensive Services

Cited by 26 publications

References 15 publications

Managing edge resources for fully autonomous aerial systems

Managing edge resources for fully autonomous aerial systems

Quality-Elasticity: Improved Resource Utilization, Throughput, and Response Times Via Adjusting Output Quality to Current Operating Conditions

Revisiting online scheduling for AI-driven internet of things

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