Embedded database libraries provide developers with a com-
mon and convenient data persistence layer. They have spread
to many systems, including interactive devices like smart-
phones, appearing in all major mobile systems. Their perfor-
mance affects the response times and resource consumption
of millions of phone apps and billions of phone users. It is
thus critical that we better understand how they work, so
they can be used more efficiently, and so developers can
make faster libraries. Mobile databases differ significantly
from server-class storage in terms of platform, usage, and
measurement. Phones are multi-tenant, end-user devices
that the database must share with other apps. Contrary to
traditional database design goals, workloads on phones are
single-app, bursty, and rarely saturate the CPU. We argue
that mobile storage design should refocus on what matters
on the mobile platform: latency and energy. As accurate per-
formance measurement tools are necessary to evaluation of
good database design, this uncovers another issue: Tradi-
tional database benchmarking methods produce misleading
results when applied to mobile devices, due to evaluating
performance at saturation. Development of databases and
measurements specifically designed for the mobile platform
is necessary to optimize user experience of the most common
database usage in the world.
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mCerebrum: A Mobile Sensing Software Platform for Development and Validation of Digital Biomarkers and Interventions
The development and validation studies of new multisensory biomarkers and sensor-triggered interventions requires collecting raw sensor data with associated labels in the natural field environment. Unlike platforms for traditional mHealth apps, a software platform for such studies needs to not only support high-rate data ingestion, but also share raw high-rate sensor data with researchers, while supporting high-rate sense-analyze-act functionality in real-time. We present mCerebrum, a realization of such a platform, which supports high-rate data collections from multiple sensors with realtime assessment of data quality. A scalable storage architecture (with near optimal performance) ensures quick response despite rapidly growing data volume. Micro-batching and efficient sharing of data among multiple source and sink apps allows reuse of computations to enable real-time computation of multiple biomarkers without saturating the CPU or memory. Finally, it has a reconfigurable scheduler which manages all prompts to participants that is burden- and context-aware. With a modular design currently spanning 23+ apps, mCerebrum provides a comprehensive ecosystem of system services and utility apps. The design of mCerebrum has evolved during its concurrent use in scientific field studies at ten sites spanning 106,806 person days. Evaluations show that compared with other platforms, mCerebrum's architecture and design choices support 1.5 times higher data rates and 4.3 times higher storage throughput, while causing 8.4 times lower CPU usage.
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- PAR ID:
- 10073900
- Date Published:
- Journal Name:
- Proceedings of the 15th ACM Conference on Embedded Network Sensor Systems
- Page Range / eLocation ID:
- 1 to 14
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Wrist-worn devices hold great potential as a platform for mobile health (mHealth) applications because they comprise a familiar, convenient form factor and can embed sensors in proximity to the human body. Despite this potential, however, they are severely limited in battery life, storage, bandwidth, computing power, and screen size. In this paper, we describe the experience of the research and development team designing, implementing and evaluating Amulet? an open-hardware, open-software wrist-worn computing device? and its experience using Amulet to deploy mHealth apps in the field. In the past five years the team conducted 11 studies in the lab and in the field, involving 204 participants and collecting over 77,780 hours of sensor data. We describe the technical issues the team encountered and the lessons they learned, and conclude with a set of recommendations. We anticipate the experience described herein will be useful for the development of other research-oriented computing platforms. It should also be useful for researchers interested in developing and deploying mHealth applications, whether with the Amulet system or with other wearable platforms.more » « less
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Serverless computing services are offered by major cloud service providers such as Google Cloud Platform, Amazon Web Services, and Microsoft Azure. The primary purpose of the services is to offer efficiency and scalability in modern software development and IT operations while reducing overall costs and operational complexity. However, prospective customers often question which serverless service will best meet their organizational and business needs. This study analyzed the features, usability, and performance of three serverless cloud computing platforms: Google Cloud’s Cloud Run, Amazon Web Service’s App Runner, and Microsoft Azure’s Container Apps. The analysis was conducted with a containerized mobile application designed to track real-time bus locations for San Antonio public buses on specific routes and provide estimated arrival times for selected bus stops. The study evaluated various system-related features, including service configuration, pricing, and memory and CPU capacity, along with performance metrics such as container latency, distance matrix API response time, and CPU utilization for each service. The results of the analysis revealed that Google’s Cloud Run demonstrated better performance and usability than AWS’s App Runner and Microsoft Azure’s Container Apps. Cloud Run exhibited lower latency and faster response time for distance matrix queries. These findings provide valuable insights for selecting an appropriate serverless cloud service for similar containerized web applications.
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3131672.3131694
