Serverless (or Function-as-a-Service) compute model enables new applications with dynamic scaling. However, all current Serverless systems are best-effort, and as we prove this means they cannot guarantee hard real-time deadlines, rendering them unsuitable for such real-time applications.
We analyze a proposed extension of the Serverless model that adds a guaranteed invocation rate to the serverless model called Real-time Serverless. This approach aims to meet real-time deadlines with dynamically allocated function invocations. We first prove that the Serverless model does not support real-time guarantees. Next, we analyze Real-time Serverless, showing it can guarantee application real-time deadlines for rate-monotonic real-time workloads. Further, we derive bounds on the required invocation rate to meet any set of workload runtimes and periods. Subsequently, we explore an application technique, pre-invocation, and show that it can reduce the required guaranteed invocation rate. We derive bounds for the feasible rate guarantee reduction, and corresponding overhead in wasted compute resources. Finally, we apply the theoretical results to improve the experience quality of a distributed virtual reality/ augmented reality application as well as simplify the application design and resource management.
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Feedback-based resource management for multi-threaded applications
Abstract Reconciling the constraint of guaranteeing to always meet deadlines with the optimization objective of reducing waste of computing capacity lies at the heart of a large body of research on real-time systems. Most approaches to doing so require the application designer to specify a deeper characterization of the workload (and perhaps extensive profiling of its run-time behavior), which then enables shaping the resource assignment to the application. In practice, such approaches are weak as they load the designer with the heavy duty of a detailed workload characterization. We seek approaches for reducing the waste of computing resources for recurrent real-time workloads in the absence of such additional characterization, by monitoring the minimal information that needs to be observable about the run-time behavior of a real-time system: its response time. We propose two resource control strategies to assign resources: one based on binary-exponential search and the other, on principles of control. Both approaches are compared against the clairvoyant scenario in which the average/typical behavior is known. Via an extensive simulation, we show that both techniques are useful approaches to reducing resource computation while meeting hard deadlines.
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- NSF-PAR ID:
- 10354274
- Date Published:
- Journal Name:
- Real-Time Systems
- ISSN:
- 0922-6443
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1007/s11241-022-09386-7
