The paradigm shift of deploying applications to
the cloud has introduced both opportunities and challenges.
Although clouds use elasticity to scale resource usage at runtime
to help meet an application’s performance requirements,
developers are still challenged by unpredictable performance,
little control of execution environment, and differences among
cloud service providers, all while being charged for their cloud
usages. Application performance stability is particularly affected
by multi-tenancy in which the hardware is shared among varying
applications and virtual machines. Developers porting their
applications need to meet performance requirements, but testing
on the cloud under the effects of performance uncertainty is
difficult and expensive, due to high cloud usage costs.
This paper presents a first approach to testing an application
with typical inputs for how its performance will be affected by
performance uncertainty, without incurring undue costs of bruteforce
testing in the cloud. We specify cloud uncertainty testing
criteria, design a test-based strategy to characterize the blackbox
cloud’s performance distributions using these testing criteria,
and support execution of tests to characterize the resource usage
and cloud baseline performance of the application to be deployed.
Importantly, we developed a smart test oracle that estimates the
application’s performance with certain confidence levels using
the above characterization test results and determines whether
it will meet its performance requirements. We evaluated our
testing approach on both the Chameleon cloud and Amazon web
services; results indicate that this testing strategy shows promise
as a cost-effective approach to test for performance effects of
cloud uncertainty when porting an application to the cloud.
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Storm-RTS: Stream Processing with Stable Performance for Multi-Cloud and Cloud-edge
Stream Processing Engines (SPEs) traditionally de-ploy applications on a set of shared workers (e.g., threads, processes, or containers) requiring complex performance man-agement by SPEs and application developers. We explore a new approach that replaces workers with Rate-based Abstract Ma-chines (RBAMs). This allows SPEs to translate stream operations into FaaS invocations, and exploit guaranteed invocation rates to manage performance. This approach enables SPE applications to achieve transparent and predictable performance. We realize the approach in the Storm-RTS system. Exploring 36 stream processing scenarios over 5 different hardware config-urations, we demonstrate several key advantages. First, Storm-RTS provides stable application performance and can enable flexible reconfiguration across cloud resource configurations. Sec-ond, SPEs built on RBAM can be resource-efficient and scalable. Finally, Storm-RTS allows the stream-processing paradigm to be extended from the cloud to the edge, using its performance stability to hide edge heterogeneity and resource competition. An experiment with 4 cloud and edge sites over 300 cores shows how Storm-RTS can support flexible reconfiguration and simple high-level declarative policies that optimize resource cost or other criteria.
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- Award ID(s):
- 1901466
- NSF-PAR ID:
- 10495146
- Publisher / Repository:
- IEEE
- Date Published:
- ISBN:
- 979-8-3503-0481-7
- Page Range / eLocation ID:
- 45 to 57
- Format(s):
- Medium: X
- Location:
- Chicago, IL, USA
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/CLOUD60044.2023.00015
