Mitigating Garbage Collection Interference on Containerized Clouds

Patros, Panagiotis; Kent, Kenneth B.; Dawson, Michael

doi:10.1109/saso.2018.00029

Cited by 2 publications

(2 citation statements)

References 15 publications

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“…In the context of language-runtime-based functions, such as those supported by serverless platforms like funcx, which runs on Python 9 , more fine-grain power capping can take place. Such algorithms could target specific sub-components that might not need to run at full speed, such as resource-intensive dynamic memory management, aka., garbage collection [PKD18]. Alternatively, the language runtime might be able to better characterize the resource requirements of its functions, enabling improved execution density via adaptive resource sharing among multitenant functions [PMK + 19].…”

Section: Designing Sustainable Serverless Platformsmentioning

confidence: 99%

Toward Sustainable Serverless Computing

Patros

Spillner

Papadopoulos

et al. 2021

IEEE Internet Comput.

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Although serverless computing generally involves executing short-lived "functions", the increasing migration to this computing paradigm requires careful consideration of energy and power requirements. Serverless computing is also viewed as an economically-driven computational approach, often influenced by the cost of computation, as users are charged for per-sub-second use of computational resources rather than the coarse-grained charging that is common with virtual machines and containers. To ensure that the startup times of serverless functions do not discourage their use, resource providers need to keep these functions hot, often by passing in synthetic data. We describe the real power consumption characteristics of serverless, based on execution traces reported in the literature, and describe potential strategies (some adopted from existing VM and container-based approaches) that can be used to reduce the energy overheads of serverless execution. Our analysis is, purposefully, biased towards the use of machine learning workloads as: (i) such workloads are increasingly being used widely across different applications; (ii) functions that implement machine learning algorithms can range in complexity from long-running (deep learning) vs. short-running (inference only), enabling us to consider serverless across a variety of possible execution behaviours. The general findings are also easily translatable to other domains.

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Section: Designing Sustainable Serverless Platformsmentioning

confidence: 99%

Toward Sustainable Serverless Computing

Patros

Spillner

Papadopoulos

et al. 2021

IEEE Internet Comput.

View full text Add to dashboard Cite

show abstract

“…In another example, Cloud Burners Java EE benchmarks are used to study the performance slowdowns and interference, however, it did not focus on GC effects 22,23 and neither did specialized Node.js microbenchmarks, which were created for assessing scalability concerns of cloud platforms 24 . ElasticGC , a cloud‐based technique for detecting periods of low load, during which it scaled down the CPU and memory resources of the GC has been used for mitigating the effect GC has on colocated tenants 25 …”

Section: Related Workmentioning

confidence: 99%

Benchmarking and learning garbage collection delays for resource‐restricted graphical user interfaces

McCarthy

Koay

Dawson³

et al. 2021

Softw Pract Exp

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Tablets, smartphones, and wearables have limited resources. Applications on these devices employ a graphical user interface (GUI) for interaction with users. Language runtimes for GUIs employ dynamic memory management using garbage collection (GC). However, GC policies and algorithms are designed for data centers and cloud computing, but they are not necessarily ideal for resource‐constrained embedded devices. In this article, we present GUI GC, a JavaFX GUI benchmark, which we use to compare the performance of the four GC policies of the Eclipse OpenJ9 Java runtime on a resource‐constrained environment. Overall, our experiments suggest that the default policy Gencon registered significantly lower execution times than its counterparts. The region‐based policy, Balanced, did not fully utilize blocking times; thus, using GUI GC, we conducted experiments with explicit GC invocations that measured significant improvements of up to 13.22% when multiple CPUs were available. Furthermore, we created a second version of GUI GC that expands on the number of controllable load‐stressing dimensions; we conducted a large number of randomly configured experiments to quantify the performance effect that each knob has. Finally, we analyzed our dataset to derive suitable knob configurations for desired runtime, GC, and hardware stress levels.

show abstract

Mitigating Garbage Collection Interference on Containerized Clouds

Cited by 2 publications

References 15 publications

Toward Sustainable Serverless Computing

Toward Sustainable Serverless Computing

Benchmarking and learning garbage collection delays for resource‐restricted graphical user interfaces

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