The judgment of forseti: economic utility for dynamic heap sizing of multiple runtimes

CameronCallum,; SingerJeremy,; VengerovDavid,

doi:10.1145/2887746.2754180

Cited by 4 publications

(1 citation statement)

References 23 publications

(8 reference statements)

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“…Another line of work introduces a central controller that can make allocation decisions across multiple heaps. Cameron et al [2015], for example, derives Pareto curves for virtual machines and combines them to achieve the best possible trade-off between memory and garbage collection time. This data-driven approach works well for virtual machines, where long run times provide lots of data.…”

Section: Related Workmentioning

confidence: 99%

Optimal Heap Limits for Reducing Browser Memory Use

Kirisame¹,

Kp²,

Panchekha³

2022

Preprint

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Garbage collected language runtimes must carefully tune heap limits to reduce garbage collection time and memory usage. However, there's a trade-off: a lower heap limit reduces memory use but increases garbage collection time. Classic methods for setting heap limits include manually-tuned heap limits and multiple-ofworking-memory rules of thumb. But because it's a trade-off, it's not clear what heap limit rule is best or how even to compare them.We address this problem with a new framework where heap limits are set for multiple heaps at once. In this framework, standard heap limit rules are non-compositional: multiple heaps using the same heap limit rule allocate memory in non-Pareto-optimal ways. We use our framework to derive a compositional "square-root" heap limit rule, which achieves minimizes total memory usage for any amount of total garbage collection time. Paradoxically, the square-root heap limit rule achieves coordination without communication: it allocates memory optimally across multiple heaps without requiring any communication between heaps.To demonstrate that this heap limit rule is effective, we prototype it for V8, the JavaScript runtime used in Google Chrome, Microsoft Edge, and other browsers, as well as in server-side frameworks like node.js and Deno. On real-world web pages, our prototype achieves reductions of approximately 16.99% of memory usage. On memory-intensive benchmarks, reductions of up to 6.55% of garbage collection time are possible with no change in total memory usage.

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Section: Related Workmentioning

confidence: 99%

Optimal Heap Limits for Reducing Browser Memory Use

Kirisame¹,

Kp²,

Panchekha³

2022

Preprint

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Towards Free Market Cloud Computing

Singer

Adhami

Perkins

2015

2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable,

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Although cloud computing has made great inroads in terms of consumer adoption, we argue that the paradigm still has a long way to go to reach an economically efficient free market state. We highlight four key problems with the current cloud computing approach. These are (1) coarse granularity of service provision, (2) limited control of resources, (3) inadequate support for small providers and prosumers, and (4) vendor lockin via APIs. In this position paper, we briefly outline interesting work that may lead to a solution, in terms of moving towards a more market-based approach to cloud resource allocation.

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Taurus

Maas

Asanović

Harris

et al. 2016

SIGOPS Oper. Syst. Rev.

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Many distributed workloads in today's data centers are written in managed languages such as Java or Ruby. Examples include big data frameworks such as Hadoop, data stores such as Cassandra or applications such as the SOLR search engine. These workloads typically run across many independent language runtime systems on different nodes.This setup represents a source of inefficiency, as these language runtime systems are unaware of each other. For example, they may perform Garbage Collection at times that are locally reasonable but not in a distributed setting.We address these problems by introducing the concept of a Holistic Runtime System that makes runtime-level decisions for the entire distributed application rather than locally. We then present Taurus, a Holistic Runtime System prototype. Taurus is a JVM drop-in replacement, requires almost no configuration and can run unmodified off-the-shelf Java applications. Taurus enforces user-defined coordination policies and provides a DSL for writing these policies.By applying Taurus to Garbage Collection, we demonstrate the potential of such a system and use it to explore coordination strategies for the runtime systems of real-world distributed applications, to improve application performance and address tail-latencies in latency-sensitive workloads.

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The judgment of forseti: economic utility for dynamic heap sizing of multiple runtimes

Cited by 4 publications

References 23 publications

Optimal Heap Limits for Reducing Browser Memory Use

Optimal Heap Limits for Reducing Browser Memory Use

Towards Free Market Cloud Computing

Taurus

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