Application-specific garbage collection

Soman, Sunil; Krintz, Chandra

doi:10.1016/j.jss.2006.12.566

Cited by 12 publications

(2 citation statements)

References 49 publications

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“…Hertz et al [12] try to avoid page faults) and experiment with different configuration for a family of algorithms (e.g. Soman et al [13] switch the GC algorithm at previously defined points or taking into account the available memory).…”

Section: Related Workmentioning

confidence: 99%

VM Economics for Java Cloud Computing: An Adaptive and Resource-Aware Java Runtime with Quality-of-Execution

Simão

Veiga

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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Resource management in Cloud Computing has been dominated by system-level virtual machines to enable the management of resources using a coarse grained approach, largely in a manner independent from the applications running on these infrastructures. However, in such environments, although different types of applications can be running, the resources are delivered equally to each one, missing the opportunity to manage the available resources in a more efficient and application driven way. So, as more applications target managed runtimes, high level virtualization is a relevant abstraction layer that has not been properly explored to enhance resource usage, control, and effectiveness.We propose a VM economics model to manage cloud infrastructures, governed by a quality-of-execution (QoE) metric and implemented by an extended virtual machine. The Adaptive and Resource-Aware Java Virtual Machine (ARA-JVM) is a clusterenabled virtual execution environment with the ability to monitor base mechanisms (e.g. thread scheduling, garbage collection, memory or network consumptions) to assess application's performance and reconfigure these mechanisms in runtime according to previously defined resource allocation policies. Reconfiguration is driven by incremental gains in quality-of-execution (QoE), used by the VM economics model to balance relative resource savings and perceived performance degradation.Our work in progress, aims to allow cloud providers to exchange resource slices among virtual machines, continually addressing where those resources are required, while being able to determine where the reduction will be more economically effective, i.e., will contribute in lesser extent to performance degradation.

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Section: Related Workmentioning

confidence: 99%

VM Economics for Java Cloud Computing: An Adaptive and Resource-Aware Java Runtime with Quality-of-Execution

Simão

Veiga

2012

2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (Ccgrid 2012)

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“…Recently, there has been work on application-specific selection of GCs, see Soman and Krintz [24] for details. The challenges they face are broadly similar to ours: when should one switch from one GC to another and what application characteristics should switching take into account.…”

Section: Related Workmentioning

confidence: 99%

Chameleon

2009

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Languages such as Java and C#, as well as scripting languages like Python, and Ruby, make extensive use of Collection classes.A collection implementation represents a fixed choice in the dimensions of operation time, space utilization, and synchronization. Using the collection in a manner not consistent with this fixed choice can cause significant performance degradation.In this paper, we present CHAMELEON, a low-overhead automatic tool that assists the programmer in choosing the appropriate collection implementation for her application. During program execution, CHAMELEON computes elaborate trace and heap-based metrics on collection behavior. These metrics are consumed on-thefly by a rules engine which outputs a list of suggested collection adaptation strategies. The tool can apply these corrective strategies automatically or present them to the programmer.We have implemented CHAMELEON on top of a IBM's J9 production JVM, and evaluated it over a small set of benchmarks. We show that for some applications, using CHAMELEON leads to a significant improvement of the memory footprint of the application.

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