BLeak: automatically debugging memory leaks in web applications

Vilk, John; Berger, Emery D.

doi:10.1145/3192366.3192376

Cited by 11 publications

(4 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It uses the number of different generations of the objects grouped by their allocation sites, to abstract the object staleness -an important attribute indicating a memory leak. Vilk et al proposed a browser leak debugger for automatically debugging memory leaks in web applications called as "BLeak" [15]. It collects heap snapshots and analyzes these snapshots over time to identify and rank leaks.…”

Section: Related Workmentioning

confidence: 99%

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Jindal,

Staab,

Kulkarni

et al. 2021

Preprint

View full text Add to dashboard Cite

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details.This paper addresses this challenge of detection of memory leaks in cloud-based infrastructure without having any internal knowledge by introducing two novel machine learning based algorithms: Linear Backward Regression (LBR) and Precog and, their two variants: Linear Backward Regression with Change Points Detection (LBRCPD) and Precog with Maximum Filteration (PrecogMF). These algorithms only use one metric i.e the system's memory utilization on which the application is deployed for detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by Huawei and it was found that the proposed PrecogMF algorithm achieves the highest accuracy score of 85%. The same algorithm also achieves this by decreasing the overall compute time by 80% when compared to LBR's compute time.The paper also presents the different memory leak patterns found in the various memory leak applications and are further classified into different classes based on their visual representation.

show abstract

Section: Related Workmentioning

confidence: 99%

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Jindal,

Staab,

Kulkarni

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…It uses the number of different generations of the objects grouped by their allocation sites, to abstract the object staleness -an important attribute indicating a memory leak. Vilk et al proposed a browser leak debugger for automatically debugging memory leaks in web applications called as "BLeak" [13]. It collects heap snapshots and analyzes these snapshots over time to identify and rank leaks.…”

Section: Related Workmentioning

confidence: 99%

Online Memory Leak Detection in the Cloud-based Infrastructures

Jindal,

Staab,

Cardoso

et al. 2021

Preprint

View full text Add to dashboard Cite

A memory leak in an application deployed on the cloud can affect the availability and reliability of the application. Therefore, to identify and ultimately resolve it quickly is highly important. However, in the production environment running on the cloud, memory leak detection is a challenge without the knowledge of the application or its internal object allocation details. This paper addresses this challenge of online detection of memory leaks in cloud-based infrastructure without having any internal application knowledge by introducing a novel machine learning based algorithm Precog. This algorithm solely uses one metric i.e the system's memory utilization on which the application is deployed for the detection of a memory leak. The developed algorithm's accuracy was tested on 60 virtual machines manually labeled memory utilization data provided by our industry partner Huawei Munich Research Center and it was found that the proposed algorithm achieves the accuracy score of 85% with less than half a second prediction time per virtual machine.

show abstract

“…Different from Mem-Lock, this technique aims at reducing memory consumption by dynamic online scheduling while MemLock is to find memory consumption bugs. BLEAK [69] is a system to debug memory leaks in web applications. It leverages the observation that users often repeatedly return to the same visual state.…”

Section: Related Workmentioning

confidence: 99%

MemLock

Cheng

Wang

et al. 2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

View full text Add to dashboard Cite

Uncontrolled memory consumption is a kind of critical software security weaknesses. It can also become a security-critical vulnerability when attackers can take control of the input to consume a large amount of memory and launch a Denial-of-Service attack. However, detecting such vulnerability is challenging, as the stateof-the-art fuzzing techniques focus on the code coverage but not memory consumption. To this end, we propose a memory usage guided fuzzing technique, named MemLock, to generate the excessive memory consumption inputs and trigger uncontrolled memory consumption bugs. The fuzzing process is guided with memory consumption information so that our approach is general and does not require any domain knowledge. We perform a thorough evaluation for MemLock on 14 widely-used real-world programs. Our experiment results show that MemLock substantially outperforms the state-of-the-art fuzzing techniques, including AFL, AFLfast, PerfFuzz, FairFuzz, Angora and QSYM, in discovering memory consumption bugs. During the experiments, we discovered many previously unknown memory consumption bugs and received 15 new CVEs. CCS CONCEPTS• Security and privacy → Software security engineering.

show abstract

BLeak: automatically debugging memory leaks in web applications

Cited by 11 publications

References 14 publications

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Memory Leak Detection Algorithms in the Cloud-based Infrastructure

Online Memory Leak Detection in the Cloud-based Infrastructures

MemLock

Contact Info

Product

Resources

About