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13th International Conference on Parallel and Distributed Computing, Applications, and Technologies (PDCAT)

作者： Sun, Qingyue Shi, Xiaohua Xie, Junru Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China

ISBN: (纸本)9780769548791

java virtual machine (JVM) has its garbage collector (GC) to manage all the java objects and the heap memory. However, in many java applications, some java objects will not be visited by the programs, but could not be automatically reclaimed by the garbage collector. These java objects would cause the memory leaks. Obtaining the life ranges of all java objects in the heap memory could help programmers to find the kind of memory leaks. This paper presents two approaches to scan the garbage-collector-managed heap memory to record the living status of all java object at runtime. One approach is called All-Scanning mechanism, and the other is called Incremental-Scanning mechanism. The All-Scanning mechanism scans heap objects after GC reclaiming the objects in a static way. The Incremental-Scanning mechanism could incrementally collect the status of heap objects when GC reclaims the objects. We use SpecJVM98 and SpecJBB2000 to evaluate the runtime performance and analyze the profiling results for both mechanisms. The profiling results of the two mechanisms are consistent, while the Incremental-Scanning mechanism is at least 1.82 times faster than the All-Scanning one on average. From the profiling results, we could figure out the life cycles of every object, the movements of the object allocations, and the potentially leaked objects. We also use three workloads, namely ListLeak, SwapLeak and MySql, to demonstrate how to find out the leaked objects from the profiling results.

关键词： java virtual machine Memory Leak Garbage Collector

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Optimization of java virtual machine Flags using Feature Model and Genetic Algorithm 21

Optimization of Java Virtual Machine Flags using Feature Mod...

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ACM/SPEC International Conference on Performance Engineering (ICPE)

作者： Canales, Felipe Hecht, Geoffrey Bergel, Alexandre Univ Chile ISCLab Dept Comp Sci Santiago Chile

ISBN: (纸本)9781450383318

Optimizing the java virtual machine ( JVM) options in order to get the best performance out of a program for production is a challenging and time-consuming task. HotSpot, the Oracle's open-source java VM implementation offers more than 500 options, called flags, that can be used to tune the JVM's compiler, garbage collector (GC), heap size and much more. In addition to being numerous, these flags are sometimes poorly documented and create a need of benchmarking to ensure that the flags and their associated values deliver the best performance and stability for a particular program to execute. Auto-tuning approaches have already been proposed in order to mitigate this burden. However, in spite of increasingly sophisticated search techniques allowing for powerful optimizations, these approaches take little account of the underlying complexities of JVM flags. Indeed, dependencies and incompatibilities between flags are non-trivial to express, which if not taken into account may lead to invalid or spurious flag configurations that should not be considered by the auto-tuner. In this paper, we propose a novel model, inspired by the feature model used in Software Product Line, which takes the complexity of JVM's flags into account. We then demonstrate the usefulness of this model, using it as an input of a Genetic Algorithm (GA) to optimize the execution times of DaCapo Benchmarks.

关键词： java virtual machine Optimization Auto-tuning Feature Model Genetic Algorithm

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An Efficient Garbage Collection in java virtual machine via Swap I/O Optimization 20

An Efficient Garbage Collection in Java Virtual Machine via ...

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35th Annual ACM Symposium on Applied Computing (SAC)

作者： Lee, Hyojeong Chen, Qichen Yeom, Heon Young Son, Yongseok Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea

ISBN: (纸本)9781450368667

Various applications, frameworks, and services are built on java virtual machine (JVM) (e.g., Big data analytics) due to its cross platform portability. However, many of them suffer from long latency of Garbage Collection (GC) which also drops throughput, efficiency, and availability of the system. For example, when clients demand larger memory than available system memory, Operating System (OS) usually uses its swap space to release the space by evicting some inactive contents stored in memory. In this case, GC latency can be longer due to the I/O time (i.e., swap in/out). In this paper, we present a performance analysis of the existing GC policy in JVM. Based on the result of analysis, we propose an efficient GC scheme to improve the GC performance via swap I/O optimization to complement the existing GC policy. In this scheme, we selectively compact JVM heap by interacting with OS swap system during GC. The experimental results demonstrate that our scheme reduces overall GC overhead by 77.5% and improves the throughput by 82.5% respectively.

关键词： java virtual machine Garbage collection Linux Swap system

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Adaptable Runtime Monitoring for the java virtual machine

Adaptable Runtime Monitoring for the Java Virtual Machine

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7th International Symposium on Leveraging Applications of Formal Methods, Verification and Validation (ISoLA)

作者： Rosa, Andrea Zheng, Yudi Sun, Haiyang Javed, Omar Binder, Walter USI Fac Informat Lugano Switzerland

ISBN: (纸本)9783319471693;9783319471686

Nowadays, many programming language implementations and programming frameworks target the java virtual machine (JVM). Examples include the java and Scala compilers, Oracle's Truffle framework and the interpreters built on top of it for a variety of dynamic programming languages, as well as big-data frameworks such as Apache Spark, Apache Flink, and Apache Storm. Unfortunately, the JVM provides only limited support for runtime monitoring. The JVM Tool Interface (JVMTI) offers only a very low-level programming model, often introduces high overhead, and does not guarantee proper isolation of the monitoring logic from the observed program. Aspect-Oriented Programming (AOP), in particular AspectJ, is often used to implement runtime monitoring tools. While offering a convenient programming model, the use of such technologies acerbates performance-and isolation-related problems. In this paper, we advocate the use of our dynamic program analysis framework DiSL for runtime monitoring on the JVM. DiSL reconciles an AOP-based programming model, full coverage of all executed bytecodes, optimizations of the monitoring logic, and support for isolation of the monitoring logic. Moreover, DiSL also offers an API to deploy, adapt, and remove monitoring logic at runtime, and it provides seamless support for monitoring also applications running on Android devices.

关键词： Runtime monitoring Runtime adaptation Bytecode optimization Bytecode instrumentation Partial evaluation java virtual machine

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Security Enhanced java: Mandatory Access Control for the java virtual machine 16

Security Enhanced Java: Mandatory Access Control for the Jav...

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16th IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing (ISORC)

作者： Venelle, Benjamin Briffaut, Jeremy Clevy, Laurent Toinard, Christian Alcatel Lucent Bell Labs F-91620 Nozay France ENSI LIFO F-18020 Bourges France

ISBN: (纸本)9781479921133

Since 70's, and despite its operational complexity, Mandatory Access Control (MAC) has demonstrated its reliability to enforce integrity and confidentiality. Surprisingly, the java technology, despite its popularity, has not yet adopted this protection principle. Current security features within the JVM (JAAS and bytecode verifier) can be bypassed, as demonstrated by summer 2012 attacks. Thus, a MAC model for java and a cross platform reference monitor are required for the java virtual machine. Security Enhanced java (SEjava) enables to control dynamically the information flows between all the java objects requiring neither bytecode nor source code instrumentations. The main idea is to consider java types as security contexts, and method calls / field accesses as permissions. SEjava allows fine-grain MAC rules between the java objects. Thus, SEjava controls all the information flows within the JVM. Our implementation is faster than concurrent approaches while allowing both finer and more advanced controls. A use case shows the efficiency to protect against Common Vulnerability and Exposures in an efficient manner.

关键词： Mandatory Access Control java virtual machine Security Context Information Flow

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CONFUZZION: A java virtual machine Fuzzer for Type Confusion Vulnerabilities 21

CONFUZZION: A Java Virtual Machine Fuzzer for Type Confusion...

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21st IEEE International Conference on Software Quality, Reliability and Security (QRS)

作者： Bonnaventure, William Khanfir, Ahmed Bartel, Alexandre Papadakis, Mike Le Traon, Yves Univ Luxembourg SnT Luxembourg Luxembourg Umea Univ Dept Comp Sci Umea Sweden

ISBN: (纸本)9781665458139

Current java virtual machine (JVM) fuzzers aim at generating syntactically valid java programs, without targeting any particular use of the standard java library. While effective, such fuzzers fail to discover specific kinds of bugs or vulnerabilities, such as type confusion, that are related to the standard API usage. To deal with this issue, we introduce a mutation-based feedback-guided black-box JVM fuzzer, called CONFUZZION. CONFUZZION, as the name suggests, targets security-relevant object-oriented flaws with a particular focus on type confusion vulnerabilities. We show that in less than 4 hours, on commodity hardware and without any predefined initialization seed, CONFUZZION automatically generates java programs that reveal JVM vulnerabilities, i.e., the Common Vulnerabilities and Exposures CVE-2017-3272. We also show that state-of-the-art fuzzers or even traditional automatic testing techniques are not capable of detecting such faults, even after 48 hours of execution in the same environment. To the best of our knowledge, CONFUZZION is the first fuzzer able to detect JVM type confusion vulnerabilities.

关键词： Fuzzing vulnerability java virtual machine

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FJProf: Profiling Fork/Join Applications on the java virtual machine 20

FJProf: Profiling Fork/Join Applications on the Java Virtual...

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13th EAI International Conference on Performance Evaluation Methodologies and Tools (VALUETOOLS)

作者： Rosales, Eduardo Rosa, Andrea Binder, Walter Univ Svizzera Italiana Fac Informat Lugano Switzerland

ISBN: (纸本)9781450376464

Fork/join applications are divide-and-conquer algorithms recursively forking tasks that are executed in parallel, waiting for them to complete, and then typically joining their results. An efficient fork/join application maximizes parallelism while minimizing overheads, and maximizes locality while minimizing contention. However, there is no unique optimal implementation that best resolves such tradeoffs and failing in balancing them may lead to fork/join applications suffering from several issues (e.g., suboptimal forking, load imbalance, excessive synchronization), possibly compromising the benefits of task-parallel execution. Moreover, there is a lack of profilers enabling performance analysis of a fork/join application. As a result, developers are often required to implement their own tools to analyze fork/join applications, which could be time-consuming, error-prone, and is often beyond the expertise of the developer. In this paper, we present FJProf, a novel profiler which accurately collects runtime metrics to allow characterizing several performance attributes specific to a fork/join application running on a single java virtual machine (JVM) in a shared-memory multicore. FJProf reports information and graphics to developers that help them understand the details of any fork/join processing exposed by a parallel application running on the JVM. We show how FJProf supports performance analysis by characterizing a fork/join application from the Renaissance benchmark suite.

关键词： Performance analysis fork/join parallelism java virtual machine

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Profiling Streams on the java virtual machine 20

Profiling Streams on the Java Virtual Machine

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4th International Conference on the Art Science and Engineering of Programming

作者： Rosero, Edgar Eduardo Rosales Rosa, Andrea Binder, Walter Univ Svizzera italiana Fac Informat Lugano Switzerland

ISBN: (纸本)9781450375078

The *** framework is becoming a popular option among developers that target the java virtual machine (JVM) to implement map-reduce-like transformations on collections. A key feature of the streams framework is enabling parallelizing a computation as easy as calling a single method. Still, developers should test whether parallelizing a stream may results in performance, liveness or safety hazards. While such issues are mainly observable at runtime, there is a lack of tools capturing information that enable understanding the dynamic behavior of a stream application. In this extended abstract, we devise a profiler focused on characterizing dynamic attributes of a stream application running on a single JVM in a shared-memory multicore. Our tool aims at collecting runtime information and key metrics to support analysis of sequential and parallel stream processing, towards helping developers make better decisions to efficiently and safely use the streams framework.

关键词： Dynamic analysis streams performance analysis java virtual machine

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Constructing exception handling chains for testing java virtual machine implementations

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JOURNAL OF SOFTWARE-EVOLUTION AND PROCESS 2024年第4期36卷

作者： Chen, Bochuan Guo, Xiao Chen, Yuting Yu, Xiaofeng Bu, Lei Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China Nanjing Univ Dept Comp Sci & Technol Nanjing Peoples R China Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Nanjing Univ Sch Business Nanjing Peoples R China Nanjing Univ Software Inst Nanjing Peoples R China

The java virtual machine (JVM) is the cornerstone of the java platforms. A JVM's exception handling implementation interrupts, when the objective application encounters an exception (or an error), the normal execution of the application and performs specific handling tasks. However, little research has been done in systematically validating JVMs' exception handling implementations-test programs or even applications need to be carefully designed for throwing/catching exceptions at runtime;a JVM's exception handling implementation is also complicated, making it challenging to design tests for testing all of its functionalities. Inspired by the recent success of fuzz testing of compilers and JVM implementations, we introduce EHCBuilder, the first technique for fuzzing JVMs' exception handling implementations. The key idea is to construct exception handling chains, each of which abstracts a program's execution into a sequence of exception throwings, catchings, and/or handlings. A classfile seed can then be mutated into test programs with diverse exception handling chains, enabling (1) exceptions to be continuously thrown and caught at runtime, and (2) JVMs' exception handling implementations to be much more thoroughly tested. We have implemented EHCBuilder and evaluated EHCBuilder on popular JVM implementations including OpenJDK's HotSpot, Eclipse's OpenJ9, Azul's Zulu, and Oracle's GraalVM. Our results show that EHCBuilder can generate programs with very intricate exception handling chains and reveal differences among JVMs' exception handling implementations: Up to thousands of lines of source code in HotSpot's exception handling implementation are covered more than the original benchmarks;during 39 K iterations, EHCBuilder generates exception handling chains of different lengths, revealing 258 runtime differences. We classify the differences into four categories, and reveal a fast throw issue confirmed by HotSpot developers and another initCause issue confirmed by the

关键词： chain exception handling java virtual machine mutation

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Analysis and Optimization of Task Granularity on the java virtual machine

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ACM TRANSACTIONS ON PROGRAMMING LANGUAGES AND SYSTEMS 2019年第3期41卷 19-19页

作者： Rosa, Andrea Rosales, Eduardo Binder, Walter Univ Svizzera Italiana Via Giuseppe Buffi 13 CH-6900 Lugano Switzerland

Task granularity, i.e., the amount of work performed by parallel tasks, is a key performance attribute of parallel applications. On the one hand, fine-grained tasks (i.e., small tasks carrying out few computations) may introduce considerable parallelization overheads. On the other hand, coarse-grained tasks (i.e., large tasks performing substantial computations) may not fully utilize the available CPU cores, leading to missed parallelization opportunities. In this article, we provide a better understanding of task granularity for task-parallel applications running on a single java virtual machine in a shared-memory multicore. We present a new methodology to accurately and efficiently collect the granularity of each executed task, implemented in a novel profiler (available open-source) that collects carefully selected metrics from the whole system stack with low overhead, and helps developers locate performance and scalability problems. We analyze task granularity in the DaCapo, ScalaBench, and Spark Perf benchmark suites, revealing inefficiencies related to fine-grained and coarse-grained tasks in several applications. We demonstrate that the collected task-granularity profiles are actionable by optimizing task granularity in several applications, achieving speedups up to a factor of 5.90x. Our results highlight the importance of analyzing and optimizing task granularity on the java virtual machine.

关键词： Task granularity task parallelism performance analysis and optimization vertical profiling actionable profiles java virtual machine

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