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Transformational advances in environmental sensing have been made possible by the convergence of Cloud Computing and the Internet of Things (IoT). The potential for these technologies is to work together and improve environmental monitoring data analytics. Integrating IoT in the cloud creates a robust environment for advanced analytics by overcoming previous hurdles in data granularity, real-time monitoring, and geographical coverage. These developments aren't without their own set of difficulties, however, including data security, interoperability, and ethical concerns. This paper explores integrating Cloud IoT with environmental sensing. The paper addresses these issues and emphasises the need to establish ethical data-gathering methods, implement standardised communication protocols, and address privacy concerns. The study concludes by examining the challenges, concerns, and potential of integrating Cloud IoT. The integration of Cloud Computing and IoT not only transforms environmental sensing but also provides a solid foundation for collaborative research, data-driven decision-making, and environmentally conscious management. [ABSTRACT FROM AUTHOR]

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 OpenJ9 community. [ABSTRACT FROM AUTHOR]

Innovative solutions are necessary for efficient monitoring and mitigation techniques as climate change increases the frequency and severity of natural catastrophes. In this study, we investigate how Cloud Internet of Things (IoT) might help mitigate climate-related calamities. Through the use of cloud computing and sensor networks, Cloud IoT facilitates the capture, processing, and distribution of data in near-real time. This strategy promotes catastrophe planning, response, and recovery by offering early warnings, predictive insights, and simplified communication. The article emphasises the cumulative influence of Cloud IoT components such as sensor networks, data analytics, decision support systems, and remote control in the context of disaster management. Cloud IoT is useful in real-world scenarios, such as the tracking of floods in Bangladesh and the identification of wildfires in California. These cases show how the technology may prevent injuries and preserve property through early warnings and well-coordinated responses. Despite its potential, it faces obstacles including ensuring the safety of data and dealing with issues related to the necessary infrastructure. In conclusion, including Cloud IoT in disaster management provides a cost-effective, scalable, and efficient solution, greatly contributing to constructing resilient communities and creating a sustainable future in the face of climate-induced natural catastrophes. [ABSTRACT FROM AUTHOR]

Java streams are receiving the attention of developers targeting the Java virtual machine (JVM) as they ease the development of data‐processing logic, while also favoring code extensibility and maintainability through a concise and declarative style based on functional programming. Recent studies aim to shedding light on how Java developers use streams. However, they consider only small sets of applications and mainly apply manual code inspection and static analysis techniques. As a result, the large‐scale dynamic analysis of stream processing remains an open research question. In this article, we present the first large‐scale empirical study on the use of streams in Java code exercised via unit tests. We present stream‐analyzer, a novel dynamic program analysis (DPA) that collects runtime information and key metrics, which enable a fine‐grained characterization of sequential and parallel stream processing. We use a fully automatic approach to massively apply our DPA for the analysis of open‐source software projects hosted on GitHub. Our findings advance the understanding of the use of Java streams. Both the scale of our analysis and the profiling of dynamic information enable us to confirm with more confidence the outcome highlighted at a smaller scale by related work. Moreover, our study reports the popularity of many features of the Stream API and highlights multiple findings about runtime characteristics unique to streams, while also revealing inefficient stream processing and stream misuses. Finally, we present implications of our findings for developers of the Stream API, tool builders and researchers, and educators. [ABSTRACT FROM AUTHOR]

Considering the large number of optimisation techniques that have been integrated into the design of the Java Virtual Machine (JVM) over the last three decades, the Java interpreter continues to persist as a significant bottleneck in the performance of bytecode execution. This paper examines the relationship between Java Runtime Environment (JRE) performance concerning the interpreted execution of Java bytecode and the effect modern compiler selection and integration within the JRE build toolchain has on that performance. We undertook this evaluation relative to a contemporary benchmark suite of application workloads, the Renaissance Benchmark Suite. Our results show that the choice of GNU GCC compiler version used within the JRE build toolchain statistically significantly affects runtime performance. More importantly, not all OpenJDK releases and JRE JVM interpreters are equal. Our results show that OpenJDK JVM interpreter performance is associated with benchmark workload. In addition, in some cases, rolling back to an earlier OpenJDK version and using a more recent GNU GCC compiler within the build toolchain of the JRE can significantly positively impact JRE performance. [ABSTRACT FROM AUTHOR]

A *memory consistency model* (or simply *memory model*) defines the possible values that a shared-memory read may return in a multithreaded programming language. Choosing a memory model involves an inherent performance-programmability tradeoff. The Java language has adopted a *relaxed* (or *weak*) memory model that is designed to admit most traditional compiler optimizations and obviate the need for hardware fences on most shared-memory accesses. The downside, however, is that programmers are exposed to a complex and unintuitive semantics and must carefully declare certain variables as `volatile` in order to enforce program orderings that are necessary for proper behavior. This paper proposes a simpler and stronger memory model for Java through a conceptually small change: *every* variable has `volatile` semantics by default, but the language allows a programmer to tag certain variables, methods, or classes as `relaxed` and provides the current Java semantics for these portions of code. This *volatile-by-default* semantics provides *sequential consistency* (SC) for all programs by default. At the same time, expert programmers retain the freedom to build performance-critical libraries that violate the SC semantics. At the outset, it is unclear if the `volatile`-by-default semantics is practical for Java, given the cost of memory fences on today's hardware platforms. The core contribution of this paper is to demonstrate, through comprehensive empirical evaluation, that the `volatile`-by-default semantics is arguably acceptable for a predominant use case for Java today -- server-side applications running on Intel x86 architectures. We present VBD-HotSpot, a modification to Oracle's widely used HotSpot JVM that implements the `volatile`-by-default semantics for x86. To our knowledge VBD-HotSpot is the first implementation of SC for Java in the context of a modern JVM. VBD-HotSpot incurs an average overhead versus the baseline HotSpot JVM of 28% for the Da Capo benchmarks, which is significant though perhaps less than commonly assumed. Further, VBD-HotSpot incurs average overheads of 12% and 19% respectively on standard benchmark suites for big-data analytics and machine learning in the widely used Spark framework.

In recent times, the subject of interoperability has become very popular. In large-scale software applications development, it is a common practice to combine multiple languages in solving peculiar problems and developing robust solutions. The ability to combine multiple languages allows an easy migration of an existing project from one language to another or use existing libraries in another language. This makes interoperability a force to be reckoned with when developing new programming languages. The Eolang programming language is a new research and development initiative aimed at achieving true Object-Oriented Programming by having all components of the program as objects. As such, the construct and syntax of Eolang is vastly different from that of Java. This makes integration and interoperability between these two languages a challenging issue related to method/object naming conventions, keywords and operators, etc. In this paper we explore the potential of Eolang interoperability with Java by looking at the interoperability mechanisms of some other languages with Java, describe ways to overcome these challenges with Eolang and develop the solution. Specifically, we focus on the possibility to call Java code from Eolang while the semantics of both languages remain preserved. Our solution allows Java code to be called in Eolang through wrappers that turn Java classes and methods into Eolang Objects. [ABSTRACT FROM AUTHOR]

Considering the large number of optimisation techniques that have been integrated into the design of the Java Virtual Machine (JVM) over the last three decades, the Java interpreter continues to persist as a significant bottleneck in the performance of bytecode execution. This paper examines the relationship between Java Runtime Environment (JRE) performance concerning the interpreted execution of Java bytecode and the effect modern compiler selection and integration within the JRE build toolchain has on that performance. We undertook this evaluation relative to a contemporary benchmark suite of application workloads, the Renaissance Benchmark Suite. Our results show that the choice of GNU GCC compiler version used within the JRE build toolchain statistically significantly affects runtime performance. More importantly, not all OpenJDK releases and JRE JVM interpreters are equal. Our results show that OpenJDK JVM interpreter performance is associated with benchmark workload. In addition, in some cases, rolling back to an earlier OpenJDK version and using a more recent GNU GCC compiler within the build toolchain of the JRE can significantly positively impact JRE performance.

While many cluster and grid computing frameworks are available, the task of building secure distributed systems or implementing distributed algorithms continue to be a challenging task due to the inherent distributed nature of such systems with multiple failure modes and security issues. In this paper, we present the design and development of remote method delegation (RMD), which is a secure lightweight grid computing platform with load balancing and code migration. RMD is focused on improving the usability issues that plague related industry solutions. The platform is implemented on the JVM (Java Virtual Machine) and supports the Java and Kotlin programming languages, however, the platform should theoretically work with other JVM languages. RMD was designed to simplify the implementation of distributed algorithms by providing a Kotlin DSL (domain specific language) that allows the programmer to define jobs within dedicated code blocks. Users from around the world can donate their own computing resources by hosting their own job server. RMD is secured by placing all untrusted code within a sandbox environment that prevents potentially malicious actions from taking place. To demonstrate the feasibility of the proposed model, a proof of concept implementation has been constructed with real examples demonstrating the usefulness of the proposed solution. [ABSTRACT FROM AUTHOR]

In programming languages with automatic memory management garbage collection plays an important role of cleaning unused memory. Garbage collection algorithms have been developed for many years and aim to maximize the application’s performance. This paper presents and compares a performance of five garbage collection algorithms present in current version of Java 12 in three applications with different object lifetime span. The analysis covered the system responsiveness, garbage collector workload and application throughput at high application load.

JShellLab is an easy to use MATLAB-like environment for the Java Virtual Machine (JVM). It implements scientific scripting based on the JShell Application Programming Interface (API) of modern Java. The paper illustrates that JShellLab can significantly facilitate and simplify the development of complex computational demanding scientific software at the JVM. The novelty at the JShellLab is that it completely hides the complexity and the intricate dependencies of optimized scientific software. As an example, the demanded field of deep learning is exploited. Specifically, the implementation of effective practical deep learning-based systems using the JShellLab environment and the Deeplearning4j Java library is considered. [ABSTRACT FROM AUTHOR]

Summary: Current computer systems separate main memory from storage, and programming languages typically reflect this distinction using different representations for data in memory and storage. However, moving data back and forth between these different layers and representations compromise both programming and execution efficiency. To remedy this, the concept of orthogonal persistence (OP) was proposed in the early 1980s advocating that, from a programmer's standpoint, there should be no differences in the way that short‐term and long‐term data are manipulated. However, at that time, the underlying implementations still had to cope with the complexity of moving data across memory and storage. Today, recent nonvolatile memory (NVM) technologies, such as resistive RAM and phase‐change memory, allow main memory and storage to be collapsed into a single layer of persistent memory, opening the way for more efficient programming abstractions for handling persistence. In this work, we revisit OP concepts in the context of NVM architectures and propose a persistent heap design for languages with automatic memory management. We demonstrate how it can significantly increase programmer and execution efficiency, removing the impedance mismatch of crossing semantic boundaries. To validate and demonstrate the presented concepts, we present JaphaVM, an implementation of the proposed design based on JamVM, an open‐source Java Virtual Machine. Our results show that JaphaVM, in most cases, executes the same operations between one and two orders of magnitude faster than regular database‐based and file‐based implementations, while requiring significantly less lines of code. [ABSTRACT FROM AUTHOR]