You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Big Data applications suffer from unpredictable and unacceptably high pause times due to Garbage Collection (GC). This is the case in latency-sensitive applications such as on-line credit-card fraud detection, graph-based computing for analysis on social networks, etc. Such pauses compromise latency requirements of the whole application stack and result from applications' aggressive buffering caching of data, exposing an ill-suited GC design, which assumes that most objects will die young and does not consider that applications hold large amounts of middle-lived data in memory. To avoid such pauses, we propose NG2C, a new GC algorithm that combines pretenuring with an N-Generational heap. By being able to allocate objects into different generations, NG2C is able to group objects with similar lifetime profiles in the same generation. By allocating objects with similar lifetime profiles close to each other, i.e. in the same generation, we avoid object promotion (copying between generations) and heap fragmentation (which leads to heap compactions) both responsible for most of the duration of HotSpot GC pause times. NG2C is implemented for the OpenJDK 8 HotSpot Java Virtual Machine, as an extension of the Garbage First GC. We evaluate NG2C using Cassandra, Lucene, and GraphChi with three different GCs: Garbage First (G1), Concurrent Mark Sweep (CMS), and NG2C. Results show that NG2C decreases the worst observable GC pause time by up to 94.8 for Cassandra, 85.0 for Lucene and 96.45 for GraphChi, when compared to current collectors (G1 and CMS). In addition, NG2C has no negative impact on application throughput or memory usage.

Cited Abstract(s):
[1]: This paper presents our experience implementing the memory management extensions in the Real-Time Specification for Java. These extensions are designed to given real-time programmers the control they need to obtain predictable memory system behavior while preserving Java's safe memory model.We describe our implementation of certain dynamic checks required by the Real-Time Java extensions. In particular, we describe how to perform these checks in a way that avoids harmful interactions between the garbage collector and the memory management system. We also found that extensive debugging support was necessary during the development of Real-Time Java programs. We therefore used a static analysis and a dynamic debugging package during the development of our benchmark applications.

[2]: In-memory caching of intermediate data and eager combining of data in shuffle buffers have been shown to be very effective in minimizing the re-computation and I O cost in distributed data processing systems like Spark and Flink. However, it has also been widely reported that these techniques would create a large amount of long-living data objects in the heap, which may quickly saturate the garbage collector, especially when handling a large dataset, and hence would limit the scalability of the system. To eliminate this problem, we propose a lifetime-based memory management framework, which, by automatically analyzing the user-defined functions and data types, obtains the expected lifetime of the data objects, and then allocates and releases memory space accordingly to minimize the garbage collection overhead. In particular, we present Deca, a concrete implementation of our proposal on top of Spark, which transparently decomposes and groups objects with similar lifetimes into byte arrays and releases their space altogether when their lifetimes come to an end. An extensive experimental study using both synthetic and real datasets shows that, in comparing to Spark, Deca is able to 1) reduce the garbage collection time by up to 99.9 , 2) to achieve up to 22.7x speed up in terms of execution time in cases without data spilling and 41.6x speedup in cases with data spilling, and 3) to consume up to 46.6 less memory.

[3]: This paper considers the problem of providing safe programming support and enabling secure online software upgrades for control software in real-time control systems. In such systems, offline techniques for ensuring code safety are greatly preferable to online techniques. We propose a language called Control-C that is essentially a subset of C, but with key restrictions designed to ensure that memory safety of code can be verified entirely by static checking, under certain system assumptions. The language permits pointer-based data structures, restricted dynamic memory allocation, and restricted array operations, without requiring any runtime checks on memory operations and without garbage collection. The language restrictions have been chosen based on an understanding of both compiler technology and the needs of real-time control systems. The paper describes the language design and a compiler implementation for Control-C. We use control codes from three different experimental control systems to evaluate the suitability of the language for these codes, the effort required to port them to Control-C, and the effectiveness of the compiler in detecting a wide range of potential security violations for one of the systems.

[4]: 

[5]: Static memory management replaces runtime garbage collection with compile-time annotations that make all memory allocation and deallocation explicit in a program. We improve upon the Tofte Talpin region-based scheme for compile-time memory management[TT94]. In the Tofte Talpin approach, all values, including closures, are stored in regions. Region lifetimes coincide with lexical scope, thus forming a runtime stack of regions and eliminating the need for garbage collection. We relax the requirement that region lifetimes be lexical. Rather, regions are allocated late and deallocated as early as possible by explicit memory operations. The placement of allocation and deallocation annotations is determined by solving a system of constraints that expresses all possible annotations. Experiments show that our approach reduces memory requirements significantly, in some cases asymptotically.

[6]: The past decade has witnessed the increasing demands on data-driven business intelligence that led to the proliferation of data-intensive applications. A managed object-oriented programming language such as Java is often the developer's choice for implementing such applications, due to its quick development cycle and rich community resource. While the use of such languages makes programming easier, their automated memory management comes at a cost. When the managed runtime meets Big Data, this cost is significantly magnified and becomes a scalability-prohibiting bottleneck. This paper presents a novel compiler framework, called Facade, that can generate highly-efficient data manipulation code by automatically transforming the data path of an existing Big Data application. The key treatment is that in the generated code, the number of runtime heap objects created for data types in each thread is (almost) statically bounded, leading to significantly reduced memory management cost and improved scalability. We have implemented Facade and used it to transform 7 common applications on 3 real-world, already well-optimized Big Data frameworks: GraphChi, Hyracks, and GPS. Our experimental results are very positive: the generated programs have (1) achieved a 3 --48 execution time reduction and an up to 88X GC reduction; (2) consumed up to 50 less memory, and (3) scaled to much larger datasets.

[7]: A fast and scalable interprocedural escape analysis algorithm is presented.T he analysis computes a description of a subset of created objects whose lifetime is bounded by the lifetime of a runtime stack frame.T he analysis results can be used for many purposes, including stack allocation of objects, thread synchronization elimination, dead-store removal, code motion, and iterator reduction. A method to use the analysis results for transforming a program to allocate some objects on the runtime stack is also presented.F or non-trivial programs, typically 10 -20 of all allocated objects are placed on the runtime stack after the transformation.

[8]: Cyclone is a type-safe programming language derived from C. The primary design goal of Cyclone is to let programmers control data representation and memory management without sacrificing type-safety. In this paper, we focus on the region-based memory management of Cyclone and its static typing discipline. The design incorporates several advancements, including support for region subtyping and a coherent integration with stack allocation and a garbage collector. To support separate compilation, Cyclone requires programmers to write some explicit region annotations, but a combination of default annotations, local type inference, and a novel treatment of region effects reduces this burden. As a result, we integrate C idioms in a region-based framework. In our experience, porting legacy C to Cyclone has required altering about 8 of the code; of the changes, only 6 (of the 8 ) were region annotations.

[9]: The goal of the Cyclone project is to investigate type safety for low-level languages such as C. Our most difficult challenge has been providing programmers control over memory management while retaining type safety. This paper reports on our experience trying to integrate and effectively use two previously proposed, type-safe memory management mechanisms: statically-scoped regions and unique pointers. We found that these typing mechanisms can be combined to build alternative memory-management abstractions, such as reference counted objects and arenas with dynamic lifetimes, and thus provide a flexible basis. Our experience---porting C programs and building new applications for resource-constrained systems---confirms that experts can use these features to improve memory footprint and sometimes to improve throughput when used instead of, or in combination with, conservative garbage collection.

[10]: Region-based memory management systems structure memory by grouping objects in regions under program control. Memory is reclaimed by deleting regions, freeing all objects stored therein. Our compiler for C with regions, RC, prevents unsafe region deletions by keeping a count of references to each region. Using type annotations that make the structure of a program's regions more explicit, we reduce the overhead of reference counting from a maximum of 27 to a maximum of 11 on a suite of realistic benchmarks. We generalise these annotations in a region type system whose main novelty is the use of existentially quantified abstract regions to represent pointers to objects whose region is partially or totally unknown. A distribution of RC is available at http: www.cs.berkeley.edu dgay rc.tar.gz.

[11]: This paper describes a memory discipline that combines region-based memory management and copying garbage collection by extending Cheney's copying garbage collection algorithm to work with regions. The paper presents empirical evidence that region inference very significantly reduces the number of garbage collections; and evidence that the fastest execution is obtained by using regions alone, without garbage collection. The memory discipline is implemented for Standard ML in the ML Kit compiler and measurements show that for a variety of benchmark programs, code generated by the compiler is as efficient, both with respect to execution time and memory usage, as programs compiled with Standard ML of New Jersey, another state-of-the-art Standard ML compiler.

[12]: The Real Time Specification for Java (RTSJ) allows a program to create real-time threads with hard real-time constraints. Real-time threads use region-based memory management to avoid unbounded pauses caused by interference from the garbage collector. The RTSJ uses runtime checks to ensure that deleting a region does not create dangling references and that real-time threads do not access references to objects allocated in the garbage-collected heap. This paper presents a static type system that guarantees that these runtime checks will never fail for well-typed programs. Our type system therefore 1) provides an important safety guarantee for real-time programs and 2) makes it possible to eliminate the runtime checks and their associated overhead.Our system also makes several contributions over previous work on region types. For object-oriented programs, it combines the benefits of region types and ownership types in a unified type system framework. For multithreaded programs, it allows long-lived threads to share objects without using the heap and without memory leaks. For real-time programs, it ensures that real-time threads do not interfere with the garbage collector. Our experience indicates that our type system is sufficiently expressive and requires little programming overhead, and that eliminating the RTSJ runtime checks using a static type system can significantly decrease the execution time of real-time programs.

[13]: Many popular systems for processing "big data" are implemented in high-level programming languages with automatic memory management via garbage collection (GC). However, high object churn and large heap sizes put severe strain on the garbage collector. As a result, applications underperform significantly: GC increases the runtime of typical data processing tasks by up to 40 . We propose to use region-based memory management instead of GC in distributed data processing systems. In these systems, many objects have clearly defined lifetimes. Hence, it is natural to allocate these objects in fate-sharing regions, obviating the need to scan a large heap. Regions can be memory-safe and could be inferred automatically. Our initial results show that region-based memory management reduces emulated Naiad vertex runtime by 34 for typical data analytics jobs.

[14]: Most "Big Data" systems are written in managed languages, such as Java, C#, or Scala. These systems suffer from severe memory problems due to the massive volume of objects created to process input data. Allocating and deallocating a sea of data objects puts a severe strain on existing garbage collectors (GC), leading to high memory management overheads and reduced performance. This paper describes the design and implementation of Yak, a "Big Data" friendly garbage collector that provides high throughput and low latency for all JVM-based languages. Yak divides the managed heap into a control space (CS) and a data space (DS), based on the observation that a typical data-intensive system has a clear distinction between a control path and a data path. Objects created in the control path are allocated in the CS and subject to regular tracing GC. The lifetimes of objects in the data path often align with epochs creating them. They are thus allocated in the DS and subject to region-based memory management. Our evaluation with three large systems shows very positive results.
* Region-based Garbage Collection The hypothesis that many objects, allocated in the same scope, share the same faith, i.e., have similar lifetimes has also been leveraged by many region-based memory management algorithms [6] [3]6 [3] [9] [3]9 [8] [10] [4] [12] [1] [5] [7] . In such algorithms, objects with similar lifetime profiles are allocated in scope-based regions, which are deallocated as a whole when objects inside these regions are no longer reachable. However, existing region-based algorithms either require sophisticated static analysis [5] [12] [1] [7] [10] [8] (which does not scale to large systems), heavily rely on manual code refactoring [6] [3]6 (to guarantee that objects in the same region die approximately at the same time), or support only simple programming models [2] [6] [14] (such as parallel bag of tasks).