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]: We present an implementation of the Train Algorithm, an incremental collection scheme for reclamation of mature garbage in generation-based memory management systems. To the best of our knowledge, this is the first Train Algorithm implementation ever. Using the algorithm, the traditional mark-sweep garbage collector employed by the Mjolner run-time system for the object-oriented BETA programming language was replaced by a non-disruptive one, with only negligible time and storage overheads.

[2]: 

[3]: We present the design and implementation of a new garbage collection framework that significantly generalizes existing copying collectors. The Beltway framework exploits and separates object age and incrementality. It groups objects in one or more increments on queues called belts, collects belts independently, and collects increments on a belt in first-in-first-out order. We show that Beltway configurations, selected by command line options, act and perform the same as semi-space, generational, and older-first collectors, and encompass all previous copying collectors of which we are aware. The increasing reliance on garbage collected languages such as Java requires that the collector perform well. We show that the generality of Beltway enables us to design and implement new collectors that are robust to variations in heap size and improve total execution time over the best generational copying collectors of which we are aware by up to 40 , and on average by 5 to 10 , for small to moderate heap sizes. New garbage collection algorithms are rare, and yet we define not just one, but a new family of collectors that subsumes previous work. This generality enables us to explore a larger design space and build better collectors.

[4]: A new garbage collection algorithm for distributed object systems, called DMOS (Distributed. Mature Object Space), is presented. It is derived from two previous algorithms, MOS (Mature Object Space), sometimes called the train algorithm, and PMOS (Persistent Mature Object Space). The contribution of DMOS is that it provides the following unique combination of properties for a distributed collector: safety, completeness, non-disruptiveness, incrementality, and scalability. Furthermore, the DMOS collector is non-blocking and does not use global tracing.

[5]: We present a parallel generational-copying garbage collector implemented for the Glasgow Haskell Compiler. We use a block-structured memory allocator, which provides a natural granularity for dividing the work of GC between many threads, leading to a simple yet effective method for parallelising copying GC. The results are encouraging: we demonstrate wall-clock speedups of on average a factor of 2 in GC time on a commodity 4-core machine with no programmer intervention, compared to our best sequential GC.

[6]: Many interactive computing environments provide automatic storage reclamation and virtual memory to ease the burden of managing storage. Unfortunately, many storage reclamation algorithms impede interaction with distracting pauses. Generation Scavenging is a reclamation algorithm that has no noticeable pauses, eliminates page faults for transient objects, compacts objects without resorting to indirection, and reclaims circular structures, in one third the time of traditional approaches. We have incorporated Generation Scavenging in Berkeley Smalltalk(BS), our Smalltalk-80 implementation, and instrumented it to obtain performance data. We are also designing a microprocessor with hardware support for Generation Scavenging .

[7]: In previous heap storage systems, the cost of creating objects and garbage collection is independent of the lifetime of the object. Since objects with short lifetimes account for a large portion of storage use, it is worth optimizing a garbage collector to reclaim storage for these objects more quickly. The garbage collector should spend proportionately less effort reclaiming objects with longer lifetimes. We present a garbage collection algorithm that (1) makes storage for short-lived objects cheaper than storage for long-lived objects, (2) that operates in real time—object creation and access times are bounded, (3) increases locality of reference, for better virtual memory performance, (4) works well with multiple processors and a large address space.
* Generational Collectors Segregating objects by age has been studied for a long time [2] as a way to take advantage of the weak generational hypothesis [6] . By promoting objects that survive a number of collections into older generations, the collector can concentrate on collecting younger generations more often (since these are more likely to contain more dead objects) [7] . The use of multiple generations (compared to using a single generation) has been shown to reduce application pauses [1] [4] [3] [5] .