A simple distributed garbage collector for distributed real-time Java

Val, Pablo Basanta; García-Valls, Marisol

doi:10.1007/s11227-014-1259-x

Cited by 7 publications

(6 citation statements)

References 35 publications

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“…A common way to detect a fault is to send a message periodically to the node. Basanta‐Val and García‐Valls used this strategy. However, none of the existing Java GPDC middleware systems consider Byzantine faults, in which a process not only fails to respond but also produces incorrect results due to a variety of reasons. Scalability: Scalability is crucial in the design of parallel and distributed systems because not all operations or applications require the same middleware improvements for GPDC.…”

Section: Gpdc Middleware Requirementsmentioning

confidence: 99%

A general‐purpose distributed computing Java middleware

Almeida

Cimino

Resende

et al. 2018

Concurrency and Computation

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Summary The middleware solutions for General‐Purpose Distributed Computing (GPDC) have distinct requirements, such as task scheduling, processing/storage fault tolerance, code portability for parallel or distributed environments, simple deployment (including over grid or multi‐cluster environments), collaborative development, low code refactoring, native support for distributed data structures, asynchronous task execution, and support for distributed global variables. These solutions do not integrate these requirements into a single deployment with a unique API exposing most of these requirements to users. The consequence is the utilization of several solutions with their particularities, thus requiring different user skills. Besides that, the users have to solve the integration and all heterogeneity issues. To reduce this integration gap, in this paper, we present Java Cá&Lá (JCL), a distributed‐shared‐memory and task‐oriented lightweight middleware for the Java community that separates business logic from distribution issues during the development process and incorporates several requirements that were presented separately in the GPDC middleware literature over the last few decades. JCL allows building distributed or parallel applications with only a few portable API calls, thus reducing the integration problems. Finally, it also runs on different platforms, including small single‐board computers. This work compares and contrasts JCL with other Java middleware systems and reports experimental evaluations of JCL applications in several distinct scenarios.

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Section: Gpdc Middleware Requirementsmentioning

confidence: 99%

A general‐purpose distributed computing Java middleware

Almeida

Cimino

Resende

et al. 2018

Concurrency and Computation

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“…This definition resembles the characterization of some real-time applications with input data that produce an output within a maximum deadline [41]. This type of analogy suggests that the models used in real-time computing may be merged with the model of stream processors to produce a more predictable computational model.…”

Section: Real-time Stream Modelmentioning

confidence: 99%

“…From the point of view of the real-time systems scheduling theory [41] periodic and sporadic patterns are simpler than aperiodic patterns to analyze.…”

Section: Stream Modelmentioning

confidence: 99%

“…of-the-art are mapped [41] to each different case to compute application response times. T i = D i is from the point of view of the different scheduling models the most beneficial situation with efficient online admission control tests [41].…”

Section: Stream Modelmentioning

confidence: 99%

“…T i = D i is from the point of view of the different scheduling models the most beneficial situation with efficient online admission control tests [41]. The last part of the model of the stream requires dealing with time taken for the execution of the acyclic graph and its structure.…”

Section: Stream Modelmentioning

confidence: 99%

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Improving the predictability of distributed stream processors

Val

Fernandez-Garcia

Wellings

et al. 2015

Future Generation Computer Systems

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h i g h l i g h t s• Model combining stream processing technology and real-time.• Extensions to the Storm processor.• Performance evaluation of the extension on a cluster. a b s t r a c t Next generation real-time applications demand big-data infrastructures to process huge and continuous data volumes under complex computational constraints. This type of application raises new issues on current big-data processing infrastructures. The first issue to be considered is that most of current infrastructures for big-data processing were defined for general purpose applications. Thus, they set aside real-time performance, which is in some cases an implicit requirement. A second important limitation is the lack of clear computational models that could be supported by current big-data frameworks. In an effort to reduce this gap, this article contributes along several lines. First, it provides a set of improvements to a computational model called distributed stream processing in order to formalize it as a real-time infrastructure. Second, it proposes some extensions to Storm, one of the most popular stream processors. These extensions are designed to gain an extra control over the resources used by the application in order to improve its predictability. Lastly, the article presents some empirical evidences on the performance that can be expected from this type of infrastructure.

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