Tuple Spaces Implementations and Their Efficiency

Buravlev, Vitaly; Nicola, Rocco De; Mezzina, Claudio Antares

doi:10.1007/978-3-319-39519-7_4

Cited by 7 publications

(5 citation statements)

References 20 publications

(34 reference statements)

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“…Moreover, the modified version allows faster execution of the tasks where many inter‐process communications are considered. It is worth noting that the plots and results concerning GIGASPACES , TUPLEWARE , and KLAIM are pretty similar to those presented in a previous version of our paper, even though the ones presented in this paper were conducted on a much more powerful machine.…”

Section: Discussionmentioning

confidence: 99%

“…An example of sorting is shown in Figure 3. and notifies all workers to terminate their work (lines [14][15]. A worker takes an unsorted array from the master (line 19), sorts it, and sends the sorted array to the master (lines [21][22].…”

Section: Introducing Case Studiesmentioning

confidence: 99%

“…Section 4 concludes the paper by collecting some remarks and highlighting directions for future work. This paper is a revised and extended version of Buravlev et al; it contains an additional case study, the thorough evaluation of a new tuple space system, and more extensive experiments on a much more powerful machine.…”

Section: Introductionmentioning

confidence: 99%

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Evaluating the efficiency of Linda implementations

Buravlev

Nicola

Mezzina

2017

Concurrency and Computation

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Summary Among the paradigms for parallel and distributed computing, the one popularized with Linda, and based on tuple spaces, is one of the least used, despite the fact of being intuitive, easy to understand, and easy to use. A tuple space is a repository, where processes can add, withdraw, or read tuples by means of atomic operations. Tuples may contain different values, and processes can inspect their content via pattern matching. The lack of a reference implementation for this paradigm has prevented its wide spreading. In this paper, first we perform an extensive analysis of a number of actual implementations of the tuple space paradigm and summarize their main features. Then, we select four such implementations and compare their performances on four different case studies that aim at stressing different aspects of computing, such as communication, data manipulation, and CPU usage. After reasoning on strengths and weaknesses of the four implementations, we conclude with some recommendations for future work towards building an effective implementation of the tuple space paradigm.

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Section: Discussionmentioning

confidence: 99%

Section: Introducing Case Studiesmentioning

confidence: 99%

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Evaluating the efficiency of Linda implementations

Buravlev

Nicola

Mezzina

2017

Concurrency and Computation

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“…Gigaspaces is a particularly simple and efficient implementation of JavaSpaces technology. Specifically, the authors of [65] find that Gigaspaces programs exhibit less inter-compute node communication latency than do JavaSpaces programs executed within other runtime systems. The primary operations on a Gigspaces space are , , , , and [66], [67].…”

Section: Methodsmentioning

confidence: 99%

Developing political-ecological theory: The need for Many-Task Computing

Haas

2019

Preprint

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8Models of political-ecological systems can inform policies for managing ecosystems that 9 contain endangered species. One way to increase the credibility of these models is to 10 subject them to a rigorous suite of data-based statistical assessments. Doing so involves 11 statistically estimating the model's parameters, computing confidence intervals for these 12 parameters, determining the model's prediction error rate, and assessing its sensitivity to 13 parameter misspecification. 14 Here, these statistical algorithms along with a method for constructing politically fea-15 sible policies from a statistically fitted model, are coded as JavaSpaces TM programs that 16 run as compute jobs on either supercomputers or a collection of in-house workstations. 17 Several new algorithms for implementing such jobs in distributed computing environments 18 are described. 19 This downloadable code is used to compute each job's output for the management 20 challenge of conserving the East African cheetah (Acinonyx jubatus). This case study shows 21 that the proposed suite of statistical tools can be run on a supercomputer to establish the 22 credibility of a managerially-relevant model of a political-ecological system that contains 23 one or more endangered species. This demonstration means that the new standard of 24 credibility that any political-ecological model needs to meet before being used to inform 25 ecosystem management decisions, is the one given herein. 26 1 27 Keywords: social-ecological systems; ecosystem policymaking; agent-based modeling; 28 high performance computing; sensitivity analysis; statistical estimation of large models 29 1 Introduction 30There is a need to acknowledge the complexity of political-ecological systems and the signif-31 icant challenges to building theories of them [1]. Such systems lie at the interface between 32 social/political science and ecology. The complexity of each of these fields coupled with an 33 additional layer of complexity introduced by the interactions between sociological/political 34 systems and natural systems can result in highly complex system dynamics, i.e., ones that 35 are stiff, nonlinear, and possess feedback loops. For example, Schoon and Van der Leeuw [2] 36 note that systems composed of interacting sociological and ecological subsystems are quick 37 to change and rarely stay in equilibrium for long. Further, many state variables are needed 38 to describe both the decision making processes of the relevant social groups, and the func-39 tioning of the involved ecosystem. A political-ecological system is also referred to as a 40 socio-ecological system or social-ecological system (e.g., see [3]). The former term is em-41 phasized herein because those political actions and processes that drive social movements 42 are often initiated by groups seeking to gain increased political power [4]. Building such 43 models is more than an academic exercise. Indeed, the alarming decline in the planet's 44 biodiversity [5], creates a crucial need for credible politi...

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“…There is no need to agree in advance on the structure of tuples. Tuples 21 representing different kinds of information are distinguished based on a field. To access a tuple from the tuple space, templates are used which is called antituple.…”

Section: Introductionmentioning

confidence: 99%

A Fault‐tolerant model for tuple space coordination in distributed environments

Kirti,

Maurya,

Yadav

2023

Concurrency and Computation

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SummaryIn distributed systems, tuple space is one of the coordination models that significantly maximizes system performance against failure due to its space and time decoupling features. With the growing popularity of distributed computing and increasing complexity in the network, host and link failure occurs frequently, resulting in poor system performance. This article proposes a fault‐tolerant model named Tuple Space Replication (TSR) for tuple space coordination in distributed environments. The model introduces a multi‐agent system that consists of multiple hosts. Each host in a multi‐agent system comprises an agent space with a tuple space for coordination. In this model, we introduce three novel fault‐tolerant algorithms for tuple space primitives to provide coordination among hosts with tolerance to multiple links and hosts failure. The first algorithm is given for out() operation to insert tuples in the tuple space. The second algorithm is presented for rdp() operation to read any tuple from the tuple space. The third algorithm is given for inp() operation to delete or withdraw tuples from the tuple space. These algorithms use less number of messages to ensure consistency in the system. The message complexity of the proposed algorithms is analyzed and found O(n) for out(), O(1) for rdp(), and O(n) for inp() operations which is comparable and better than existing works, where n is the number of hosts. The testbed experiment reveals that the proposed TSR model gives performance improvement up to 88%, 70.94%, and 63.80% for out(), rdp(), and inp() operations compared to existing models such as FT‐SHE, LBTS, DEPSPACE, and E‐DEPSPACE.

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Tuple Spaces Implementations and Their Efficiency

Cited by 7 publications

References 20 publications

Evaluating the efficiency of Linda implementations

Evaluating the efficiency of Linda implementations

Developing political-ecological theory: The need for Many-Task Computing

A Fault‐tolerant model for tuple space coordination in distributed environments

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