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This systematic review offers a comprehensive analysis of scheduling algorithms designed for scientific workflows, particularly those handling Big Data. By examining research published between 2010 and 2023, we aim to provide a structured overview of the field, highlighting key trends and identifying existing gaps. This review will be valuable to researchers and practitioners in computer science who are involved in developing workflow management systems, deploying, and managing complex computational workflows that process Big Data across various scientific domains, including astronomy, bioinformatics and genomics, climate science, earth and environmental sciences, high-energy physics, and social sciences. These workflows are frequently executed on High-performance, Cloud, Fog, Grid, IoT, and hybrid computing systems. We focus on scheduling algorithms tailored for batch and stream processing workflows, cataloging them based on their optimization objectives (e.g., monetary cost, makespan, resource efficiency, energy consumption), optimization techniques (e.g., heuristics, meta-heuristics, machine learning), and assessment criteria (e.g., workflow complexity, computing environment scale, dataset size). Our analysis reveals that cloud computing remains the dominant environment for scientific workflow scheduling, but significant gaps persist in addressing stream processing workflows, resource efficiency, energy consumption, data movement, and applications in High-performance computing, IoT, and Hybrid systems. Moreover, the potential of machine learning techniques for scheduling algorithms remains largely untapped. By providing a comprehensive catalog of existing scheduling algorithms, this review aims to support future research and practical implementations in the field of scientific workflow scheduling.

The widespread adoption of cloud computing necessitates privacy-preserving techniques that allow information to be processed without disclosure. This paper proposes a method to increase the accuracy and performance of privacy-preserving Convolutional Neural Networks with Homomorphic Encryption (CNN-HE) by Self-Learning Activation Functions (SLAF). SLAFs are polynomials with trainable coefficients updated during training, together with synaptic weights, for each polynomial independently to learn task-specific and CNN-specific features. We theoretically prove its feasibility to approximate any continuous activation function to the desired error as a function of the SLAF degree. Two CNN-HE models are proposed: CNN-HE-SLAF and CNN-HE-SLAF-R. In the first model, all activation functions are replaced by SLAFs, and CNN is trained to find weights and coefficients. In the second one, CNN is trained with the original activation, then weights are fixed, activation is substituted by SLAF, and CNN is shortly re-trained to adapt SLAF coefficients. We show that such self-learning can achieve the same accuracy 99.38% as a non-polynomial ReLU over non-homomorphic CNNs and lead to an increase in accuracy (99.21%) and higher performance (6.26 times faster) than the state-of-the-art CNN-HE CryptoNets on the MNIST optical character recognition benchmark dataset.

Improving the characteristics of digital signal processing devices is an important task in many practical problems. The paper proposes the architecture of a two-dimensional digital filter with a $5\times 5$ mask, in which calculations are performed according to the Winograd method in the Residue Number System (RNS) with moduli of a special type. Theoretical analysis and hardware Field-Programmable Gate Array simulation are presented. The results show that the fragment throughput fr/s (number of fragments per second) of the device is 29.6%– 724.7% higher than state-of-the-art solutions. This is achieved by the combination of the Winograd method, which reduces the number of multiplications, with the RNS arithmetic, which performs addition and multiplication under smaller operands in parallel. However, our experiments showed that the proposed method requires up to 2.54%– 11.01% more Look-Up Tables and 3.58%– 19.83% higher power consumption compared to known analogues.

The security issues that arise in public cloud environments raise several concerns about privacy-preserving. Conventional security practices successfully protect stored and transmitted data by encryption, but not during data processing where the data value extraction requires decryption. It creates critical exposure points for sensitive sectors like healthcare, pharmaceutical, genomics, government, and financial, among many others that cause hesitation to use these third-party services and prevent widespread practical adoption of cloud solutions. Homomorphic Encryption (HE) emerges as a mechanism for expanding the scope of public cloud services for sensitive data processing. However, high-demand solutions such as artificial intelligence and machine learning require efficient operations beyond HE additions and multiplications. In this paper, we analyze the current homomorphic comparison methods across their strengths and weaknesses and present theoretical concepts, state-of-the-art techniques, challenges, opportunities, and open problems. We theoretically prove the limits of the representability of sign and comparison functions in polynomial forms for HE schemes. We show that both functions can be represented as polynomials over the Galois field and cannot be represented over a residue ring with zero divisors. We compare the efficiency, accuracy, and computational complexity of different homomorphic comparison approaches. The experimental results demonstrate that Newton-Raphson is the fastest method for generating polynomial approximations and evaluating comparisons, and the Fourier method is the most accurate considering the $L_{1}$ , $L_{2}$ , $L_{\infty }$ norms and $R^{2}$ measure. The bi-objective analysis presents the performance compromise between complexity and accuracy.

Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy infrastructure research needs multi-variant planning and tools to allocate and dispatch distributed computing resources that pool together to let applications share data, taking into account the subject domain specificity, resource characteristics, and quotas for resource use. To that end, we propose an approach to implement HPC-based resilience analysis using our Orlando Tools (OT) framework. To dynamically scale computing resources, we provide their integration with the relevant software, identifying key application parameters that can have a significant impact on the amount of data processed and the amount of resources required. We automate the startup of the IMDG cluster to execute workflows. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our solution allows us to investigate large infrastructures by modeling multiple simultaneous failures of different types of elements down to the number of network elements. In terms of task and resource utilization efficiency, we achieve almost linear speedup as the number of nodes of each resource increases.

Urban mobility and strategies for public transportation play a vital role in promoting a more sustainable, accessible, and livable urban environment in smart cities. Data-driven decision making, multi-modal integration, and innovative means are key elements in developing effective public transportation. The COVID-19 pandemic had a significant impact on public transportation worldwide, including decreased travel, health and security concerns, and a shift in travel patterns. In particular, the use of public transportation reduced by up to 90% in developed countries. This article studies the mobility demands and patterns related to public transportation in Montevideo, Uruguay, amidst the COVID-19 pandemic. The study follows an urban data analysis approach, using open data from various sources, including citizen mobility, the public transportation system, COVID-19 case records, and economic indicators. The urban data methodology allows the deriving of significant findings, encompassing the assessment of trip reductions in Montevideo in March 2020 (71.4%, lower than similar cities in the world), the correlation between trip numbers and COVID-19 cases during the different pandemic waves, the recovery of public transportation usage, and the correlation with socio-economic indicators. These results offer valuable insights for quantifying and comprehending the behavior of citizens concerning public transportation throughout the COVID-19 pandemic, providing valuable understandings for policymakers and researchers to elaborate mobility strategies and policies.

A smart city has a complex hierarchical communication system with various components. It must meet the requirements of fast connection, reliability, and security without data compromise. Internet of Things technology is widely used to provide connectivity and control solutions for smart sensors and other devices using heterogeneous networking technologies. In this paper, we propose a routing solution for Wireless Sensor Networks (WSN) and Mobile Ad hoc NETworks (MANET) with increasing speed, reliability, and sufficient security. Many routing protocols have been proposed for WSNs and MANETs. We combine the Secret Sharing Schemes (SSS) and Redundant Residual Number Systems (RRNS) to provide an efficient mechanism for a Distributed dynamic heterogeneous network Transmission (DT) with new security and reliability routing protocol (DT-RRNS). We analyze the concept of data transmission based on RRNS that divides data into smaller encoded shares and transmits them in parallel, protecting them from attacks on routes by adaptive multipath secured transmission and providing self-correcting properties that improve the reliability and fault tolerance of the entire system.

Homomorphic encryption (HE) is a promising solution for handling sensitive data in semi-trusted third-party computing environments, as it enables processing of encrypted data. However, applying sophisticated techniques such as machine learning, statistics, and image processing to encrypted data remains a challenge. The computational complexity of some encrypted operations can significantly increase processing time. In this paper, we focus on the analysis of two state-of-the-art HE matrix multiplication algorithms with the best time and space complexities. We show how their performance depends on the libraries and the execution context, considering the standard Cheon–Kim–Kim–Song (CKKS) HE scheme with fixed-point numbers based on the Microsoft SEAL and PALISADE libraries. We show that Windows OS for the SEAL library and Linux OS for the PALISADE library are the best options. In general, PALISADE-Linux outperforms PALISADE-Windows, SEAL-Linux, and SEAL-Windows by 1.28, 1.59, and 1.67 times on average for different matrix sizes, respectively. We derive high-precision extrapolation formulas to estimate the processing time of HE multiplication of larger matrices.

Containers have emerged as a more portable and efficient solution than virtual machines for cloud infrastructure providing both a flexible way to build and deploy applications. The quality of service, security, performance, energy consumption, among others, are essential aspects of their deployment, management, and orchestration. Inappropriate resource allocation can lead to resource contention, entailing reduced performance, poor energy efficiency, and other potentially damaging effects. In this paper, we present a set of online job allocation strategies to optimize quality of service, energy savings, and completion time, considering contention for shared on-chip resources. We consider the job allocation as the multilevel dynamic bin-packing problem that provides a lightweight runtime solution that minimizes contention and energy consumption while maximizing utilization. The proposed strategies are based on two and three levels of scheduling policies with container selection, capacity distribution, and contention-aware allocation. The energy model considers joint execution of applications of different types on shared resources generalized by the job concentration paradigm. We provide an experimental analysis of eighty-six scheduling heuristics with scientific workloads of memory and CPU-intensive jobs. The proposed techniques outperform classical solutions in terms of quality of service, energy savings, and completion time by 21.73-43.44%, 44.06-92.11%, and 16.38-24.17%, respectively, leading to a cost-efficient resource allocation for cloud infrastructures.

Cloud storage as service is the mainstream technology used to retain digital data. However, there are significant risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level 2Lbp-RRNS scheme based on a Redundant Residue Number System with a backpropagation and hamming distance mechanisms for increasing reliability of a configurable and secure multi-cloud data storage. We provide a theoretical analysis of the 2Lbp-RRNS solution as an extension of the classical 2L-RRNS and a variant of fully homomorphic encryption for privacy-preserving, parallel processing, and scalability. We formulate, explain, and prove its main properties to extend existing knowledge within the limits of the critical bounding RRNS assumptions. We show that 2Lbp-RRNS can identify and recover more errors than traditional 2L-RRNS. We provide the upper bounds of the traditional threshold 2L-RRNS and our solution to estimate the number of detectable and correctable errors. We study various data access scenarios and show that it detects 1.58$\times $ and corrects 3.37$\times $ more errors than 2L-RRNS, on average. We also provide efficient implementations of encoding and decoding algorithms MRC8, and MRC16 based on the Mixed-Radix system, Finite Ring Neuronal Network, and signed binary window method. We evaluate encoding/decoding speeds using three algorithms: Mignotte, MRC8, and MRC16. The experimental system includes seven cloud storages: DropBox, GoogleDrive, OneDrive, Sharefile, Box, Egnyte, and Salesforce. To assess the efficiency of the system on real data, we vary scenarios of the first and second levels. The results show that our solution outperforms MRC8 by 2.53$\times $ (1.78$\times $ ), and Mignotte by 4.83$\times $ (11.43$\times $ ) for the encoding (decoding) speed, respectively.

This article presents the application of soft computing methods for solving the problem of designing and optimizing cloud-based Content Distribution Networks (CDN). A multi-objective approach is applied to solve the resource provisioning problem for building the infrastructure for the network, considering the objectives of minimizing the cost of the virtual machines, network, and storage, and the maximization of the quality-of-service provided to end-users. A specific brokering model is proposed to allow a single cloud-based CDN to be able to host multiple content providers applying a resource sharing strategy. Following the proposed brokering model, three multiobjective evolutionary approaches are studied for the offline optimization of resource provisioning and a greedy heuristic method is proposed for addressing the online routing of contents. The experimental evaluation of the proposed approach is performed over a set of realistic problem instances. The obtained experimental results indicate that the proposed approach is effective for designing and optimizing cloud-based Content Distribution Networks: total costs are reduced by up to 10.34% while maintaining high quality-of-service values.

An important operation for data processing is a number comparison. In Residue Number System (RNS), it consists of two steps: the computation of the positional characteristic of the number in RNS representation and comparison of its positional characteristics in the positional number system. In this paper, we propose a new efficient method to compute the positional characteristic based on the approximate method. The approximate method as a tool to compare numbers does not require resource-consuming non-modular operations that are replaced by fast bit right shift operations and taking the least significant bits. We prove that in case when the dynamic range of RNS is an odd number, the size of the operands is reduced by the size of the module. If one of the RNS moduli is a power of two, then the size of the operands is less than the dynamic range. It makes our method efficient for hardware implementation of cryptographic primitives and digital signal processing.

This special issue contains selected papers that had been submitted to Proceedings of the Institute for System Programming of the Russian Academy of Sciences. Thirteen submissions from nine countries (England, Mexico, China, Uruguay, Spain, Pakistan, Cuba, Dominican Republic, and Russia) cover several important topics in rapidly expanding area of research and development related with Advanced Computing. Authors show a spectrum of approaches to solve complex problems such as: data-oriented scheduling, scientific workflows, cloud computing, evolutionary algorithms, content distribution networks, soft computing, parallel programming model for multicore machines, high performance computing, data mining, software birth-marking, anomaly detection, swarm robotics, neural networks, machine learning, security, secret-sharing schemes, heterogeneous distributed computing, and Internet of Things.

In this paper, we present an analysis of information dispersal methods for using in distributed storage systems, processing, and transmission of data. We provide a comparative study of the methods most widely used in practice considering performance, reliability and cryptographic security. There are three main approaches to the information dispersal: Information Dispersal Algorithm by Rabin, Residue Number System (RNS) and Polynomial Residue Number System. We propose an efficient data recovery algorithm based on data representation in the RNS. Comprehensive experimental analysis shows that the most productive approach for bit length up to 256 bits is the use of the RNS with our developed algorithm. We show that the use of the RNS for the design of distributed storage systems, data transmission, and data processing, can significantly reduce the time of information processing.

Software-defined networking (SDN) is the fastest growing and most widely deployed network infrastructure due to its adaptability to new networking technologies and intelligent applications. SDN simplifies network management and control by separating the control plane from the data plane. The SDN controller performs the routing process using the traditional shortest path approach to obtain end-to-end paths. This process usually does not consider the nodes’ capacity and may cause network congestion and delays, affecting flow performance. Therefore, we evaluate the most conventional routing criteria in the SDN scenario based on Dijkstra’s algorithm and compare the found paths with our proposal based on a cellular genetic algorithm for multi-objective optimization (MOCell). We compare our proposal with another multi-objective evolutionary algorithm based on decomposition (MOEA/D) for benchmark purposes. We evaluate various network parameters such as bandwidth, delay, and packet loss to find the optimal end-to-end path. We consider a large-scale inter-domain SDN scenario. The simulation results show that our proposed method can improve the performance of data streams with TCP traffic by up to 54% over the traditional routing method of the shortest path and by 33% for the highest bandwidth path. When transmitting a constant data stream using the UDP protocol, the throughput of the MOCell method is more than 1.65% and 9.77% for the respective paths.

This article presents a parallel evolutionary algorithm for public transport optimization by synchronizing traffic lights in the context of Bus Rapid Transit systems. The related optimization problem is NP-hard, so exact computational methods are not useful to solve real-world instances. Our research introduces a parallel evolutionary algorithm to efficiently configure and synchronize traffic lights and improve the average speed of buses and other vehicles. The Bus Rapid Transit on Garzón Avenue (Montevideo, Uruguay) is used as a case study. This is an interesting complex urban scenario due to the number of crossings, streets, and traffic lights in the zone. The experimental analysis compares the numerical results computed by the parallel evolutionary algorithm with a scenario that models the current reality. The results show that the proposed evolutionary algorithm achieves better quality of service when compared with the current reality, improving up to 15.3% the average bus speed and 24.8% the average speed of other vehicles. A multiobjective optimization analysis also demonstrates that additional improvements can be achieved by assigning different priorities to buses and other vehicles. In addition, further improvements can be achieved on a modified scenario simply by deleting a few bus stops and changing some traffic lights rules. The benefits of using a parallel solver are also highlighted, as the parallel version is able to accelerate the execution times up to 26.9× when compared with the sequential version. Keywords: Bus Rapid Transit, Traffic lights synchronization, Evolutionary algorithms

Homomorphic encryption with the ability to compute over encrypted data without access to the secret key provides benefits for the secure and powerful computation, storage, and communication of resources in the cloud. One of its important applications is fast-growing robot control systems for building lightweight, low-cost, smarter robots with intelligent brains consisting of data centers, knowledge bases, task planners, deep learning, information processing, environment models, communication support, synchronous map construction and positioning, etc. It enables robots to be endowed with secure, powerful capabilities while reducing sizes and costs. Processing encrypted information using homomorphic ciphers uses the sign function polynomial approximation, which is a widely studied research field with many practical results. State-of-the-art works are mainly focused on finding the polynomial of best approximation of the sign function (PBAS) with the improved errors on the union of the intervals [−1,−ϵ]∪[ϵ,1]. However, even though the existence of the single PBAS with the minimum deviation is well known, its construction method on the complete interval [−1,1] is still an open problem. In this paper, we provide the PBAS construction method on the interval [−1,1], using as a norm the area between the sign function and the polynomial and showing that for a polynomial degree n≥1, there is (1) unique PBAS of the odd sign function, (2) no PBAS of the general form sign function if n is odd, and (3) an uncountable set of PBAS, if n is even.

In this paper, we present a Big Data analysis paradigm related to smart cities using cloud computing infrastructures. The proposed architecture follows the MapReduce parallel model implemented using the Hadoop framework. We analyse two case studies: a quality-of-service assessment of public transportation system using historical bus location data, and a passenger-mobility estimation using ticket sales data from smartcards. Both case studies use real data from the transportation system of Montevideo, Uruguay. The experimental evaluation demonstrates that the proposed model allows processing large volumes of data efficiently.

Error detection and correction codes based on redundant residue number systems are powerful tools to control and correct arithmetic processing and data transmission errors. Decoding the magnitude and location of a multiple error is a complex computational problem: it requires verifying a huge number of different possible combinations of erroneous residual digit positions in the error localization stage. This paper proposes a modified correcting method based on calculating the approximate weighted characteristics of modular projections. The new procedure for correcting errors and restoring numbers in a weighted number system involves the Chinese Remainder Theorem with fractions. This approach calculates the rank of each modular projection efficiently. The ranks are used to calculate the Hamming distances. The new method speeds up the procedure for correcting multiple errors and restoring numbers in weighted form by an average of 18% compared to state-of-the-art analogs.

The residue number system (RNS) is widely used in different areas due to the efficiency of modular addition and multiplication operations. However, non-modular operations, such as sign and division operations, are computationally complex. A fractional representation based on the Chinese remainder theorem is widely used. In some cases, this method gives an incorrect result associated with round-off calculation errors. In this paper, we optimize the division operation in RNS using the Akushsky core function without critical cores. We show that the proposed method reduces the size of the operands by half and does not require additional restrictions on the divisor as in the division algorithm in RNS based on the approximate method.

Fully Homomorphic Encryption (FHE) permits processing information in the form of ciphertexts without decryption. It can ensure the security of information in common technologies used today, such as cloud computing, the Internet of Things, and machine learning, among others. A primary disadvantage for its practical application is the low efficiency of sign and comparison operations. Several FHE schemes use the Residue Number System (RNS) to decrease the time complexity of these operations. Converting from the RNS to the positional number system and calculating the positional characteristic of a number are standard approaches for both operations in the RNS domain. In this paper, we propose a new method for comparing numbers and determining the sign of a number in RNS. We focus on the even ranges that are computationally simple due to their peculiarities. We compare the performance of several state-of-art algorithms based on an implementation in C++ and relatively simple moduli with a bit depth from 24 to 64 bits. The experimental analysis shows a better performance of our approach for all the test cases; it improves the sign detection between 1.93 and 15.3 times and the number comparison within 1.55–11.35 times with respect to all the methods and configurations.

Storage-as-a-service offers cost savings, convenience, mobility, scalability, redundant locations with a backup solution, on-demand with just-in-time capacity, syncing and updating, etc. While this type of cloud service has opened many opportunities, there are important considerations. When one uses a cloud provider, their data are no longer on their controllable local storage. Thus, there are the risks of compromised confidentiality and integrity, lack of availability, and technical failures that are difficult to predict in advance. The contribution of this paper can be summarized as follows: (1) We propose a novel mechanism, En-AR-PRNS, for improving reliability in the configurable, scalable, reliable, and secure distribution of data storage that can be incorporated along with storage-as-a-service applications. (2) We introduce a new error correction method based on the entropy (En) paradigm to correct hardware and software malfunctions, integrity violation, malicious intrusions, unexpected and unauthorized data modifications, etc., applying a polynomial residue number system (PRNS). (3) We use the concept of an approximation of the rank (AR) of a polynomial to reduce the computational complexity of the decoding. En-AR-PRNS combines a secret sharing scheme and error correction codes with an improved multiple failure detection/recovery mechanism. (4) We provide a theoretical analysis supporting the dynamic storage configuration to deal with varied user preferences and storage properties to ensure high-quality solutions in a non-stationary environment. (5) We discuss approaches to efficiently exploit parallel processing for security and reliability optimization. (6) We demonstrate that the reliability of En-AR-PRNS is up to 6.2 times higher than that of the classic PRNS.

This article presents a system for detecting pedestrian movement patterns in urban environments, by applying computational intelligence methods for image processing and pattern detection. The proposed system is capable of processing multiple images and video sources in real-time. Furthermore, it has a flexible design, as it is based on a pipes and filters architecture that makes it easy to evaluate different computational intelligence techniques to address the subproblems involved in each stage of the process. Two main stages are implemented in the proposed system: the first stage is in charge of extracting relevant features of the processed images, by applying image processing and object tracking, and the second stage is responsible for the patterns detection. The experimental analysis of the proposed system was performed over more than 1450 problem instances, using PETS09-S2L1 videos, and the results were compared with part of the Multiple Object Tracking Challenge benchmark results. Experiments covered the two main stages of the system. Results indicate that the proposed system is competitive yet simpler than other similar software methods. Overall, this article provides the theoretical frame and a proof of concept needed for the implementation of a real-time system that takes as input a group of image sequences, extracts relevant features, and detects a set of predefined patterns. The proposed implementation is a reliable proof of the viability of building pedestrian movement pattern detection systems.

Existe en la actualidad un crecimiento acelerado de las ciudades, debido principalmente a la migración de las personas buscando mejorar su calidad de vida. Esto ha provocado el surgimiento de diversas problemáticas, como por ejemplo: abastecimiento de agua y energía eléctrica, problemas de salud, alimentación, educación y contaminación, entre otros. Uno de los problemas más importantes y del cual se derivan en mayor o menor medida algunos de los antes mencionados, es el del transporte público. Desde hace varias décadas algunos investigadores han abordado esta problemática desde diferentes perspectivas y aplicando diferentes técnicas para solucionarlos y generalmente poniéndole un acrónimo particular. El presente trabajo muestra una revisión de los principales tipos de problemas de redes de transporte público abordados a lo largo de estos últimos años y busca clasificar los tipos de problemáticas abordadas. Como se puede ver, varios de los problemas planteados con diferentes nombre corresponden al mismo tipo de problema. En general, este tipo de problemas sigue teniendo un gran interés en la actualidad debido a la importancia que representa la solución de éstos.

We address a scheduling problem in an actual environment of the tortilla industry. Since the problem is NP hard, we focus on suboptimal scheduling solutions. We concentrate on a complex multistage, multiproduct, multimachine, and batch production environment considering completion time and energy consumption optimization criteria. The production of wheat-based and corn-based tortillas of different styles is considered. The proposed bi-objective algorithm is based on the known Nondominated Sorting Genetic Algorithm II (NSGA-II). To tune it up, we apply statistical analysis of multifactorial variance. A branch and bound algorithm is used to assert obtained performance. We show that the proposed algorithms can be efficiently used in a real production environment. The mono-objective and bi-objective analyses provide a good compromise between saving energy and efficiency. To demonstrate the practical relevance of the results, we examine our solution on real data. We find that it can save 48% of production time and 47% of electricity consumption over the actual production.

En este artículo, describimos el caché de software I-Structure para entornos de memoria distribuida (D-ISSC), lo cual toma ventaja de la localidad de los datos mientras mantiene la capacidad de tolerancia a la latencia de sistemas de memoria I-Structure. Las facilidades de programación de los programas MPI, le ocultan los problemas de sincronización al programador. Nuestra evaluación experimental usando un conjunto de pruebas de rendimiento indica que clusters de PC con I-Structure y su mecanismo de cache D-ISSC son más robustos. El sistema puede acelerar aplicaciones de comunicación intensiva regulares e irregulares.

En este articulo se describe un modelo de computación paralela denominado máquina abstracta de red (ANM). Nos centramos en el papel que juega la evaluación parcial en la transformación, optimización y especialización de programas ANM, y el revelado automático de su paralelismo inherente La técnica se basa en computación paralela sobre redes asociativas implementadas en ANM. ANM usa redes asociativas tanto para representar tanto información completa como información incompleta. ANM sigue el estilo de transformación paralela de la computación basada en un proceso de unificación de red, el cual es una variante del mecanismo de reducción de grafos para computación paralela granular y para el procesamiento de información incompleta. Se da una breve descripción de esta aproximación, así como también una comparación con el esquema tradicional de evaluación parcial. Adicionalmente, se puntualiza sobre algunos de los problemas asociados con esta clase de computación paralela.