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Current edge computing systems are deployed in highly complex application scenarios with dynamically changing requirements. In order to provide the expected performance and energy efficiency values in these situations, the use of heterogeneous hardware/software platforms at the edge has become widespread. However, these computing platforms still suffer from the lack of unified software-driven programming models to efficiently deploy multi-purpose hardware-accelerated solutions. In parallel, edge computing systems also face another huge challenge: operating under multiple conditions that were not taken into account during any of the design stages. Moreover, these conditions may change over time, forcing self-adaptation mechanisms to become a must. This paper presents an integrated architecture to exploit hardware-accelerated data-parallel models and transparent hardware/software multithreading. In particular, the proposed architecture leverages the \ARTICo framework and ReconOS to allow developers to select the most suitable programming model to deploy their edge computing applications onto run-time reconfigurable hardware devices. An evolvable hardware system is used as an additional architectural component during validation, providing support for continuous lifelong learning in smart edge computing scenarios. In particular, the proposed setup exhibits online learning capabilities that include learning by imitation from software-based reference algorithms.

Web3.0, often cited to drastically shape our lives, is ubiquitous. However, few literatures have discussed the crucial differentiators that separate Web3.0 from the era we are currently living in. Via a thorough analysis of the recent blockchain infrastructure evolution, we capture a key invariant featuring the evolution, based on which we provide the first academic definition for Web3.0. Our definition is not the only way of understanding Web3.0, yet, it captures the fundamental and defining trait of Web3.0, and meanwhile it is has two desirable properties. Under this definition, we articulate three key infrastructural enablers for Web3.0: individual blockchains, federated or centralized platforms capable of publishing verifiable states, and an interoperability platform to hyperconnect those state publishers. While innovations in all categories are necessary to fully enable Web3.0, in this paper, we present a design for the third enabler, namely HyperService, that delivers interoperability and programmability across heterogeneous blockchains and state publishers. HyperService is powered by two innovative designs: a developer-facing programming framework that allows developers to build cross-chain applications in a unified programming model; and a secure blockchain-facing cryptography protocol that provably realizes those applications on blockchains. We implement a prototype of HyperService in 62,000 lines of code to demonstrate its practicality, usability, and scalability.

We consider the design of a robust procurement mechanism for an assembler who must procure multiple complementary components under limited information regarding the unit production costs of the potential suppliers. We abandon the standard assumption that there exists a common prior distribution for the suppliers’ costs and we employ the max-min criterion in which we design a mechanism to maximize the assembler’s worst-case expected profit when only the means of the suppliers’ costs are known. We formulate this problem using a linear semi-infinite programming model and we apply a primal-dual approach to reduce the problem to a single joint optimization model. We characterize the optimal procurement mechanism (payments and ordering quantities) under the assumption of balanced ordering and uncertain end-customer demand. We use these results to compare the performance of component production, under which the components are sourced from separate suppliers, and integrated production, under which the components are sourced from the same supplier. We find that component production is preferred when the production costs of the components are heterogeneous. We also propose two heuristic mechanisms which are easier to compute and implement in practice, particularly when the number of components is large. Finally, we extend our analysis to the case in which the average and variance of the suppliers’ costs are known.

Coarse-grained FPGA overlays built around the runtime programmable DSP blocks in modern FPGAs can achieve high throughput and improved scalability compared to traditional overlays built without detailed consideration of FPGA architecture. These overlays can be mapped to using higher level compilers, achieving fast compilation, software-like programmability and run-time management, and high-level design abstraction. OpenCL allows programs running on a host computer to launch accelerator kernels which can be compiled at run-time for a specific architecture, thus enabling portability. However, prohibitive hardware compilation times in traditional design flows mean that the tools cannot effectively use just-in-time (JIT) compilation or runtime performance scaling on FPGAs. We present a methodology for runtime compilation of dataflow graphs expressed as OpenCL kernels onto coarse-grained overlays. The methodology benefits from the high level of abstraction afforded by using the OpenCL programming model, while the mapping to the overlay significantly reduces compilation and load times. Key characteristics of this work include highly performant DSP-optimized functional units that scale to large overlays on modern devices and the ability to perform automatic resource-aware kernel replication up to the size of the overlay. We demonstrate place and route times orders of magnitude better than traditional HLS flows, even when running on an embedded processor in the Xilinx Zynq.

Taskflow aims to streamline the building of parallel and heterogeneous applications using a lightweight task graph-based approach. Taskflow introduces an expressive task graph programming model to assist developers in the implementation of parallel and heterogeneous decomposition strategies on a heterogeneous computing platform. Our programming model distinguishes itself as a very general class of task graph parallelism with in-graph control flow to enable end-to-end parallel optimization. To support our model with high performance, we design an efficient system runtime that solves many of the new scheduling challenges arising out of our models and optimizes the performance across latency, energy efficiency, and throughput. We have demonstrated the promising performance of Taskflow in real-world applications. As an example, Taskflow solves a large-scale machine learning workload up to 29% faster, 1.5x less memory, and 1.9x higher throughput than the industrial system, oneTBB, on a machine of 40 CPUs and 4 GPUs. We have opened the source of Taskflow and deployed it to large numbers of users in the open-source community., 18 pages, 24 figures

We derive a nested risk measure for a maximization problem and implement it in a scenario-based formulation of a multi-stage stochastic mixed-integer programming problem. We apply the risk-averse formulation to the surveillance and control of a non-native forest insect, the emerald ash borer (EAB), a wood-boring beetle native to Asia and recently discovered in North America. Spreading across the eastern United States and Canada, EAB has killed millions of ash trees and cost homeowners and local governments billions of dollars. We present a mean-Conditional Value-at-Risk (CVaR), multi-stage, stochastic mixed-integer programming model to optimize a manager’s decisions about surveillance and control of EAB. The objective is to maximize the benefits of healthy ash trees in forests and urban environments under a fixed budget. Combining the risk-neutral objective with a risk measure allows for a trade-off between the weighted expected benefits from ash trees and the expected risks associated with experiencing extremely damaging scenarios. We define scenario dominance cuts (sdc) for the maximization problem and under the decision-dependent uncertainty. We then solve the model using the sdc cutting plane algorithm for varying risk parameters. Computational results demonstrate that scenario dominance cuts significantly improve the solution performance relative to that of CPLEX. Our CVaR risk-averse approach also raises the objective value of the least-benefit scenarios compared to the risk-neutral model. Results show a shift in the optimal strategy from applying less expensive insecticide treatment to more costly tree removal as the manager becomes more risk-averse. We also find that risk-averse managers survey more often to reduce the risk of experiencing adverse outcomes.

Taskflow tackles the long-standing question: How can we make it easier for developers to program parallel and heterogeneous computer-aided design (CAD) applications with high performance and simultaneous high productivity? Taskflow introduces a new powerful task graph programming model to assist developers in the implementation of parallel and heterogeneous algorithms with complex control flow. We develop an efficient system runtime to solve many of the new scheduling challenges arising out of our models and optimize the performance across latency, energy efficiency, and throughput. Taskflow has demonstrated promising performance on both micro-benchmarks and real-world applications. As an example, Taskflow solved a large-scale circuit placement problem up to 17% faster, with 1.3× fewer memory, 2.1× less power consumption, and 2.9× higher throughput than two industrial-strength systems, oneTBB and StarPU, on a machine of 40 CPUs and 4 GPUs.

Recent applications in low-power (1-20 mW) near-sensor computing require the adoption of floating-point arithmetic to reconcile high precision results with a wide dynamic range. In this article, we propose a low-power multi-core computing cluster that leverages the fined-grained tunable principles of transprecision computing to provide support to near-sensor applications at a minimum power budget. Our solution - based on the open-source RISC-V architecture - combines parallelization and sub-word vectorization with a dedicated interconnect design capable of sharing floating-point units (FPUs) among the cores. On top of this architecture, we provide a full-fledged software stack support, including a parallel low-level runtime, a compilation toolchain, and a high-level programming model, with the aim to support the development of end-to-end applications. We performed an exhaustive exploration of the design space of the transprecision cluster on a cycle-accurate FPGA emulator, varying the number of cores and FPUs to maximize performance. Orthogonally, we performed a vertical exploration to identify the most efficient solutions in terms of non-functional requirements (operating frequency, power, and area). We conducted an experimental assessment on a set of benchmarks representative of the near-sensor processing domain, complementing the timing results with a post place-&-route analysis of the power consumption. A comparison with the state-of-the-art shows that our solution outperforms the competitors in energy efficiency, reaching a peak of 97 Gflop/s/W on single-precision scalars and 162 Gflop/s/W on half-precision vectors. Finally, a real-life use case demonstrates the effectiveness of our approach in fulfilling accuracy constraints. ISSN:1045-9219 ISSN:1558-2183 ISSN:2161-9883

Modern industrial robots can work alongside human workers and coordinate with other robots. This means they can perform complex tasks, but doing so requires complex programming. Therefore, robots are typically programmed by experts, but there are not enough to meet the growing demand for robots. To reduce the need for experts, researchers have tried to make robot programming accessible to factory workers without programming experience. However, none of that previous work supports coordinating multiple robot arms that work on the same task. In this paper we present four block-based programming language designs that enable end-users to program two-armed robots. We analyze the benefits and trade-offs of each design on expressiveness and user cognition, and evaluate the designs based on a survey of 273 professional participants of whom 110 had no previous programming experience. We further present an interactive experiment based on a prototype implementation of the design we deem best. This experiment confirmed that novices can successfully use our prototype to complete realistic robotics tasks. This work contributes to making coordinated programming of robots accessible to end-users. It further explores how visual programming elements can make traditionally challenging programming tasks more beginner-friendly.

In the era of big data analytics, data owner is more concern about the data privacy. Data anonymization approaches such as k-anonymity, l-diversity, and t-closeness are used for a long time to preserve privacy in published data. However, these approaches cannot be directly applicable to a large amount of data. Distributed programming framework such as MapReduce and Spark are used for big data analytics which add more challenges to privacy preserving data publishing. Recently, we identified few scalable approaches for Privacy Preserving Big Data Publishing in literature and majority of them are based on k-anonymity and l-diversity. However, these approaches require a significant improvement to reach the level of existing privacy preserving data publishing approaches, therefore, we propose Improved Scalable l-Diversity (ImSLD) approach which is the extension of Improved Scalable k-Anonymity (ImSKA) for scalable anonymization in this paper. Our approaches are based on scalable k-anonymization that uses MapReduce as a programming paradigm. We use poker dataset and synthesize big data versions of poker dataset to test our approaches. The result analysis shows significant improvement in terms of running time due to the lesser number of MapReduce iterations and also exhibits lower information loss as compared to existing approaches while providing the same level of privacy due to tight arrangement of the records in the initial equivalence class.

Quantum Computers, one fully realized, can represent an exponential boost in computing power. However, the computational power of the current quantum computers, referred to as Noisy Internediate Scale Quantum, or NISQ, is severely limited because of environmental and intrinsic noise, as well as the very low connectivity between qubits compared to their total amount. We propose a virtual quantum processor that emulates a generic hybrid quantum machine which can serve as a logical version of quantum computing hardware. This hybrid classical quantum machine powers quantum-logical computations which are substitutable by future native quantum processors., IEEE peer reviewed, published September 2021

The message-passing programming model is based on the abstraction of a parallel computer with a distributed address space where each processor has a local memory to which it has exclusive access, see Sect. 2.3.1. There is no global memory. Data exchange must be performed by message passing: to transfer data from the local memory of one processor \(A\) to the local memory of another processor \(B\), \(A\) must send a message containing the data to \(B\), and \(B\) must receive the data in a buffer in its local memory. To guarantee portability of programs, no assumptions on the topology of the interconnection network is made. Instead, it is assumed that each processor can send a message to any other processor.

Frequency competition is critical for a full-service airline in gaining market share, and adopting a proper strategy can improve an airline’s profits. This study proposes a new equilibrium programming model with flow balance to address frequency competition on airports network with time slot constraints. We first show that a pure-strategy Nash equilibrium may not always exist, and thus forming a pure strategy profile in frequency competition among airlines may naturally lead to deviation from current frequency. Therefore, we formulate the problem as a programming model with a mixed-strategy Nash equilibrium. To avoid shocks from dramatic frequency changes across the network, airlines tend to fine-tune frequencies on select segments during each adjustment. We propose a procedure to generate a computationally tractable amount of representative strategies from a finite set of feasible strategies to demonstrate mixed-strategy Nash equilibrium. We conduct an empirical analysis using an example in which industry profitability increased by as much as 7.89%. We then extend the model to formulate frequency competition among metal-neutral alliances. The results show that forming metal-neutral alliances can improve total industry profits by 10.59%. In particular, a sensitivity analysis with real data on the tolerance of flow imbalance demonstrates that deducting the potential costs due to the relaxation of flow balance between congested airports may earn additional total industry profits in a frequency competition.

The aggravation of environmental issues makes green manufacturing become inevitable and fuzzy uncertainties prevail in supply chain management. To further promote the development of green supply chain, this paper considers a two-echelon green supply chain models with governmental interventions composed of one supplier and one retailer under fuzzy uncertainties, in which the parameters of demand function and manufacturing cost are all characterized as fuzzy variables. Then the equilibrium decisions of the expected value and chance-constrained programming models are derived considering the different risk references of the supplier and the retailer. At the end, numerical examples are presented to demonstrate the theoretical underpinning of proposed models. Analytical results indicate that the supplier and the retailer can obtain different equilibrium decisions by adjusting different confidence levels. The equilibrium decisions reflect the risk attitudes of the supplier and the retailer to the uncertainties in supply chain system and their different predictions on possible level. Then, whether the supplier as a leader has the first-mover advantage is related to the risk attitudes of the supplier and the retailer when dealing with the uncertainties of the green supply chain system. Moreover, the strong governmental interventions can coordinate not only the conflict between pricing and green level decisions, but also the contradiction between the consumers and the supplier. In addition, the retailer may be the main puller of the green supply chain in developing the green product.

This study introduces new probabilistic constraints and objective functions to manage the uncertain nature of the renewable energy sources in single-item capacitated lot sizing problem for flow-shop configurations by integrating the capacity contract selection problem with multiple energy sources. The aim of the probabilistic models built by considering the different probabilistic constraints and objective functions is to provide a decision-making tool and to promote the use of renewable energy sources in manufacturing industry despite of their stochastic nature. Mixed Integer Non-Linear Programming models are proposed by integrating the uncertainty of the renewable energy sources based on different features. The developed models are tested on a small-size instance and the results of the models are compared in terms of economical, ecological and reliability aspects.

Disassembly is an essential step in a remanufacturing process via which valuable parts and material of end-of-life (EOL) products can be well reused and resource waste is reduced. Disassembly sequence planning focuses on finding the best disassembly sequence for a given EOL product by considering economic and environmental performance. In a practical disassembly process, one may face a disassembly operation failure risk due to the difficulty of knowing EOL products' exact information in advance. Despite its importance in impacting disassembly outcomes, the existing work fails to consider it comprehensively. This work proposes a stochastic biobjective DSP problem with the objectives of maximizing disassembly profit and minimizing energy consumption by doing so. A chance-constrained programming model is established, where a chance constraint ensures a fixed confidence level of disassembly failure. To solve it efficiently, a multiobjective multiverse optimization algorithm with stochastic simulation is proposed. Experiments are carried out on four products. Results demonstrate that it outperforms some state-of-the-art algorithms in terms of solution performance.

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link. We propose a new interval type-2 fuzzy (IT2F) programming method for risky multicriteria decision-making (MCDM) problems with IT2F truth degrees, where the criteria exhibit a heterogeneous relationship and decision-makers behave according to bounded rationality. First, we develop a technique to calculate the Banzhaf-based overall perceived utility values of alternatives based on 2-additive fuzzy measures and regret theory. Subsequently, considering pairwise comparisons of alternatives with IT2F truth degrees, we define the Banzhaf-based IT2F risky consistency index (BIT2FRCI) and the Banzhaf-based IT2F risky inconsistency index (BIT2FRII). Next, to identify the optimal weights, an IT2F programming model is established based on the concept that BIT2FRII must be minimized and must not exceed the BIT2FRCI using a fixed IT2F set. Furthermore, we design an effective algorithm using an external archive-based constrained state transition algorithm to solve the established model. Accordingly, the ranking order of alternatives is derived using the Banzhaf-based overall perceived utility values. Experimental studies pertaining to investment selection problems demonstrate the state-of-the-art performance of the proposed method, that is, its strong capability in addressing risky MCDM problems.

Considering the increased trend that power systems are closer to the operating bounds because of the increased demand and new challenges in consideration of gas systems and wind power, this paper investigates long-term voltage stability-constrained integrated electric and gas system optimal scheduling in consideration of wind energy integration. A sufficient condition, which is represented as an explicit function of voltage and injected power, is used to constrain power system long-term voltage stability. Due to bilinear terms in this condition, tightening piecewise McCormick envelope relaxation is used to convert it into convex constraints. The second-order cone programming (SOCP) formulation is employed to represent the operational constraints of the integrated electric and gas system. The loss of wind power probability, representing wind power uncertainties, is established by a chance-constrained programming model, which is transformed into a deterministic optimization model by means of the star-inequality-based extended formulation of sample average approximation. Two test systems, the 9-bus electric system with the 6-node gas system and the IEEE 118-bus electric system with the 40-node gas system, are used to validate the proposed model.

In this work, the possibility of cross-border activities between two regions in the framework of the investment contract is viewed as optimal allocation problems. The problems of determining the optimal proportion of funds to be invested in liquidity and technology are analyzed in two different environments. In the first case, we consider a two-region and two-technology economy in which both regions possess the same productive technology or project, but a different stream of return. While in the second case, we examine an economy where two regions (i.e., Indonesia and Malaysia) hold different Islamic productive projects with identical returns. Allocation models are formulated in terms of investors’ expected utility maximization problem under budget constraints with respect to regional and sectoral shocks. It is revealed that optimal parameters for liquidity ratio, technological investment profile, and bank repayment are analytically characterized by the return of a more productive project and the proportion of impatient and patient investors in the region. Even though both cases employ different assumptions, they provide the same expressions of optimal parameters. The model suggests that cross-border Islamic investment activities between two regions might be realized, provided both regions hold productive projects with an identical stream of return. This paper also shows that by increasing the lower return of the project approaching the higher return, a room for inter-region investment can be created. An analytical framework of an investment contract in terms of optimal allocation model is provided.

Supercomputing applications are increasingly adopting the MPI+threads programming model over the traditional “MPI everywhere” approach to better handle the disproportionate increase in the number of cores compared with other on-node resources. In practice, however, most applications observe a slower performance with MPI+threads primarily because of poor communication performance. Recent research efforts on MPI libraries address this bottleneck by mapping logically parallel communication, that is, operations that are not subject to MPI’s ordering constraints to the underlying network parallelism. Domain scientists, however, typically do not expose such communication independence information because the existing MPI-3.1 standard’s semantics can be limiting. Researchers had initially proposed user-visible endpoints to combat this issue, but such a solution requires intrusive changes to the standard (new APIs). The upcoming MPI-4.0 standard, on the other hand, allows applications to relax unneeded semantics and provides them with many opportunities to express logical communication parallelism. In this article, we show how MPI+threads applications can achieve high performance with logically parallel communication. Through application case studies, we compare the capabilities of the new MPI-4.0 standard with those of the existing one and user-visible endpoints (upper bound). Logical communication parallelism can boost the overall performance of an application by over 2×.

With the advancement of automation in transportation, the need to improve the operation efficiency of container terminals has increased. The most important determinant of container-handling efficiency is the productivity of equipment, such as quay cranes, automated lifting vehicles, storage yards, and yard cranes. Most previous studies have sought to optimize equipment assignments and scheduling independently and have considered only a loading or an unloading process. As loading and unloading processes occur simultaneously and the equipment operations are highly interrelated, it is important to direct the operations in an integrated manner that reflects the characteristics of automated container terminals. This paper presents a new mixed-integer programming model for analyzing the integrated problem of assigning resources and scheduling, which also considers the limited quantity of critical equipment. To solve the integrated optimization model, a genetic algorithm (GA) is developed. Since the critical equipment, such as yard cranes, are limited, and thus, restricting the efficiency of terminals, a sharing policy is proposed to improve the GA to shorten the operation time of both the loading and unloading processes. Experiments show that the improved GA proposed in this paper can obtain the optimal/near-optimal solutions in short CPU times, therefore it is efficient in solving the integrated equipment assignment and scheduling problem. The results obtained from the sharing policy are superior to those obtained from a non-sharing approach.

One of the key obstacles to pervasive deployment of FPGA accelerators in data centers is their cumbersome programming model. Open source tooling is suggested as a way to develop alternative EDA tools to remedy this issue. Open source FPGA CAD tools have traditionally targeted academic hypothetical architectures, making them impractical for commercial devices. Recently, there have been efforts to develop open source back-end tools targeting commercial devices. These tools claim to follow an alternate data-driven approach that allows them to be more adaptable to the domain requirements such as faster compile time. In this paper, we present RWRoute, the first open source timing-driven router for UltraScale+ devices. RWRoute is built on the RapidWright framework and includes the essential and pragmatic features found in commercial FPGA routers that are often missing from open source tools. Another valuable contribution of this work is an open-source lightweight timing model with high fidelity timing approximations. By leveraging a combination of architectural knowledge, repeating patterns, and extensive analysis of Vivado timing reports, we obtain a slightly pessimistic, lumped delay model within 2% average accuracy of Vivado for UltraScale+ devices. Compared to Vivado, RWRoute results in a 4.9× compile time improvement at the expense of 10% Quality of Results (QoR) loss for 665 synthetic and six real designs. A main benefit of our router is enabling fast partial routing at the back-end of a domain-specific flow. Our initial results indicate that more than 9× compile time improvement is achievable for partial routing. The results of this paper show how such a router can be beneficial for a low touch flow to reduce dependency on commercial tools.

The dew computing paradigm is emerging as a complement of cloud computing to cover its limitations. Independence is one of the essential features of dew computing. It means that it can continue to provide services without an Internet connection. These characteristics of dew computing allow it to find a niche in real-time applications. The importance of real-time applications in daily human life is not hidden due to the growing development of the Internet of Things. In this paper, the hierarchical architecture of cloud-fog-dew is presented to overcome the limitations of cloud computing in real-time applications such as latency and resource management. Also, a Mixed Integer Non-Linear Programming model is presented for the scheduling of real-time applications in the proposed architecture. It aims to reduce power consumption and Internet traffic. Besides, the proposed model is supported by Non-dominated Sorting Genetic Algorithm II to provide scalability. The simulation results demonstrate that completing tasks in the dew computing layer can reduce Internet dependency while also reducing power consumption and traffic. As a result, under the suggested paradigm, the number of tasks missed due to stoppage or Internet disturbance is reduced.

Machine learning, neural networks, and metaheuristic algorithms are relatively new subjects, closely related to each other: learning is somehow an intrinsic part of all of them. On the other hand, cell formation (CF) and facility layout design are the two fundamental steps in the CMS implementation. To get a successful CMS design, addressing the interrelated decisions simultaneously is important. In this article, a new nonlinear mixed-integer programming model is presented which comprehensively considers solving the integrated dynamic cell formation and inter/intracell layouts in continuous space. In the proposed model, cells are configured in flexible shapes during the planning horizon considering cell capacity in each period. This study considers the exact information about facility layout design and material handling cost. The proposed model is an NP-hard mixed-integer nonlinear programming model. To optimize the proposed problem, first, three metaheuristic algorithms, that is, Genetic Algorithm (GA), Keshtel Algorithm (KA), and Red Deer Algorithm (RDA), are employed. Then, to further improve the quality of the solutions, using machine learning approaches and combining the results of the aforementioned algorithms, a new metaheuristic algorithm is proposed. Numerical examples, sensitivity analyses, and comparisons of the performances of the algorithms are conducted.

This paper presents a mixed-integer non-linear programming model to design a green closed-loop supply chain comprising hybrid plants, hybrid collection centers, customer zones, secondary markets, and disposal centers. To ensure a smooth reverse flow, our model determines a return rate for each customer zone with respect to the consumers’ environmental awareness and the optimal acquisition price offered for returned products. Considering the environmental awareness levels and optimal green levels, specific cost and price functions for green products are proposed. Further, government subsidy as a financial incentive for manufacturers is considered to make the model more realistic and challenging. The effectiveness of the proposed model is analyzed by an illustrative example generated based on an Iranian straw factory. Beneficial managerial insights are obtained by conducting several sensitivity analyses. Further, the illustrative example and several generated examples in all scales are assessed by Sine Cosine Crow Search Algorithm using an efficient solution representation based on the priority-based encoding. As there is no benchmark available in the related literature to validate the results of Sine Cosine Crow Search Algorithm and to evaluate its performance, Particle Swarm Optimization and Genetic Algorithm are utilized to solve the examples. Finally, the obtained results of different metaheuristic algorithms and the exact solution are compared in terms of the CPU time and the objective function value.

Системы SAPFOR и DVM были спроектированы и предназначены для упрощения разработки параллельных программ научно-технических расчетов. Главной целью системы SAPFOR является автоматизация процесса отображения последовательных программ на параллельные архитектуры в модели DVMH. В некоторых случаях пользователь системы SAPFOR может рассчитывать на полностью автоматическое распараллеливание, если программа была написана или приведена к потенциально параллельному виду. DVMH модель представляет собой расширение стандартных языков C и Fortran спецификациями параллелизма, которые оформлены в виде директив и не видимы стандартным компиляторам. В статье будет рассмотрено автоматизированное дополнительное распараллеливание существующих MPI-программ с помощью системы SAPFOR, где, в свою очередь, будут использованы новые возможности DVMH модели по распараллеливанию циклов в MPI программе внутри узла. Данный подход позволяет существенно снизить трудоемкость распараллеливания MPI программ на графические ускорители и многоядерные процессоры, сохранив при этом удобство сопровождения уже написанной программы. Данная возможность в системе SAPFOR была реализована для языков Fortran и C. Эффективность данного подхода показана на примере некоторых приложений из пакета NAS Parallel Benchmarks. The SAPFOR and DVM systems are primarily designed to simplify the development of parallel programs of scientific-technical calculations. SAPFOR is a software development suite that aims to produce a parallel version of a sequential program in a semi-automatic way. The fully automatic parallelization is also possible if the program is well-formed and satisfies certain requirements. SAPFOR uses the DVMH directive-based programming model to expose parallelism in the code. The DVMH model introduces CDVMH and Fortran-DVMH (FDVMH) programming languages which extend the standard C and Fortran languages by parallelism specifications. We present MPI-aware extension of the SAPFOR system that exploits opportunities provided by the new features of the DVMH model to extend existing MPI programs with intra-node parallelism. In that way, our approach reduces the cost of parallel program maintainability and allows an MPI program to utilize accelerators and multicore processors. SAPFOR extension has been implemented for both Fortran and C programming languages. In this paper, we use the NAS Parallel Benchmarks to evaluate the performance of generated programs.

Many programming models, e.g., MapReduce, introduce precedence constraints between the jobs. This paper formalizes a notion of precedence constraints, called fractional precedence constraints, where the progress of follower jobs only has to lag behind (fractionally) their leads. For a general set of fractional precedence constraints between the jobs, this paper provides a new class of preemptive scheduling algorithms on unrelated machines that have arbitrary processing speeds. In particular, for a given makespan, we establish both sufficient and necessary conditions on the existence of a feasible job schedule, and then propose an efficient scheduling algorithm based on a novel matrix decomposition method, if the sufficient conditions are satisfied. The algorithm is shown to be a Polynomial-Time Approximation Scheme (PTAS), i.e., its solution is able to achieve any feasible makespan with an approximation bound of 1 + ϵ , for an arbitrary ϵ > 0 .

Natural disasters cause heavy casualties and financial losses annually. To reduce these damages, the rescue teams need to be planned effectively. In this regard, in this research, a mixed-integer programming model is offered to allocate and schedule rescue teams in a response phase of disaster management under uncertainty. The objective function minimizes the incident’s total weighted completion times. The literature review shows that the uncertain condition and time windows have been less addressed in the previous studies. To cover these gaps, this paper investigates the problem under uncertainty and considers time windows for incidents. Besides, the fatigue effect is considered in this paper. Accordingly, within a planning horizon, incident processing times are not fixed. Since the considered problem is an NP-hard one and exact methods cannot solve it within a reasonable amount of time, this research develops a heuristic-based simulated annealing algorithm. The presented model is solved using the developed algorithm and three known meta-heuristic algorithms. Then, the results obtained by algorithms are compared and analyzed. Finally, the sensitivity analysis is carried out on some crucial parameters of the presented model, and the related results are reported.

The Software Defined Networking (SDN) promises exciting new networking functionality. However, there is always remains a chance of programming errors that result in unreliable data communication. The centralized programming model helps decrease bugs' probability where a single controller manages the whole network. Yet, many real-time events occur at switches and end hosts, which often affect and add a delay in the communication process. One of those events includes unannounced destination host migration after installing flow rules during receiving of data packets. Such destination host movement results in the loss of packets because controller is not aware of this recent event. Therefore, we need an efficient approach to transmit packets without any packet loss despite destination host migration. This paper proposed a design to achieve the objective as mentioned above by defining a layer named Intelligent Transmission Control Layer (ITCL). It monitors all the end hosts' connections at their specific locations and performs necessary actions whenever the connection state changes for one or multiple hosts. The controller collects information of end nodes and state change through ITCL using A star search algorithm. After that, it updates flow tables accordingly to accommodate a location-change scenario with a route-change policy. ICTL is developed on prototype-based implementation using a popular POX controller platform. By comparing ITCL with the existing solution, we conclude that our proposed approach exhibits efficient performance in terms of Packet loss, Bandwidth usage, and Network Throughput.