SMT-Based Contention-Free Task Mapping and Scheduling on 2D/3D SMART NoC with Mixed Dimension-Order Routing

SMART NoCs achieve ultra-low latency by enabling single-cycle multiple-hop transmission via bypass channels. However, contention along bypass channels can seriously degrade the performance of SMART NoCs by breaking the bypass paths. Therefore, contention-free task mapping and scheduling are essential for optimal system performance. In this article, we propose an SMT (Satisfiability Modulo Theories)-based framework to find optimal contention-free task mappings with minimum application schedule lengths on 2D/3D SMART NoCs with mixed dimension-order routing. On top of SMT’s fast reasoning capability for conditional constraints, we develop efficient search-space reduction techniques to achieve practical scalability. Experiments demonstrate that our SMT framework achieves 10× higher scalability than ILP (Integer Linear Programming) with 931.1× (ranges from 2.2× to 1532.1×) and 1237.1× (ranges from 4× to 4373.8×) faster average runtimes for finding optimum solutions on 2D and 3D SMART NoCs and our 2D and 3D extensions of the SMT framework with mixed dimension-order routing also maintain the improved scalability with the extended and diversified routing paths, resulting in reduced application schedule lengths throughout various application benchmarks.

Evolutionary Large-Scale Multi-Objective Optimization: A Survey

Multi-objective evolutionary algorithms (MOEAs) have shown promising performance in solving various optimization problems, but their performance may deteriorate drastically when tackling problems containing a large number of decision variables. In recent years, much effort been devoted to addressing the challenges brought by large-scale multi-objective optimization problems. This article presents a comprehensive survey of stat-of-the-art MOEAs for solving large-scale multi-objective optimization problems. We start with a categorization of these MOEAs into decision variable grouping based, decision space reduction based, and novel search strategy based MOEAs, discussing their strengths and weaknesses. Then, we review the benchmark problems for performance assessment and a few important and emerging applications of MOEAs for large-scale multi-objective optimization. Last, we discuss some remaining challenges and future research directions of evolutionary large-scale multi-objective optimization.

Computing Time Series Data During Index Based De-Duplication of Industrial IoT Data in Cloud Environment

As a result of the development in Industry 4.0, the data generated within the Industries are increasing rapidly every day to attain the innovative environment within the industry through maximal asset utilization. Meanwhile, the redundancy rate in the server is also increasing, which has an impact on the storage as well as in the analysis of data. Most existing de-duplication techniques partition the data with respect to memory. However if the time period is considered for partition, time-series analysis would be achieved during the de-duplication process. To address the above issue, the proposed work presents the Index Based De-duplication technique with Categorized Region Method for computing time-series data. The Merkle Tree with a super feature called reckoning of occurrence is combined in the proposed system to rapidly identify the existence of similar data in the distributed system with an accurate existence count that significantly helps in predicting the future drifts of the industrial environment. Finally, the proposed system also concludes with optimal transportation cost to reach the storage nodes in the cloud using MODI method. The experimental results reveal that the proposed model is efficient since it facilitates less memory and less computation overhead. The proposed technique achieves space reduction by 98%, reduces the computation overhead during analysis by 55%, and increases the efficacy of cloud storage by 60%.

A novel state space reduction algorithm for team formation in social networks

Team formation (TF) in social networks exploits graphs (i.e., vertices = experts and edges = skills) to represent a possible collaboration between the experts. These networks lead us towards building cost-effective research teams irrespective of the geolocation of the experts and the size of the dataset. Previously, large datasets were not closely inspected for the large-scale distributions & relationships among the researchers, resulting in the algorithms failing to scale well on the data. Therefore, this paper presents a novel TF algorithm for expert team formation called SSR-TF based on two metrics; communication cost and graph reduction, that will become a basis for future TF’s. In SSR-TF, communication cost finds the possibility of collaboration between researchers. The graph reduction scales the large data to only appropriate skills and the experts, resulting in real-time extraction of experts for collaboration. This approach is tested on five organic and benchmark datasets, i.e., UMP, DBLP, ACM, IMDB, and Bibsonomy. The SSR-TF algorithm is able to build cost-effective teams with the most appropriate experts–resulting in the formation of more communicative teams with high expertise levels.

Optimal value of Economic Load Dispatch using Swarm Algorithm

In this paper space reduction particle swarm optimization(SRPSO) is proposed for solving single-objective optimization problems. Minimization of cost is considered as an objective in the economic dispatch problem. The valve point loading effect is incorporated with the cost function which transfigures to the nonlinear problem. To improve the convergence speed, space reduction is essential and parameter variation keeps away the struck of local optima. Particle swarm optimization (PSO) emphasizes global search and is encountered as a stochastic population-based method. The proposed method is validated on a 26 bus system with 6 generators and the performance results are compared with the other existing techniques.

Telling stories, performing operations as a design method

Exploration of stories in architectural discourse has yet to generate a structured discussion in the methods of its operations as part of a design endeavour. This issue of ARSNET presents a collection of articles that demonstrates a variety of everyday stories emerging from the experience of space and objects, and outlines the methodologies of their corresponding spatial operations. Through the stories of occupying, manoeuvring, navigating, dispersing, and sensing, this issue highlights various methods of spatial operations. With operations ranging from tracing as a way of revealing space, reduction as manoeuvring strategy, connecting the virtual and the real as a way of finding new meaning and use of space, and creating a spatial atmosphere based on trajectories of senses; the issue expands the discussion of stories-driven architectural design methods.