This paper investigates how parallel computing techniques, such as OpenMP and CUDA, can be optimized to enhance the computational efficiency of detecting chaotic regions in the parameter space of recurrent equations, a critical task in chaos theory. Leveraging the embarrassingly parallel nature of maximum Lyapunov exponent calculations, our method targets systems with known recurrence relations, where governing equations are analytically defined. Applied to a discretized recurrent neural model, the proposed approach achieves significant speedups, addressing the computational intensity of chaos detection. While building on established parallel techniques, this work fills a gap in their systematic application to chaos detection in high-dimensional systems, offering a scalable solution with potential for real-time analysis. We provide detailed performance metrics, parallel I/O guidelines, and visualization strategies, demonstrating adaptability to other analytically defined chaotic systems.

Social networks reflect, to a wide extent, the real-world relationships that allow users to connect and share information. The number of people that interact in social networks keeps increasing, and the devices used are equipped with more and more computational capacities. This gives rise to the formulation of social clouds, which refer to resource-sharing infrastructures that enable friends to share their resources within the social network. As modern applications become more and more sophisticated, users should be able to share their own services and computing resources through social networks. This poses many challenges for the design options of a computing system composed of a set of trusted friends. The spotlight turns on the design of a proper trust model that considers the suitability of the trusted users to execute an application’s tasks and on the fair distribution of these tasks among these users. Therefore, social networks and their trust-based applications in a distributed environment have seen increasing attention in the research community. In this regard, we present a social community cloud implementation model, where friendly relationships determine resource provisioning. The issues of fairness and allocation of time are of great importance and they are thoroughly investigated. We use extensive simulations to illustrate that the communities can be employed to construct cloud infrastructures, such that the shared resources can be utilized fairly and efficiently. Our experiments have shown that our model achieves a higher allocation rate (percentage of tasks successfully allocated and completed) than competitive models and reduces the average response time and the total execution time. Finally, our work does not overutilize the resources.

Nowadays, there is an accelerating need to efficiently and timely handle large amounts of data that arrives continuously. Streams of big data led to the emergence of Distributed Stream Processing Systems (DSPS) that assign processing tasks to the available resources (dynamically or not) and route streaming data between them. Efficient scheduling of processing tasks of data flows can reduce application latencies and eliminate network congestion. In this work, we propose a linear complexity scheme for the task allocation and scheduling problem to improve system’s performance, load balancing and memory efficiency, in applications where there is need for heavy communication (all-to-all) between the tasks assigned to pairs of components.

Communities are important attributes of today's networking, since people who join networks tend to join communities. Thus, community detection is an important issue in designing algorithms for delay-tolerant networks (DTNs). Multicasting is an appropriate method to share information within a community or between communities, because it delivers messages from a source to a group of targets using limited resources. This paper addresses the problem of detecting communities in weighted networks with irregular topologies. Based on communities, we propose an efficient data distribution algorithm for DTNs. The distribution strategy is based on the construction of a number of multicast trees, where each tree can be used to select the best relay node for each target to improve multicast efficiency. The proposed strategy provides better delivery ratio compared with other strategies and it reduces latencies when multiple nodes from a community need to multicast to other community members.

Data replication is used to track the most popular files (i.e., the ones with most requests) and replicate them in selected nodes. In this way, more requests for such popular files can be completed over a period of time and bandwidth consumption is reduced, since these files do not need to be transferred from remote nodes. In this article, we extend our previous work [1] to make it more efficient in terms of memory and total computation cost, so that it becomes more efficient and suitable for larger grids. To reduce the memory costs, we present a centralized strategy which estimates the potential for selected batches of files. The computations required for these estimations are executed in a pipelined way, so their cost is also reduced.

Recently, data replication has received considerable attention in the field of grid computing. The main goal of data replication algorithms is to optimize data access performance by replicating the most popular files. When a file does not exist in the node where it was requested, it necessarily has to be transferred from another node, causing delays in the completion the file requests. The general idea behind data replication is to keep track of the most popular files requested in the grid and create copies of them in selected nodes. In this way, more file requests can be completed over a period of time and average job execution time is reduced. In this paper, we introduce an algorithm that estimates the potential of the files located in each node of the grid, using a binary tree structure. Also, the file scope and the file type are taken into account. By potential of a file, we mean its increasing or decreasing demand over a period of time. The file scope generally refers to the extent of the group of users which are interested or potentially interested in a file. The file types are divided into read and write intensive. Our scheme mainly promotes the high-potential files for replication, based on the temporal locality principle. The simulation results indicate that the proposed scheme can offer better data access performance in terms of the hit ratio and the average job execution time, compared to other state-of-the-art strategies.