Journal of Parallel and Distributed Computing最新文献

With the promise of higher throughput, and better response times, 6G networks provide a significant enabler for smart cities to evolve. The rapidly-growing reliance on connected devices within the smart city context encourages malicious actors to target these devices to achieve various malicious goals. In this paper, we present a novel defense technique that creates a cloud-based virtualized honeypot/twin that is designed to receive malicious traffic through edge-based machine learning-enabled detection system. The proposed system performs early identification of malicious traffic in a software defined network-enabled edge routing point to divert that traffic away from the 6G-enabled smart city endpoints. Testing of the proposed system showed an accuracy exceeding 99.8%, with an $F_1$ score of 0.9984.

Publish–subscribe communication is a fundamental service used for message-passing between decoupled applications in distributed simulation. When abundant unnecessary data transfer is introduced, interest-matching services are needed to filter irrelevant message traffic. Frequent demands during simulation execution makes interest matching a bottleneck with increased simulation scale. Contemporary algorithms built for serial processing inadequately leverage multicore processor-based parallel resources. Parallel algorithmic improvements are insufficient for large-scale simulations. Therefore, we propose a hierarchical sort-based parallel algorithm for dynamic interest matching that embeds all update and subscription regions into two full binary trees, thereby transferring the region-matching task to one of node-matching. It utilizes the association between adjacent nodes and the hierarchical relation between parent‒child nodes to eliminate redundant operations, and achieves incremental parallel matching that only compares changed regions. We analyze the time and space complexity of this process. The new algorithm performs better and is more scalable than state-of-the-art algorithms.

This work revisits I/O bandwidth-sharing strategies for HPC applications. When several applications post concurrent I/O operations, well-known approaches include serializing these operations (

Time series analysis is a key technique for extracting and predicting events in domains as diverse as epidemiology, genomics, neuroscience, environmental sciences, economics, etc. Matrix Profile, a state-of-the-art algorithm to perform time series analysis, finds out the most similar and dissimilar subsequences in a time series in deterministic time and it is exact. Matrix Profile has low arithmetic intensity and it operates on large amounts of time series data, which can be an issue in terms of memory requirements. On the other hand, Hardware Transactional Memory (HTM) is an alternative optimistic synchronization method that executes transactions speculatively in parallel while keeping track of memory accesses to detect and resolve conflicts.

This work evaluates one of the best implementations of Matrix Profile exploring multiple multiprocessor variants and proposing new implementations that consider a variety of synchronization methods (HTM, locks, barriers), as well as algorithm organizations. We analyze these variants using real datasets, both short and large, in terms of speedup and memory requirements, the latter being a major issue when dealing with very large time series. The experimental evaluation shows that our proposals can achieve up to 100× speedup over the sequential algorithm for 128 threads, and up to 3× over the baseline, while keeping memory requirements low and even independent of the number of threads.

The RAID2.0 architecture, which uses dozens or even hundreds of disks, is widely adopted for large-capacity data storage. However, limited resources like memory and CPU cause RAID2.0 to execute batch recovery for disk failures. The traditional random data placement and recovery schemes result in highly skewed I/O access within a batch, which slows down the recovery speed. To address this issue, we propose DR-RAID, an efficient reconstruction scheme that balances local rebuilding workloads across all surviving disks within a batch. We dynamically select a batch of tasks with almost balanced read loads and make intra-batch adjustments for tasks with multiple solutions of reading source chunks. Furthermore, we use a bipartite graph model to achieve a uniform distribution of write loads. DR-RAID can be applied with homogeneous or heterogeneous disk rebuilding bandwidth. Experimental results demonstrate that in offline rebuilding, DR-RAID enhances the rebuilding throughput by up to 61.90% compared to the random data placement scheme. With varied rebuilding bandwidth, the improvement can reach up to 65.00%.

Adaptive routing is effective in maintaining higher processor performance and avoids packets over minimal or non-minimal alternate routes without congestion for a multiprocessor system on chip. However, many systems cannot deal with the fact that sending packets over an alternative path rather than the shorter, fixed-priority route can result in packets arriving at the destination node out of order. This can occur if packets belonging to the same communication flow are adaptively routed through a different path. In real-world network systems, there are strategies and algorithms to efficiently handle out-of-order packets without requiring infinite memory. Techniques like buffering, sliding windows, and sequence number management are used to reorder packets while considering the practical constraints of available memory and processing power. The specific method used depends on the network protocol and the requirements of the application. In the proposed technique, a novel technique aimed at improving the performance of multiprocessor systems on chip by implementing adaptive routing based on the Bat algorithm. The framework employs 5 stage pipeline router, that completely gained and forward a packet at the perfect direction in an adaptive mode. Bat algorithm is used to enhance the performance, which can optimize route to transmit packets at the destination. A test was carried out on various NoC sizes (6 X 6 and 8 X 8) under multimedia benchmarks, compared with other related algorithms and implemented on Kintex-7 FPGA board. The outcomes of the simulation illustrate that the proposed algorithm reduces delay and improves the throughput over the other traditional adaptive algorithms.

In the dispersion problem, a set of k co-located mobile robots must relocate themselves in distinct nodes of an unknown network. The network is modeled as an anonymous graph $G=(V,E)$, where the graph's nodes are not labeled. The edges incident to a node v with degree d are labeled with port numbers in the range $\{0,1,\dots ,d-1\}$ at v. The robots have unique IDs in the range $[0,L]$, where $L\ge k$, and are initially placed at a source node s. The task of the dispersion was traditionally achieved based on the assumption of two types of communication abilities: (a) when some robots are at the same node, they can communicate by exchanging messages between them, and (b) any two robots in the network can exchange messages between them. This paper investigates whether this communication ability among co-located robots is absolutely necessary to achieve dispersion. We establish that even in the absence of the ability of communication, the task of the dispersion by a set of mobile robots can be achieved in a much weaker model, where a robot at a node v has access to following very restricted information at the beginning of any round: (1) am I alone at v? (2) did the number of robots at v increase or decrease compared to the previous round?

We propose a deterministic distributed algorithm that achieves the dispersion on any given graph $G=(V,E)$ in time $O(k\log L+k^2\log \Delta )$, where Δ is the maximum degree of a node in G. Further, each robot uses $O(\log L+\log \Delta )$ additional memory, i.e., memory other than the memory required to store its id. We also prove that the task of the dispersion cannot be achieved by a set of mobile robots with $o(\log L+\log \Delta )$ additional memory.

In recent decades, the exponential growth of applications has intensified traffic demands, posing challenges in ensuring optimal user experiences within modern networks. Traditional congestion avoidance and control mechanisms embedded in conventional routing struggle to promptly adapt to new-generation networks. Current routing approaches risk-averse outcomes such as (1) scalability constraints, (2) high convergence times, and (3) congestion due to inadequate real-time traffic prioritization. To address these issues, this paper introduces a QoS-Driven Routing Optimization in Software-Defined Networking (SDN) using Deep Reinforcement Learning (DRL) to optimize routing and enhance QoS efficiency. Employing DRL, the proposed DQS optimizes routing decisions by intelligently distributing traffic, guided by a multi-objective function-driven DRL agent that considers both link and queue metrics. Despite the complexity of the network, DQS sustains scalability while significantly reducing convergence times. Through a Docker-based Openflow prototype, results highlight a substantial 20-30% reduction in end-to-end delay compared to baseline methods.

The rapid development of Deep Neural Networks (DNNs) lays solid foundations for Internet of Things systems. However, mobile devices with limited processing capacity and short battery life confront the difficulties of executing complex DNNs. To satisfy different Quality of Service requirements, a feasible solution is offloading DNN layers to edge nodes and the cloud. The energy-efficient offloading problem for DNN-based applications with the deadline and budget constraints in the edge-cloud environment is still an open and challenging issue. To this end, this paper proposes a Hybrid Chaotic Evolutionary Algorithm (HCEA) incorporating diversification and intensification strategies and a DVFS-enabled version of it (HCEA-DVFS). The Archimedes Optimization Algorithm-based diversification strategy exploits global and local guiding information to improve population diversity during the updating process and employs Metropolis acceptance rule of Simulated Annealing to avoid premature convergence. The Genetic Algorithm-based chaotic intensification strategy is designed to enhance the local search capability of HCEA. Moreover, the Dynamic Voltage Frequency Scaling-enabled adjustment strategies can be embedded into HCEA to further reduce energy consumption by resetting frequency levels and reallocating DNN layers. Experimental results over four DNN-based applications demonstrate that HCEA-DVFS can reduce more energy consumption under different deadlines, budgets, and workloads on average by 7.93, 9.68, 11.02, 11.84, and 19.38 percent in comparison with HCEA, PSO-GA, MCEA, AOA, and Greedy, respectively.