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Recently, with the large-scale outbreak of the global financial crisis and public safety incidents (such as COVID-19), high-performance computing has been widely applied to risk prediction, vaccine development, and other fields. In scenarios where high-performance computing infrastructure responds to the instantaneous explosion of computing demands, a crucial issue is to provide large-scale flexible allocation and adjustment of computing capability by rapidly constructing computing clusters. Existing large-scale computing cluster deployment solutions usually utilize source code deployment or other deployment tools. The great challenge of existing deployment methods is to reduce excessive image distribution time and refrain from configuration defects. In this article, we design an intelligent distributed registry deployment (IDRD) architecture based on the OpenStack cloud platform, which adaptively places distributed image repositories using the containerized deployment of multiple registries. We propose a server load priority algorithm to solve multiple registries placement problems in IDRD. Furthermore, we devise a clustering algorithm based on demand density that can optimize the global performance of IDRD and improve large-scale cluster load balancing capabilities, which has been implemented in the TianHe Supercomputing environment. Extensive experimental results demonstrate that IDRD can effectively reduce 30% − 50% of the distribution time of component images and significantly improve the efficiency of large-scale cluster deployment. [ABSTRACT FROM AUTHOR]

For the rapid growth computation requirements in big data and artificial intelligence area, CPU-GPU heterogeneous clusters can provide more powerful computing capacity compared to CPU clusters. The high parallel computing capabilities of GPUs greatly accelerate computation-intensive applications. And the number of GPUs on single computing node is scalable, which greatly improves the computing capacity of the cluster under the condition of limited cluster size. However, there is a lack of the effective load-balancing scheduling model in multi-GPU hardware environment. This article proposes AEML, an acceleration engine for multi-GPU load-balancing in distributed heterogeneous environment. AEML can effectively integrate GPUs into distributed processing framework and achieve great load-balance among multiple heterogeneous GPUs. We propose a heterogeneous task execution model based on multiple GPUs and multiple streams (MGMS), which can effectively balance the workload of multiple GPUs. MGMS model utilizes four core techniques: a fine-grained task mapping mechanism, a device resource unified management scheme, a novel resource-aware GPU task scheduling strategy, and a feedback-based streams adjustment scheme. The implementation of AEML system is based on Spark 3.0.0 and NVIDIA CUDA 10.0. We comprehensively evaluate the performance of AEML with multiple typical benchmarks. Experimental results show that AEML can fully exploit the computing power of GPUs and achieve great load-balance among multiple heterogeneous GPUs. [ABSTRACT FROM AUTHOR]

The excavated information will become obsolete when the data changes in dynamic graphs. To compute the up-to-date results, the graph algorithm has to re-compute the entire data from scratch, which will consume huge computation time and resources. To reduce the cost of such calculations, this paper proposes a model called IncGraph to support incremental iterative computation over dynamic graphs. Different from the way of traditional iteration, IncGraph executes the graph algorithm through reusing the results of the previous graph and performs computation on the part of the graph that has changed. IncGraph has two critical components: (1) an incremental iterative computation model that consists of two steps: an incremental step to calculate the results on the changed vertices of the graph, and a merge step to calculate the results on the entire graph by using the results of the previous graph and the incremental step; and (2) an incremental update method to accelerate the iterative process within the iterative graph algorithm. We implement IncGraph model on GraphX and evaluate its performance by using several representative iterative graph algorithms: PageRank, Connected components, and Single Source Shortest Path. The results show that compared with the traditional iteration, when adding the 100k of vertices in different size data sets, the performance optimization ratio of IncGraph is 31.79 percent averagely, and 50.2 percent maximum; and when the percentage of added vertices varied from 0.01 to 10 percent in different data sets, the performance optimization ratio of IncGraph varied from 19.9 to 66.1 percent. Moreover, the result errors of IncGraph is small and can be neglected. [ABSTRACT FROM AUTHOR]

Large-scale data centers have been widely used for cloud services, and the stability of various cloud services has received additional attention from users. Although service disruptions are not as catastrophic as they once were, their impact might be more extensive than before. These outages may trigger the migration of virtual machines (VMs) located in the failure node. However, the access time of each VM is random, unlike the accident time, which can be predicted. This means that traditional migration caused by service interruptions may result in a large number of unwanted migrations, regardless of the user’s downtime experience. Migration is an expensive process in terms of the resources needed as well as the degradation of application performance during migration. A balance between the recovery time of the service (to minimize the migration resulting from a given placement) and the downtime experience of the users (to minimize the impact of access interruptions) is needed. In this paper, we propose HMGOWM, a hybrid decision-making mechanism for automating the migration of VMs. Our proposed mechanism extends the original VM migration performance cost model, greatly reducing the downtime experience of the users. To achieve high performance and a good load balance, a multi-objective monitoring system for both VMs and physical machine nodes and an adaptive VM migration-scheduling scheme for the OpenStack cloud platform are proposed. Extensive experiment results indicate that the downtime experienced by users can be efficiently reduced and that the implementation of HMGOWM outperforms the original scheduling of the OpenStack cloud platform. [ABSTRACT FROM AUTHOR]

Closer to mobile users geographically, mobile edge computing (MEC) can provide some cloud-like capabilities to users more efficiently. This enables it possible for resource-limited mobile users to offload their computation-intensive and latency-sensitive tasks to MEC nodes. For its great benefits, MEC has drawn wide attention and extensive works have been done. However, few of them address task migration problem caused by distributed user mobility, which can’t be ignored with quality of service (QoS) consideration. In this article, we study task migration problem and try to minimize the average completion time of tasks under migration energy budget. There are multiple independent users and the movement of each mobile user is memoryless with a sequential decision-making process, thus reinforcement learning algorithm based on Markov chain model is applied with low computation complexity. To further facilitate cooperation among users, we devise a distributed task migration algorithm based on counterfactual multi-agent (COMA) reinforcement learning approach to solve this problem. Extensive experiments are carried out to assess the performance of this distributed task migration algorithm. Compared with no migrating (NM) and single-agent actor-critic (AC) algorithms, the proposed distributed task migration algorithm can achieve up 30-50 percent reduction about average completion time. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things, the data that needs to be processed is increasing rapidly. Therefore, the collaboration of cloud and edge emerges as the times require. Edge nodes are mainly responsible for collecting data, and decide to process the data locally or offload to cloud data centers. Cloud data centers are suitable for data analysis, model training, and managing edge nodes. In this article, we focus on the task assignment problems in collaborative edge and cloud environments and study it in a distributed, non-cooperative environment. An M/M/1 queueing model is established to characterize the task transmission. Because of the multi-core processors, we set an M/M/C queueing model to characterize the task computation. We consider the problem from the perspective of game theory and formulate it into a non-cooperative game among multi-agents (multiple edge data centers) in which each agent is informed with incomplete information (allocation strategies) of others. For each agent, we define a function of the expected cost of tasks as the disutility function, and minimize it subject to the QoS constraint. We analyze the existence of Nash equilibrium and develop a Greedy Energy-aware Algorithm (GEA) to choose active servers using the Limit Searching Algorithm (LSA) to find the ceiling utilization. Then we propose the Best Response Algorithm (BRA) to optimize the utility function. The convergence of the BRA algorithm has been discussed. Finally, the results demonstrate that the BRA algorithm can get a solution close to Nash equilibrium and reach it quickly. [ABSTRACT FROM AUTHOR]

Once defined, the access control policies and regulations would never be changed in a running and state transition process. However, it will give attackers the possibility of discovering vulnerabilities in the system, and the control systems lack the ability of dynamic perception of security state and risk, causing the systems to be exposed to risks. In this article, a dynamic Bell–LaPadula (BLP) model is proposed. The conditional random field (CRF) is introduced into the BLP model to optimize the rules. First, the model formalizes the security attributes, states of system, transition rules, and constraint models on the basis of the state transition of CRFs. After the historical system access logs are processed as the original dataset, a feature selection method is proposed to extract the requests and current states as feature vectors. Second, this article presents a rules training algorithm based on L-BFGS to implement the study and training of datasets, and then marks the logs in the test set through Viterbi algorithm automatically. On the base of these, a rule generation algorithm is proposed to dynamically adjust the access control rules based on the current security status and events of the system. Third, the security of CRFs-BLP is proved by theoretical analysis. Finally, the validity and accuracy of the model are verified by estimating the value of the precision, recall, and $F1$ -score. As the system threats are shown to be decreased obviously from these experiments, this dynamic model can decrease the vulnerabilities and risk effectively. [ABSTRACT FROM AUTHOR]

In this article, we investigate code-oriented partitioning computation offloading strategy for multiple user equipments (UEs) and multiple mobile edge computing servers with limited resources (i.e., limited computing power and waiting task queues with finite capacity). This article aims to develop an offloading strategy to decide the execution location, CPU frequency, and transmission power for UE while minimizing the execution overhead (i.e., a weighted sum of energy consumption and computational time) of UE's applications, which is an NP-hard problem. To achieve the objective, first, we transform the problem into a convex optimization problem and find the optimal solution. Second, we propose a decentralized computation offloading strategy (DCOS) algorithm for UE, and define a dictionary data structure for recording the strategy of the UE to reduce the algorithm complexity. Finally, the effectiveness of DCOS, and the impact of various key parameters on the strategy and overhead are demonstrated by simulation experiments. [ABSTRACT FROM AUTHOR]

In this article, two improved finite-time convergent complex-valued zeroing neural network (IFTCVZNN) models are presented and investigated for real-time solution of time-varying reciprocal of complex matrices on account of two equivalent processing ways of complex calculations for nonlinear activation functions. Furthermore, a novel nonlinear activation function is explored to modify the comprehensive performance of such two IFTCVZNN models. Compared with existing complex-valued neural networks converging within the limited time, the proposed IFTCVZNN models with the new activation function have better finite-time convergence and less conservative upper bound. Numerical simulations verify that the maximum of convergence time estimated via Lyapunov stability is theoretically much closer to the actual convergence time. [ABSTRACT FROM AUTHOR]

In currently popular access control models, the security policies and regulations never change in the running system process once they are identified, which makes it possible for attackers to find the vulnerabilities in a system, resulting in the lack of ability to perceive the system security status and risks in a dynamic manner and exposing the system to such risks. By introducing the maximum entropy (MaxENT) models into the rule optimization for the Bell–LaPadula (BLP) model, this paper proposes an improved BLP model with the self-learning function: MaxENT-BLP. This model first formalizes the security properties, system states, transformational rules, and a constraint model based on the states transition of the MaxENT. After handling the historical system access logs as the original data sets, this model extracts the user requests, current states, and decisions to act as the feature vectors. Second, we use $k$ -fold cross validation to divide all vectors into a training set and a testing set. In this paper, the model training process is based on the Broyden–Fletcher–Goldfarb–Shanno algorithm. And this model contains a strategy update algorithm to adjust the access control rules dynamically according to the access and decision records in a system. Third, we prove that MaxENT-BLP is secure through theoretical analysis. By estimating the precision, recall, and F1-score, the experiments show the availability and accuracy of this model. Finally, this paper provides the process of model training based on deep learning and discussions regarding adversarial samples from the malware classifiers. We demonstrate that MaxENT-BLP is an appropriate choice and has the ability to help running information systems to avoid more risks and losses. [ABSTRACT FROM PUBLISHER]

The extreme learning machine (ELM) has become one of the most important and popular algorithms of machine learning, because of its extremely fast training speed, good generalization, and universal approximation/classification capability. The proposal of hierarchical ELM (H-ELM) extends ELM from single hidden layer feedforward networks to multilayer perceptron, greatly strengthening the applicability of ELM. Generally speaking, during training H-ELM, large-scale datasets (DSTs) are needed. Therefore, how to make use of H-ELM framework in processing big data is worth further exploration. This paper proposes a parallel H-ELM algorithm based on Flink, which is one of the in-memory cluster computing platforms, and graphics processing units (GPUs). Several optimizations are adopted to improve the performance, such as cache-based scheme, reasonable partitioning strategy, memory mapping scheme for mapping specific Java virtual machine objects to buffers. Most importantly, our proposed framework for utilizing GPUs to accelerate Flink for big data is general. This framework can be utilized to accelerate many other variants of ELM and other machine learning algorithms. To the best of our knowledge, it is the first kind of library, which combines in-memory cluster computing with GPUs to parallelize H-ELM. The experimental results have demonstrated that our proposed GPU-accelerated parallel H-ELM named as GPH-ELM can efficiently process large-scale DSTs with good performance of speedup and scalability, leveraging the computing power of both CPUs and GPUs in the cluster. [ABSTRACT FROM AUTHOR]

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining data-parallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious. [ABSTRACT FROM PUBLISHER]

Processing large volumes of data has presented a challenging issue, particularly in data-redundant systems. As one of the most recognized models, the conditional random fields (CRF) model has been widely applied in biomedical named entity recognition (Bio-NER). Due to the internally sequential feature, performance improvement of the CRF model is nontrivial, which requires new parallelized solutions. By combining and parallelizing the limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) and Viterbi algorithms, we propose a parallel CRF algorithm called MapReduce CRF (MRCRF) in this paper, which contains two parallel sub-algorithms to handle two time-consuming steps of the CRF model. The MapReduce L-BFGS (MRLB) algorithm leverages the MapReduce framework to enhance the capability of estimating parameters. Furthermore, the MapReduce Viterbi (MRVtb) algorithm infers the most likely state sequence by extending the Viterbi algorithm with another MapReduce job. Experimental results show that the MRCRF algorithm outperforms other competing methods by exhibiting significant performance improvement in terms of time efficiency as well as preserving a guaranteed level of correctness. [ABSTRACT FROM PUBLISHER]