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State synchronization in process-oriented chaincode
Lian YU, Wei-Tek TSAI
Front. Comput. Sci.    https://doi.org/10.1007/s11704-017-6484-z
Abstract   PDF (1076KB)

Business processes often involve operational processes, contracts, and regulations. The modeling of such processes must address regulation monitoring and enforcement and maintain a reliable history of data for evidence. This study proposes modeling business processes as chaincode (CC) on permissioned blockchains (BCs). The challenges encountered by the proposed approach are state synchronizations among distributed nodes (called authnodes)and realtime requirements. This study separates CC executions from the state management of multiple BCs and demonstrates the validity of the proposed approach with a payment authorization system at a Chinese bank.

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A maximum margin clustering algorithm based on indefinite kernels
Hui XUE, Sen LI, Xiaohong CHEN, Yunyun WANG
Front. Comput. Sci.    https://doi.org/10.1007/s11704-018-7402-8
Abstract   PDF (1875KB)

Indefinite kernels have attracted more and more attentions in machine learning due to its wider application scope than usual positive definite kernels. However, the research about indefinite kernel clustering is relatively scarce. Furthermore, existing clustering methods are mainly designed based on positive definite kernels which are incapable in indefinite kernel scenarios. In this paper, we propose a novel indefinite kernel clustering algorithm termed as indefinite kernel maximum margin clustering (IKMMC) based on the state-of-the-art maximum margin clustering (MMC) model. IKMMC tries to find a proxy positive definite kernel to approximate the original indefinite one and thus embeds a new F-norm regularizer in the objective function to measure the diversity of the two kernels, which can be further optimized by an iterative approach. Concretely, at each iteration, given a set of initial class labels, IKMMC firstly transforms the clustering problem into a classification one solved by indefinite kernel support vector machine (IKSVM) with an extra class balance constraint and then the obtained prediction labels will be used as the new input class labels at next iteration until the error rate of prediction is smaller than a prespecified tolerance. Finally, IKMMC utilizes the prediction labels at the last iteration as the expected indices of clusters. Moreover, we further extend IKMMC from binary clustering problems to more complexmulti-class scenarios. Experimental results have shown the superiority of our algorithms.

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Safe semi-supervised learning: a brief introduction
Yu-Feng LI, De-Ming LIANG
Front. Comput. Sci.    https://doi.org/10.1007/s11704-019-8452-2
Abstract   PDF (467KB)

Semi-supervised learning constructs the predictive model by learning from a few labeled training examples and a large pool of unlabeled ones. It has a wide range of application scenarios and has attracted much attention in the past decades. However, it is noteworthy that although the learning performance is expected to be improved by exploiting unlabeled data, some empirical studies show that there are situations where the use of unlabeled data may degenerate the performance. Thus, it is advisable to be able to exploit unlabeled data safely. This article reviews some research progress of safe semi-supervised learning, focusing on three types of safeness issue: data quality, where the training data is risky or of low-quality;model uncertainty, where the learning algorithm fails to handle the uncertainty during training; measure diversity, where the safe performance could be adapted to diverse measures.

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SLA-driven container consolidation with usage prediction for green cloud computing
Jialei LIU, Shangguang WANG, Ao ZHOU, Jinliang XU, Fangchun YANG
Front. Comput. Sci.    https://doi.org/10.1007/s11704-018-7172-3
Abstract   PDF (542KB)

Since service level agreement (SLA) is essentially used to maintain reliable quality of service between cloud providers and clients in cloud environment, there has been a growing effort in reducing power consumption while complying with the SLA by maximizing physical machine (PM)-level utilization and load balancing techniques in infrastructure as a service. However, with the recent introduction of container as a service by cloud providers, containers are increasingly popular and will become the major deployment model in the cloud environment and specifically in platform as a service. Therefore, reducing power consumption while complying with the SLA at virtual machine (VM)-level becomes essential. In this context, we exploit a container consolidation scheme with usage prediction to achieve the above objectives. To obtain a reliable characterization of overutilized and underutilized PMs, our scheme jointly exploits the current and predicted CPU utilization based on local history of the considered PMs in the process of the container consolidation. We demonstrate our solution through simulations on real workloads. The experimental results show that the container consolidation scheme with usage prediction reduces the power consumption, number of container migrations, and average number of active VMs while complying with the SLA.

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AAMcon: an adaptively distributed SDN controller in data center networks
Waixi LIU, Yu WANG, Jie ZHANG, Hongjian LIAO, Zhongwei LIANG, Xiaochu LIU
Front. Comput. Sci.    https://doi.org/10.1007/s11704-019-7266-6
Abstract   PDF (691KB)

When evaluating the performance of distributed software-defined network (SDN) controller architecture in data center networks, the required number of controllers for a given network topology and their location are major issues of interest. To address these issues, this study proposes the adaptively adjusting and mapping controllers (AAMcon) to design a stateful data plane. We use the complex network community theory to select a key switch to place the controller which is closer to switches it controls in a subnet. A physically distributed but logically centralized controller pool is built based on the network function virtualization (NFV). And then we propose a fast start/overload avoid algorithm to adaptively adjust the number of controllers according to the demand. We performed an analysis for AAMcon to find the optimal distance between the switch and controller. Finally, experiments show the following results. (1) For the number of controllers, AAMcon can greatly follow the demand; for the placement location of controller, controller can respond to the request of switch with the least distance to minimize the delay between the switch and it. (2) For failure tolerance, AAMcon shows good robustness. (3) AAMcon requires less delay to the network with more significant community structure. In fact, there is an inverse relationship between the community modularity and average distance between the switch and controller, i.e., the average delay decreases when the community modularity increases.(4) AAMcon can achieve the load balance between the controllers. (5) Compared to DCP-GK and k-critical, AAMcon shows good performance

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Ordinal factorization machine with hierarchical sparsity
Shaocheng GUO, Songcan CHEN, Qing TIAN
Front. Comput. Sci.    https://doi.org/10.1007/s11704-019-7290-6
Abstract   PDF (821KB)

Ordinal regression (OR) or classification is a machine learning paradigm for ordinal labels. To date, there have been a variety of methods proposed including kernel based and neural network based methods with significant performance. However, existing OR methods rarely consider latent structures of given data, particularly the interaction among covariates, thus losing interpretability to some extent. To compensate this, in this paper, we present a new OR method: ordinal factorization machine with hierarchical sparsity (OFMHS), which combines factorization machine and hierarchical sparsity together to explore the hierarchical structure behind the input variables. For the sake of optimization, we formulate OFMHS as a convex optimization problem and solve it by adopting the efficient alternating directions method of multipliers (ADMM) algorithm. Experimental results on synthetic and real datasets demonstrate the superiority of our method in both performance and significant variable selection.

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