In this letter, we reveal redundant control traffic in the optimized link state routing protocol (OLSR) for MANET. Topology control (TC) messages, which occupy a part of control traffic in OLSR, are used to exchange topology information with other nodes. TC messages are generated and forwarded by only nodes that have been selected as multipoint relays (MPRs) by at least one neighbor node. These nodes selected as MPRs are called TC message senders in this letter. One of solutions to reduce the number of TC messages is to reduce the number of TC message senders. We describe a non-distributed algorithm to minimize the number of TC message senders. Through simulation of static-node scenarios, we show 18% to 37% of TC message senders in RFC-based OLSR are redundant. By eliminating redundant TC message senders, the number of TC packets, each of which contains one or more TC messages, is also reduced from 19% to 46%. We also show that high density scenarios have more redundancy than low density scenarios. This observation can help to consider a cooperative MPR selection in OLSR.

Data accessed by many sites are replicated in distributed environments for performance and availability. In this paper, replication schemes are examined in parallel image convolution processing. This paper presents a system architecture that we have developed with CORBA (Common Object Request Broker Architecture) for the processing. Employing CORBA enables us to make use of a cluster of workstations, each of which has a different level of computing power. The paper also describes a parallel and distributed image convolution processing model using replicas stored in a network of workstations, and reports some experimental results showing that our analytical model can agree with practical situations.

In this research, we propose a novel method to determine fingerprint liveness to improve the discriminative behavior and classification accuracy of the combined features. This approach detects if a fingerprint is from a live or fake source. In this approach, fingerprint images are analyzed in the differential excitation (DE) component and the centralized binary pattern (CBP) component, which yield the DE image and CBP image, respectively. The images obtained are used to generate a two-dimensional histogram that is subsequently used as a feature vector. To decide if a fingerprint image is from a live or fake source, the feature vector is processed using support vector machine (SVM) classifiers. To evaluate the performance of the proposed method and compare it to existing approaches, we conducted experiments using the datasets from the 2011 and 2015 Liveness Detection Competition (LivDet), collected from four sensors. The results show that the proposed method gave comparable or even better results and further prove that methods derived from combination of features provide a better performance than existing methods.

This paper proposes a time-interval data model in which all temporal representation and operations can be expressed with time intervals. The model expresses not only real time intervals, in which an event exists, but also null time intervals, in which an event is suspended. We model the history of a real-world event as a composite time interval, which is defined in this paper. Operations on the composite time intervals are also defined, and it is shown how these operations can be used to express temporal constraints with time intervals.

When a set of objects is shared among several applications, multiple implementations for the set are required in order to suit each application as much as possible. Furthermore, if a set of objects could have multiple implementations, the following issues arise: (1) how to select the best implementation when processing queries on the set, and (2) how to propagate updates on an implementation of the set to the others. In this paper we propose a mechanism of multiple implementations for a set, and also give a solution for the latter issue. In the proposal a set can be of multiple types, and each of the types corresponds to an implementation already contained within the set. Update propagation can be achieved by a rewriting technique at compilation time. We also present a performance study in which the feasibility and effectiveness of our proposal were examined.

The Infrastructure as a Service (IaaS) Clouds are emerging as a promising platform for the execution of resource demanding and computation intensive workflow applications. Scheduling the execution of scientific applications expressed as workflows on IaaS Clouds involves many uncertainties due to the variable and unpredictable performance of Cloud resources. These uncertainties are modeled by probability distribution functions in past researches or totally ignored in some cases. In this paper, we propose a novel robust deadline constrained workflow scheduling algorithm which handles the uncertainties in scheduling workflows in the IaaS Cloud environment. Our proposal is a static scheduling algorithm aimed at addressing the uncertainties related to: the estimation of task execution times; and, the delay in provisioning computational Cloud resources. The workflow scheduling problem was considered as a cost-optimized, deadline-constrained optimization problem. Our uncertainty handling strategy was based on the consideration of knowledge of the interval of uncertainty, which we used to modeling the execution times rather than using a known probability distribution function or precise estimations which are known to be very sensitive to variations. Experimental evaluations using CloudSim with synthetic workflows of various sizes show that our proposal is robust to fluctuations in estimates of task runtimes and is able to produce high quality schedules that have deadline guarantees with minimal penalty cost trade-off depending on the length of the interval of uncertainty. Scheduling solutions for varying degrees of uncertainty resisted against deadline violations at runtime as against the static IC-PCP algorithm which could not guarantee deadline constraints in the face of uncertainty.

We present an economical and fault-tolerant load balancing strategy (EFTLBS) based on an operator replication mechanism and a load shedding method, that fully utilizes the network resources to realize continuous and highly-available data stream processing without dynamic operator migration over wide area networks. In this paper, we first design an economical operator distribution (EOD) plan based on a bin-packing model under the constraints of each stream bandwidth as well as each server's CPU capacity. Next, we devise super-operator (SO) that load balances multi-degree operator replicas. Moreover, for improving the fault-tolerance of the system, we color the SOs based on a coloring bin-packing (CBP) model that assigns peer operator replicas to different servers. To minimize the effects of input rate bursts upon the system, we take advantage of a load shedding method while keeping the QoS guarantees made by the system based on the SO scheme and the CBP model. Finally, we substantiate the utility of our work through experiments on ns-3.

This paper describes a way of implementing a conceptual model for temporal data on a commercial object database system. The implemented version is provided as a class library. The library enables applications to handle temporal data. Any application can employ the library because it does not depend on specific applications. Furthermore, we propose an enhanced version of Time Index. The index efficiently processes event queries in particular. These queries search time intervals in which given events are all valid. We also investigate the effectiveness of the enhanced Time Index.

New deblocking artifact, or blocking artifact reduction, algorithms based on nonlinear adaptive soft-threshold anisotropic filter in wavelet are proposed. Our deblocking algorithm uses soft-threshold, adaptive wavelet direction, adaptive anisotropic filter, and estimation. The novelties of this paper are an adaptive soft-threshold for deblocking artifact and an optimal intersection of confidence intervals (OICI) method in deblocking artifact estimation. The soft-threshold values are adaptable to different thresholds of flat area, texture area, and blocking artifact. The OICI is a reconstruction technique of estimated deblocking artifact which improves acceptable quality level of estimated deblocking artifact and reduces execution time of deblocking artifact estimation compared to the other methods. Our adaptive OICI method outperforms other adaptive deblocking artifact methods. Our estimated deblocking artifact algorithms have up to 98% of MSE improvement, up to 89% of RMSE improvement, and up to 99% of MAE improvement. We also got up to 77.98% reduction of computational time of deblocking artifact estimations, compared to other methods. We have estimated shift and add algorithms by using Euler++(E++) and Runge-Kutta of order 4++ (RK4++) algorithms which iterate one step an ordinary differential equation integration method. Experimental results showed that our E++ and RK4++ algorithms could reduce computational time in terms of shift and add, and RK4++ algorithm is superior to E++ algorithm.

Data replication in cloud storage systems brings a lot of benefits, such as fault tolerance, data availability, data locality and load balancing both from reliability and performance perspectives. However, each time a datanode fails, data blocks stored on the failed datanode must be restored to maintain replication level. This may be a large burden for the system in which resources are highly utilized with users' application workloads. Although there have been many proposals for replication, the approach of re-replication has not been properly addressed yet. In this paper, we present a deferred re-replication algorithm to dynamically shift the re-replication workload based on current resource utilization status of the system. As workload pattern varies depending on the time of the day, simulation results from synthetic workload demonstrate a large opportunity for minimizing impacts on users' application workloads with the simple algorithm that adjusts re-replication based on current resource utilization. Our approach can reduce performance impacts on users' application workloads while ensuring the same reliability level as default HDFS can provide.