Current Issue : April - June Volume : 2015 Issue Number : 2 Articles : 6 Articles
With the great risk exposed in IT outsourcing, how to assess IT outsourcing risk becomes a critical issue. However, most of\napproaches to date need to further adapt to the particular complexity of IT outsourcing risk for either falling short in subjective bias,\ninaccuracy, or efficiency. This paper proposes a dynamic algorithm of risk assessment. It initially forwards extended three layers\n(risk factors, risks, and risk consequences) of transferring mechanism based on transaction cost theory (TCT) as the framework\nof risk analysis, which bridges the interconnection of components in three layers with preset transferring probability and impact.\nThen, it establishes an equation group between risk factors and risk consequences, which assures the ââ?¬Å?attributionââ?¬Â more precisely\nto track the specific sources that lead to certain loss. Namely, in each phase of the outsourcing lifecycle, both the likelihood and\nthe loss of each risk factor and those of each risk are acquired through solving equation group with real data of risk consequences\ncollected. In this ââ?¬Å?reverseââ?¬Â way, risk assessment becomes a responsive and interactive process with real data instead of subjective\nestimation, which improves the accuracy and alleviates bias in risk assessment. The numerical case proves the effectiveness of the\nalgorithm compared with the approach forwarded by other references....
Voting is an important operation in multichannel computation paradigm and realization of ultrareliable and real-time control\nsystems that arbitrates among the results of N redundant variants. These systems include ????-modular redundant (NMR) hardware\nsystems and diversely designed software systems based on ????-version programming (NVP). Depending on the characteristics of\nthe application and the type of selected voter, the voting algorithms can be implemented for either hardware or software systems. In\nthis paper, a novel voting algorithm is introduced for real-time fault-tolerant control systems, appropriate for applications in which\nN is large. Then, its behavior has been software implemented in different scenarios of error-injection on the system inputs. The\nresults of analyzed evaluations through plots and statistical computations have demonstrated that this novel algorithm does not\nhave the limitations of some popular voting algorithms such as median and weighted; moreover, it is able to significantly increase\nthe reliability and availability of the system in the best case to 2489.7% and 626.74%, respectively, and in the worst case to 3.84%\nand 1.55%, respectively....
Animal migration optimization (AMO) is one of the most recently introduced algorithms based on the behavior of animal swarm\nmigration. This paper presents an improved AMO algorithm (IAMO), which significantly improves the original AMO in solving\ncomplex optimization problems. Clustering is a popular data analysis and data mining technique and it is used in many fields.\nThe well-known method in solving clustering problems is K-means clustering algorithm; however, it highly depends on the initial\nsolution and is easy to fall into local optimum. To improve the defects of the K-means method, this paper used IAMO for the\nclustering problem and experiment on synthetic and real life data sets. The simulation results show that the algorithm has a better\nperformance than that of the K-means, PSO, CPSO, ABC, CABC, and AMO algorithm for solving the clustering problem....
Resource allocation is expected to be amost important factor especially for heterogeneous applications in wireless ad hoc networks.\nIn this paper, a novel heterogeneous resource allocation algorithm (HRA) is presented for ad hoc networks, supporting both elastic\nand inelastic traffic. First, by combining the first order Lagrangian method with pseudo utility, the original nonconvex problem\nis converted into a new convex one. Then, we successfully solve the heterogeneous problem with the dual-based decomposition\napproach. In addition,we integrate utility fairness into the resource allocation framework,which can adaptivelymanage the tradeoff\nbetween elastic and inelastic flows. Simulations show and prove that HRA converges fast and can achieve the global optimum\nstarting from many different network conditions, such as elastic, inelastic, and hybrid scenario. With both considerations of flow\nrate and utility fairness, HRA improves the overall network utility and system throughput greatly....
The present study investigates the Haar-Sinc collocation method for the solution of the hyperbolic partial telegraph equations. The\nadvantages of this technique are that not only is the convergence rate of Sinc approximation exponential but the computational\nspeed also is high due to the use of the Haar operational matrices. This technique is used to convert the problem to the solution\nof linear algebraic equations via expanding the required approximation based on the elements of Sinc functions in space and Haar\nfunctions in time with unknown coefficients. To analyze the efficiency, precision, and performance of the proposed method, we\npresented four examples through which our claim was confirmed....
Heterogeneous network concept was introduced to satisfy the demands of network�s traffic capacity and data rate. It consists of\nmultiplatformnetworks with various radio access technologies.Conventionally, a mobile usermay roamand accomplish the vertical\nhandover using single criteria, such as received signal strength (RSS). Single criteria vertical handover decision, however, may cause\ninefficient handoff, unbalanced network load, and service interruption. This paper proposed an improved vertical handover decision\nusing multicriteria metrics in the environment of heterogeneous network consisting of three network interfaces: (i) wireless local\narea network (WLAN), (ii) wideband code divisionmultiple access (WCDMA), and (iii) worldwide interoperability for microwave\naccess (WiMAX). In the vertical handover decision, four metrics are considered: (i) RSS, (ii) mobile speed, (iii) traffic class, and (iv)\nnetwork occupancy.There are three types of the vertical handover decision algorithm: (i) equal priority, (ii) mobile priority, and (iii)\nnetwork priority. Equal prioritymulticriteria handover algorithmimproved the number of handoffs by 46.60 whilemobile priority\nmulticriteria algorithm improved the number of handoffs by 90.41% and improved the balance index by 0.09%. Network priority\nmulticriteria method improved the number of handoffs by 84.60%, balance index by 18.03%, and average blocking probability by\n20.23%....
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