Cloud computing, a key enabling technology for speedy wide-area networks are one of the emerging technologies in Computer Science. One of the pressing challenges in the ongoing research in this particular domain is da...
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ISBN:
(纸本)9781728194417
Cloud computing, a key enabling technology for speedy wide-area networks are one of the emerging technologies in Computer Science. One of the pressing challenges in the ongoing research in this particular domain is data confidentiality. There is an immense need to preserve important data and confidentiality in cloud computing, which means a more critical requirement for robust mechanisms to protect it from attackers or intruders. This paper proposes a broad and thorough probing multiple simulation-based software that examines and evaluates the execution, extensibility, strength, and intricacy of Cloud data center. This paper analyzes different Cloud services using Cloud-Sim, Cloud Analyst, and OPNET simulation tool. Services, cloud management, load-distributing, cloud strength, and expand-ability are the main scope of this paper. Hence, results are compared in the real-time framework to identify the performance and extensibility, as well as consider the experiment as challenges for further discussion.
This article discusses an extension of censored quantile regression to a distributed setting. With the growing availability of massive datasets, it is oftentimes an arduous task to analyze all the data with limited co...
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This article discusses an extension of censored quantile regression to a distributed setting. With the growing availability of massive datasets, it is oftentimes an arduous task to analyze all the data with limited computational facilities efficiently. Our proposed method, which attempts to overcome this challenge, is comprised of two key steps, namely: (i) estimation of both Kaplan-Meier estimator and model coefficients in a parallel computing environment;(ii) aggregation of coefficient estimations from individual machines. We study the upper limit of the order of the number of machines for this computing environment, which, if fulfilled, guarantees that the proposed estimator converges at a comparable rate to that of the oracle estimator. In addition, we also provide two further modifications for distributed systems including (i) a communication-facilitated adaptation in the sense of Chen, Liu, and Zhang and (ii) a nonparametric counterpart along the direction of Kong and Xia for censored quantile regression. Numerical experiments are conducted to compare the proposed and the existing estimators. The promising results demonstrate the computation efficiency of the proposed methods. Finally, for practical concerns, a cross-validation procedure is also developed which can better select the hyperparameters for the proposed methodologies. for this article are available online.
We have developed a support vector regression (SVR) accelerated variant of the distributed derivative-free optimization (DFO) method using the limited-memory BFGS Hessian updating formulation (LBFGS) for subsurface fi...
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We have developed a support vector regression (SVR) accelerated variant of the distributed derivative-free optimization (DFO) method using the limited-memory BFGS Hessian updating formulation (LBFGS) for subsurface field-development optimization problems. The SVR-enhanced distributed LBFGS (D-LBFGS) optimizer is designed to effectively locate multiple local optima of highly nonlinear optimization problems subject to numerical noise. It operates both on single- and multiple-objective field-development optimization problems. The basic D-LBFGS DFO optimizer runs multiple optimization threads in parallel and uses the linear interpolation method to approximate the sensitivity matrix of simulated responses with respect to optimized model parameters. However, this approach is less accurate and slows down convergence. In this paper, we implement an effective variant of the SVR method, namely epsilon-SVR, and integrate it into the D-LBFGS engine in synchronous mode within the framework of a versatile optimization library inside a next-generation reservoir simulation platform. Because epsilon-SVR has a closed-form of predictive formulation, we analytically calculate the approximated objective function and its gradients with respect to input model variables subject to optimization. We investigate two different methods to propose a new search point for each optimization thread in each iteration through seamless integration of epsilon-SVR with the D-LBFGS optimizer. The first method estimates the sensitivity matrix and the gradients directly using the analytical epsilon-SVR surrogate and then solves a LBFGS trust-region subproblem (TRS). The second method applies a trust-region search LBFGS method to optimize the approximated objective function using the analytical epsilon-SVR surrogate within a box-shaped trust region. We first show that epsilon-SVR provides accurate estimates of gradient vectors on a set of nonlinear analytical test problems. We then report the results of nume
Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand *** expansion limits exist for traditional standalone ...
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Recently,research on a distributed storage system that efficiently manages a large amount of data has been actively conducted following data production and demand *** expansion limits exist for traditional standalone storage systems,such as I/O and file system ***,the existing distributed storage system does not consider where data is consumed and is more focused on data dissemination and optimizing the lookup cost of data *** this leads to system performance degradation due to low locality occurring in a Wide Area Network(WAN)environment with high network *** problem hinders deploying distributed storage systems to multiple data centers over *** lowers the scalability of distributed storage systems to accommodate data storage *** paper proposes a method for distributing data in a WAN environment considering network latency and data locality to solve this problem and increase overall system *** proposed distributed storage method monitors data utilization and locality to classify data temperature as hot,warm,and *** assigned data temperature,the proposed algorithm adaptively selects the appropriate data center and places data accordingly to overcome the excess latency from the WAN environment,leading to overall system performance *** paper also conducts simulations to evaluate the proposed and existing distributed storage *** result shows that our proposed method reduced latency by 38%compared to the existing ***,the proposed method in this paper can be used in large-scale distributed storage systems over a WAN environment to improve latency and performance compared to existing methods,such as consistent hashing.
Feature selection and classification efficiency and accuracy are key to improving decision-making regarding medical data analysis. Since the medical datasets are large and complex, they give rise to certain problemati...
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Feature selection and classification efficiency and accuracy are key to improving decision-making regarding medical data analysis. Since the medical datasets are large and complex, they give rise to certain problematic issues such as computational complexity, limited memory space, and a lesser number of correct classifications. In order to overcome these drawbacks, the new integrated algorithm is presented here: Synergistic Kruskal-RFE Selector and distributed Multi-Kernel Classification Framework (SKR-DMKCF). The innovative architecture of SKR-DMKCF results in the reduction of dimensionality while preserving useful characteristics of the image utilizing recursive feature elimination and multi-kernel classification in a distributed environment. Detailed evaluations were performed on four broad medical datasets and established our performance advantage. The average feature reduction ratio was 89 % for the proposed method, SKR-DMKCF, which can outperform all the methods by achieving the best classification average accuracy of 85.3 %, precision of 81.5 %, and recall 84.7 %. On the efficiency calculations, it was seen that the memory usage is a 25 % reduction compared to the existing methods and the speed-up time was a significant improvement as well to assure scalability for resource-limited environments. center dot Innovative Synergistic Kruskal-RFE Selector for efficient feature selection in medical datasets. center dot distributed Multi-Kernel Classification Framework achieving superior accuracy and computational efficiency.
distributed computing is a forceful idea in disseminated registering which depicts versatile information to the executives for a minimal price dependent on client interest to various business associations. Because of ...
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distributed computing is a forceful idea in disseminated registering which depicts versatile information to the executives for a minimal price dependent on client interest to various business associations. Because of multi-cloud identity-based encryption over distributed environment, in this document, the authors present and implement a novel identity-based multi-cloud security access control approach (NIMSACPA) for efficient security in multi data security and privacy based on three basic parametric concepts: 1) open minded security between autonomous user privacy using Byzantine protocol, 2) to classify the security privileges with respect to multi-cloud data sharing is described using DepSky Architecture, and 3) for identity-based information distribution between diverse users in CC described using Shamir secret key sharing procedure. This execution gives better and critical execution as far as data stockpiling and information investigation contrast and existing cryptographic techniques alongside practical multi-cloud information.
The parallel processing is an effective approach to solving those high complexity problems which may be represented as a set of independent or loosely coupled subproblems. In the latter case, however, the critical fac...
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The parallel processing is an effective approach to solving those high complexity problems which may be represented as a set of independent or loosely coupled subproblems. In the latter case, however, the critical factor for a computation time is an overhead generated by communication among particular subtasks. The decomposition of a graph-based computational problem allows transforming it into a set of subproblems to be processed in parallel. A decomposition method should guarantee a good performance of Parallel computations with respect to communication and synchronization among agents managing a distributed representation of a considered system. In this paper we present the novel method of a decomposition, reducing coupling among subproblems and thus minimizing a required cooperation among agents. Comparison and performance tests are also included.
We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributed computing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) ...
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ISBN:
(纸本)9798350369458;9798350369441
We study the Traveling Salesman Problem (TSP) in the Congested Clique Model (CCM) of distributed computing. We present a deterministic distributed algorithm that computes a tour for the TSP using O(1) rounds and O(m) messages for a given undirected weighted complete graph of n nodes and m edges with an approximation factor 2 of the optimal. The TSP has wide applications in logistics, planning, manufacturing and testing microchips, DNA sequencing etc., and we claim that our proposed O(1)-rounds approximation algorithm to the TSP, which is fast and efficient, can also be used to minimize the energy consumption in Wireless Sensor Networks.
As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To ...
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ISBN:
(纸本)9798350369205;9798350369199
As semiconductor design approaches physical limits, computer processing speeds are stagnating. This poses significant challenges for traffic simulations, which are becoming more and more computationally demanding. To maintain fast execution times while accommodating more complex simulations, it is essential to utilize the parallel computing capabilities of modern hardware. This paper discusses the need for an updated architectural design in the MATSim traffic simulation framework to take advantage of parallel computing infrastructures. We introduce a prototype that adapts the existing traffic simulation logic to a distributed parallel algorithm. Extensive benchmarks have been conducted to evaluate the prototype's performance and identify its limitations. The results demonstrate that the prototype performs up to 100 times faster than the current implementation. Based on these findings, we advocate for the integration of a distributed traffic simulation within the MATSim framework and outline necessary steps to enhance the prototype.
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