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Shear wave velocity (VS) is one of the most important parameters in deep and surface studies and the estimation of geotechnical design parameters. This parameter is widely utilized to determine permeability and porosity, lithology, rock mechanical parameters, and fracture assessment. However, measuring this important parameter is either impossible or difficult due to the challenges related to horizontal and deviation wells or the difficulty in reaching cores. Artificial Intelligence (AI) techniques, especially Machine Learning (ML), have emerged as efficient approaches for dealing with such challenges. Therefore, considering the advantage of the ML, the current research proposes a novel Fully-Self-Adaptive Harmony Search—Group Method of Data Handling (GMDH)-type neural network, named FSHS-GMDH, to estimate the VS parameter. In this way, the Harmony Memory Consideration Rate (HMCR) and Pitch Adjustment Rate (PAR) parameters are calculated automatically. A novel method is also introduced to adjust the value of the Bandwidth (BW) parameter based on the cosine wave and each decision variable values. In addition, a variable-size harmony memory is proposed to enhance both the diversification and intensification. Our proposed FSHS-GMDH algorithm quickly explores the problem space and exploits the best regions at the late iterations. This algorithm allows for the training of the prediction model based on the P-wave velocity (VP) and the bulk density of rock (RHOB). Applying the proposed algorithm to a carbonate petroleum reservoir in the Persian Gulf demonstrates that it is capable of accurately estimating the VS parameter better than state-of-the-art machine learning methods in terms of the coefficient of determination (R2), Mean Square Error (MSE), and Root Mean Square Error (RMSE). [ABSTRACT FROM AUTHOR]

Virtualization as one of the leading technologies has assisted datacenters to cloudify their products and provide versatile platforms and variety of Internet services. This technology also has facilitated agile deployment of complex Internet services such as Cloud-based multi-tier applications. However implementing multi-tier applications assists providers to set up flexible and scalable services, deploying such applications in virtualized environments faces challenges, which harden collocation of disparate multi-tier applications in a shared environment. In this article, we propose a placement strategy to consolidate multi-tier applications' tiers in a virtualized datacenter regarding workload demands of individual tiers, utilization of physical hosts' resources and operational status of virtual machines. The placement strategy identifies over-utilized hosts based on the birth--death stochastic process and ranks tiers based on Data Envelopment Analysis-Analytic Hierarchy Process modeling to be placed on target hosts. A prioritized tier will be placed on an appropriate host though least operational interference with hosted tiers incurred. The efficiency of the proposed model is evaluated using a series of in-depth experiments. We introduce results derived from quantitative and qualitative analyses that are useful for multi-tier applications placement in virtualized datacenters. Simulation results reveal that the proposed solution excels in terms of both load distribution and energy consumption in the datacenter, while the number of unnecessary migrations and consecutive Service Level Agreement violations is considerably reduced. [ABSTRACT FROM AUTHOR]

Recent developments in the field of virtualization technologies have led to renewed interest in performance evaluation of these systems. Nowadays, maturity of virtualization technology has made a fuss of provisioning IT services to maximize profits, scalability and QoS. This pioneer solution facilitates deployment of datacenter applications and grid and Cloud computing services; however, there are challenges. It is necessary to investigate a trade-off among overall system performance and revenue and to ensure service-level agreement of submitted workloads. Although a growing body of literature has investigated virtualization overhead and virtual machines interference, there is still lack of accurate performance evaluation of virtualized systems. In this paper, we present in-depth performance measurements to evaluate a Xen-based virtualized Web server. Regarding this experimental study; we support our approach by queuing network modeling. Based on these quantitative and qualitative analyses, we present the results that are important for performance evaluation of consolidated workloads on Xen hypervisor. First, demands of both CPU intensive and disk intensive workloads on CPU and disk are independent from the submitted rate to unprivileged domain when dedicated core(s) are pinned to virtual machines. Second, request response time not only depends on processing time at unprivileged domain but also pertains to amount of flipped pages at Domain 0. Finally, results show that the proposed modeling methodology performs well to predict the QoS parameters in both para-virtualized and hardware virtual machine modes by knowing the request content size. Copyright © 2015 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Quality of service (QoS) provisioning in mobile ad hoc networks (MANETs) is one of the fundamental concerns in this area. Autonomous mobility of nodes is one of the main factors affecting the network QoS. A lack of fixed infrastructure and central control on the nodes complicate guaranteeing the QoS in MANETs. Security is a major factor affecting the QoS in such networks. This paper proposes a new routing method based on trust among the nodes and applies a clustering approach to MANETs. This routing scheme can calculate the trustworthiness of the route and the shortest routes to the destination by avoiding identified malicious nodes. The effects of node mobility and number of malicious nodes on routing performances are investigated in the reported research. Simulation is used to show the effectiveness of the proposed approach in comparison with a categorized trust method, Ad hoc On-demand Distance Vector, trust vector-based Ad hoc On-demand Distance Vector, and trust vector-based dynamic source routing in terms of end-to-end delay, network throughput, and packet delivery ratio at the expense of a moderate increase in the routing overhead. The proposed trust-based routing method finds the trustworthy route by applying end-to-end trust calculation and finds the optimal route by performing clustering scheme from the source node to the destination node. In the extended proposed routing method, adding a nonce-based encryption scheme results in improved packet delivery ratio, network throughput, and malicious node detection rate. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Various studies have shown the benefits of using distributed fog computing for healthcare systems. The new pattern of fog and edge computing reduces latency for data processing compared to cloud computing. Nevertheless, the proposed fog models still have many limitations in improving system performance and patients' response time.This paper, proposes a new performance model by integrating fog computing, priority queues and certainty theory into the Edge computing devices and validating it by analyzing heart disease patients' conditions in clinical decision support systems (CDSS). In this model, a Certainty Factor (CF) value is assigned to each symptom of heart disease. When one or more symptoms show an abnormal value, the patient's condition will be evaluated using CF values in the fog layer. In the fog layer, requests are categorized in different priority queues before arriving into the system. The results demonstrate that network usage, latency, and response time of patients' requests are respectively improved by 25.55%, 42.92%, and 34.28% compared to the cloud model. Prioritizing patient requests with respect to CF values in the CDSS provides higher system Quality of Service (QoS) and patients' response time.