A survey and taxonomy on energy efficient resource allocation techniques for cloud computing systems

In a cloud computing paradigm, energy efficient allocation of different virtualized ICT resources (servers, storage disks, and networks, and the like) is a complex problem due to the presence of heterogeneous application (e.g., content delivery networks, MapReduce, web applications, and the like) workloads having contentious allocation requirements in terms of ICT resource capacities (e.g., network bandwidth, processing speed, response time, etc.). Several recent papers have tried to address the issue of improving energy efficiency in allocating cloud resources to applications with varying degree of success. However, to the best of our knowledge there is no published literature on this subject that clearly articulates the research problem and provides research taxonomy for succinct classification of existing techniques. Hence, the main aim of this paper is to identify open challenges associated with energy efficient resource allocation. In this regard, the study, first, outlines the problem and existing hardware and software-based techniques available for this purpose. Furthermore, available techniques already presented in the literature are summarized based on the energy-efficient research dimension taxonomy. The advantages and disadvantages of the existing techniques are comprehensively analyzed against the proposed research dimension taxonomy namely: resource adaption policy, objective function, allocation method, allocation operation, and interoperability.     

A Unified Variational Volume Registration Method Based on Automatically Learned Brain Structures

We introduce a new volumetric registration technique that effectively combines active surfaces with the finite element method. The method simultaneously aligns multi-label automatic structural segmentation results, which can be obtained by the application of existing segmentation software, to produce an anatomically accurate 3D registration. This registration is obtained by the minimization of a single energy functional. Just like registering raw images, obtaining a 3D registration this way still requires solving a fundamentally ill-posed problem. We explain through academic examples as well as an MRI dataset with manual anatomical labels, which are hidden from the registration method, how the quality of a registration method can be measured and the advantages our approach offers.     

Tri‐band composite cylindrical dielectric resonator antenna with hybrid mode excitation and cross‐polarization suppression

This communication investigates composite cylindrical dielectric resonator antenna (CDRA) for various wireless applications. Three important features of proposed antenna design are (i) realization of two different hybrid modes, that is, HEM_11δ and HEM_12δ mode in CDRA with the help of modified annular ring printed line (work as both magnetic dipole and electric dipole), both the hybrid modes support broadside radiation characteristics (ii) suppression of HEM_21δ mode, in order to reduce the cross‐polarization level in H‐plane of other hybrid modes (HEM_11δ and HEM_12δ mode) by an amount of 8‐10 dB (iii) creation of triple‐band attribute using the concept of composite antenna. The proposed antenna design has been fabricated and practically tested. Simulated outcomes show good agreement with measured outcomes. It works in three frequency bands, that is, 2.25‐2.79 GHz, 3.1‐4.0 GHz, and 5.05‐5.6 GHz. The designed antenna structure is appropriate for WLAN and WiMAX applications.     

A novel bacterial foraging technique for edge detection

A new approach for edge detection using a combination of bacterial foraging algorithm (BFA) and probabilistic derivative technique derived from Ant Colony Systems, is presented in this paper. The foraging behavior of some species of bacteria like Escherichia coli can be hypothetically modeled as an optimization process. A group of bacteria search for nutrients in a way that maximizes the energy obtained per unit time spent during the foraging. The proposed approach aims at driving the bacteria through the edge pixels. The direction of movement of the bacteria is found using a direction probability matrix, computed using derivatives along the possible directions. Rules defining the derivatives are devised to ensure that the variation of intensity due to noise is discarded. Quantitative analysis of the feasibility of the proposed approach and its comparison with other standard edge detection operators in terms of kappa and entropy are given. The effect of initial values of parameters of BFA on the edge detection is discussed.     

Adaptive simulation for system reliability analysis of large structures

This article proposes an efficient simulation-based methodology to estimate the system reliability of large structures. The proposed method uses a hybrid approach: first, a probabilistic enumeration technique is used to identify a significant system failure sequence. This provides an initial sampling domain for an adaptive importance sampling procedure. As further simulations are performed, information about other significant sequences is incorporated to refine the sampling domain and to estimate the failure probability of the system. The adaptive sampling overcomes the restrictive assumptions of analytical techniques, yet achieves the robustness and accuracy of basic Monte Carlo simulation in an efficient manner. In this article, the proposed method is implemented using the ANSYS finite element software, and is applied to the system reliability estimation of two redundant structural systems, a six-story building frame and a transmission tower. Both ductile and brittle failure modes are considered. The method is structured in a modular form such that it can be easily applied to different types of problems and commercial software, thus facilitating practical application.     

Towards the development of a virtual environment-based training system for mechanical assembly operations

In this paper, we discuss the development of Virtual Training Studio (VTS), a virtual environment-based training system that allows training supervisors to create training instructions and allows trainees to learn assembly operations in a virtual environment. Our system is mainly focused on the cognitive side of training so that trainees can learn to recognize parts, remember assembly sequences, and correctly orient the parts during assembly operations. Our system enables users to train using the following three training modes: (1) Interactive Simulation, (2) 3D Animation, and (3) Video. Implementing these training modes required us to develop several new system features. This paper presents an overview of the VTS system and describes a few main features of the system. We also report user test results that show how people train using our system. The user test results indicate that the system is able to support a wide variety of training preferences and works well to support training for assembly operations.

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Distributed Symbolic Bounded Property Checking

In this paper we describe an algorithm for distributed, BDD-based bounded property checking and its implementation in the verification tool SymC. The distributed algorithm verifies larger models and returns results faster than the sequential version.The core algorithm distributes partitions of the state set to computation nodes after reaching a threshold size. The nodes proceed with image computation on the nodes asynchronously. The main scalability problem of this scheme is the overlap of state set partitions. We present static and dynamic overlap reduction techniques.