3 Non-linear Objective functions 4 3.1 Production problem with set-up costs . . . . . . . . . . . . . . . . . . . . . . . . . . 4 3.2 Piecewise linear cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 3.3 Piecewise linear convex cost function . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3.4 Disjunctive constraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Integer Programming and Combinatorial Optimization

Security and the smart city: A systematic review

Big data (BD) analytics has been increasingly gaining attraction in both practice and theory in light of its opportunities, barriers and expected benefits. In particular, emerging economics view big data analytics as having great importance despite the fact that it has been in a constant struggle with the barriers that prevent its adoption. Thus, this study primarily attempted to determine the drivers of big data analytics in the context of a developing economy, Jordan. The study examined the influence of technological, organizational and environmental factors on big data adoption in the Jordanian SMEs context, using PLS-SEM for the analysis. The empirical results revealed that the relative advantage, complexity, security, top management support, organizational readiness and government support influence the adoption of BD, whilst pressure of competition and compatibility appeared to be of insignificant influence. The findings are expected to contribute to enterprise management and strategic use of data analytics in the present dynamic market environment, for both researcher and practitioner circles concerned with the adoption of big data in developing countries.

/pdf/factors-influencing-the-adoption-of-big-data-analytics-in-1kj94xy2.pdf

Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs

Multiobjective virtual machine placement mechanisms using nature‐inspired metaheuristic algorithms in cloud environments: A comprehensive review

Despite the higher scalability and parallelism integration offered by Network-on-Chip (NoC) over the traditional shared-bus based systems, it is still not an ideal solution for future large-scale Systems-on-Chip (SoCs), due to limitations such as high power consumption, high-cost communication, and low throughput. Recently, extending 2D-NoC to the third dimension (3D-NoC) has been proposed to deal with these problems; however, 3D-NoC systems are exposed to a variety of manufacturing and design factors making them vulnerable to different faults that cause corrupted message transfer or even catastrophic system failures. Therefore, a 3D-NoC system should be fault tolerant to transient malfunctions or permanent physical damages.

In this paper, we propose a low-latency, high-throughput, and fault-tolerant routing algorithm named Look-Ahead-Fault-Tolerant (LAFT). LAFT reduces the communication latency and enhances the system performance while maintaining a reasonable hardware complexity and ensuring fault tolerance. We implemented the proposed algorithm on a real 3D-NoC architecture (3D-OASIS-NoC) and prototyped it on FPGA, then we evaluated its performance over various applications. Evaluation results show that the proposed algorithm efficiently reduces the communication latency that can reach an average of 38 % and 16 %, when compared to conventional XYZ and our early designed Look-Ahead-XYZ routing algorithms, respectively, and enhances the throughput with up to 46 % and 29 %.

http://web-ext.u-aizu.ac.jp/~benab/publications/journals/super13/super13_preprint.pdf

Architecture and design of high-throughput, low-latency, and fault-tolerant routing algorithm for 3D-network-on-chip (3D-NoC)

An increasingly important advancement in information and communication technologies is cloud computing, and a remarkably increasing trend is its adoption by various organizations. The trend is attributed to the potential of this growing computing paradigm to improve the scalability, efficiency, and reliability of IT systems. Diffusion of cloud computing innovation is changing the way business information systems are developed, paid for, and maintained Alshamaila et al. 2013, Low et al. 2011, thus contributing to efficiency and better value for enterprises. This not only applies to large organizations, but also progressively more to small and medium-sized enterprises (SMEs). However, little is known about the factors most likely to be associated to the adoption behavior of cloud computing services among small and medium enterprises operating in Lebanon. This study deploys the technology–organization–environment (TOE) framework and the Contextual Theory to empirically examine the determinants of cloud computing service adoption in a developing country, namely Lebanon. A model is proposed, and data collected from 139 respondents working in SMEs in Lebanon and analyzed using confirmatory factor analysis and logistic regression in SPSS provide strong support for the model. Results indicate that technological (i.e., complexity and security) and organizational (i.e., top management support and prior IT experience) factors are positively related to the decision to adopt cloud computing services. Moreover, one of the areas of potential interest is the effect of country-specific, or contextual factors, among those who intend to adopt cloud computing. The analysis shows that context-specific factors (i.e. poor infrastructure and lack of government initiatives) are negatively related to the adoption decision. Implications and limitations are discussed, and recommendations for future research are proposed.

/pdf/factors-influencing-smes-adoption-of-cloud-computing-2j8lyczq3x.pdf

Factors Influencing SMEs’ Adoption of Cloud Computing Services in Lebanon: An Empirical Analysis Using TOE and Contextual Theory

Type-aware virtual machine management for energy efficient cloud data centers

Cloud computing is an emerging technology in the field of computing that provides access to a wide range of shared resources. The rapid growth of cloud computing has led to establishing numerous data centers around the world. As data centers consume huge amounts of power, enhancing their power efficiency has become a major challenge in cloud computing. This paper surveys previous studies and researches that aimed to improve power efficiency of virtualized data centers. This survey is a valuable guide for researchers in the field of power efficiency in virtualized data centers following the cloud computing model.

/pdf/power-management-in-virtualized-data-centers-state-of-the-46qvjs613b.pdf

Power management in virtualized data centers: state of the art

The two-dimensional (2D) forward/inverse discrete Fourier transform (DFT), discrete cosine transform (DCT), discrete sine transform (DST), discrete Hartley transform (DHT), discrete Walsh-Hadamard transform (DWHT), play a fundamental role in many practical applications. Due to the separability property, all these transforms can be uniquely defined as a triple matrix product with one matrix transposition. Based on a systematic approach to represent and schedule different forms of the $n\times n$ matrix-matrix multiply-add (MMA) operation in 3D index space, we design new orbital highly-parallel/scalable algorithms and present an efficient $n\times n$ unified array processor for computing {\it any} $n\times n$ forward/inverse discrete separable transform in the minimal $2n$ time-steps. Unlike traditional 2D systolic array processing, all $n^2$ register-stored elements of initial/intermediate matrices are processed simultaneously by all $n^2$ processing elements of the unified array processor at each time-step. Hence the proposed array processor is appropriate for applications with naturally arranged multidimensional data such as still images, video frames, 2D data from a matrix sensor, etc. Ultimately, we introduce a novel formulation and a highly-parallel implementation of the frequently required matrix data alignment and manipulation by using MMA operations on the same array processor so that no additional circuitry is needed.

Orbital Algorithms and Unified Array Processor for Computing 2D Separable Transforms

General-purpose microprocessors are augmented with short-vector instruction extensions in order to simultaneously process more than one data element using the same operation. This type of parallelism is known as data-parallel processing. Many scientific, engineering, and signal processing applications can be formulated as matrix operations. Therefore, accelerating these kernel operations on microprocessors, which are the building blocks or large high-performance computing systems, will definitely boost the performance of the aforementioned applications. In this paper, we consider the acceleration of the matrix transpose operation using the 256-bit Intel advanced vector extension (AVX) instructions. We present a novel vector-based matrix transpose algorithm and its optimized implementation using AVX instructions. The experimental results on Intel Core i7 processor demonstrates a 2.83 speedup over the standard sequential implementation, and a maximum of 1.53 speedup over the GCC library implementation. When the transpose is combined with matrix addition to compute the matrix update, B + A T , where A and B are

/pdf/enhancing-the-matrix-transpose-operation-using-intel-avx-4142inwlvw.pdf

Ahmed Zekri

Papers

Factors Influencing SMEs’ Adoption of Cloud Computing Services in Lebanon: An Empirical Analysis Using TOE and Contextual Theory

Type-aware virtual machine management for energy efficient cloud data centers

Power management in virtualized data centers: state of the art

Orbital Algorithms and Unified Array Processor for Computing 2D Separable Transforms

Enhancing the matrix transpose operation using intel avx instruction set extension