Review on Cloud Computing Security Challenges

In this paper, security issues associated with cloud computing are reviewed. Additionally, the types of cloud and service models have also been pointed out. Cloud computing has ruled the data innovation industry as of late. Giant data centersthat provide cloud services are been set up due to the global approval of cloud and virtualization innovations. Cloud computing is characterized as a web-based software service since Information Technology (IT) resources like network, server, storage, and so on are based on the Web. Along these lines, cloud computing services can be utilized at any place and whenever on the Personal Computer (PC) or smart mobile phones. In light of the on-demand, adaptable and versatile administration it can give, a considerable measure of companies that beforehand deployed locally has moved their organizations to the cloud. Although cloud computing brings a whole lot of advantages, many security challenges have been brought up to both cloud providers and clients

Can We Reckon Bitcoin as a Hedge, a Safe Haven or a Diversifier for US Dollars?

This article aims to analyse the hedging, diversifier and safe-haven properties of Bitcoin for the US Dollar Index (USDI). We explore the long-term relationship between USDI and Bitcoin by estimating a Markov-switching autoregressive (MS-AR) model with two regimes. Thus, the data used cover the period from 18 January 2010 to 30 June 2017 for both USDI and Bitcoin. The empirical findings based on the analysis of the MS-AR model report that investing in Bitcoin involves more benefits than USDI even if the economy is in a recession. However, by examining Bitcoin and USDI volatility, the research findings underline positive dependency between the two. Such results denote that Bitcoin does not act as a hedge, and not even as a safe haven, against USDI. We found that Bitcoin is merely a diversifier for USDI. Accordingly, the outcomes will help investors and portfolio risk managers to make more up-to-date investment analyses and decisions.</jats:p

Multi-objective optimization for VM placement in homogeneous and heterogeneous cloud service provider data centers

International audienc

The Dependence and Risk Spillover Between Energy Market and BRICS Stock Markets: A Copula-MGARCH Model Approach

The article examines the dynamic dependence structure and risk spillover between the future market of energy commodities and Brazil, Russia, India, China and South Africa (BRICS) stock markets for different market conditions. The study used copula-based multivariate GARCH model, or in short C-MGARCH model, to explore the conditional correlation by multivariate generalized autoregressive conditional heteroskedastic (MGARCH) and the remaining dependence by different copula models. Our results provide significant positive dynamic dependency among crude oil markets (natural gas market) and BRICS stock markets. We then explore the financial implications of volatility spillovers regarding portfolio risk management through an analysis of risk spillovers from energy market to BRICS countries using the value at Risk (VaR), conditional value at risk (CVaR) and delta CVaR. Our findings support the existence of significant risk spillover between crude oil markets (natural gas market) and BRICS stock markets. The presence of volatility spillover among oil prices, natural gas prices and BRICS stock market implies that oil market information (natural gas market information) enhances the volatility forecast in stock markets. Consequently, investors must take oil markets and natural gas markets into account at the time of financial portfolios structuring and in improving their hedging strategies. </jats:p

A Multi-Objective Approach for Optimizing Virtual Machine Placement Using ILP and Tabu Search

Efficient Virtual Machine (VM) placement is a critical challenge in optimizing resource utilization in cloud data centers. This paper explores both exact and approximate methods to address this problem. We begin by presenting an exact solution based on a Multi-Objective Integer Linear Programming (MOILP) model, which provides an optimal VM Placement (VMP) strategy. Given the NP-completeness of the MOILP model when handling large-scale problems, we then propose an approximate solution using a Tabu Search (TS) algorithm. The TS algorithm is designed as a practical alternative for addressing these complex scenarios. A key innovation of our approach is the simultaneous optimization of three performance metrics: the number of accepted VMs, resource wastage, and power consumption. To the best of our knowledge, this is the first application of a TS algorithm in the context of VMP. Furthermore, these three performance metrics are jointly optimized to ensure operational efficiency (OPEF) and minimal operational expenditure (OPEX). We rigorously evaluate the performance of the TS algorithm through extensive simulation scenarios and compare its results with those of the MOILP model, enabling us to assess the quality of the approximate solution relative to the optimal one. Additionally, we benchmark our approach against existing methods in the literature to emphasize its advantages. Our findings demonstrate that the TS algorithm strikes an effective balance between efficiency and practicality, making it a robust solution for VMP in cloud environments. The TS algorithm outperforms the other algorithms considered in the simulations, achieving a gain of 2% to 32% in OPEF, with a worst-case increase of up to 6% in OPEX