Sajjad Ahmad Madani

Geographically distributed cloud data centers (DCs) consume enormous amounts of energy to meet the ever-increasing processing and storage demands of users. The brown energy generated using fossil fuels is expensive and significantly contributes to global warming. Considering the environmental impact caused by the high carbon emissions and relatively high energy cost of brown energy, we propose the integration of renewable energy sources (RES), especially solar and wind energy, with brown energy to power cloud data centers. In our earlier study, we addressed the intermittency of renewable energy sources, where we replaced the random initialization of artificial neural network (ANN) edge weights with the harmony search algorithm (HSA)-optimized assignment of weights. This study incorporated reliably forecast solar and wind energy into the input parameters of our proposed green energy manager (GEM), for cost minimization, carbon emission minimization, and better energy management of cloud DCs, to make our current study more reliable and trustworthy. Four power sources, on-site solar energy and wind energy, off-site solar energy and wind energy, energy stored in energy storage devices, and brown energy, were considered in this study and simulations were carried out for three different cases. The simulation results showed that case 1 (all brown) was 58% more expensive and caused 71% higher carbon emissions than case 2.1 (cost minimization). Case 1 (all brown) was 39% more expensive and had 80% higher carbon emissions than case 2.2 (carbon emission minimization). The simulation results justify the necessity and importance of the GEM, and finally the results proved that our proposed GEM is less expensive and more environmentally friendly.

Fossil-fuel-based power generation leads to higher energy costs and environmental impacts. Solar and wind energy are abundant important renewable energy sources (RES) that make the largest contribution to replacing fossil-fuel-based energy consumption. However, the uncertain solar radiation and highly fluctuating weather parameters of solar and wind energy require an accurate and reliable forecasting mechanism for effective and efficient load management, cost reduction, green environment, and grid stability. From the existing literature, artificial neural networks (ANN) are a better means for prediction, but the ANN-based renewable energy forecasting techniques lose prediction accuracy due to the high uncertainty of input data and random determination of initial weights among different layers of ANN. Therefore, the objective of this study is to develop a harmony search algorithm (HSA)-optimized ANN model for reliable and accurate prediction of solar and wind energy. In this study, we combined ANN with HSA and provided ANN feedback for its weights adjustment to HSA, instead of ANN. Then, the HSA optimized weights were assigned to the edges of ANN instead of random weights, and this completes the training of ANN. Extensive simulations were carried out and our proposed HSA-optimized ANN model for solar irradiation forecast achieved the values of MSE = 0.04754, MAE = 0.18546, MAPE = 0.32430%, and RMSE = 0.21805, whereas our proposed HSA-optimized ANN model for wind speed prediction achieved the values of MSE = 0.30944, MAE = 0.47172, MAPE = 0.12896%, and RMSE = 0.55627. Simulation results prove the supremacy of our proposed HSA-optimized ANN models compared to state-of-the-art solar and wind energy forecasting techniques.

Fault tolerance, performance, and throughput have been major areas of research and development since the evolution of large-scale networks. Internet-based applications are rapidly growing, including large-scale computations, search engines, high-definition video streaming, e-commerce, and video on demand. In recent years, energy efficiency and fault tolerance have gained significant importance in data center networks and various studies directed the attention towards green computing. Data centers consume a huge amount of energy and various architectures and techniques have been proposed to improve the energy efficiency of data centers. However, there is a tradeoff between energy efficiency and fault tolerance. The objective of this study is to highlight a better tradeoff between the two extremes: (a) high energy efficiency and (b) ensuring high availability through fault tolerance and redundancy. The main objective of the proposed Energy-Aware Fault-Tolerant (EAFT) approach is to keep one level of redundancy for fault tolerance while scheduling resources for energy efficiency. The resultant energy-efficient data center network provides availability as well as fault tolerance at reduced operating cost. The main contributions of this article are: (a) we propose an Energy-Aware Fault-Tolerant (EAFT) data center network scheduler; (b) we compare EAFT with energy efficient resource scheduling techniques to provide analysis of parameters such as, workload distribution, average task per servers, and energy consumption; and (c) we highlight effects of energy efficiency techniques on the network performance of the data center.

Energy estimation of applications helps developers greening the smartphone- and Internet-of-Things-based devices. Traditional energy estimation schemes consider smartphone component's power measurement or code analysis methods for energy estimation of applications. The existing code analysis method considers the energy cost of software operations to minimize the energy estimation overhead of dynamic estimation methods. However, it overlooked cache storage analysis and overheads associated with it due to concurrent program execution at runtime. As a result, the performance of estimation tools is affected. To handle these issues, this study put forward an enhanced static-code-analysis-based lightweight energy estimation (SA-LEE) framework that has considered overheads associated with the application runtime execution environment, cache storage analysis, and the application inactivity period for energy estimation of applications. The experiments revealed that the SA-LEE model has minimized the estimation time and the energy overhead by 98% and 97%, respectively. Also, the accuracy is observed to be 82-88%.

Recent advancements in location-based recommendation system (LBRS) and the availability of online applications, such as Twitter, Instagram, Foursquare, Path, and Facebook have introduced new research challenges in the area of LBRS. Use of content, such as geo-tagged media, point location-based, and trajectory-based information help in connecting the gap between the online social networking services and the physical world. In this article, we present a systematic review of the scientific literature of LBRS and summarize the efforts and contributions proposed in the literature. We have performed a qualitative comparison of the existing techniques used in the area of LBRS. We present the basic filtration techniques used in LBRS followed by a discussion on the services and the location features the LBRS utilizes to perform the recommendations. The classification of criteria for recommendations and evaluation metrics are also presented. We have critically investigated the techniques proposed in the literature for LBRS and extracted the challenges and promising research topics for future work.

Virtualization is generally adopted in server and desktop environments to provide for fault tolerance, resource management, and energy efficiency. Virtualization enables parallel execution of multiple operating systems (OSs) while sharing the hardware resources. Virtualization was previously not deemed as feasible technology for mobile and embedded devices due to their limited processing and memory resource. However, the enterprises are advocating Bring Your Own Device (BYOD) applications that enable co-existence of heterogeneous OSs on a single mobile device. Moreover, embedded device require virtualization for logical isolation of secure and general purpose OSs on a single device. In this paper, we investigate the processor architectures in the mobile and embedded space while examining their formal visualizability. We also compare the virtualization solutions enabling coexistence of multiple OSs in Multicore Processor System-on-Chip (MPSoC) mobile and embedded systems. We advocate that virtualization is necessary to manage resource in MPSoC designs and to enable BYOD, security, and logical isolation use cases.