Hydroeconomic modeling for assessing water scarcity and agricultural pollution abatement policies in the Ebro River Basin, Spain

Water scarcity and water quality degradation are major problems in many basins across the world, especially in arid and semiarid regions. The severe pressures on basins are the consequence of the intensification of food production systems and the unrelenting growth of population and income. Agriculture is a major factor in the depletion and degradation of water resources, and contributes to the emissions of greenhouse gases (GHG). Our study analyzes water allocation and agricultural pollution into watercourses and the atmosphere, with the purpose of identifying cost-effective policies for sustainable water management in the Ebro River Basin (Spain). The study develops an hydroeconomic model that integrates hydrological, economic and water quality aspects, capturing the main spatial and sectoral interactions in the basin. The model is used to analyze water scarcity and agricultural pollution under normal and droughts conditions, providing information for evaluating mitigation and adaptation policies. Results indicate that drought events increase nitrate concentration by up to 63% and decrease water availability by 42% at the mouth of Ebro River, highlighting the tradeoffs between water quantity and quality. All mitigation and adaptation policies reduce the effects of climate change, improving water quality and reducing GHGs’ emissions, thus lowering environmental damages and enhancing social well-being. Manure fertilization and optimizing the use of synthetic fertilizers are important cost-effective policies increasing social benefits in a range between 50 and 160 million Euros. Results show that irrigation modernization increases the efficient use of nitrogen and water, augmenting social benefits by up to 90 million Euros, and enlarging stream flows at the river mouth. In contrast, manure treatment plants reduce private and social benefits even though they achieve the lowest nitrate concentrations. Our study provides insights on the synergies and tradeoffs between environmental and economic objectives. Another finding is that drought conditions decrease the effectiveness of policies, and increase the tradeoffs between water availability and nitrate pollution. The results contribute to the discussion of designing cost-effective policies for the abatement of agricultural polluting emissions into water and the atmosphere

Measurement of energy efficiency metrics of data centers. case study: higher education institution of Barranquilla

Data centers have become fundamental pillars of the network infrastructures of the various companies or entities regardless of their size. Since they support the processing, analysis, assurance of the data generated in the network, and by the applications in the cloud, which every day increases its volume thanks to diverse and sophisticated technologies. The management and storage of this large volume of information make the data centers consume a lot of energy, generating great concern to owners and administrators. Green Data Center (GDC) is a solution for this problem, reducing the impact produced by the data centers in the environment through the monitoring and control of these and to the application of standards-based on metrics. Although each data center has its particularities and requirements, the metrics are the tools that allow us to measure the energy efficiency of the data center and evaluate if it is friendly to the environment (1.Adv. Intell. Syst. Comput. 574:329–340). The objective of the study is to calculate these metrics in the data centers of a Higher Education Institution in Barranquilla, on both campuses, and the analysis of these will be carried out. It is planned to extend this study by reviewing several metrics to conclude, which is the most efficient and which allows defining the guidelines to update or convert the data center in a friendly environment. The research methodology used for the development of the project is descriptive and no-experimental

Achieving energy efficiency in data centers with a performance-guaranteed power aware routing

Nowadays, data centers are designed to offer the highest performance in case of high traffic load and peak utilisation of the network. However, in a realistic data center environment, the peak capacity of the network is rarely reached and the average utilisation of devices varies between 5% and 25% which results into a huge loss of energy since most of the time links and servers are idle or under-utilized. The high impact of this wasted power on environmental effects, energy needs and electricity costs raised the concerns to seek for an efficient solution to make data centers more power effective while keeping the desired quality of service. In this paper, we propose a power-aware routing algorithm that saves a considerable amount of energy with a negligible trade-off on the performance of the network and a guaranteed reliability of the system. The key idea is to keep active only the vital and critical nodes participating in the communication traffic and ensuring the reliability while the unneeded devices are turned-off. Vital nodes between clusters (parts of the network) are calculated only once during the initialization of the system and consequently used with a constant time complexity. Besides its short computation time, our routing algorithm guarantees over 50% of energy saving by maintaining the minimum number of needed devices and over 20% when adding backup routes. This power efficiency is accompanied by a guaranteed performance and reliability against failures. 1 2017 Elsevier B.V.Scopu

PCCP: Proactive Video Chunks Caching and Processing in edge networks

Mobile Edge Computing (MEC) networks have been proposed to extend the cloud services and bring the cloud computing capabilities near the end-users at the Mobile Base Stations (MBS). To improve the efficiency of pushing the cloud features to the edge, different MEC servers assist each others to effectively select videos to cache and transcode. In this work, we adopt a joint caching and processing model for Video On Demand (VOD) in MEC networks. Our goal is to proactively cache only the chunks of videos to be watched and instead of caching the whole video content in one edge server (as performed in most of the previous works), neighboring MBSs will collaborate to store different video chunks to optimize the storage resources usage. Then, by coping with the Adaptive BitRate streaming technology (ABR), different representations of each chunk can be generated on the fly and cached in multiple MEC servers. To maximize the caching efficiency, we study the videos viewing pattern and design a Proactive caching Policy (PcP) and a Caching replacement Policy (CrP) to cache only highest probability video chunks. Servers performing caching and transcoding tasks should be thoroughly selected to optimize the storage and computing resources usage. Hence, we formulate this collaborative problem as a NP-hard Integer Linear Program (ILP). In addition to the CrP and PcP policies, we also propose a sub-optimal relaxation and an online heuristic, which are adequate for real-time chunks fetching. The simulation results prove that our model and policies perform more than 20% better than other edge caching approaches in terms of cost, average delay and cache hit ratio for different network configurations. 2019Qatar Foundation;Qatar National Research FundScopu

CE-D2D: Collaborative and Popularity-aware Proactive Chunks Caching in Edge Networks

Leveraging video caching to collaborative Mobile Edge Computing (MEC) servers is an emerging paradigm, where cloud computing services are extended to edge networks to allocate multimedia contents close to end-users. However, despite minimizing the traffic over the content delivery networks (CDN), congestions may occur in peak hours characterized by high load demands. Involving users' devices in data offloading through Device-to-Device (D2D) connections has proved its efficiency in relieving the cellular spectrum utilization. In this paper, the Collaborative Edge network (CE) and the devices (D2D) cluster are combined to form a CE-D2D framework aiming at maximizing video caching and efficiently using cellular and backhaul bandwidths. However, since we are dealing with large sized contents, the small storage and bandwidth capacities offered by users limit the number of cached videos and restrict offloading large volume data. This makes the CE-D2D framework, so far, an incomplete solution for multimedia contents. Therefore, we propose a caching strategy to cache only the chunks of videos to be watched and instead of caching or offloading each video content by one edge node (as performed in literature), helpers (MEC and mobiles) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. In this work, we model both CE and D2D frameworks as linear programs and schedule the collaboration between them constrained by resource availability. Due to the NP-hardness of the problem, we introduce an online heuristic that presents a proactive chunks caching (HLPC) and a near-optimal data offloading with polynomial complexity. 2020 IEEE.Qatar National Research FundScopu

Collaborative hierarchical caching and transcoding in edge network with CE-D2D communication

To support multimedia applications, Mobile Edge Computing (MEC) servers offer storage and computing capacities to handle videos close to end-users. However, the high load in peak hours consumes the limited available bandwidth of existing cellular and backhaul links leading to low network performance. Hence, an elastic system model is required to maintain the high Quality of Experience (QoE) as the resource demands increase. Caching popular videos at mobile devices is considered a promising technique for content delivery. Yet, mobile users offer small capacities that are not adequate for large-sized video sharing. In this paper, we extend the collaborative caching and processing framework in edge networks (Collaborative Edge - CE) to include the users' mobile video sharing (Device-to-Device - D2D). We propose a caching strategy to cache only the chunks of videos to be watched and instead of offloading one video content by one edge node, helpers (MEC servers and users) will collaborate to store and share different chunks to optimize the storage/transmission resources usage. To only cache popular contents, we designed a D2D-aware proactive chunks caching on users devices based on our chunks popularity model. Next, we formulate this CE-D2D collaborative problem as a linear program. Due to the NP-hardness of the problem, we introduce a sub-optimal relaxation and an online heuristic using the proactive caching and presenting a near optimal data offloading and a profitable payment determination, with polynomial time complexity. The simulation results show that our policies and heuristics outperform other edge caching approaches by more than 10% in terms of hit ratio, average delay, and cost. 2020 Elsevier LtdQatar Foundation;Qatar National Research FundScopu

DistPrivacy: Privacy-Aware Distributed Deep Neural Networks in IoT surveillance systems

With the emergence of smart cities, Internet of Things (IoT) devices as well as deep learning technologies have witnessed an increasing adoption. To support the requirements of such paradigm in terms of memory and computation, joint and real-time deep co-inference framework with IoT synergy was introduced. However, the distribution of Deep Neural Networks (DNN) has drawn attention to the privacy protection of sensitive data. In this context, various threats have been presented, including black-box attacks, where a malicious participant can accurately recover an arbitrary input fed into his device. In this paper, we introduce a methodology aiming to secure the sensitive data through re-thinking the distribution strategy, without adding any computation overhead. First, we examine the characteristics of the model structure that make it susceptible to privacy threats. We found that the more we divide the model feature maps into a high number of devices, the better we hide proprieties of the original image. We formulate such a methodology, namely DistPrivacy, as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy level of the data, and the limited-resources of IoT participants. Due to the NP-hardness of the problem, we introduce an online heuristic that supports heterogeneous IoT devices as well as multiple DNNs and datasets, making the pervasive system a general-purpose platform for privacy-aware and low decision-latency applications. 2020 IEEE.Scopu

Transcoding resources forecasting and reservation for crowdsourced live streaming

During the last decade, empowered by the technological advances of mobile devices and the revolution of wireless mobile network access, crowdsourced live streaming has become more popular. Ensuring a stable high-quality playback experience is necessary to maximize the number of viewers and profits for content providers. Additionally, because of the instability of network conditions and the heterogeneity of the end-users capabilities, transcoding the original video into multiple bitrates is required. Video transcoding is a computationally exhaustive process, where generally a single cloud instance needs to be reserved to produce one single video bitrate representation. On-demand renting of resources or inadequate resources pre-renting may cause delay of the video playback or serving the viewers with a lower quality. On the other hand, if resources provisioning is much higher than required, the extra resources will be wasted. In this paper, we introduce our resources reservation framework for geo-distributed cloud sites, to maximize the Quality of Experience (QoE) of viewers and minimize the cost to the content providers. First, we formulate an offline optimization problem to allocate transcoding resources at the viewers' proximity, while creating a trade off between the network cost and viewers QoE. Second, based on the optimizer resource allocation decisions on historical live videos, we create our time series datasets containing historical records of the optimal resources needed at each geo-distributed cloud site. Finally, we adopt machine learning to build our distributed time series forecasting models to proactively forecast the exact needed transcoding resources ahead of time at each geo-distributed cloud site. 2019 IEEE.Qatar Foundation;Qatar National Research FundScopu

QoE-Aware Resource Allocation for Crowdsourced Live Streaming: A Machine Learning Approach

Driven by the tremendous technological advancement of personal devices and the prevalence of wireless mobile network accesses, the world has witnessed an explosion in crowdsourced live streaming. Ensuring a better viewers quality of experience (QoE) is the key to maximize the audiences number and increase streaming providers' profits. This can be achieved by advocating a geo-distributed cloud infrastructure to allocate the multimedia resources as close as possible to viewers, in order to minimize the access delay and video stalls. Moreover, allocating the exact needed resources beforehand avoids over-provisioning, which may lead to significant costs by the service providers. In the contrary, under-provisioning might cause significant delays to the viewers. In this paper, we introduce a prediction driven resource allocation framework, to maximize the QoE of viewers and minimize the resource allocation cost. First, by exploiting the viewers locations available in our unique dataset, we implement a machine learning model to predict the viewers number near each geo-distributed cloud site. Second, based on the predicted results that showed to be close to the actual values, we formulate an optimization problem to proactively allocate resources at the viewers proximity. Additionally, we will present a trade-off between the video access delay and the cost of resource allocation. 2019 IEEE.Qatar Foundation;Qatar National Research FundScopu

RL-PDNN: Reinforcement Learning for Privacy-Aware Distributed Neural Networks in IoT Systems

Due to their high computational and memory demand, deep learning applications are mainly restricted to high-performance units, e.g., cloud and edge servers. Particularly, in Internet of Things (IoT) systems, the data acquired by pervasive devices is sent to the computing servers for classification. However, this approach might not be always possible because of the limited bandwidth and the privacy issues. Furthermore, it presents uncertainty in terms of latency because of the unstable remote connectivity. To support resource and delay requirements of such paradigm, joint and real-time deep co-inference framework with IoT synergy was introduced. However, scheduling the distributed, dynamic and real-time Deep Neural Network (DNN) inference requests among resource-constrained devices has not been well explored in the literature. Additionally, the distribution of DNN has drawn the attention to the privacy protection of sensitive data. In this context, various threats have been presented, including white-box attacks, where malicious devices can accurately recover received inputs if the DNN model is fully exposed to participants. In this paper, we introduce a methodology aiming at distributing the DNN tasks onto the resource-constrained devices of the IoT system, while avoiding to reveal the model to participants. We formulate such an approach as an optimization problem, where we establish a trade-off between the latency of co-inference, the privacy of the data, and the limited resources of devices. Next, due to the NP-hardness of the problem, we shape our approach as a reinforcement learning design adequate for real-time applications and highly dynamic systems, namely RL-PDNN. Our system proved its ability to outperform existing static approaches and achieve close results compared to the optimal solution. 2013 IEEE.Scopu