Utility-Oriented Placement of Actuator Nodes with a Collaborative Serving Scheme for Facilitated Business and Working Environments

Places to be served by cyber-physical systems (CPS) are usually distributed unevenly over the area. Thus, different areas usually have different importance and values of serving. In other words, serving power can be excessive or insufficient in practice. Therefore, actuator nodes (ANs) in CPS should be focused on serving around points of interest (POIs) with considerations of “service utility.” In this paper, a utility-oriented AN placement framework with a collaborative serving scheme is proposed. Through spreading serving duties among correctly located ANs, deployment cost can be reduced, utility of ANs can be fully utilized, and the system longevity can be improved. The problem has been converted into a binary integer linear programming optimization problem. Service fading, 3D placements, multiscenario placements, and fault-tolerant placements have been modeled in the framework. An imitated example of a CPS deployment in a smart laboratory has been used for evaluations.published_or_final_versio

Service Systems, Smart Service Systems and Cyber- Physical Systems—What’s the difference? Towards a Unified Terminology

As businesses and their networks transform towards co-creation, several concepts describing the resulting systems emerge. During the past years, we can observe a rise of the concepts Service Systems, Smart Service Systems and Cyber-Physical Systems. However, distinct definitions are either very broad or contradict each other. As a result, several characteristics appear around these terms, which also miss distinct allocations and relationships to the underlying concepts. Previous research only describes these concepts and related characteristics in an isolated manner. Thus, we perform an inter-disciplinary structured literature review to relate and define the concepts of Service Systems, Smart Service Systems and Cyber-Physical Systems as well as related characteristics. This article can, therefore, serve as a basis for future research endeavors as it delivers a unified terminology

Distributed multi-agent Gaussian regression via finite-dimensional approximations

We consider the problem of distributedly estimating Gaussian processes in multi-agent frameworks. Each agent collects few measurements and aims to collaboratively reconstruct a common estimate based on all data. Agents are assumed with limited computational and communication capabilities and to gather $M$ noisy measurements in total on input locations independently drawn from a known common probability density. The optimal solution would require agents to exchange all the $M$ input locations and measurements and then invert an $M \times M$ matrix, a non-scalable task. Differently, we propose two suboptimal approaches using the first $E$ orthonormal eigenfunctions obtained from the \ac{KL} expansion of the chosen kernel, where typically $E \ll M$. The benefits are that the computation and communication complexities scale with $E$ and not with $M$, and computing the required statistics can be performed via standard average consensus algorithms. We obtain probabilistic non-asymptotic bounds that determine a priori the desired level of estimation accuracy, and new distributed strategies relying on Stein's unbiased risk estimate (SURE) paradigms for tuning the regularization parameters and applicable to generic basis functions (thus not necessarily kernel eigenfunctions) and that can again be implemented via average consensus. The proposed estimators and bounds are finally tested on both synthetic and real field data

Exploratory data analysis for data center energy management

The continuous improvement in energy efficiency of existing data centers would help reduce their environmental footprints. Greening of Data Centers could be attained using renewable energy sources or more energy efficient compute systems and effective cooling systems. A reliable cooling system is necessary to generate a persistent flow of cold air to cool servers that are subjected to increasing computational load demand. As a matter of fact, servers' dissipated heat effects a strain on the cooling systems and consequently, on electricity consumption. Generated heat in the data center is categorized into different granularity levels namely: server level, rack level, room level, and data center level. Several datasets are collected at ENEA Portici Data Center from CRESCO 6 cluster-A High-Performance Computing Cluster. The cooling and environmental aspects of the data center is also considered for data analysis. This research aims to conduct a rigorous exploratory data analysis on each dataset separately and collectively followed in various stages. This work presents descriptive and inferential analyses for feature selection and extraction process. Furthermore, a supervised Machine learning modelling and correlation estimation is performed on all the datasets to abstract relevant features.That would have an impact on energy efficiency in data centers

Data Mining for Thermal Analysis of Big Dataset of HPC-Data Center

Greening of Data Centers could be achieved through energy savings in two major areas namely: compute systems and cooling systems. A reliable cooling system is necessary to produce a persistent flow of cold air to cool the servers due to increasingly demanding computational load. Servers’ dissipated heat effects a strain on the cooling systems. Consequently, it is imperative to individual servers that frequently occur in the hotspot zones. This is facilitated through the application of data mining techniques to an available big data set with thermal characteristics of HPC-ENEA-Data Center, namely Cresco 6. This work involves the implementation of an advanced algorithm on the workload management platform produces hotspots maps with the goal to reduce data centre wide thermal-gradient, and cooling effectiveness

Joint Computing and Electric Systems Optimization for Green Datacenters

This chapter presents an optimization framework to manage green datacenters using multilevel energy reduction techniques in a joint approach. A green datacenter exploits renewable energy sources and active Uninterruptible Power Supply (UPS) units to reduce the energy intake from the grid while improving its Quality of Service (QoS). At server level, the state-of-the-art correlation-aware Virtual Machines (VMs) consolidation technique allows to maximize server’s energy efficiency. At system level, heterogeneous Energy Storage Systems (ESS) replace standard UPSs, while a dedicated optimization strategy aims at maximizing the lifetime of the battery banks and to reduce the energy bill, considering the load of the servers. Results demonstrate, under different number of VMs in the system, up to 11.6% energy savings, 10.4% improvement of QoS compared to existing correlation-aware VM allocation schemes for datacenters and up to 96% electricity bill savings

Optimized Thermal-Aware Job Scheduling and Control of Data Centers

Analyzing data centers with thermal-aware optimization techniques is a viable approach to reduce energy consumption of data centers. By taking into account thermal consequences of job placements among the servers of a data center, it is possible to reduce the amount of cooling necessary to keep the servers below a given safe temperature threshold. We set up an optimization problem to analyze and characterize the optimal set points for the workload distribution and the supply temperature of the cooling equipment. Furthermore, under mild assumptions, we design and analyze controllers that regulate the system to the optimal state without knowledge of the current total workload to be handled by the data center. The response of our controller is validated by simulations and convergence to the optimal set points is achieved under varying workload conditions

Advanced data analytics modeling for evidence-based data center energy management

Over the past few decades, the demand for Data Center (DC) services has significantly increased due to the world's growing need for internet access, social networking, and data storage. Data Centers are among the most energy-intensive businesses, so optimizing IT operations in DC requires energy-efficient techniques. This paper presents AI based modeling strategies for effective energy management with a particular emphasis on DC's two most energy intensive systems (i.e., cooling and IT systems). This study addresses the issues of IT equipment performance degradation, inappropriate IT room thermal conditions, inefficient workload placement, and excessive energy waste. This research entails the application of machine learning for DC thermal classification, and deployment of deep learning models to predict resource utilization and energy consumption in DC. Furthermore, a comparative analysis is performed with existing relevant methods to demonstrate the effectiveness and accuracy of the proposed AI techniques. The findings of this study also provide evidence-based recommendations for DC efficient energy management

Energy-Aware System-Level Design of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are heterogeneous systems in which one or several computational cores interact with the physical environment. This interaction is typically performed through electromechanical elements such as sensors and actuators. Many CPSs operate as part of a network and some of them present a constrained energy budget (for example, they are battery powered). Examples of energy constrained CPSs could be a mobile robot, the nodes that compose a Body Area Network or a pacemaker. The heterogeneity present in the composition of CPSs together with the constrained energy availability makes these systems challenging to design. A way to tackle both complexity and costs is the application of abstract modelling and simulation. This thesis proposed the application of modelling at the system level, taking energy consumption in the different kinds of subsystems into consideration. By adopting this cross disciplinary approach to energy consumption it is possible to decrease it effectively. The results of this thesis are a number of modelling guidelines and tool improvements to support this kind of holistic analysis, covering energy consumption in electromechanical, computation and communication subsystems. From a methodological point of view these have been framed within a V-lifecycle. Finally, this approach has been demonstrated on two case studies from the medical domain enabling the exploration of alternative systems architectures and producing energy consumption estimates to conduct trade-off analysis