Placement Solution Research Articles

Unlocking Modern VLSI Placement with Cutting-Edge GPU Acceleration Integrated with Deep Learning Tool Kit

Objectives: In VLSI, cell placement is critical in determining the overall performance, area, runtime efficiency, and power consumption of integrated circuits. The objective of this work is to find the best possible locations for the cells to meet the mentioned constraints. Methods: The proposed method utilizes a deep reinforcement learning strategy with heuristics to address the complexities of modern VLSI design challenges. A multi-objective deep reinforcement learning approach fused with GPU acceleration is explored to optimize placement metrics like wirelength, congestion, and runtime to gain a globally optimal placement solution. The suggested method dynamically selects appropriate parameters, a process known as Parameter Tuning, to produce high-quality placement solutions. The strategy's potency is demonstrated using open-source benchmarks sourced from storage, with BlackParrot and MemPool. Findings: The trial outcomes of the strategy on benchmark data show considerable improvements in placement quality. It reduces wire length by up to 4% and congestion by about 10%. Moreover, it is highly scalable and reliable in providing global placement solutions. The reduced aspect ratio indicates lower chip area utilization and reduced power consumption. Novelty: The integration of GPU acceleration and deep learning methodology as a strategy for VLSI global placement has not been reported in prior placement work; however, similar approaches are available for legalization and detailed placement. Keywords: Placement, VLSI, Deep Learning, GPU Acceleration, Parameter Tuning

Optimal spatial arrangement of modules for large‐scale photovoltaic farms in complex topography

AbstractThe existing methods for determining the module arrangement in photovoltaic (PV) farms are considered insufficient as they are generally limited to the environment of flat ground without considering both physical and electrical factors. The orientations of PV modules may be very diverse when installed in places with complex topography, e.g. mountains and abandoned mine sites. Thus, the received irradiance by the modules is inconsistent directly results in current differences and hence leads to significant mismatch loss in the PV arrays. This paper proposes a solution to determine the most appropriate combination of tilts and orientations of PV modules as well as the arrangement of PV arrays. The complex topographies are fully considered to minimize the mismatch loss phenomenon, and hence the power generation degradation. The solution adopts a set of models, i.e. the irradiation model for the calculation of optimal module tilts and orientations, the shadow model for shadow effect analysis, and the mismatch model for mismatch condition analysis. A two‐layer multi‐objective optimization is implemented for the optimal arrangement. The proposed solution is assessed through the case study of a 30 MW PV farm. The result confirms the effectiveness of the proposed solution for the optimal spatial module placement.

A Dynamic Edge Server Placement Scheme Using the Improved Snake Optimization Algorithm

In the paradigm of mobile edge computing (MEC), providing low-latency and high-reliability services for users is garnering increasing attention. Appropriate edge-server placement is the crucial first step to realizing such services, as it can meet computation requirements and enhance resource utilization. This study delves into efficient and intelligent dynamic edge-server placement by taking into account time-varying network scenarios and deployment costs. Firstly, edge servers are classified into static and dynamic ones. Subsequently, an improved snake optimization algorithm is proposed to determine the number and placement locations of dynamic servers while adhering to delay requirements. Finally, a minimum placement-cost algorithm is put forward to further reduce the service cost. Experimental results demonstrate that compared to classic algorithms, the proposed algorithms can achieve a reduction in latency of 5% to 12%. And compared to the state-of-the-art methods, they can reduce service costs by 20% to 43%. This research offers an effective solution for dynamic edge-server placement and holds great theoretical and practical significance.

Optimal PMU placement in the Southern Cameroon interconnected grid considering the effect of a group of ZIBs for complete observability and dynamic stability

In this paper, a solution for optimal PMU placement in the southern Cameroon interconnected grid (SIG) is proposed to the issue of dynamic stability within the network. The power system under consideration relies on SCADA parameter monitoring, which offers unsynchronized measurements and inconsistent state estimation of the grid. Moreover, SCADA system's data scan rate hinders its efficiency in capturing the dynamic and transient behaviours in the power grid. The Approach in this research is based on the integer linear programming (ILP) considering the effect of a group of zero-injection buses (ZIBs). Subsequently, a comparative analysis is conducted to evaluate the performance of the ILP method in terms of the number of PMUs, the number of buses observable from one or more nodes, and the sum of redundancy index (SORI) in comparison to other optimization algorithms. The results obtained using MATLAB 2020a demonstrate that the ILP method, considering the effect of a group of ZIBs and the SORI, provides the most optimal solution. Furthermore, a PMU-based dynamic stability assessment is performed, validating the improvement of the visibility of voltage signals.

Data-Driven Sparse Sensor Placement Optimization on Wings for Flight-By-Feel: Bioinspired Approach and Application.

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm extracts information from a dense array of flow data using singular value decomposition and linear discriminant analysis, thereby identifying the most information-rich sparse subset of sensor locations. In this research, the SSPOP algorithm is evaluated for the placement of artificial hair sensors on a 3D delta wing model with a 45° sweep angle and a blunt leading edge. The sensor placement solution, or design point (DP), is shown to rank within the top one percent of all possible solutions by root mean square error in angle of attack prediction. This research is the first to evaluate SSPOP on a 3D model and the first to include variable length hairs for variable velocity sensitivity. A comparison of SSPOP against conventional greedy search and gradient-based optimization shows that SSPOP DP ranks nearest to optimal in over 90 percent of models and is far more robust to model variation. The successful application of SSPOP in complex 3D flows paves the way for experimental sensor placement optimization for artificial hair-cell airflow sensors and is a major step toward biomimetic flight-by-feel.

Calculation of the main operational characteristics of a tethered high-altitude ship-based system

Objectives. Currently, UAVs are actively used in many military and civilian fields such as object surveillance, telecommunications, radar, photography, video recording, and mapping, etc. The main disadvantage of autonomous UAVs is their limited operating time. The long-term operation of UAVs on ships can be ensured by tethered high-altitude systems in which the power supply of engines and equipment is provided from the onboard energy source through a thin cable tether. This paper aims to select and justify the appearance of such system, as well as to calculate the required performance characteristics.Methods. The study used methods of systemic and functional analysis of tethered system parameters, as well as methods and models of the theory of relations and measurement.Results. The issues of design and implementation of new generation tethered high-altitude ship-based systems were considered. A rational type of aerodynamic design for unmanned aerial vehicles was determined based on existing tethered platforms. The optimal architecture of the tethered system was defined and justified. The paper presents the appearance and solution for placement onboard the ship, and describes its operation. The main initial parameters for designing high-altitude systems such as take-off weight, optimal lift altitude, maximum power required for operation, structure of the energy transfer system, as well as deployment and lift time to the design altitude were selected and calculated.Conclusions. The methodology for calculating the necessary characteristics described in the paper can be used for developing and evaluating tethered high-altitude systems. These systems are capable of performing a wide range of tasks, without requiring a separate storage and launch location, which is especially important in the ship environment. The system presented herein possesses significant advantages over well-known analogues.

Sparse Flow Sensor Placement Optimization for Flight-by-Feel Control of 2D Airfoils

This research introduces the Sparse Sensor Placement Optimization for Prediction algorithm and explores its use in bioinspired flight-by-feel control system design. Flying animals have velocity-sensing structures on their wings and are capable of highly agile flight in unsteady conditions, a proof-of-concept that artificial flight-by-feel control systems may be effective. Constrained by size, weight, and power, a flight-by-feel sensory system should have the fewest optimally placed sensors which capture enough information to predict the flight state. Flow datasets, such as from computational fluid dynamics, are discrete, often highly discontinuous, and ill-suited for conventional sensor placement optimization techniques. The data-driven Sparse Sensor Placement Optimization for Prediction approach reduces high-dimensional flow data to a low-dimensional sparse approximation containing nearly all of the original information, thereby identifying a near-optimal placement for any number of sensors. For two or more airflow velocity magnitude sensors, this algorithm finds a placement solution (design point) which predicts angle of attack of airfoils to within 0.10° and ranks within the top 1% of all possible design points validated by combinatorial search. The scalability and adaptability of this algorithm is demonstrated on several 2D model variations in clean and noisy data, and model sensitivities are evaluated and compared against conventional optimization techniques. Applications for this sensor placement algorithm are explored for aircraft design, flight control, and beyond.

An easy method for simultaneously enhancing power system voltage and angle stability using STATCOM

The global electricity demand is growing rapidly and is expected to double by 2050. This growth requires significantly expanding the current high-voltage transmission lines, which is a long-term and costly solution. As an alternative, utilities use advanced technologies like Flexible Alternating Current Transmission System (FACTS) devices as a temporary measure, postponing the expansion. However, it is necessary to locate these devices in the network properly to avoid negative outcomes. Traditional methods for FACTS placement focus on voltage profile improvement and power loss reduction, neglecting rotor angle stability. This can lead to oscillatory instability, especially in weak power grids. To address this issue, the paper proposes a simple approach to optimize the placement of shunt FACTS devices in power systems. The method improves both voltage and rotor angle stability without compromising power loss. The proposed approach considers rotor angle stability constraints, which is essential for system stability during transient events. The study was demonstrated on Nigeria’s 50-bus 330 kV power system, with results indicating a 31% improvement in angle stability with an enhanced voltage profile. The method is straightforward, non-iterative, and provides a cost-effective solution for FACTS device placement in power systems.

ComPipe: A Novel Flow Placement and Measurement Algorithm for Programmable Composite Pipelines

Programmable networks comprise heterogeneous network devices based on both hardware and software. Hardware devices provide superior bandwidth and low latency but encounter challenges in managing large table entries. Conversely, software devices offer abundant flow tables but have a limited forwarding capacity. To overcome this limitation, some commercial switches offer implementations that combine both hardware and software devices. In this context, this paper presents the Composite Pipeline (ComPipe), an algorithm for high-performance and high-precision flow placement and measurement. ComPipe utilizes a multi-level hashing algorithm for the real-time identification of heavy hitters, incorporates a unique flow eviction strategy, and is implemented on commercial programmable hardware. For non-heavy flows, ComPipe employs sketch structures to accomplish a high-performance flow synopsis within limited memory constraints. This design allows to replace flow rules entirely in the data plane, ensuring the timely detection and offloading of heavy-hitter flows, and offering a unified interface to the controller. The ComPipe prototype has been implemented in both testbed and simulation environments. The results indicate that ComPipe is an effective solution for dynamic flow placement in programmable networks, distinguished by its low cost, high performance, and high accuracy.

A Multiscale Approach for Free-Float Bike-Sharing Electronic Fence Location Planning: A Case Study of Shenzhen City

As an emerging technological means for managing free-float bike-sharing parking, electronic fences have attracted increasing attention in major cities as a solution to the challenges posed by disorderly parking of free-float bikes. Existing research has predominantly focused on employing clustering methods from the perspectives of free-float bike-sharing companies and users to plan and deploy electronic fences. However, the results often deviate significantly from the actual phenomenon. Therefore, scientific location selection is particularly important to fully harness the effectiveness of electronic fences. This paper proposes a multiscale clustering method based on free-float bike-sharing parking features to determine the optimal locations for electronic fences. A multiobjective mixed-integer programming model is established to address the location planning problem of electronic fences, determining the planning positions, quantities, and areas of electronic fences. A case study is conducted using a local area free-float bike-sharing dataset from Shenzhen city to validate the effectiveness of the proposed method. Comparative results with traditional approaches solely relying on K-means or DBSCAN methods demonstrate that the proposed approach achieves efficient location selection, through multiscale fusion site selection in the study area of 1.5∗1 km, and only 25 electronic fences need to be planned and deployed, covering a total area of 1691.88 square meters, which can provide rational placement solutions and better utilize the effectiveness of electronic fences. This method can thus offer decision-making support for the planning and location selection of electronic fences in free-float bike-sharing systems.

Optimizing the Fixed Number Detector Placement for the Nuclear Reactor Core Using Reinforcement Learning

Monitoring three-dimensional flux distribution in a nuclear reactor core is essential for improving safety and economics, which requires strategically placed in-core detectors. However, the deployment of these sensors is often constrained by physical, industrial, and economic limitations. This study treats optimizing the location of in-core detectors as a Markov decision process and develops a reinforcement learning (RL)–based framework to provide a solution for detector placement given a fixed number of detectors and available detector positions. The RL-based framework contains an environment consisting of a Proper Orthogonal Decomposition–based power reconstruction function paired with a novel reward function based on the power reconstruction error and a well-educated agent that updates the detector placement. Four RL algorithms including Proximal Policy Optimization, Deep Q-Network, Advantage Actor-Critic, and Monte Carlo Tree Search are investigated to optimize the detector placement and are analyzed. Genetic Algorithm (GA), a traditional optimization approach, is applied for comparison. The findings reveal that RL outperforms GA in terms of the quality of optimal solutions, demonstrating an inclination toward locating a global solution. Moreover, the flexible nature of RL enables the integration of developed novel reward functions from a specific reactor core into other reactors, considering the particular engineering requirements within the RL-based framework, thereby enhancing the optimization of in-core detector configurations.

Multiple objectives dynamic VM placement for application service availability in cloud networks

Ensuring application service availability is a critical aspect of delivering quality cloud computing services. However, placing virtual machines (VMs) on computing servers to provision these services can present significant challenges, particularly in terms of meeting the requirements of application service providers. In this paper, we present a framework that addresses the NP-hard dynamic VM placement problem in order to optimize application availability in cloud computing paradigm. The problem is modeled as an integer nonlinear programming (INLP) optimization with multiple objectives and constraints. The framework comprises three major modules that use optimization methods and algorithms to determine the most effective VM placement strategy in cases of application deployment, failure, and scaling. Our primary goals are to minimize power consumption, resource waste, and server failures while also ensuring that application availability requirements are met. We compare our proposed heuristic VM placement solution with three related algorithms from the literature and find that it outperforms them in several key areas. Our solution is able to admit more applications, reduce power consumption, and increase CPU and RAM utilization of the servers. Moreover, we use a deep learning method that has high accuracy and low error loss to predict application task failures, allowing for proactive protection actions to reduce service outage. Overall, our framework provides a comprehensive solution by optimizing dynamic VM placement. Therefore, the framework can improve the quality of cloud computing services and enhance the experience for users.

Feasibility study of functional near-infrared spectroscopy in the ventral visual pathway for real-life applications.

fNIRS-based neuroenhancement depends on the feasible detection of hemodynamic responses in target brain regions. Using the lateral occipital complex (LOC) and the fusiform face area (FFA) in the ventral visual pathway as neurofeedback targets boosts performance in visual recognition. However, the feasibility of utilizing fNIRS to detect LOC and FFA activity in adults remains to be validated as the depth of these regions may exceed the detection limit of fNIRS. This study aims to investigate the feasibility of using fNIRS to measure hemodynamic responses in the ventral visual pathway, specifically in the LOC and FFA, in adults. We recorded the hemodynamic activities of the LOC and FFA regions in 35 subjects using a portable eight-channel fNIRS instrument. A standard one-back object and face recognition task was employed to elicit selective brain responses in the LOC and FFA regions. The placement of fNIRS optodes for LOC and FFA detection was guided by our group's transcranial brain atlas (TBA). Our findings revealed selective activation of the LOC target channel (CH2) in response to objects, whereas the FFA target channel (CH7) did not exhibit selective activation in response to faces. Our findings indicate that, although fNIRS detection has limitations in capturing FFA activity, the LOC region emerges as a viable target for fNIRS-based detection. Furthermore, our results advocate for the adoption of the TBA-based method for setting the LOC target channel, offering a promising solution for optrode placement. This feasibility study stands as the inaugural validation of fNIRS for detecting cortical activity in the ventral visual pathway, underscoring its ecological validity. We suggest that our findings establish a pivotal technical groundwork for prospective real-life applications of fNIRS-based research.

IoT based antenna positioning system

Antennas are fundamental to every form of wireless communication system. Antenna placement is crucial for successful wireless communication, according to satellites and transmitters. So, to enable the use of IoT for remote antenna deployment, this paper provides an IoT-based antenna placement solution. In this case, this paper examines the transmitted orientation of each antenna over the Internet of Things using a sensor-based system that includes a motor on each antenna. When a satellite's or transmitting station's orientation changes, it is necessary to reposition the antenna. The receiving antennas might be located in different parts of the world, at great distances from each other. So, extremely long-distance antenna placement is within the realm of possibility because of modern technology. Online visibility of antenna sites is available to the operator in charge of the IoT. The antenna monitoring GUI system is utilized by the IoT. With IoT-based antenna placement solution technology, the antenna's orientation can be monitored and updated coordinates provided to the motor, allowing it to position the antenna correctly.

A qualitative evaluation of a student midwife placement teaching English to speakers of other languages (ESOL).

A shortage of UK midwives has put pressure on clinical placements and supervision of student midwives. Alternative placement solutions are needed to provide students with meaningful learning experiences. One such learning experience was a placement undertaken by student midwives who attended a program teaching English to speakers of other languages (ESOL). This study evaluated the impact of the placement on student midwife learning and experiences of the ESOL participants. The 2022 study employed a qualitative design using Kolb's model of experiential learning as a framework. Ten student midwives placed with the ESOL program and three women enrolled in the program participated. Data were collected via online focus groups with the student midwives and a face-to-face focus group with the women. Data were analyzed using thematic analysis and Kolb's model of experiential learning. Four themes were constructed: 'Putting the scripts aside: expectations versus the reality of being an educator', 'Adapting and personalizing teaching', 'We are learning too: an environment for mutual learning', and 'Taking our learning forwards'. Students faced barriers during their placement and had to adapt their teaching accordingly. They gained crucial knowledge of the challenges faced by women who speak other languages. The women valued the students' input and together they forged a reciprocal learning environment. This study demonstrates how placing student midwives in a unique non-maternity setting has benefits for student learning which are transferrable to future practice. Importantly, it confirms that quality of learning during a novel placement is not compromised for students or participants.

Energy-Efficient Relay Node Placement in Wireless Heterogeneous Networks with Capacity Constraints

In wireless heterogeneous networks (HetNets), rational relay node (RN) placement can not only expand network coverage and capacity, but also significantly reduce energy consumption of the network. However, existing RN placement schemes mainly focus on improving network performance, and do not take the network energy consumption and capacity constraints of RNs into consideration, which makes their solutions impractical. Therefore, in this paper, we investigate energy-efficient RN placement (EERPL) problem, and try to reduce energy consumption of a wireless HetNet within the range of RN’s capacity constraints. More specifically, we first present a fine-grained energy consumption model and formulate the EERPL problem, which can be translated to the degree constrained minimum Steiner tree problem. Then, we propose a heuristic RN-placement algorithm HBAPS, which involves two steps, i.e., generating an initial minimum-energy RN placement solution and adjusting the solution to be with in the capacity constraints. Finally, extensive simulations are conducted in both randomly generated scenarios and real trace-based scenarios. Experimental results demonstrate that HBAPS achieves nearly optimal results when the scenario is small, and even in a large scenario, the proposed algorithm still obtains proper solutions within the acceptable time, and outperforms the other two existing heuristic algorithms.

Modelling optimal water retention in hydrogenic habitats using LIDAR laser data

Degradation of hydrogenic habitats in climate change increased rapidly. It is important that we take actions to stop this process. Solution is to increase efficiency of water usage by ecosystems - especially water based ones. Building devices for delaying surface water runoff – like locks and dams – should improve hydrogenic habitats conditions and allow surrounding ecosystems use rainwater more efficient. Modelling of small retention in forests is an important aspect in decision making schema. Aim of this paper is to point optimal solutions for height and placement of devices which delay surface water runoff to set necessary water table level for renaturalization and maintenance of degrading natural habitats. Data used for analyses were acquired in the Polanów Forest Inspectorate in West Pomeranian voivodeship because of the topography diversification and the drainage infrastructure presence. There were three research plots selected based on decreased stability of habitats and historic data stated that there were natural water reservoirs, which were drained in past. Based on 2012 LiDAR (Light Detection and Ranging) point cloud the digital terrain model (DTM) was built. Water outflow points – melioration canals – were identified and analysed for optimal device localization. In following part of research specific data for each hydrogenic habitat were used to model height of devices which delay surface water runoff. Optimal level of device and area covered by water were set for each site separately. The results were handed over to the investor for implementation, then the compliance of the assumptions of the simulation of raising the water table with the as-built field measurements was checked. Study shows that it is possible to use laser technology to optimize location and height of devices which delay surface water runoff what allows to restore degraded hydrogenic habitats. Presented method supports small local retention what increases limited water resources in this region, decreases rapid runoff of surface water which causes frequent floods. Proposed method of modelling the location and height of the dams or locks is universal. Even though results are unique for each object the method is possible to be applied to every other situation.

Passive-seismic sensor placement optimization for geologic carbon storage

The implementation of passive-seismic monitoring is essential for the geological carbon storage projects. For secure and continuous observing of induced passive-seismic events by CO2 injecting, a ground geophone network would be required. By determining the ideal number of seismic stations within a regular network, recent research has enhanced monitoring capabilities while also accommodating budgetary limitations. The primary objective of the optimal placement strategy for the surface geophone network is to create a cost-effective monitoring system. Given the cost limitations, a restricted quantity of sensors can be deployed at each location to maximize monitoring performance. To address this challenge, our approach involves the P-median stochastic programming formulation. The formulation aims to minimize the expected value of a monitoring target metric, which ultimately results in the optimal placement of detectors exhibiting superior expected behaviors. Our methodology is designed to choose the arrangement of detectors with best performance to improve both the early alarm detection time and localization capabilities of the sensor network. We utilize site-specific passive-seismic scenarios that capture the uncertainty in the characteristics of carbon leakage. The sensor grids provided by the optimization method invariably improve the capacity to detect passive-seismic events in comparison to sensors positioned in a regular grid configuration. We test the effectiveness of our approach on synthetic data based on a carbon storage site named Kimberlina. In conclusion, our approach provides a cost-effective solution for the optimal placement of sensors to achieve superior monitoring performance for the detection of passive-seismic events. The incorporation of site-specific scenarios with stochastic uncertainty coverage allows for more accurate and reliable results, leading to better decision-making for long-term geological carbon storage.

Optimal hybrid photovoltaic distributed generation and distribution static synchronous compensators planning to minimize active power losses using adaptive acceleration coefficients particle swarm optimization algorithms

The paper aims to identify the optimum size and location of photovoltaic distributed generation systems and distribution static synchronous compensators (DSTATCOMs) systems to minimize active power losses in the distribution network and enhance the voltage profile. The methodology employed in this article begins by thoroughly discussing various acceleration algorithms used in Particle Swarm Optimization (PSO) and their variations with each iteration. Subsequently, a range of PSO algorithms, each incorporating different variations of acceleration coefficients was verified to solve the problem of active power losses and voltage improvement. Simulation results attained on Standard IEEE-33 bus radial distribution network prove the efficiency of acceleration coefficients of PSO; it was evaluated and compared with other methods in the literature for improving the voltage profile and reducing active power. Originality. Consists in determining the most effective method among the various acceleration coefficients of PSO in terms of minimizing active power losses and enhancing the voltage profile, within the power system. Furthermore, demonstrates the superiority of the selected method over others for achieving significant improvements in power system efficiency. Practical value of this study lies on its ability to provide practical solutions for the optimal placement and sizing of distributed generation and DSTATCOMs. The proposed optimization method offers tangible benefits for power system operation and control. These findings have practical implications for power system planners, operators, and policymakers, enabling them to make informed decisions on the effective integration of distributed generation and DSTATCOM technologies.

HADES: An NFV solution for energy-efficient placement and resource allocation in heterogeneous infrastructures

Network Function Virtualization (NFV) aims to replace traditional network functions running in proprietary hardware with software instances (i.e., Virtual Network Functions, VNFs) embedded in general-purpose virtualization solutions. Aware that the transition to a fully virtualized network infrastructure will pay a high energy cost, especially in IoT systems composed of a myriad of devices, energy efficiency is one of the key innovative targets of future networks. Edge computing should be considered in IoT environments to save time and energy by processing data near the producer devices. However, applying NFV in the context of IoT/Edge/Cloud environments complicates the placement of VNFs, due to the inherent heterogeneous nature of such environments and the variety of resource demands. This paper proposes an energy-aware placement of service function chains of VNFs and a resource-allocation solution for heterogeneous edge infrastructures that considers the computation and communication delays according to the VNFs’ location in the infrastructure. The solution has been integrated with the ETSI-sponsored project Open Source Management and Orchestration (OSM) as an extension called HADES, which allows the configuration of VNFs and their subsequent resource allocation and deployment at the edge, minimizing energy consumption and ensuring a quality of service. We have applied the deployment of augmented reality services in real and simulated scenarios. The results show up to a 59% reduction in power consumption and QoS compliance in all scenarios considered compared to default OSM placement and four other allocation policies. We prove that our solution has negligible power overhead, and validate the scalability and applicability of HADES.