Placement Algorithm Research Articles

Enhancing Scalability through Extended Hybrid Switching with Mixture Path Deployment (EHS-MPD): A Novel Framework

In today's digital landscape, the exponential growth of data has created significant challenges for archiving systems. Existing solutions often struggle to cope with the ever-increasing data volumes, leading to scalability limitations, reduced efficiency, and inflexibility. To address these challenges, this research paper introduces a novel approach that extends hybrid switching with a mixture path deployment framework. By combining the benefits of hybrid switching techniques and the adaptability of the mixture path deployment framework, our proposed approach aims to enhance scalability, efficiency, and flexibility in archiving systems while minimizing the risks of data loss and system failures. This paper provides a comprehensive analysis of the design, implementation, and evaluation of our approach, highlighting its key features, benefits, and potential impact on archiving systems. We review existing archiving approaches, identifying their limitations and shortcomings. Then, we delve into the concept of hybrid switching, exploring its advantages in optimizing network performance. Additionally, we introduce the mixture path deployment framework, emphasizing its benefits in load balancing, fault tolerance, and performance improvement. The proposed approach's architecture is presented, showcasing the integration of hybrid switching techniques and the mixture path deployment framework. Strategies for efficient data placement and management are discussed, including dynamic data placement algorithms and metadata management techniques. The design and implementation of the extended archiving system are described, outlining the interactions among its components. Overall, our novel approach to archiving high scalability through extending hybrid switching with a mixture path deployment framework offers a promising solution to address the limitations of existing archiving systems. It enables improved performance, flexibility, and fault tolerance, paving the way for efficient and scalable archiving solutions in the era of big data.

VLSI Placement Optimization Algorithms

Placement is a fundamental optimization step to compute cell locations. The quality of results in Clock Tree Synthesis (CTS) and routing stages is impacted by the placement solution. The placement optimization flow is split into (1)global placement, (2) legalization, and (3) detailed placement. In global placement, cell locations are computed to minimize total wire length subject to a maximum cell density threshold. The cell overlapping and cell alignment to site row boundaries are relaxed. In legalization, cells are placed in locations free of overlapping and aligned to site row boundaries. Legalization algorithms compute cell locations with minimized cell displacement. In detailed placement, objectives are optimized locally. Detailed placement algorithms iterate over one cell or a small set of cells. The traditional optimization objective is total wire length. Placement algorithms also address timing violations, routability, design rules, and so forth. The placement algorithms rely on heuristics and formal methods to compute optimized cell locations. Moreover, placement algorithms require models to address signal delay propagation, area density, routing congestion, hyper-edge nets, and so forth. In the literature, several algorithms have been presented to improve placement solutions. On the other hand, placement is a really challenging problem that continues to have space for further improvement and for innovative algorithms.

Enhanced Real Coded Genetic Algorithm for Optimal DG Placement in a Radial Distribution System

Enhanced Real Coded Genetic Algorithm for Optimal DG Placement in a Radial Distribution System

КО-ЭВОЛЮЦИОННЫЙ АЛГОРИТМ РАЗМЕЩЕНИЯ НА ОСНОВЕ ВЗАИМОДЕЙСТВИЯ СУБПОПУЛЯЦИЙ, ОТЛИЧАЮЩИХСЯ СТРАТЕГИЯМИ ПОИСКА

A new methodology and method for placing VLSI elements has been developed, which differin that the solution of the placement problem is based on the use of a fixed order of position selection,focused on the effective solution of the placement problem, and a heuristic procedure fordistributing elements by positions, which reduces the overall complexity and improves the qualityof the solution. The process of forming a list of positions on the switching field is carried out usingthe mechanisms of the wave algorithm. The choice of the final list is based on the principle of constructinga route with a minimum estimate of the total linear length of distances between routepositions. To solve the placement problem, a search algorithm based on the modified ant colonymethod has been developed. To exclude premature convergence and localization of the globalextremum of the problem, the development of the algorithm was carried out on the basis of the coevolutionaryapproach. The architecture of the co-evolutionary placement algorithm is developedon the basis of the ant colony algorithm paradigm. In the search space, sub-populations implementfour optimization strategies in parallel. In the work, the coevolution process is implemented on thebasis of the interaction of subpopulations that differ in search strategies. A distinctive feature ofthe co-evolutionary approach used is that subpopulations of solutions are actually virtual. Theprocess of co-evolution is implemented by one population of agents Z by sequential formation andmerging of virtual subpopulations of solutions into one population. In this paper, the solution ofthe placement problem is aimed at improving traceability by minimizing the resources required toimplement connections. A significant contribution to minimizing the spatial and temporal complexityof the search procedure was made by: the use by virtual sub-populations of a common evolutionarymemory, a common solution search graph, the formation of a single interpretation of thesolution in the form of a route on a complete directed graph with binary directed edges. Testingwas carried out on benchmarks 19s, PrimGA1, PrimGA2. The results compared to existing algorithmsare improved by 7-8%. The probability of obtaining a global optimum was 0.96. On average,solutions differ from the optimal by less than 1.5%. The time complexity of the algorithm forfixed values of the population size and the number of generations is O(n). The total time complexityof the hybrid algorithm is O(n2)−O(n3).

An improved parallel processing-based strawberry optimization algorithm for drone placement

It is challenging to place drones in the best possible locations to monitor all sensor targets while keeping the number of drones to a minimum. Strawberry optimization (SBA) has been demonstrated to be more effective and superior to current methods in evaluating engineering functions in various engineering problems. Because the SBA is a new method, it has never been used to solve problems involving optimal drone placement. SBA is preferred for optimizing drone placement in this study due to its promising results for nonlinear, mixed, and multimodal problems. Based on the references listed below, no study has investigated the need to develop a parallelized strategy version. Several studies have been conducted on the use of drones for coverage. However, no optimization algorithms have been evaluated regarding time complexity or execution time. Despite what has been said thus far, no study has looked into the significance of a systematic framework for assessing drone coverage techniques using test suits. An optimized drone placement algorithm based on strawberry optimization is presented in the paper. The strawberry optimization algorithm will solve the drone placement problem through parallelization. In addition, the authors deploy test suits that vary in size from small to large. The dataset consists of four categories with three problems each. Results indicate that strawberry optimizers outperform Genetic algorithms (GA) and particle swarm optimization algorithms (PSO) in the number of drones, convergence, and computation time. Furthermore, the proposed approach achieves the best solution in a finite number of steps. In small-scale problems, the performance of all algorithms is convergent. As the size of the data set increases, the superiority of Strawberry optimization algorithms becomes evident. Overall, Strawberry comes out on top for eleven out of twelve comparisons.

Simultaneous seismic input and state estimation with optimal sensor placement for building structures using incomplete acceleration measurements

Simultaneous real-time input and state estimation and optimal sensor placements are investigated in this paper, focusing on systems without direct feedthrough, such as earthquake-excited building structures with absolute floor acceleration measurements. Current studies showed that system properties such as strong observability, strong* detectability (the asterisk distinguishes strong* detectability from strong detectability), and invertibility conditions are crucial to the stability and convergence of unknown input and state estimation, but they can often be violated in practice. Consequently, uncertainties such as modeling errors and measurement noise can greatly degrade the accuracy and stability of the estimations. Estimation in this case has remained challenging due to the above reasons. To fill this gap, this paper develops an optimal sensor placement algorithm (OSPA) for real-time unknown input and state estimation, which ensures the required system conditions are met. The developed OSPA is integrated with two optimal real-time Kalman-based filters, a minimum-variance unbiased input and state estimation filter (MVUIS) and an Augmented State Kalman Filter (ASKF), for simultaneous input and state estimation. In particular, the MVUIS is presented in a recursive three-step structure without using the arbitrary matrix in the gain, which makes no assumptions on the input but requires strong* detectability. To avoid the requirement, ASKF is derived from the MVUIS by incorporating prior knowledge of the input. The developed OSPA along with the MVUIS and ASKF provide the optimal input and state estimation in real-time without incurring low-frequency drift or unstable estimations. Notably, the OSPA improves the performance of MVUIS by reducing amplitude errors and enhances the accuracy of ASKF by reducing phase errors. The developed OSPA integrated with the MVUIS and ASKF are validated through numerical and experimental studies as well as a real-world instrumented building structure under earthquakes using incomplete absolute acceleration measurements.

Providing Aerial MEC Service in Areas Without Infrastructure: A Tethered-UAV-Based Energy-Efficient Task Scheduling Framework

The evolution of information and communication technology, besides the proliferation of mobile devices, pushes the horizon of the Internet of Things. The main challenges for mobile devices are limited battery power and insufficient computational resources. Multiaccess edge computing (MEC) is an emerging paradigm that can provide task offloading services in the proximity of mobile devices to alleviate their load. In addition, mobile devices cannot work in emergency scenarios (e.g., post-earthquake, post-flood, and post-hurricane) or areas without infrastructure. Tethered unmanned aerial vehicles (UAVs) have received widespread attention as an alternative base station because of their line-of-sight solid links status, flexible deployment, and sufficient energy supply. With this in mind, we propose a tethered-unmanned-aerial-vehicle-based aerial MEC network to provide communication and task offloading services in areas without infrastructure. In addition, an energy-efficient task scheduling framework is proposed to achieve more energy-efficient task scheduling. First, we propose a geometry-based placement algorithm to generate optimal placement positions for the UAV placement problem. Then, for the nonconvex task scheduling and the resource allocation problem, we propose a low complexity divide and conquer scheme, which decomposes the original problem into three subproblems and solves them separately. Extensive simulations demonstrate the better energy efficiency of the proposed framework.

Efficient sensor placement and online scheduling of bin collection

Several spatially distributed tasks can be completed more efficiently by using a combination of better sensing, prediction, and planning. When an agent needs to regularly move to serve different locations, it is necessary to find a compromise solution between the travel cost and the required quality of service. This problem is present in several contexts, namely waste collection, maintenance, and surveillance. This problem is very complex since it includes sensor placement and routing problems that, due to the uncertainty and combinatorial decision making, cannot be solved optimally in an efficient way. In this work, we develop a new efficient algorithm for sensor placement. It takes into account scheduling and route planning to serve distributed locations, considering not only the value of the information provided by each sensor but also the impact on travel efficiency and quality of service. We present examples of sensor placement and route planning in a waste collection environment.

Edge server placement for offloading real‐time statistics tasks of bus passenger

AbstractWith the continuous development of smart bus systems, higher requirements are expected for the accuracy and timeliness of the real‐time statistics of bus passengers. Although the statistical method of image and video processing based on deep learning has higher accuracy, it has higher requirements on the computing power and hardware equipment of the computer. A traditional solution is cloud computing, but cloud computing cannot meet real‐time requirements due to long‐distance transmission. In order to meet the real‐time demand, it can be offloaded to the edge of the network and processed by edge servers. In edge computing, the location of the edge server will have a great impact on the access delay and the traffic load in the edge network. Currently, few people optimize the traffic load in the edge network during the placement process. In view of this, an edge server placement algorithm for task offloading, named ESPTO, is designed to balance the average delay and traffic load under the control of each edge server while minimizing the average delay and traffic load in the edge network. First, a decomposition‐based multi‐objective evolutionary algorithm (MOEA/D) is used to find a better set of placement strategies, and then the optimal placement strategy is obtained through TOPSIS. Experimental results based on the Hangzhou bus station dataset prove the effectiveness of ESPTO.

Energy-Efficient Initial Deployment and ML-Based Postdeployment Strategy for UAV Network With Guaranteed QoS

Nowadays, the extensive use of unmanned aerial vehicle (UAV)-enabled networks in different applications demands intelligent deployment planning to utilize several benefits of UAVs. This article proposes a complete solution for deploying a UAV network over an unprecedented public meet-up area that offers a guaranteed quality-of-service demand with no interference and capacity limit violation. We call that the proposed solution is complete as it includes both initial and postdeployment planning. Under initial deployment, we offer three different placement algorithms, known as anticlockwise spiral algorithm, clockwise spiral algorithm, and hexagonal circle packing algorithm, to determine the energy-efficient 3-D positions of capacity-limited UAVs with no inter-UAV interference. After deployment, the random walk by users demands postdeployment planning for UAVs. We propose a <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning-based algorithm to realign the existing UAVs to maintain the outage. In order to do a more realistic performance assessment of the proposed algorithms, we model the user distribution for a hotspot region by the Thomas cluster point process and the Matern cluster point process. The obtained results exhibit that all three initial deployment algorithms show better performance than a random deployment. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$Q$</tex-math></inline-formula> -learning algorithm under postdeployment offers network lifetime enhancement in addition to outage improvement.

Parallelism-Optimizing Data Placement for Faster Data-Parallel Computations

Systems performing large data-parallel computations, including online analytical processing (OLAP) systems like Druid and search engines like Elasticsearch, are increasingly being used for business-critical real-time applications where providing low query latency is paramount. In this paper, we investigate an underexplored factor in the performance of data-parallel queries: their parallelism. We find that to minimize the tail latency of data-parallel queries, it is critical to place data such that the data items accessed by each individual query are spread across as many machines as possible so that each query can leverage the computational resources of as many machines as possible. To optimize parallelism and minimize tail latency in real systems, we develop a novel parallelism-optimizing data placement algorithm that defines a linearly-computable measure of query parallelism, uses it to frame data placement as an optimization problem, and leverages a new optimization problem partitioning technique to scale to large cluster sizes. We apply this algorithm to popular systems such as Solr and MongoDB and show that it reduces p99 latency by 7-64% on data-parallel workloads.

Efficient gravitational wave template bank generation with differentiable waveforms

The most sensitive search pipelines for gravitational waves from compact binary mergers use matched filters to extract signals from the noisy data stream coming from gravitational wave detectors. Matched-filter searches require banks of template waveforms covering the physical parameter space of the binary system. Unfortunately, template bank construction can be a time-consuming task. Here we present a new method for efficiently generating template banks that utilizes automatic differentiation to calculate the parameter space metric. Principally, we demonstrate that automatic differentiation enables accurate computation of the metric for waveforms currently used in search pipelines, whilst being computationally cheap. Additionally, by combining random template placement and a Monte Carlo method for evaluating the fraction of the parameter space that is currently covered, we show that search-ready template banks for frequency-domain waveforms can be rapidly generated. Finally, we argue that differentiable waveforms offer a pathway to accelerating stochastic placement algorithms. We implement all our methods into an easy-to-use Python package based on the jax framework, diffbank, to allow the community to easily take advantage of differentiable waveforms for future searches.

Efficient Flow-Based Scheduling for Geo-Distributed Simulation Tasks in Collaborative Edge and Cloud Environments

Edge computing is a good complement to cloud computing for deploying large-scale geo-distributed simulation applications, which are very sensitive to the communication delay among different simulation components (also called tasks in this paper) and users. We mainly focus on the efficient scheduling of simulation components in collaborative edge and cloud environments. As components should be deployed jointly with the consideration of capacity constraints of hosts, it is actually an NP-complete multi-dimensional bin packing problem. Meanwhile, dynamic changes of component and host states require the low deployment latency of scheduling algorithms. Unfortunately, most of the existing schedulers for modern clusters are queue-based, in which tasks are scheduled sequentially, thus lacking the ability to process tightly coupled tasks jointly. Other batching-based placement algorithms are usually time-consuming. This paper describes Pond, a novel flow-based scheduler with the awareness of interactions among tasks and users as well as heterogeneous multi-dimensional resources. First, characteristics of distributed simulation tasks are analysed and the scheduling problem is formulated as a min-cost max-flow (MCMF) problem over the flow network by mapping the communication overhead among tasks and users to the costs of arcs in the network. Considering the inherent defects of existing flow-based schedulers in dealing with multi-dimensional resources, a new method based on dominant resource is proposed and some problem specific heuristics are also designed. Extensive simulation experiments based on Alibaba production trace and some random synthetic parameters are conducted. Results show that Pond can reduce the average communication cost for each task significantly in a quite low deployment latency compared with some baselines.

Decentralized Application Placement in Fog Computing

In recent years, cloud computing concepts have been extended towards the network edge, leading to paradigms like fog and edge computing. As a result, applications can be placed on a variety of resources, including fog nodes and cloud data centers. Application placement has significant impact on important metrics like latency. Finding an optimal application placement is computationally challenging, particularly because of the potentially huge number of infrastructure nodes and application components. To overcome the limited scalability of application placement algorithms, optimization can be decentralized, i.e., performed separately for different parts of the infrastructure. The infrastructure can be split into fog colonies, where a fog colony consists of the computational resources in a given geographical region. Application placement can then be performed for the individual fog colonies, thus mitigating the scalability problem. However, independent optimization of application placement in different fog colonies may lead to missed synergies and thus to sub-optimal overall results. Hence, some kind of coordination between fog colonies may be beneficial. In this article, we analyze the effects of decentralization and coordination on the optimization results. In particular, we compare empirically four different approaches: (i) centralized decision-making, where decisions are made in one go for the entire infrastructure, (ii) independent fog colonies, where optimization is carried out in each fog colony independently from each other, (iii) fog colonies with communication, where excess application components in one fog colony can be sent to a neighboring fog colony, and (iv) fog colonies with overlaps, where shared resources may be dynamically distributed between neighboring fog colonies. Our experiments show that, for large problem instances, decentralization combined with coordination leads to the best results.

Novel Proximal Group ADMM for Placement Considering Fogging and Proximity Effects

Fogging and proximity effects (FPEs) are two major factors that cause inaccurate exposure and layout pattern distortions in e-beam lithography. In this article, we propose an analytical placement algorithm that considers both FPEs. We formulate the global placement problem as a separable minimization problem with linear constraints, where different objectives can be tackled one by one in an alternating fashion. Then, we propose a novel proximal group alternating direction method of multipliers (ADMM) to solve the separable minimization problem with two subproblems, where the first subproblem (associated with wirelength and density) is solved by the steepest descent method without line search, and the second one (associated with the FPEs) is handled by an analytical scheme. We prove the property of global convergence of the proximal group ADMM method. Finally, the FPEs-aware legalization and detailed placement are employed to legalize and improve the placement result. The experimental results show that our algorithm is effective and efficient for the addressed problem. Our algorithm achieved 5.7% smaller fogging variation, 6.8% lower proximity variation, and 5.4% lower runtime with a minor wirelength overhead compared with the state-of-the-art work.

A Novel Strategy for VNF Placement in Edge Computing Environments

Network function virtualization (NFV) is a novel technology that virtualizes computing, network, and storage resources to decouple the network functions from the underlying hardware, thus allowing the software implementation of such functions to run on commodity hardware. By doing this, NFV provides the necessary flexibility to enable agile, cost-effective, and on-demand service delivery models combined with automated management. Different management and orchestration challenges arise in such virtualized and distributed environments. A major challenge in the selection of the most suitable edge nodes is that of deploying virtual network functions (VNFs) to meet requests from multiple users. This article addresses the VNF placement problem by providing a novel integer linear programming (ILP) optimization model and a novel VNF placement algorithm. In our definition, the multi-objective optimization problem aims to (i) minimize the energy consumption in the edge nodes; (ii) minimize the total latency; and (iii) reducing the total cost of the infrastructure. Our new solution formulates the VNF placement problem by taking these three objectives into account simultaneously. In addition, the novel VNF placement algorithm leverages VNF sharing, which reuses VNF instances already placed to potentially reduce computational resource usage. Such a feature is still little explored in the community. Through simulation, numerical results show that our approach can perform better than other approaches found in the literature regarding resource consumption and the number of SFC requests met.

A Graph-Theory-Based PRV Placement Algorithm for Reducing Water Age in Water Distribution Systems

Water age is the time taken for water to travel through a distribution system and reach the consumer. Generally, there is a trade-off between water pressure and water age in a water distribution system—higher pressure results in higher flow velocity, which often means shorter traveling time for the water, while lower pressure leads to slower flow and thus higher water age. Low pressure is a desired objective in a distribution system, as it reduces the physical stress on its components and minimizes water losses in the event of a leak. Low water age is a desired objective as well, as increased age is regarded as having a low water quality. Therefore, the two objectives compete with one another. The problem of trying to minimize both water pressure and age is a common problem in water distribution systems’ design and management. This paper introduces an algorithm for pressure reducing valves’ (PRVs) placement for reducing water age in water distribution systems. The algorithm is based on graph-theory elements and uses EPANET 2.2 for simulation and analysis. The method is demonstrated on two small scale examples, and the results present relatively significant improvements in respect to water age.

Optimising Viscous Damper Placement in Frame Buildings with Element Exchange Method for Multiple Seismic Hazard Levels

ABSTRACT Viscous dampers enhance the seismic performance of buildings by increasing the capacity for energy dissipation, or damping. Many placement optimisation algorithms currently exist, though they often do not align with code-based verification procedures nor consider optimising performance at multiple seismic hazard levels, at serviceability and ultimate limits. This paper proposes new damper placement algorithms and compares them against eight existing methods on seismic performance at different hazard levels, design iterations required and total computation effort. The comparison is conducted by means of numerical case studies. All methods are applied to a ten-storey reinforced concrete (RC) moment resisting frame (MRF) building, initially meeting the design requirements of typical buildings in a moderate seismic zone in New Zealand. The result showed that the proposed methods and two existing methods can generate a solution that dominates solutions of other existing methods in at least one aspect (e.g. the objective at a hazard level or running time).

Optimal Micro-PMUs Placement with Channel Limits using Dynamically Controlled Taguchi Binary Particle Swarm Optimization

The two-directional flow of electrical power has necessitated real-time monitoring of the distribution system (DS), which can otherwise result in power system failure. A micro-phasor measurement unit (micro-PMU) is proficient monitoring equipment that is presently employed to keep track of the changes taking place in DS. However, due to the higher cost of micro-PMU, an optimal number of micro-PMUs with channel limits must be strategically placed in the system. Therefore, an optimization problem for the optimal placement of micro-PMU has been formulated and solved in the present work under different operating scenarios for complete observability with sufficient redundancy. The optimal locations of micro-PMUs with channel limits in DS are obtained using an innovative technique that combines the concepts of Taguchi method (TM) and dynamically controlled binary particle swarm optimization (DBPSO). The proposed micro-PMU placement algorithm has been tested and analyzed on standard IEEE-33 bus and IEEE-69 bus distribution feeders with two different S-shaped and V-shaped sigmoidal functions. The results demonstrate that the proposed technique utilizing V sigmoidal function provides a better solution for optimal micro-PMU placement problem. A comparative investigation of the presented work to the existing micro-PMUs placement techniques for the DS establishes the efficacy of the proposed technique.

Real-time assessment of asphalt pavement moduli and traffic loads using monitoring data from Built-in Sensors: Optimal sensor placement and identification algorithm

Asphalt pavement performance is gradually attenuated subjected to repeated traffic loads. Grasping the relationship between the repeated traffic loads and pavement modulus attenuation is crucial for understanding pavement mechanical behavior, improving pavement design methods, and guiding maintenance decisions. However, the current bottleneck is that neither the existing non-destructive testing nor monitoring methods can simultaneously obtain the information of traffic loads and pavement moduli. To fill this gap, an innovative real-time assessment method of asphalt pavement moduli and traffic loads from the aspect of structural health monitoring was proposed. On the one hand, the optimal sensor placement was determined by the theoretical relationships between moduli, loads, and mechanical responses based on elastic multi-layered theory. On the other hand, the corresponding algorithm for stepwise decoupling identification of asphalt pavement moduli and traffic loads to match the optimal sensor placement was developed. Therefrom, the validity of the proposed assessment method was theoretically verified, and the effects of the viscoelastic property of asphalt materials, measurement errors, and layer thickness on the identification accuracy were comprehensively discussed. The proposed assessment method was applied to the realistic pavement to further prove its applicability. Results show that the proposed assessment method can both evaluate pavement moduli and traffic loads accurately. The proposed assessment method has application prospects for simultaneous real-time monitoring of the pavement modulus attenuation and traffic loads, which helps understand the pavement damage behavior, improve pavement design methods, and guide maintenance decisions.