Reconfiguration Cost Research Articles

Digital Twin-Empowered Network Planning for Multi-Tier Computing

In this paper, we design a resource management scheme to support stateful applications, which will be prevalent in 6G networks. Different from stateless applications, stateful applications require context data while executing computing tasks from user terminals (UTs). Using a multi-tier computing paradigm with servers deployed at the core network, gateways, and base stations to support stateful applications, we aim to optimize long-term resource reservation by jointly minimizing the usage of computing, storage, and communication resources and the cost from reconfiguring resource reservation. The coupling among different resources and the impact of UT mobility create challenges in resource management. To address the challenges, we develop digital twin (DT) empowered network planning with two elements, i.e., multi-resource reservation and resource reservation reconfiguration. First, DTs are designed for collecting UT status data, based on which UTs are grouped according to their mobility patterns. Second, an algorithm is proposed to customize resource reservation for different groups to satisfy their different resource demands. Last, a Meta-learning-based approach is developed to reconfigure resource reservation for balancing the network resource usage and the reconfiguration cost. Simulation results demonstrate that the proposed DT-empowered network planning outperforms benchmark frameworks by using less resources and incurring lower reconfiguration costs.

A skill- and feature-based approach to planning process monitoring in assembly planning

Frequent changes in customer needs and large product variety are forcing manufacturing companies to move from mass production to mass customization. Customized production can be achieved by introducing reconfigurable production systems (RMS). The customized flexibility and several characteristics of RMSs provide many opportunities in terms of process and production planning. However, those characteristics greatly increase the complexity of the design and planning of production systems. This paper presents a decision support system relying on a skill-based approach to design a reconfigurable assembly line considering the planning of assembly processes and monitoring. The proposed decision aid system is modular in design and is composed of four modules. The main input data is a CAD model of a new product variant for the identification of the assembly and monitoring requirements. Besides, a current assembly system layout with its resource descriptions exists. In the first developed module, assembly-by-disassembly and a skill-based approach are used to generate different assembly plans. In the second module, feature recognition and skill-based approaches generate process monitoring alternatives. The third module uses a linear program (LP) that aims to minimize the total cost of workstation activation and reconfiguration, as well as cycle time, and to maximize the process quality of the assembly tasks. A user-based generative model design approach is applied to optimize the values of three objective functions. In the fourth and final module, a simulation of the optimized assembly plan allows either the validation of the assembly plan and process monitoring plan or initiates a new iteration due to their infeasibility. To further demonstrate how the proposed methodology works, some computational experiments are provided for two use cases.

Budget-Conscious Fine-Grained Configuration Optimization for Spatio-Temporal Applications

Based on the performance requirements of modern spatio-temporal data mining applications, in-memory database systems are often used to store and process the data. To efficiently utilize the scarce DRAM capacities, modern database systems support various tuning possibilities to reduce the memory footprint (e.g., data compression) or increase performance (e.g., additional indexes). However, the selection of cost and performance balancing configurations is challenging due to the vast number of possible setups consisting of mutually dependent individual decisions. In this paper, we introduce a novel approach to jointly optimize the compression, sorting, indexing, and tiering configuration for spatio-temporal workloads. Further, we consider horizontal data partitioning, which enables the independent application of different tuning options on a fine-grained level. We propose different linear programming (LP) models addressing cost dependencies at different levels of accuracy to compute optimized tuning configurations for a given workload and memory budgets. To yield maintainable and robust configurations, we extend our LP-based approach to incorporate reconfiguration costs as well as a worst-case optimization for potential workload scenarios. Further, we demonstrate on a real-world dataset that our models allow to significantly reduce the memory footprint with equal performance or increase the performance with equal memory size compared to existing tuning heuristics.

Economic Dispatch of Distribution Network With Dispersed Wind Power Considering Network Reconfiguration

With the large-scale development of renewable energy sources such as wind and solar energy, the grid connection of renewable energy sources poses a certain threat to the security and stability of the power system, and also brings a great challenge to the economic dispatch of the distribution network. Traditional and single dispatching methods such as load demand response or network reconfiguration alone cannot meet the needs of a safe and economic operation of the distribution network. This article proposes an economic dispatching method for distribution networks with dispersed wind power considering network reconfiguration, and establishes an economic dispatching model with the objective function of minimizing distribution network operation cost, reconfiguration cost, and total system network loss. Based on the optimal scheduling of energy storage and reactive power compensation devices in the distribution network and the full utilization of demand response, a mixed integer second-order cone programming (MISOCP) method with multi-objective collaborative optimization is proposed. The results of the tests using the IEEE33 node system verify the feasibility and applicability of the proposed method in this article.

Design and analysis of flexible multi-layer staged deployment for satellite mega-constellations under demand uncertainty

Internet satellite constellations are expected to play an important role in accommodating the rising global demand for internet access. Such rise in demand, however, is highly uncertain. Staged deployment is an approach that provides flexibility to tackle demand uncertainty by enabling the real option to reconfigure a constellation if demand changes. Advancements in satellite technology have led to the emergence of multi-layered constellations. This opens the opportunity to enhance staged deployment by enabling an additional real option: adding a new layer to a constellation. This real option has no associated reconfiguration costs, and therefore has the potential to reduce the cost of staged systems deployment. This paper proposes a framework to design multi-layer staged deployment systems and analyse their effectiveness in modern mega-constellations under global demand uncertainty. The framework is applied to four case studies based on market projections. Results show that multi-layer staged deployment decreases the expected life-cycle cost (ELCC) by 42.8% compared to optimal traditional single-layer deployment. Multi-layer staged deployment is more cost effective than single-layer staged deployment in all practical cases, which decreases ELCC by 22.9% compared to traditional deployment. Several cost altering mechanisms in staged deployment are identified. The results and analysis provide improved economic performance and better resource utilization, thus contributing in the long term to improved sustainability and market resilience. An accompanying decision support system provides system engineers with valuable insights on how to reduce deployment costs using the proposed multi-layered staged strategy.

Do Safe Bike Lanes Really Slow Down Cars? A Simulation-Based Approach to Investigate the Effect of Retrofitting Safe Cycling Lanes on Vehicular Traffic

Cycling is a sustainable transportation mode that provides many health, economic and environmental benefits to society. Cities with high rates of cycling are better placed to address modern challenges of densification, carbon-neutral and connected 20-min neighbourhood goals. Despite the known benefits of cycling, participation rates in Australian cities are critically low and declining. Frequently, this low participation rate is attributed to the dangers of Australian cycle infrastructure that often necessitates the mixing of cyclists with car traffic. In addition, residents of car-dependent Australian suburbs can be resistant to the installation of cycle infrastructure where threats to traffic flow, or decreased on-street parking availability are perceived and the prohibitive cost of reconfiguration of other infrastructure maintained by the local councils to retrofit safe bike paths. This study investigates the effects on traffic behaviour of retrofitting safe, separate cycling lanes into existing residential streets in a Melbourne suburb suitable for accessing the primary neighbourhood destinations. We utilise only the widths available on the existing roadway of these streets, with minimal incursion on other facilities, such as the vehicle network and parking. Using only the existing roadway reflects the common need for municipal asset managers to minimise disruption and costs associated with street redesign. Using a traffic simulation approach, we modelled travel demand that suits suburban trips to services and shops, and we selectively applied separate cycling lanes to suitable residential streets and varied the effect of lowering speed limits. Simulations show that the selective inclusion of safe cycling lanes in some streets leads to a mere 7% increase in the average car travel times in the worst case, while requiring cyclists to increase their travel distance only marginally to avoid streets without dedicated cycling lanes. These results demonstrate that reasonable compromises are possible to make suburbs safer for cyclists and bring them closer to the 20-min neighbourhood goal. There is significant potential to enhance the result by including more street types and alternative designs. The results can inform councils in their cycle path infrastructure decisions and disprove assumptions about the influence of cyclists on car infrastructure.

HoneyCar: A Framework to Configure Honeypot Vulnerabilities on the Internet of Vehicles

The Internet of Vehicles (IoV), whereby interconnected vehicles that communicate with each other and with road infrastructure on a common network, has promising socio-economic benefits but also poses new cyber-physical threats. To protect these entities and learn about adversaries, data on attackers can be realistically gathered using decoy systems like honeypots. Admittedly, honeypots introduces a trade-off between the level of honeypot-attacker interactions and incurred overheads and costs for implementing and monitoring these systems. Deception through honeypots can be achieved by strategically configuring the honeypots to represent components of the IoV to engage attackers and collect cyber threat intelligence. Here, we present HoneyCar, a novel decision support framework for honeypot deception in IoV. HoneyCar benefits from the repository of known vulnerabilities of the autonomous and connected vehicles found in the Common Vulnerabilities and Exposure (CVE) database to compute optimal honeypot configuration strategies. The adversarial interaction is modelled as a repeated imperfect-information zero-sum game where the IoV network administrator strategically chooses a set of vulnerabilities to offer in a honeypot and a strategic attacker chooses a vulnerability to exploit under uncertainty. Our investigation examines two different versions of the game, with and without the re-configuration cost, to empower the network administrator to determine optimal honeypot investment strategies given a budget. We show the feasibility of this approach in a case study that consists of the vulnerabilities in autonomous and connected vehicles gathered from the CVE database and data extracted from the Common Vulnerability Scoring System (CVSS).

Data-Driven Adaptive Network Slicing for Multi-Tenant Networks

Network slicing to support multi-tenancy plays a key role in improving the performance of 5G networks. In this paper, we propose a two time-scale framework for the reservation-based network slicing in the backhaul and Radio Access Network (RAN). In the proposed two time-scale scheme, a subset of network slices is activated via a novel sparse optimization framework in the long time-scale with the goal of maximizing the expected utilities of tenants while in the short time-scale the activated slices are reconfigured according to the time-varying user traffic and channel states. Specifically, using the statistics from users and channels and also considering the expected utility from serving users of a slice and the reconfiguration cost, we formulate a sparse optimization problem to update the configuration of a slice resources such that the maximum isolation of reserved resources is enforced. The formulated optimization problems for long and short time-scales are non-convex and difficult to solve. We use the $\ell_q$-norm, $0<q<1$, and group LASSO regularizations to iteratively find convex approximations of the optimization problems. We propose a Frank-Wolfe algorithm to iteratively solve approximated problems in long time-scales. To cope with the dynamical nature of traffic variations, we propose a fast, distributed algorithm to solve the approximated optimization problems in short time-scales. Simulation results demonstrate the performance of our approaches relative to optimal solutions and the existing state of the art method.

Посткризисная регионализация глобальных цепочек создания стоимости в стратегиях транснациональных компаний

The current global crisis led to the transformation of global value chains (GVC) in strategies of transnational companies (TNCs). The present study aims to establish a causal relationship between the volatility of the world economy and regionalisation of global value chains. Based on historical analysis and a systemic-functional approach, scattered information about the impact of global value chains on the development of TNC strategies was integrated. Thus, various aspects of TNC activities (their participation in international trade, international capital flows, etc.) were analysed together in order to identify the consequences of regionalisation for the strategic development of such companies. The study revealed that the established international production system carries many risks, which are exacerbated by continuing protectionism in international trade. Under the influence of digitalisation, regionalisation of value chains and partial reshoring of production facilities are the key directions for further development of GVCs. Given the rapid cross-border transmission of exogenous shocks, the stability and sustainability of global value chains will determine their development; however, there is no evidence whether GVCs will lengthen or shorten. The high cost of the complex reconfiguration of GVCs towards regionalisation will lead to only minor changes in their architecture. The redesign will affect only some aspects of activities, in particular, the organisation of work with global suppliers. While the weak integration of developing countries into GVCs will remain, the barriers to internationalisation will be reduced under the influence of digitalisation, creating new opportunities for the incorporation of small business into the international production system. In general, global value chains will still be considered a reliable source to reduce the risks of global operating activities of TNCs. The research results can be used for further theoretical and methodological development of internationalisation strategies.

Benders decomposition approach to solve a Bi-objective cell formation in dynamic conditions

Cellular Manufacturing System (CMS) is an alternative to production systems based on process and product layout that combines the advantage of high throughput rate of flow shop system with the flexibility of job shop layout. CMS design has four steps: 1) Cell formation: grouping the parts with similar geometric design features and processing requirements in part families to use similarities to build, manufacture and assemble machines in machine lines and cells, 2) group layout: placing machines in each cell that includes intra-cell and inter-cell layout, 3) group scheduling: part family scheduling, and 4) source allocation: allocation of tools and primary resources and human force. Dynamic CMS (DCMS) can be divided into smaller periods, each of which is different from the previous one as these periods have various demands and compositions. The study presented a mixed-integer programming model for DCMS. The model considered some significant real-world conditions. In this model, the first objective function tries to minimize the costs related to the machines purchase cost, machines variable cost, the cost of movement workers, the cost of cell formation, the cost of inter-cell and intra-cell part movement, the cost of overtime, and the cost of reconfiguration.

Automatic Generation of Reconfiguration Blueprints for IMA Systems Using Reinforcement Learning

Reconfiguration is a crucial method to improve the fault-tolerant capability of the integrated modular avionics (IMA) system. The quality of the blueprints has a substantial influence on operational performance after the system reconfiguration. Hence, this letter proposes an intelligent reconfiguration algorithm, which applies reinforcement learning (RL) to the IMA system. The proposed algorithm combines the critical elements of the RL model with the established IMA reconfiguration model to generate low-cost and high-quality blueprints. The experiment results demonstrate that the proposed method can not only generate feasible blueprints rapidly but also enhance system performance by optimizing load balance and reconfiguration costs.

Speculative Dynamic Reconfiguration and Table Prefetching Using Query Look-Ahead in the ReProVide Near-Data-Processing System

FPGAs are promising target architectures for hardware acceleration of database query processing, as they combine the performance of hardware with the programmability of software. In particular, they are partially reconfigurable at runtime, which allows for the runtime adaptation to a variety of queries. However, reconfiguration costs some time, and a region of the FPGA is not available for computations during its reconfiguration. Techniques to avoid or at least hide the reconfiguration latencies can improve the overall performance. This paper presents optimizations based on query look-ahead, which follows the idea of exploiting knowledge about subsequent queries for scheduling the reconfigurations such that their overhead is minimized. We evaluate our optimizations with a calibrated model for various parameter settings. Improvements in execution time can be “calculated” even if only being able to look one query ahead.

Reconfiguration Aware Orchestration for Network Function Virtualization With Time-Varied Workload in Virtualized Datacenters

Network function virtualization (NFV) is an emerging paradigm that has revolutionized the deployment and provisioning of telecommunication network services (NS). In NFV, a network service is implemented as a chain of network functions (NFs). NFs are virtualized and packaged into virtual network functions (VNF), so that they can be decoupled from the physical middleboxes and dynamically deployed on standard virtualization platforms in cloud or datacenters. Therefore, NFV has the potential to provide significant cost reductions in operating expenses (OPEX) and capital expenses (CAPEX) and increased agility for network service deployments. However, to build and manage a network function virtualization infrastructure (NFVI) is challenging. One of the key problems is to determine the mapping between underlying physical resources and VNFs in order to minimize energy consumption without violating service-level agreements (SLA). Many previous studies use embedding solutions to solve the problem by only considering the resource demand of NS and the resource capacity of physical network at a given time instance. But in practice, the resource demands of NS can vary over time, hence the embedding decision must be reconfigured accordingly. Many techniques can be used for reconfiguration, including resource scaling, instance migration and traffic re-direction. But each of these operation is associated with some costs, such as service interruptions, and additional resource or energy consumption. In this work, we investigate various reconfiguration strategies, formulate their costs by designing a model in the NFV placement problem, and propose Integer Linear Programming (ILP) and heuristic solutions, called REAP. In our evaluation, we observed 60% and 25% cost reduction for energy and reconfiguration costs, and we analyze the cost reduction from each of the reconfiguration methods to validate the design of our heuristic algorithm.

Dynamic construction of virtual manufacturing cell considering workload balancing and machine failure

Machine is the key element in manufacturing system. Machine failure will lead to order delivery delay and production cost rise. Aiming at the random disturbance of equipment failure, a dynamic non-linear programming model of virtual manufacturing cell (VMC) is established, which aims to minimize the equipment cost, logistics transportation cost, delay delivery cost and the differences of workload among cells, based on the comprehensive consideration of equipment reliability, process route constraints, variable processing path and workload. Due to the high complexity of the problem, the hybrid particle swarm optimization algorithm is used to solve the model, and the rationality and effectiveness of the method are verified by a numerical example. Experimental results show that this method has advantages in reducing the cost of cell reconfiguration, improving the utilization of equipment and shortening the completion time.

A multihorizon approach for the reliability oriented network restructuring problem, considering learning effects, construction time, and cables maintenance costs

This paper presents a techno-economic optimisation tool to study how the power system expansion decisions can be taken in a more economical and efficient way, by minimising the consequent costs of network reinforcement and reconfiguration. Analyses are performed to investigate how the network reinforcement and reconfiguration should be planned, within a time horizon of several years, by continuously keeping the network feasibility and ability to satisfy the load. The main contribution of this study is the inclusion of key features within the mathematical model to enhance the investment decision making process. A representative maintenance cost of existing cables and apparatus is included, to analyse the influence of the historical performance of the electric items on the investment decisions. A multihorizon methodology is developed to take into account the long term variation of the demand, combined with the long term variation of cables maintenance costs. Moreover, technological learning coefficients are considered, to take into account the investment costs reductions that arise when an investment in network restructuring and/or reconfiguration is repeated throughout the years. Finally, construction time constraints are included to find a proper investment scheduling that allows a feasible power flow, also during the years required to build a new connection or restructure an existing one. This study is also providing recommendations for future research directions within the power system reliability field. The analyses show the important and urgent need of proper methodologies for a better definition of cables projected maintenance costs and learning coefficients dedicated to network restructuring, reconfiguration and expansion.

Підхід щодо розміщення реклоузерів в розподільних мережах

Today, the electricity supplier is not able to declare the possible level of reliability of electricity supply, and the consumer simply does not have the opportunity to buy such “reliable” electricity. In such conditions, a differentiated approach to tariff formation is inevitable, which has been implemented in practice today, albeit in its infancy. Further improvement of the tariff-forming mechanism is impossible without creating a "flexible" dependence of the tariff on the indicators of the consumer's power supply reliability. Reliability indicators, in turn, are based, on the one hand, on the methods and approaches for their determination, and on the other, on statistically reliable data on the reliability of the elements that make up the equipment in the "generation-consumer" chain. Localization by automatic sectioning points at the design stage in the context of RAB tariff setting (incentive tariff setting) ensures the target nature of reducing SAIDI (index of average duration of outages in the system) and, as a result, assessing the possibility of achieving the target ENS (estimated volume of non-supplied electrical energy) significantly reduces the cost of network design and reconfiguration. In this regard, it is necessary to revise the state and topology of problem networks, including the operated partitioning hardware; to define support services in the energy company; be trained in software (for example, TELARM) as a design tool; to create the standards of the company (group of companies), taking into account the experience of colleagues from Moldova, the Security Council, the Baltic States, far abroad. As part of a scientific search, an approach was proposed, which is the basis for calculations to determine the optimal installation locations for automatic sectioning points for an average air line, as well as a parallel-serial air line with "targeted" automatic reserve input, corresponding to the optimal SAIDI value. The results obtained are presented in the framework of a joint technical meeting of leading specialists of operating enterprises, the customer and the staff of the Department of Power Supply named after A. V.M. Sinkova NULES of Ukraine.

Edge Caching and Computation Management for Real-Time Internet of Vehicles: An Online and Distributed Approach

Vehicular Edge Computing (VEC) is expected to be an effective solution to meet the ultra-low delay requirements of many emerging Internet of Vehicles (IoV) services by shifting the service caching and the computation capacities to the network edge. However, due to the constraints of the multidimensional (storage-computing-communication) resources capacities and the cost budgets of vehicles, there are two main issues need to be addressed: 1) How to collaboratively optimize the service caching decision among edge nodes to better reap the benefits of the storage resource and save the time-correlated service reconfiguration cost? 2) How to allocate resources among various vehicles and where vehicular requests are scheduled to improve the efficiency of the computing and communication resources utilization? In this paper, we formulate an edge caching and computation management problem that jointly optimizes the service caching, the request scheduling, and the resource allocation strategies. Our focus is to minimize the time-average service response delay of the random arriving service requests in a cost-efficient way. To cope with the dynamic and unpredictable challenges of IoVs, we leverage the combined power of Lyapunov optimization, matching theory, and consensus alternating direction method of multipliers to solve the problem in an online and distributed manner. Theoretical analysis shows that the developed approach achieves a close-to-optimal delay performance without relying on any prior knowledge of the future network information. Moreover, simulation results validate the theoretical analysis and demonstrate that our algorithm outperforms the baselines substantially.

Scheduling and Dynamic Pilot Allocation for Massive MIMO With Varying Traffic

In this letter, we consider the problem of joint user scheduling and dynamic pilot allocation in a Time-Division Duplex (TDD) based Massive MIMO network under varying traffic condition. One of the main problems with Massive MIMO systems is that the number of available orthogonal pilot signals is limited, and the dynamic allocation of these signals to different users is crucially needed to utilize the full benefit of these systems. In addition, pilot signals are radio resource control (RRC) configured in practice, and hence the frequent reconfiguration causes high signaling overhead and is costly. Using Lyapunov optimization framework, we develop an optimal algorithm that first assigns pilots dynamically based on queue sizes and the channel conditions of users as well as the reconfiguration cost at large time-scale. Then, it schedules users on a small-time scale. Numerical results show the efficacy of our algorithm and demonstrate that pilots do not need to be configured frequently at the expense of increased queue delay.

An Optimal Solution for Eco-reliability Stochastic Modeling of Simultaneous Reconfiguration of Distribution Networks and Allocation of Renewable Energy Resources, Capacitors and D-facts

Objective: A new method based on stochastic modeling of simultaneous optimal reconfiguration of distribution networks and allocation of multiple Distribution STATic COMpensator and renewable energy resources as new developed technologies is suggested in this paper. Background: This problem involves vital uncertain parameters such as: intermittent nature of renewable energy sources, load forecasting and market price errors. Krill Herd Algorithm as a powerful optimization solution is applied to solve suggested problem. Methods: Several effective cost based consequences can be achieved by proposed technique like: DG units installation and operation costs, D-Statcom installation cost, capacitor costs, cost of purchased energy from the transmission network, active and reactive power losses cost, line upgrade cost and distribution system reconfiguration cost. Results: In addition, energy not supplied as a vital reliability index incorporated in the cost function aims to improve network’s reliability efficiently. Conclusion: IEEE 33 bus radial distribution system is used here to check the practicability of the proposed scheme.

Dynamic Partial Reconfiguration Profitability for Real-Time Systems

Modern field-programmable gate arrays offer dynamic partial reconfiguration (DPR) capabilities, a characteristic that opens new scheduling opportunities for real-time applications running on heterogeneous platforms. To evaluate when it is really useful to exploit a DPR, in this letter, we present the characterization of its reconfiguration cost in terms of time and a definition of the “DPR Profitability” concept targeting real-time systems. To obtain such results, the components involved in a DPR process have been identified and an innovative approach to calculate the DPR time and its worst-case bound is provided. We validate our approach on a real DPR-compliant platform, showing that our proposal is general enough to be applied to modern DPR-compliant platforms.