Reconfiguration Cost Research Articles

Anchor View Allocation for Collaborative Free Viewpoint Video Streaming

In free viewpoint video, a viewer can choose at will any camera angle or the so-called “virtual view” to observe a dynamic 3-D scene, enhancing his/her depth perception. The virtual view is synthesized using texture and depth videos of two anchor camera views via depth-image-based rendering (DIBR). We consider, for the first time, collaborative live streaming of a free viewpoint video, where a group of users may interactively pull and cooperatively share streams of different anchor views. There is a cost to access the anchor views from the live source, a cost to “reconfigure” the peer network due to a change in selected anchors during view switching, and a distortion cost due to the distance of the virtual views to the received anchor views at users. We optimize the anchor views allocated to users so as to minimize the overall streaming cost given by the access cost, reconfiguration cost, and view distortion cost. We first show that, if the reconfiguration cost due to view switching is negligible, the view allocation problem can be optimally and efficiently solved in polynomial time using dynamic programming. For the case of non-negligible reconfiguration cost, the problem becomes NP-hard. We thus present a locally optimal and centralized algorithm inspired by Lloyd's algorithm used in non-uniform scalar quantization. We further propose a distributed algorithm with convergence guarantee, where each peer group independently makes merge-and-split decisions with a well-defined fairness criteria. Simulation results show that our algorithms achieve low streaming cost due to its excellent anchor view allocation.

A Stimulus-Response Framework for Robot Control

We propose in this article a new approach to robot cognitive control based on a stimulus-response framework that models both a robot’s stimuli and the robot’s decision to switch tasks in response to or inhibit the stimuli. In an autonomous system, we expect a robot to be able to deal with the whole system of stimuli and to use them to regulate its behavior in real-world applications. The proposed framework contributes to the state of the art of robot planning and high-level control in that it provides a novel perspective on the interaction between robot and environment. Our approach is inspired by Gibson’s constructive view of the concept of a stimulus and by the cognitive control paradigm of task switching. We model the robot’s response to a stimulus in three stages. We start by defining the stimuli as perceptual functions yielded by the active robot processes and learned via an informed logistic regression. Then we model the stimulus-response relationship by estimating a score matrix that leads to the selection of a single response task for each stimulus, basing the estimation on low-rank matrix factorization. The decision about switching takes into account both an interference cost and a reconfiguration cost. The interference cost weighs the effort of discontinuing the current robot mental state to switch to a new state, whereas the reconfiguration cost weighs the effort of activating the response task. A choice is finally made based on the payoff of switching. Because processes play such a crucial role both in the stimulus model and in the stimulus-response model, and because processes are activated by actions, we address also the process model, which is built on a theory of action. The framework is validated by several experiments that exploit a full implementation on an advanced robotic platform and is compared with two known approaches to replanning. Results demonstrate the practical value of the system in terms of robot autonomy, flexibility, and usability.

Task allocation in organic computing systems: networks with reconfigurable helper units

In this paper, computing systems that have no central control and consist of many partially autonomous components are studied. Two types of components are distinguished which are called workers and helpers respectively. The components are connected via a network and the helper components perform service tasks for the worker components. It is assumed that the helper components exhibit reconfigurable hardware to be able to execute different service tasks efficiently. The problem addressed in this paper is how to organise such a decentralised system in a way that requests of the workers are executed by suitable helpers and the total reconfiguration costs of the helpers are small. Several decentralised task allocation methods are proposed. One of them uses a combination of a fully decentralised dynamic clustering algorithm and a self-organised task allocation system. The clustering algorithm is used to classify the service requests that are sent through the network in order to give the helpers hints which requests are suitable to be executed by them. Simulations are done for static and dynamic scenarios to investigate the reconfiguration costs and the number of dropped requests, i.e., requests that could not be satisfied. The results show that the clustering-based system has a strong adaptive behaviour and that the decentralised clustering is able to reduce the reconfiguration costs significantly.

Re-balancing problem for assembly lines: new mathematical model and exact solution method

Purpose – The purpose of this study is to develop a new mathematical model and an exact solution method for an assembly line rebalancing problem. When an existing assembly line has to be adapted to a new production context, the line balancing, resources allocation and component management solutions have to be revised. The objective is to minimize the number of modifications to be done in the initial line in order to reduce the time and investment needed to meet new production requirements. The proposed model is evaluated via a computational experiment. The obtained results the efficacy of the proposed method. Design/methodology/approach – This paper develops a new mathematical model and an exact solution method for an assembly line rebalancing problem with the objective to minimize the number of modifications to be done in the initial line to reduce the time and investments needed to meet new production requirements. Findings – The computational experiments show the efficacy of the proposed method. Originality/value – These reconfiguration costs were analysed for different part-feeding policies that can be adopted in an assembly line.

Retraction Note to: Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems

Retraction Note to: Effect of reconfiguration costs on planning for capacity scalability in reconfigurable manufacturing systems

Study of Migration Policies in Energy-Aware Virtual Router Networks

The definition of a virtual router layer allows the application of energy saving techniques based on the migration of virtual routers within the physical infrastructure as traffic conditions change. As traffic decreases, physical resources can be vacated through virtual router migration, thus, saving energy. Such resources can be reclaimed if needed because of traffic increase. Unfortunately, the migration of virtual routers involves reconfiguration costs depending on implementation details and characterized, for instance, by the revenue loss due to quality-of-service (QoS) degradation during the migration process. In this paper, we propose a methodology based on the Markov decision processes for the evaluation of optimal migration policies taking into account both the operation (in terms of energy consumption) and reconfiguration costs.

Energy-efficient adaptive networked datacenters for the QoS support of real-time applications

In this paper, we develop the optimal minimum-energy scheduler for the adaptive joint allocation of the task sizes, computing rates, communication rates and communication powers in virtualized networked data centers (VNetDCs) that operate under hard per-job delay-constraints. The considered VNetDC platform works at the Middleware layer of the underlying protocol stack. It aims at supporting real-time stream service (such as, for example, the emerging big data stream computing (BDSC) services) by adopting the software-as-a-service (SaaS) computing model. Our objective is the minimization of the overall computing-plus-communication energy consumption. The main new contributions of the paper are the following ones: (i) the computing-plus-communication resources are jointly allotted in an adaptive fashion by accounting in real-time for both the (possibly, unpredictable) time fluctuations of the offered workload and the reconfiguration costs of the considered VNetDC platform; (ii) hard per-job delay-constraints on the overall allowed computing-plus-communication latencies are enforced; and, (iii) to deal with the inherently nonconvex nature of the resulting resource optimization problem, a novel solving approach is developed, that leads to the lossless decomposition of the afforded problem into the cascade of two simpler sub-problems. The sensitivity of the energy consumption of the proposed scheduler on the allowed processing latency, as well as the peak-to-mean ratio (PMR) and the correlation coefficient (i.e., the smoothness) of the offered workload is numerically tested under both synthetically generated and real-world workload traces. Finally, as an index of the attained energy efficiency, we compare the energy consumption of the proposed scheduler with the corresponding ones of some benchmark static, hybrid and sequential schedulers and numerically evaluate the resulting percent energy gaps.

A Robust Model for a Dynamic Cellular Manufacturing System with Production Planning

In this paper, a robust optimization approach is proposed to design a dynamic cellular manufacturing system (DCMS) under uncertainty of processing time of products. In addition, a mathematical model considering cell formation, inter-cell design and production planning under a dynamic environment (i.e., product mix and demand are changed in each period) is presented. Therefore, reconfiguration becomes necessary for one period in relative with previous periods. The aim of this model is to minimize inter and intra-cell material handling movements, inventory holding, back order and reconfiguration costs. Furthermore, alternative routing for each part is considered, in which one of its routing with the lowest cost is chosen among its other routings for each part. Finally, a numerical example is solved to demonstrate the behavior of the presented model and verify the performance of the developed approach.

A Virtual Machine Placement and Reconfiguration Framework for Cloud Computing Platforms

As a rising application paradigm and technology, cloud computing can leverage the efficient pooling of on-demand, self-managed virtual infrastructure. How to maximize the resource utilization and how to reduce the cost of configuration are essential issues in cloud computing. In this paper, the authors propose a framework to achieve these objectives by optimizing VM placement and deciding when and how to perform the VM reconfigurations. The authors leverage the vector arithmetic to model the objective of balancing the multiple resource utilization and propose an optimization method for the static VM placement. Then the authors propose a two-level runtime reconfiguration policy, including the local adjustment and the parallel migration, to minimize the reconfiguration cost. Finally, the authors implement a prototype to validate and evaluate the proposed mechanism with a set of preliminary experiments, which shows that our work can maximize the resource utilization while effectively reducing the cost of the runtime reconfiguration.

A mathematical programming model for reconfiguration of flexible manufacturing cells

In flexible manufacturing cells, changes in demand size, product mix, part variety, existing routings and set-up/operation times may require reconfiguration of the cells. In this study, an approach is developed to decide when and how such a reconfiguration should be carried out for existing cells. This study considers reconfiguration in terms of changing part routings, adding a new machine type in a cell, duplicating an existing machine type, removing an existing machine from a cell and transferring a machine to another cell. The study also shows the total number of tools of each type on each machine located in each cell after reconfiguration. To make the optimal reconfiguration decisions, a mathematical programming model to minimize the total reconfiguration cost is developed. The developed model considers the lower and upper bounds on machine utilizations and the time limits on machine cycle times to decide when to reconfigure the system.

A survey on reducing reconfiguration cost: reconfigurable PID control as a special case

Reconfigurable computing is a paradigm in computing architecture that refers to the practice of using interchangeable hardware modules to enhance the performance of conventional Von-Neumann style computing. Despite the numerous advantages of reconfiguration, it is only suitable for quasi-static applications with slowly changing reconfiguration criteria. In general, it is only advantageous to reconfigure if the execution time exceeds reconfiguration time. Since the execution time of real-time systems are quite limited, the control of dynamic non-linear systems are typically not reconfigured. Instead, adaptivity is mostly gained from reading coefficients or gains from memory. Where different controller architectures are required, the route of parallel implementation could be taken, switching between architectures. The drawback of this approach is an increase in area required to implement the controllers. Reconfiguration on the other hand could allow the different control architectures to be swapped on the fly without interrupting operation of the controller, while minimizing the area required. Unfortunately, this process is limited by the overhead introduced by the reconfiguration. Even though various survey papers exist on the topic of reconfiguration, none really focus on methods to reduce the cost of reconfiguration. This survey summarizes different means of reducing configuration overhead in an attempt to allow reconfiguration of applications with limited execution time. A block RAM-based (BRAM) architecture is proposed as the optimal architecture for reconfiguring dynamic applications. As an example, this architecture is used to discuss the methodology used to design a reconfigurable PID controller.

Network Topology Reconfiguration for State Estimation Over Sensor Networks With Correlated Packet Drops

This paper studies the problem of optimal network topology reconfiguration in sensor networks for state estimation. Multiple sensors make observations of a process, which are then transmitted, possibly via intermediate sensors, to a central gateway. Transmission over each link can experience packet drops. The time-varying wireless network environment is modelled by the notion of a network state as in Quevedo et al. (2013a). For each network state, different network configurations can be used, which govern the network topology and routing of packets. Switching between different configurations incurs a cost, in that unwanted links will need to be removed before the establishment of new links, leading to a transient time in which some links may not be available. The problem is to determine the optimal network configurations to use in each network state, in order to minimize an expected error covariance measure that takes into account the cost of reconfiguration. A simpler sub-optimal method which minimizes the upper bound of the expected error is also proposed, which in numerical simulations gives essentially identical results to the optimal method.

Service Level as Performance Index for Reconfigurable Manufacturing System Involving Multiple Part Families

In this global manufacturing era, one of the important challenges faced by manufacturer is how to deal with stochastic demand and ever changing customer needs and requirements. Reconfigurable manufacturing systems are recognized as next generation manufacturing systems capable of providing the exact functionality and capacity as and when required. Some important performance indices studied in the past include cost, ease of reconfigurability, productivity, reliability and availability for assessing the performance of these systems. The economics while carrying out the reconfiguration process of these systems must include parameters such as cost incurred while producing orders of part families on a particular configuration and the reconfiguration cost associated while changing over from initial configuration to another. The complexity and cost involved from changing one configuration to another depends on the existing initial configuration and the new configuration required for subsequent production. In this paper, based on the different efforts associated with the reconfiguration process, a new index of performance termed as “Service Level” is proposed. The proposed indicator is modeled for a multiple part family reconfigurable manufacturing system. The methodology proposed is explained using a numerical example. The results obtained along with their important implications were discussed.

Research on Measure Method for Reconfigurability

Some certain criteria will be required to evaluate the reconfiguration of a system or an individual as well as its reconfiguration value. In the paper a methodology on evaluating the reconfigurability has been proposed. It was intended for the paper to study quantitatively and qualitatively the feasibility of the system to reconfigurate. The reconfiguration distance has been put forward based on the information distance and its calculation model has been given. In the paper, reconfiguration distance was proposed to measure the reconfiguration value. This method can be used to determine the level of its reconfigurability, providing a basis for decision-makers. For the system's or individual's reconfiguration, the focus of this paper is the determination of indicators to measure the reconfiguration, i.e., costs of reconfiguration, and reconfiguration realizability used to describe the obstacles, costs needed to pay, and the degree to realize when reconfigurating the system or individual, which will further improve the reconfiguration theory. Only thus the truly meaningful reconfiguration can be achieved.

Assembly System Reconfiguration Planning

Decreasing product life cycles and reduced product development times have led to a need for new strategies for coping with the rapid rate of product family design changes. In this paper, assembly system reconfiguration planning (ASRP) is introduced as a method for cost effectively designing several generations of assembly systems in order to produce a product family that gradually evolves over time. In the ASRP approach, the possible assembly systems for each generation are first considered and then the sequence of assembly system configurations that minimize the life cycle cost of the process are selected. A nonlinear integer optimization formulation is developed for finding the cost minimizing assembly system reconfiguration plan using the ASRP approach. Dynamic programming and genetic algorithm are used to solve the optimization problem. Simulation results indicate that the ASRP approach leads to the minimum life cycle costs of the assembly system, and the relative cost of reconfiguration and production have an impact on the assembly system reconfiguration plan selected. Comparison of the results of the dynamic program and genetic algorithm indicate that the dynamic program is more computationally efficient for small problems and genetic algorithm is preferred for larger problems.

System scenarios-based architecture level exploration of SDR application using a network-on-chip simulation framework

Software-defined radio (SDR) terminals are critical to enable concrete and consecutive inter-working between fourth generation wireless access systems or communication modes. Next generation of SDR terminals will have heavy hardware requirements and the switching between the different resource utilization modes will introduce dynamism in respect to timing and size of resource requests. This study exploits the flexibility of a system-level framework, combining a cycle-accurate Network-on-Chip (NoC) simulation environment with an SDR simulator, applying the systems scenario methodology. The aim of our framework is to characterize an SDR platform considering the design trade-offs at an early development phase. The main scope is to define a design space of optimal platform configuration points for a potential SDR resource manager. The purpose is to achieve an efficient resource utilization retaining the reconfiguration cost in reasonable levels. To handle the complexity of the design space, we introduced in the framework the concept of the system scenarios. The system scenario methodology classifies all the run-time situations of the system into cost representative situations. However, this introduces an overhead which is admeasured at the reconfiguration cost. In our case study, we achieve significant resource savings decreasing the energy consumption by 45–73% (based on the system’s deadlines).

Cell formation and scheduling of part families for reconfigurable cellular manufacturing systems using Tabu search

A reconfigurable cellular manufacturing system (RCMS) consists of multiple reconfigurable machining cells, each of which has one or more reconfigurable machine tools (RMTs), a setup station, and an automatic material handling and storage system. As part of the RCMS design process, similar parts must be grouped into part families and the RMTs must be arranged to form parallel cell configurations. A RCMS is designed at the outset for rapid changes in its components, allowing the production of multiple part families in each parallel cell. This paper proposes a new approach to simultaneously solve the cell formation and the scheduling of part families for an effective working of a RCMS. A new mixed integer linear programming model is used to represent both problems at the same time with the objective of minimizing production costs. Two types of production costs are considered: reconfiguration (i.e. setup) costs for changing from one family to the next one, and under-utilization costs for not using the RMT resources. A small size example is used to illustrate this integrated methodology. Computational experiments have been carried out adapting some larger instances from the literature on cellular manufacturing systems. Solving large instances optimally becomes prohibitive in terms of computational effort. That is why an approximate method, based on a Tabu search (TS) algorithm, has also been developed. Results show the ability of this algorithm to find good-quality production schedules of part families in a RCMS without requiring long computing times. It can be concluded that a RCMS can attain manufacturing flexibility without losing cost-effectiveness and that the approach proposed in this paper can efficiently solve real-world problems.

Wavelength Path Reconfiguration Design in Transparent Optical WDM Networks

This paper studies the reconfiguration design and migration from an old path set to a new path set in multifiber wavelength-division-multiplexing networks. For the reconfiguration design phase, we introduce the functions of wavelength fragmentation cost and reconfiguration cost, and we propose a reconfiguration design that minimizes both wavelength fragmentation and the number of changed wavelengths. For the migration phase, we propose a migrating sequence algorithm that uses spare wavelengths to break the dependency cycle between before and after reconfiguration design sets and thus prevent service disruption. A numerical evaluation shows that the number of fibers is suppressed 4%–15% compared to the design without employing fragmentation cost and that the number of changed wavelengths is reduced by approximately 50%–90%.

Algorithms for the Multi-Period Power-Aware Logical Topology Design With Reconfiguration Costs

We tackle the problem of reducing power consumption in IP-over-WDM networks, targeting the power-aware logical topology design (LTD). Unlike the previous work in the literature, our solution reduces the power consumption with consideration of the cost (in terms of reconfigured traffic) incurred when the network is reconfigured. We first formulate the LTD with reconfiguration costs as an optimization problem. Then, we present three heuristics to effectively solve it. We compare our algorithms over an extensive set of networks and scenarios. Results indicate that our algorithms are effective in reducing power consumption while limiting the amount of traffic that is reconfigured. Moreover, we show that the input parameters are intuitive and easy to set, which makes our algorithms more practical.

UNIFICATION OF PR REGION FLOORPLANNING AND FINE-GRAINED PLACEMENT FOR DYNAMIC PARTIALLY RECONFIGURABLE FPGAS

Dynamic Partially Reconfiguration (DPR) designs provide additional benefits compared to traditional FPGA application. However, due to the lack of support from automatic design tools in current design flow, designers have to manually define the dimensions and positions of Partially Reconfigurable Regions (PR Regions). The following fine-grained placement for system modules is also limited because it takes the floorplanning result as a rigid region constraint. Therefore, the manual floorplanning is laborious and may lead to inferior fine-grained placement results. In this paper, we propose to integrate PR Region floorplanning with fine-grained placement to achieve the global optimization of the whole DPR system. Effective strategies for tuning PR Region floorplanning and apposite analytical evaluation models are customized for DPR designs to handle the co-optimization for both PR Regions and static region. Not only practical reconfiguration cost and specific reconfiguration constraints for DPR system are considered, but also the congestion estimation can be relaxed by our approach. Especially, we established a two-stage stochastic optimization framework which handles different objectives in different optimization stages so that automated floorplanning and global optimization can be achieved in reasonable time. Experimental results demonstrate that due to the flexibility benefit from the unification of PR Region floorplanning and fine-grained placement, our approach can improve 20.9% on critical path delay, 24% on reconfiguration delay, 12% on congestion, and 8.7% on wire length compared to current DPR design method.