Reconfiguration Cost Research Articles

Smoothed Online Resource Allocation in Multi-Tier Distributed Cloud Networks

The problem of dynamic resource allocation for service provisioning in multi-tier distributed clouds is particularly challenging due to the coexistence of several factors: the need for joint allocation of cloud and network resources, the need for online decision-making under time-varying service demands and resource prices, and the reconfiguration cost associated with changing resource allocation decisions. We study this problem from an online optimization perspective to address all these challenges. We design an online algorithm that decouples the original offline problem over time by constructing a series of regularized subproblems, solvable at each corresponding time slot using the output of the previous time slot. We prove that, without prediction beyond the current time slot, our algorithm achieves a parameterized competitive ratio for arbitrarily dynamic workloads and resource prices. If prediction is available, we demonstrate that existing prediction-based control algorithms lack worst case performance guarantees for our problem, and we design two novel predictive control algorithms that inherit the theoretical guarantees of our online algorithm, while exhibiting improved practical performance. We conduct evaluations in a variety of settings based on real-world dynamic inputs and show that, without prediction, our online algorithm achieves up to nine times total cost reduction compared with the sequence of greedy one-shot optimizations and at most three times the offline optimum; with moderate predictions, our control algorithms can achieve two times total cost reduction compared with existing prediction-based algorithms.

Integrated real-time production scheduling of a multiple cryogenic air separation unit and compressor plant

The development and application of an integrated real-time production scheduling and control strategy for a multiple cryogenic air separation unit (ASU) and compressor plant is discussed. Using a top-down optimisation approach, the operational targets for ASU production and compressor configuration are obtained for a given customer demand and subsequently managed using a real-time optimisation strategy. This is integrated with existing control to implement the steady-state configuration targets subject to process disturbances, power price fluctuations and against network change penalty weightings. Network material balance and network component operating constraints are met while simultaneously minimising plant reconfiguration costs during transient operation which occurs as a result of changing demands. Implemented using mixed integer linear programming, it is demonstrated that the two-stage optimisation strategy improves site operating costs by an average of 5% over the considered trial period (which would translate into substantial cost savings for such an energy intensive process).

Transmission network switching for reducing market power cost in generation sector: A Nash-equilibrium approach

The transmission network switching is proposed in the literature as a way to improve social welfare in liberalized power markets. 〈7〉 Moreover, exercise of market power by strategic generating companies (Gencos) causes some extra cost in electricity market which can be alleviated by implementing appropriate switching policies. This paper contributes to the existing literature by developing a mathematical model that explores, from an economic perspective, the transmission network switching in the context of market power. The strategic Gecnos are modelled based on the Cournot game. The Nash equilibrium of the game between Gencos is formulated as an equilibrium problem with equilibrium constraint (EPEC). The EPEC problem is transformed to a mixed-integer linear feasibility problem. To handle the multiple-Nash-equilibria situations, the solution concept of the extremal-Nash equilibrium (ENE) is introduced. A mixed-integer linear program (MILP) is derived for finding ENE. The transmission switching decisions are modelled as binary variables controlled by the system operator (TSO). The TSO minimizes the system dispatch cost calculated at ENE and network reconfiguration cost. The TSO minimizes the cost using its transmission switching decisions. The problem faced by the TSO is a mixed-integer bilevel linear program (MIBLP) with binary variables in both upper and lower levels. The upper level models the TSO's action and the lower level the oligopolistic Gencos (competing in Cournot game). A (parallel) branch-and-bound technique is used to solve the developed MIBLP model. An illustrative 3-bus example system and the IEEE-RTS96 are modelled and carefully studied. 〈8〉The numerical results demonstrate that: 1 – the (parallel) branch-and-bound technique can effectively solve the developed MIBLP, 2 – using the developed model, the system operator can change topology of the network by switching the lines in order to reduce the adverse effect of the strategic behaviour of Gecnos.

A MULTI-PERIOD GROUP TECHNOLOGY METHOD FOR DYNAMIC CELLULAR MANUFACTURING SYSTEMS

A dynamic cellular manufacturing system is a system where cell configurations change in order to respond to changing production requirements. This paper proposes methods for multi-period cell and part family formation. The method of fuzzy C-means clustering is used for part family formation while balancing the part load between cells in each period; a genetic algorithm is presented to solve the model. An integer programming model is presented for multi-period cell formation with the objective of determining an optimal trade-off between reconfiguration cost and the cost of deficit capacity.

Path reuse-aware routing for non-volatile memory based FPGAs

Non-volatile memory-based FPGAs (NV-FPGAs) are expected to replace traditional SRAM-based FPGAs to achieve higher scalability and lower power consumption. Yet the slow write performance of NVMs not only challenges FPGA reconfiguration speed and overhead but also constrains the programming cycles of FPGAs. To efficiently configure switch boxes, the majority component of an FPGA, this paper presents a routing path reuse technique. The reconfiguration cost of routing resources is first modeled mathematically and then minimized through a reuse-aware routing algorithm, which is incorporated into the standard VTR CAD tool. Experiments on standard MCNC and Titan benchmarks show that the proposed scheme is able to achieve as much as 58% path reuse rate and reduce as much as 45% configuration cost for routing resources.

Reconfigure

Background. Organization design is a subject that is covered in business school courses such as strategy implementation, organizational theory, and organizational behaviour. However, students sometimes perceive organization design concepts as abstract. Aim. To develop and pilot a simulation that may improve participants’ understanding of two key organization design concepts - work process interdependency and coordination cost. In addition, to develop an approach that may contribute to better operationalization of these concepts in future research. Method. Participants are divided into groups representing departments and are then asked to reconfigure the groups while considering both benefits (reduced coordination costs) and costs (of reconfiguration). The exercise is supported by a MicrosoftExcelTM tool, quantifying the concepts of interdependency and coordination cost. Results. The pilot application of the simulation achieved its pedagogical aims. The participants performed somewhat better than anticipated (some possible causes are discussed). Conclusions. Experiential simulations can be used to convey complex organization design principles to students and managers. They may also be used to study the capacity of individuals and groups to take into account multiple interdependencies in optimizing a design at the individual, group and system level. Recommendation. Simulations should be used to enhance the pedagogical value of organization design lectures (or seminars for employees in organizations).

A vibration damping optimization algorithm for solving a new multi-objective dynamic cell formation problem with workers training

This paper presents a comprehensive multi-objective mixed integer mathematical programming model which considers cell formation and production planning problems simultaneously. This comprehensive model includes dynamic system reconfiguration, multi period production planning, operation sequence, alternative process plans for part types, machine and worker flexibility, duplicate machines, machine capacity, available time of workers and worker assignment. The aim of the proposed model is to minimize inter and intra-cell movement costs, machine and reconfiguration costs, setup costs, production planning costs (holding, backorder and subcontracting costs) and workers hiring, firing, training and salary costs, as well as minimizing summation of machines idle times as a second objective. Due to NP-hardness of the problem, a recent and efficient meta-heuristic algorithm namely multi-objective vibration damping optimization (MOVDO) is designed for finding Pareto-optimal frontier. In order to check the efficiency of the developed algorithm, it is compared with two salient multi-objective genetic algorithms named NSGAII and NRGA. Finally, by generating some test problems in small and large scales and using some multi-objective comparison metrics, the algorithms are compared and analyzed statistically.

Constrained Resource Assignments: Fast Algorithms and Applications in Wireless Networks

Resource assignment problems occur in a vast variety of applications, from scheduling problems over image recognition to communication networks. Often these problems can be modeled by a maximum weight matching problem in (bipartite) graphs or generalizations thereof, and efficient and practical algorithms are known for these problems. Although in some of the applications an assignment of the resources may be needed only once, in many of these applications, the assignment has to be computed more often for different scenarios. In that case it is often essential that the assignments can be computed very fast. Moreover, implementing different assignments in different scenarios may come with a certain cost for the reconfiguration of the system. In this paper, we consider the problem of determining optimal assignments sequentially over a given time horizon, where consecutive assignments are coupled by constraints that control the cost of reconfiguration. We develop fast approximation and online algorithms for this problem with provable approximation guarantees and competitive ratios. Moreover, we present an extensive computational study about the applicability of our model and our algorithms in the context of orthogonal frequency division multiple access (OFDMA) wireless networks, finding a significant performance improvement for the total bandwidth of the system using our algorithms. For this application (the downlink of an OFDMA wireless cell) , the run time of matching algorithms is extremely important, having an acceptable range of a few milliseconds only. For the considered realistic instances, our algorithms perform extremely well: the solution quality is, on average, within a factor of 0.8–0.9 of optimal off-line solutions, and the running times are at most 5 ms per phase even in the worst case. Thus, our algorithms are well suited to be applied in the context of OFDMA systems. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mnsc.2015.2221 . This paper was accepted by Teck-Hua Ho, optimization.

Message Routing in Wireless and Mobile Networks Using TDMA Technology

In an era where communication has a most important role in modern societies, designing efficient algorithms for data transmission is of the outmost importance. TDMA is a technology used in many communication systems such as satellite, cell phone as well as other wireless or mobile networks. Most 2G cellular systems as well as some 3G are TDMA based. In order to transmit data in such systems we need to cluster them in packages. To achieve a faster transmission we are allowed to preempt the transmission of any packet in order to resume at a later time. Preemption can be used to reduce idleness of some stations. Such preemptions though come with a reconfiguration cost in order to setup for the next transmission. In this paper we propose two algorithms which yield improved transmission scheduling. These two algorithms we call MGA and IMGA (Improved MGA). We have proven an approximation ratio for MGA and ran experiments to establish that it works even better in practice. In order to conclude that MGA will be a very helpful tool in constructing an improved schedule for packet routing using preemtion with a setup cost, we compare its results to two other efficient algorithms designed by researchers in the past: A-PBS(d+1) and GWA. To establish the efficiency of IMGA we ran experiments in comparison to MGA as well as APBS(d+1) and GWA. IMGA has proven to produce the most efficient schedule on all counts.

Costly Circuits, Submodular Schedules and Approximate Carathéodory Theorems

Hybrid switching -- in which a high bandwidth circuit switch (optical or wireless) is used in conjunction with a low bandwidth packet switch -- is a promising alternative to interconnect servers in today's large scale data centers. Circuit switches offer a very high link rate, but incur a non-trivial reconfiguration delay which makes their scheduling challenging. In this paper, we demonstrate a lightweight, simple and nearly-optimal scheduling algorithm that trades-off reconfiguration costs with the benefits of reconfiguration that match the traffic demands. Seen alternatively, the algorithm provides a fast and approximate solution towards a constructive version of Caratheodory's Theorem for the Birkhoff polytope. The algorithm also has strong connections to submodular optimization, achieves a performance at least half that of the optimal schedule and strictly outperforms state of the art in a variety of traffic demand settings. These ideas naturally generalize: we see that indirect routing leads to exponential connectivity; this is another phenomenon of the power of multi-hop routing, distinct from the well-known load balancing effects.

Study of Reconfiguration Cost and Energy Aware VNE Policies in Cycle-Stationary Traffic Scenarios

Network virtualization techniques allow for the coexistence of many virtual networks hosted in the same substrate network. Virtual router migration allows for resource consolidation with the consequence to reduce the power consumption in low traffic periods. Unfortunately, virtual router migration has the effect of degrading the quality of service during the downtime in which the router is not able to carry on its forwarding function. For this reason, despite the advantages in power consumption saving, the migration technique should be applied with parsimony to avoid an excessive QoS degradation. The objective of this paper is to study virtual network embedding (VNE) problems aware of both operation and reconfiguration costs that are characterized by the energy consumption and the revenue loss due to QoS degradation. Both embedding optimization problem formulation and feasible solutions to the problem in cycle-stationary traffic scenario and when the possible embeddings are determined a priori will be given. Two migration policies will be introduced and compared. The first one, referred to as “global,” is based on the knowledge of the entire daily traffic profile and it is determined by solving a Markov decision process. The second one, referred to as “local,” is only based on the knowledge of the current traffic. The results achieved show how the application of the global policy allows better performance than the local one and its application can lead to a cost reduction in the order of 35% with respect to traditional migration policies.

Bi-objective optimization of integrating configuration generation and scheduling for reconfigurable flow lines using NSGA-II

Reconfigurable manufacturing system (RMS) is a new manufacturing system paradigm suited to the era of mass customization. A RMS is able to change its configuration by physical reconfiguration and/or logical reconfiguration, to provide exact functionality and capacity needed for every demand period. For the reconfigurable flow line (RFL) in which multiple parts within the same part family can be produced simultaneously, optimal design of physical configuration (called configuration generation) and proper scheduling of multiple products are crucial to operate RFL in a cost-effective manner. Considering the close coupling between configuration generation and scheduling of multiple products for RFL, a new multi-objective mixed integer programming model is established for the integrated optimization of configuration generation and scheduling. The two conflicting objectives are to minimize total cost including capital cost and reconfiguration cost and to minimize total tardiness. The validity of the model is verified through case analysis implemented by LINGO software. Subsequently, the nondominated sorting genetic algorithm II (NSGA-II) is adopted to obtain a set of nondominated solutions. In the NSGA-II, a hybrid encoding method ensuring any chromosome a feasible solution is devised. Case study is utilized to illustrate the effectiveness of the NSGA-II for solving the problem. The case study also reveals that only if performing configuration generation and scheduling concurrently can a set of solutions balancing cost and tardiness be identified effectively for the RFL.

Production Planning of a Failure-Prone Manufacturing System under Different Setup Scenarios

This paper presents a control problem for the optimization of the production and setup activities of an industrial system operating in an uncertain environment. This system is subject to random disturbances (breakdowns and repairs). These disturbances can engender stock shortages. The considered industrial system represents a well-known production context in industry and consists of a machine producing two types of products. In order to switch production from one product type to another, a time factor and a reconfiguration cost for the machine are associated with the setup activities. The parts production rates and the setup strategies are the decision variables which influence the inventory and the capacity of the system. The objective of the study is to find the production and setup policies which minimize the setup and inventory costs, as well as those associated with shortages. A modeling approach based on stochastic optimal control theory and a numerical algorithm used to solve the obtained optimality conditions are presented. The contribution of the paper, for industrial systems not studied in the literature, is illustrated through a numerical example and a comparative study.

Multi-UAVs Formation Autonomous Control Method Based on RQPSO-FSM-DMPC

For various threats in the enemy defense area, in order to achieve covert penetration and implement effective combat against enemy, the unmanned aerial vehicles formation needs to be reconfigured in the process of penetration; the mutual collision avoidance problems and communication constraint problems among the formation also need to be considered. By establishing the virtual-leader formation model, this paper puts forward distributed model predictive control and finite state machine formation manager. Combined with distributed cooperative strategy establishing the formation reconfiguration cost function, this paper proposes that adopting the revised quantum-behaved particle swarm algorithm solves the cost function, and it is compared with the result which is solved by particle swarm algorithm. Simulation result shows that this algorithm can control multiple UAVs formation autonomous reconfiguration effectively and achieve covert penetration safely.

Cognitive caching promotes flexibility in task switching: evidence from event-related potentials.

Time-consuming processes of task-set reconfiguration have been shown to contribute to the costs of switching between cognitive tasks. We describe and probe a novel mechanism serving to reduce the costs of task-set reconfiguration. We propose that when individuals are uncertain about the currently valid task, one task set is activated for execution while other task sets are maintained at a pre-active state in cognitive cache. We tested this idea by assessing an event-related potential (ERP) index of task-set reconfiguration in a three-rule task-switching paradigm involving varying degrees of task uncertainty. In high-uncertainty conditions, two viable tasks were equally likely to be correct whereas in low-uncertainty conditions, one task was more likely than the other. ERP and performance measures indicated substantial costs of task-set reconfiguration when participants were required to switch away from a task that had been likely to be correct. In contrast, task-set-reconfiguration costs were markedly reduced when the previous task set was chosen under high task uncertainty. These results suggest that cognitive caching of alternative task sets adds to human cognitive flexibility under high task uncertainty.

The Online Control Framework on Computational Optimization of Resource Provisioning in Cloud Environment

The resource monitoring and management system for data center are capable of huge amount of activities. In terms of resource allocation and workload using a variety of K-means clustering algorithm. Workload and machine heterogeneity as well as an optimization problem that is described to see that considers reconfiguration costs. It will solve the problem by using control framework based on computational optimization algorithm. More efficient transfer of energy consumption and delay between each machine by using scheduling algorithm. Energy consumption characteristics of the various machines provide different capacities. Cloud data center are capable of sensing the machine and power consumption. In my proposed using the hybrid method for resource provisioning in data center. This method is used to allocate the resource at the working condition and also energy stored in the power consumption.

High-Speed and Area-Efficient Reconfigurable Multiplexer Bank for RAM-Based Finite State Machine Implementations

This work is focused on the problem of designing efficient reconfigurable multiplexer banks for RAM-based implementations of reconfigurable state machines. We propose a new architecture (called combination-based reconfigurable multiplexer bank, CRMUX) that use multiplexers simpler than that of the state-of-the-art architecture (called variation-based reconfigurable multiplexer bank, VRMUX). The performance (in terms of speed, area and reconfiguration cost) of both architectures is compared. Experimental results from MCNC finite state machine (FSM) benchmarks show that CRMUX is faster and more area-efficient than VRMUX. The reconfiguration cost of both multiplexer banks is studied using a behavioral model of a reconfigurable state machine. The results show that the reconfiguration cost of CRMUX is lower than that of VRMUX in most cases.

Measuring and Modeling the Energy Cost of Reconfiguration in Sensor Networks

As wireless sensor networks (WSNs) must operate for long periods on a limited power budget, estimating the energy cost of software operations is critical. Contemporary reconfiguration approaches for WSN allow for software evolution at various granularities; from reflashing of a complete software image, through replacement of complete applications, to the reconfiguration of individual software components. This paper contributes a generic model for measuring and modeling the energy cost of reconfiguration in WSN. We validate that this model is accurate in the face of different hardware platforms, software stacks, and software encapsulation approaches. We have embedded this model in the loosely coupled component infrastructure middleware, resulting in the first energy aware reconfigurable component model for sensor networks. We evaluate our approach using two real-world WSN applications and demonstrate that our model predicts the energy cost of reconfiguration with 93% accuracy. Using this model, we demonstrate that selecting the most appropriate software modularization approach is key to minimizing energy consumption.

Minimum maximum reconfiguration cost problem

This paper discusses the problem of minimizing the reconfiguration cost of some types of reconfigurable systems. A formal definition of the problem and a proof of its NP-completeness are provided. In addition, an Integer Linear Programming formulation is proposed. The proposed problem has been used for optimizing a design stage of Finite Virtual State Machines.

Software-Defined Networking for RSU Clouds in Support of the Internet of Vehicles

We propose a novel roadside unit (RSU) cloud, a vehicular cloud, as the operational backbone of the vehicle grid in the Internet of Vehicles (IoV). The architecture of the proposed RSU cloud consists of traditional and specialized RSUs employing software-defined networking (SDN) to dynamically instantiate, replicate, and/or migrate services. We leverage the deep programmability of SDN to dynamically reconfigure the services hosted in the network and their data forwarding information to efficiently serve the underlying demand from the vehicle grid. We then present a detailed reconfiguration overhead analysis to reduce reconfigurations, which are costly for service providers. We use the reconfiguration cost analysis to design and formulate an integer linear programming (ILP) problem to model our novel RSU cloud resource management (CRM). We begin by solving for the Pareto optimal frontier (POF) of nondominated solutions, such that each solution is a configuration that minimizes either the number of service instances or the RSU cloud infrastructure delay, for a given average demand. Then, we design an efficient heuristic to minimize the reconfiguration costs. A fundamental contribution of our heuristic approach is the use of reinforcement learning to select configurations that minimize reconfiguration costs in the network over the long term. We perform reconfiguration cost analysis and compare the results of our CRM formulation and heuristic. We also show the reduction in reconfiguration costs when using reinforcement learning in comparison to a myopic approach. We show significant improvement in the reconfigurations costs and infrastructure delay when compared to purist service installations.