Accurate Cost Model Research Articles

A User-Driven Prioritisation Process implementation and optimisation for ATFM hotspot resolution

The current and forecast air traffic levels lead to demand-capacity imbalances, which are dealt with by delaying flights through the allocation of air traffic flow management (ATFM) slots. To mitigate the delay impact on airspace users (AUs) and passengers, User Driven Prioritisation Process (UDPP) solutions are under development, with the goal to enhance flexibility for airlines to prioritise their own flights in the ATFM regulations. UDPP solutions are developed in collaboration with AUs, achieving high maturity level and even operational use at some airports.While UDPP solutions in reality are still based on manual or semi-automated procedures, in this paper we show that when an airline has an accurate delay cost model at disposal, the prioritisation process can be fully automated via an integer programming model that provides the prioritisation that optimises the AUs’ UDPP exploitation. We use this automated process and the implementation of the UDPP mechanism to provide an estimation of the benefits of UDPP in terms of cost with respect to the current ATFM regulation process.

Locally Differentially Private Personal Data Markets Using Contextual Dynamic Pricing Mechanism

Data is becoming the world's most valuable asset and the ultimate renewable resource. This phenomenon has led to online personal data markets where data owners and collectors engage in the data sale and purchase. From the collector's standpoint, a key question is how to set a proper pricing rule that brings profitable tradings. One feasible solution is to set the price slightly above the owner's data cost. Nonetheless, data cost is generally unknown by the collector as being the owner's private information. To bridge this gap, we propose a novel learning algorithm, modified stochastic gradient descent (MSGD) that infers the owner's cost model from her interactions with the collector. To protect owners' data privacy during trading, we employ the framework of local differential privacy (LDP) that allows owners to perturb their genuine data and trading behaviors. The vital challenge is how the collector can derive the accurate cost model from noisy knowledge gathered from owners. For this, MSGD relies on auxiliary parameters to correct biased gradients caused by noise. We formally prove that the proposed MSGD algorithm produces a sublinear regret of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mathcal {O}(T^{\frac{5}{6}}\sqrt{\log (T^{\frac{1}{3}})})$</tex-math></inline-formula> . The effectiveness of our design is further validated via a series of in-person experiments that involve 30 volunteers.

Kepler: Robust Learning for Parametric Query Optimization

Most existing parametric query optimization (PQO) techniques rely on traditional query optimizer cost models, which are often inaccurate and result in suboptimal query performance. We propose Kepler, an end-to-end learning-based approach to PQO that demonstrates significant speedups in query latency over a traditional query optimizer. Central to our method is Row Count Evolution (RCE), a novel plan generation algorithm based on perturbations in the sub-plan cardinality space. While previous approaches require accurate cost models, we bypass this requirement by evaluating candidate plans via actual execution data and training anML model to predict the fastest plan given parameter binding values. Our models leverage recent advances in neural network uncertainty in order to robustly predict faster plans while avoiding regressions in query performance. Experimentally, we show that Kepler achieves significant improvements in query runtime on multiple datasets on PostgreSQL.

Adaptive constraint satisfaction for Markov decision process congestion games: Application to transportation networks

Under the Markov decision process (MDP) congestion game framework, we study the problem of enforcing population distribution constraints on a population of players with stochastic dynamics and coupled congestion costs. Existing research demonstrates that the constraints on the players’ population distribution can be satisfied by enforcing tolls. However, computing the minimum toll value for constraint satisfaction requires accurate modeling of the player’s congestion costs. Motivated by settings where an accurate congestion cost model may be unavailable (e.g. transportation networks), we consider an MDP congestion game with unknown congestion costs. We assume that a constraint-enforcing authority can repeatedly enforce tolls on a population of players that converges to an ϵ-optimal population distribution for any given toll. We then construct a myopic update algorithm to compute the minimum toll value while ensuring that the constraints are satisfied on average. We analyze how the players’ sub-optimal responses to tolls impact the rates of convergence towards the minimum toll value and constraint satisfaction. Finally, we construct a congestion game model for Uber drivers in Manhattan, New York City (NYC) using data from the Taxi and Limousine Commission (TLC) to illustrate how to efficiently reduce congestion while minimizing the impact on driver earnings.

Electricity market clearing considering demand response cost of thermostatically controlled loads

• According to the analysis the equivalent energy storage (EES) model of the aggregated thermostatically controlled loads (TCLs), non-monotonic characteristics of the TCLs’ cost curve is discovered. • Due to the non-monotonic characteristics of the marginal cost, the proposed non-monotonic step-wise bidding model by the TCLs aggregators can reflect the actual cost when participating in power system scheduling. • Based on the bidding model, a novel electricity market clearing method considering TCLs is proposed, so that accurate market clearing can be guaranteed. Among various kinds of flexible loads resources in power system, the thermostatically controlled loads are considered as the excellent candidates for the demand response programs due to the heating/cooling storage characteristics. Market-orientated demand-side management has become a new focus, and the participation of the thermostatically controlled loads in the electricity market requires an accurate cost model. In this paper, a cost model is established based on the response characteristics of the thermostatically controlled loads in day-ahead scheduling, and considering the impact of non-monotonic bidding for thermostatically controlled loads on market clearing is considered, then the augmented constraints of the thermostatically controlled loads bidding are introduced into the traditional clearing method. By this way, proper sequence of bid quantity of each segment and the economic revenue can be guaranteed. The numeral testing results verify the effectiveness of the proposed method.

Optimus: An Operator Fusion Framework for Deep Neural Networks

The reduction of neural parameters and operations for the applications on embedded and IoT platforms in current deep neural network (DNN) architectures has received increasing attention. Relatively, the intermediate feature maps of such lightweight neural networks begin to grow and usually outsize the on-chip memory as the new bottleneck, which introduces considerable power-consuming off-chip memory accesses. To reduce the feature-induced memory accesses, operator fusion has been proposed to parallelize the execution of multiple convolutional layers and shown significant reduction of off-chip memory accesses. However, how to fuse the neural operators is still a challenging issue that heavily depends on both the neural network (NN) topology and the specific DNN accelerator configuration. In this work, we observed prior operator fusion approaches fail to guarantee memory-level optimality as they search in the constrained operator fusion design space. Considering the complexity of the NN topologies and the constrained resources of the DNN accelerators, we develop a novel operator fusion framework, Optimus. Optimus includes an accurate memory cost model dedicated to the scheduler to evaluate the potential operator-fusion schemes and a directed acyclic graph-based operator fusion algorithm for both off-line and on-line workload deployment scenarios, which altogether generates high-efficiency operator-fusion solutions for arbitrary network models running on DNN accelerators. The experimental results show that Optimus reduces 17–75% off-chip memory accesses and obtains 1.86×–3.66× energy efficiency on state-of-the-art DNN workloads when compared to the baselines and brings significant power-efficiency boost to the DNN accelerators of different architectures and dataflows.

Parametric estimation of supplier's plant construction costs

Cost engineers of buying enterprises perform detailed product cost calculations of externally manufactured components. The aim of these calculations is to determine what a product should cost and to support purchasing functions in fact-based negotiations. While product cost engineers have deep knowledge in the calculation of direct cost, they need support in the calculation of supplier´s indirect cost categories. The calculation of industrial rent, which is expressed in annual cost per m² of occupied plant building floor space can be improved by providing accurate construction cost estimates. Construction costs are strongly impacting the calculation of supplier´s annual building depreciation, which is a crucial cost driver for the determination of the industrial rent. Academic literature is actually not providing an accurate and suitable cost model for product cost engineers, which is estimating construction cost per m² depending on different industrial building categories and alternative supplier plant locations. The paper aims to close this gap by applying linear regression analysis on a set of European construction cost data considering two industrial building categories: “warehouses/basic factory units” and “high-tech factories”. By regressing construction cost against construction labor rates within different supplier plant locations it was possible to form suitable and accurate parametric regression functions with R² values between 0.74 and 0.88. Next to high R² values acceptable mean average percentage errors between 7.45% and 11.77% could be realized by comparing estimated with observed construction cost. The estimation of industrial construction costs based on the paper´s results can be used to improve the calculation of industrial rent, which is one cost element, that has to be covered within product cost engineer´s Should Cost Calculations.

Affine-arithmetic-based microgrid interval optimization considering uncertainty and battery energy storage system degradation

Microgrids can effectively integrate renewable energy sources (RESs) and provide power for local customers. However, uncertainties of RESs and loads pose challenges to microgrid operation. The traditional point optimization method is unrealistic, and the widely used stochastic optimization (SO) method is time-consuming. Besides, battery energy storage systems (BESSs) are critical dispatchable devices to alleviate adverse effects of uncertainty, so an accurate nonlinear degradation cost model of BESSs should also be proposed. To handle such problems, the paper proposes an affine–arithmetic (AA)-based microgrid interval optimization (IO) method considering uncertainty and BESS degradation. First, the AA theory is introduced to model the RES and load variation ranges as intervals and calculate the interval uncertainty. Then, a nonlinear BESS degradation cost model is proposed, which can assess battery degradation costs considering different charging and discharging behaviors. The nondominated sorting genetic algorithm-II (NSGA-II) is employed to solve the proposed microgrid IO framework. For validation, the proposed IO method was compared with the point optimization method and SO method under various uncertainty realizations in a modified IEEE 33 bus system. The simulation results indicated the effectiveness of the proposed IO method in terms of an equilibrium between the simulation time and optimization performance.

A Query Language for Workflow Logs

A business process (workflow) is an assembly of tasks to accomplish a business goal. Real-world workflow models often demanded to change due to new laws and policies, changes in the environment, and so on. To understand the inner workings of a business process to facilitate changes, workflow logs have the potential to enable inspecting, monitoring, diagnosing, analyzing, and improving the design of a complex workflow. Querying workflow logs, however, is still mostly an ad hoc practice by workflow managers. In this article, we focus on the problem of querying workflow log concerning both control flow and dataflow properties. We develop a query language based on “incident patterns” to allow the user to directly query workflow logs instead of having to transform such queries into database operations. We provide the formal semantics and a query evaluation algorithm of our language. By deriving an accurate cost model, we develop an optimization mechanism to accelerate query evaluation. Our experiment results demonstrate the effectiveness of the optimization and achieves up to 50× speedup over an adaption of existing evaluation method.

DeepCM: Deep neural networks to improve accuracy prediction of database cost models

AbstractA major challenge for many database management tasks including admission control, query scheduling, progress monitoring and self‐driving data storage systems is to enhance queries performances which are based on computational models known as database cost models. One of the most challenging aspects of developing accurate database cost models is identifying their parameters and capturing their relationships, consequently we can derive the query execution cost on the basis of a specific database hosted on a given platform. Furthermore, the highly dynamic workload (i.e., a set of queries) and the query execution variation lead to performance degradation risk, therefore cost models need to be improved by considering newer software configuration and future workload characteristics. In this article, we propose a framework called DeepCM that is based on a min–max optimization for building robust database cost model against uncertainty parameters. Furthermore, our framework is based on Robust Deep Neural Networks to build database cost models that guarantee a high accuracy regardless of variations from software configuration and workload characteristics. Several experiments have been done to evaluate the robustness of produced cost models and findings show that DeepCM provides a high cost model prediction accuracy and stable performance.

Understanding and Optimizing Conjunctive Predicates Under Memory-Efficient Storage Layouts

Database queries can contain multiple predicates. The optimization of conjunctive predicates is still vital to the overall performance of analytic data processing tasks. Prior work proposes several memory-efficient storage layouts, e.g., BitWeaving and ByteSlice, to significantly accelerate predicate evaluation, as circuit-level intra-cycle parallelism available in modern CPUs can be exploited such that the total number of instructions can be dramatically reduced. However, the performance potential of conjunctive predicates has not been harvested yet under such storage layouts as there is no accurate cost model to provide necessary insights that guide the optimization process. In this paper, we propose a hybrid empirical/analytical cost model (Understanding) to unveil the performance characteristics of such storage layouts when applying to predicate evaluation. Our cost model takes into account effect of non-linear factors, e.g., cache miss and branch misprediction, and easily applies to different CPUs. The main finding from our cost model is to distinguish high-cost instruction (which suffers from cache miss and/or branch misprediction) from low-cost instruction (which enjoys cache hit and correct branch prediction) in the context of predicate evaluation under these storage layouts. Guided by such a finding, we propose a simple execution scheme Hebe (Optimizing), which is order-oblivious while maintaining high performance. Hebe is attractive to the query optimizer (QO), as the QO does not need to go through a sampling process to decide the optimal evaluation order in advance. The intuition behind Hebe is to significantly reduce the number of high-cost instructions while keeping low-cost instructions unchanged. Our finding from Hebe sheds light on the importance of accurate cost model that guide us to derive an efficient execution scheme for query processing on modern CPUs.

Energy management system of low voltage dc microgrid using mixed-integer nonlinear programing and a global optimization technique

Integration of a group of distributed generators (DGs) and loads in the form of a microgrid (MG) enhances the supply reliability, reduces the amount of greenhouse gasses (GHG) emission, and reduces the generation cost. However, this integration requires a refined energy management system (EMS). The MG-EMS dispatches the power between DGs to optimize either of the generation cost, the amount of GHG emissions, or both simultaneously. Minimum cost scenario needs an accurate generation cost model for each DG. In this paper, generation cost model includes the no-load cost and nonlinear behavior of losses inside the DG. In addition, the start/stop cost of DGs and power losses within the MG are also considered. The proposed optimization problem becomes a non-convex mixed integer nonlinear (MINL) problem which is hard to solve. The paper suggests using branch and reduce optimization navigator (BARON) algorithm, a global optimization technique. Minimum cost and minimum emission scenarios are single objective problems, while operation at reasonable emission and cost is a multi-objective scenario. Results of the single objective scenarios are exploited to determine feasible constraints to optimize the multi-objective scenario using constraints approach. Eventually, the capability of an energy storage system (ESS) to compensate generation shortage and minimize the emission is also investigated. The study is carried out on a DC microgrid comprised of photovoltaic (PV), fuel cell (FC), microturbine (MT), diesel generator (DE), battery ESS, and Egyptian grid load profiles over four seasons of the year.

Drone data for decision making in regeneration forests: from raw data to actionable insights1

In this study, we aim at developing ways to directly translate raw drone data into actionable insights, thus enabling us to make management decisions directly from drone data. Drone photogrammetric data and data analytics were used to model stand-level immediate tending need and cost in regeneration forests. Field reference data were used to train and validate a logistic model for the binary classification of immediate tending need and a multiple linear regression model to predict the cost to perform the tending operation. The performance of the models derived from drone data was compared to models utilizing the following alternative data sources: airborne laser scanning data (ALS), prior information from forest management plans (Prior) and the combination of drone +Prior and ALS +Prior. The use of drone data and prior information outperformed the remaining alternatives in terms of classification of tending needs, whereas drone data alone resulted in the most accurate cost models. Our results are encouraging for further use of drones in the operational management of regeneration forests and show that drone data and data analytics are useful for deriving actionable insights.

Hull geometry optimisation of wave energy converters: On the choice of the optimisation algorithm and the geometry definition

It is key in the development of wave energy systems to aim at designing economically competitive solutions that enable maximal annual energy production. Previous studies identify the Wave Energy Converter (WEC) structure, i.e. the hull, to have one of the largest cost reduction potentials. Due to this potential, geometry optimisation of WECs has been previously considered, however, most of these studies have been limited by the simplicity of the employed geometrical shapes and the lack of accurate cost models. It is, therefore, important to include an adaptable geometry definition capable of generating diverse WEC shapes, and to account for other factors that can have an effect on costs. These considerations result in a more challenging optimisation problem, and a more complex objective function. The goal of this study is to address the challenge of finding a suitable and efficient optimisation method for WEC geometry design. In this paper, different geometry definitions, such as using simple shapes or B-spline surfaces, and different meta-heuristic optimisation algorithms, such as genetic algorithms or particle swarm optimisation are applied to this problem to find the most suitable choices. Results show an improvement in final objective function values of up to 224% when using an adaptable geometry definition and up to 11% when employing the most suitable optimisation algorithm compared to previous results. In conclusion, the choice of the different elements of the optimisation formulation have a large impact on the quality of the optimisation results and should be based on preliminary studies as presented here.

A Vector-Length Agnostic Compiler for the Connex-S Accelerator with Scratchpad Memory

Compiling sequential C programs for Connex-S, a competitive, scalable and customizable, wide vector accelerator for intensive embedded applications with 32 to 4,096 16-bit integer lanes and a limited capacity local scratchpad memory, is challenging. Our compiler toolchain uses the LLVM framework and targets OPINCAA, a JIT vector assembler and coordination C++ library for Connex-S accelerating computations for an arbitrary CPU. Therefore, we address in the compiler middle end aspects of efficient vectorization, communication, and synchronization. We perform quantitative static analysis of the program useful, among others, for the symbolic-size compiler memory allocator and the coordination mechanism of OPINCAA. We also discuss the LLVM back end for the Connex-S processor and the methodology to automatically generate instruction selection code for emulating efficiently arithmetic and logical operations for non-native types such as 32-bit integer and 16-bit floating-point. By using JIT vector assembling and by encoding the vector length of Connex-S as a parameter in the generated OPINCAA program, we achieve vector-length agnosticism to support execution on distinct embedded devices, such as several digital cameras with different resolutions, each equipped with custom-width Connex-S accelerators meant to save energy for the image processing kernels. Since Connex-S has a limited capacity local scratchpad memory of 256 KB normally, we present how we also use the PPCG C-to-C code generator to perform data tiling to minimize the total kernel execution time, subject to fitting larger program data in the local memory. We devise an accurate cost model for the Connex-S accelerator to choose optimal performance tile sizes at compile time. We successfully compile several simple benchmarks frequently used, for example, in high-performance and computer vision embedded applications. We report speedup factors of up to 11.33 when running them on a Connex-S accelerator with 128 16-bit integer lanes w.r.t. the dual-core ARM Cortex A9 host clocked at a frequency 6.67 times higher, with a total of two 128-bit Neon SIMD units.

Multi-objective Pareto and GAs nonlinear optimization approach for flyback transformer

Design and optimization of high-frequency inductive components is a complex task because of the huge number of variables to manipulate, the strong interdependence and the interaction between variables, the nonlinear variation of some design variables as well as the problem nonlinearity. This paper proposes a multi-objective design methodology of a 200-W flyback transformer in continuous conduction mode using genetic algorithms and Pareto optimality concept. The objective is to minimize loss, volume and cost of the transformer. Design variables such as the duty cycle, the winding configuration and the core shape, which have great effects on the former objectives but were neglected in previous works, are considered in this paper. The optimization is performed in discrete research space at different switching frequencies. In total, 24 magnetic materials, 6 core shapes and 2 winding configurations are considered in the database. Accurate volume and cost models are also developed to deal with the optimization in the discrete research space. The bi-objective (loss–volume) and tri-objective (loss–volume–cost) optimization results are presented, and the variations of the design variables are analyzed for the case of 60 kHz. An example of a design (30 kHz) is experimentally verified. The registered efficiency is 88% at full load.

Cost Impression on Human Resource Accounting

The main resource in every organization is "Human resource". They are asset for the organization and they develop organization's productivity and profitability. Human resource is to direct and control the production and profit in the organization. Employees are considered as main factors but they do not belong in the balance sheet as an asset.Many organization do not use any accurate cost model to measure employees cost and value.The employees value does not considered in the organisation. We can measure that value of an employeein different ways, are1.standard cost2.replacement cost3.opportunity cost4.economic value.These cost methods are involved capitalisation of actual cost on recruitment, selection, learning and development, hiring, etc., of personnel in the organization. This research article is focused on theoretical conceptual paper of HRA, it helps the organisation to make important decision making by the help of Human Resource Cost Accounting method.

Extended overlay architectures for heterogeneous FPGA cluster management

This paper proposes a novel approach for the hardware virtualization of FPGA resources, based on overlay architectures. Overlays are reconfigurable architectures synthesized on top of commercial-of-the-shelf (COTS) FPGAs. They have demonstrated to improve portability, speed up reconfiguration, and promote resource abstraction hence durability. This work demonstrates how slightly extending the architecture overlaying on top of COTS FPGAs can bring novel features for sake of improved management of hardware tasks, and ensure the binary compatibility among heterogeneous FPGAs. This comes along with a deployment platform and a software stack offering an operating system service. As a result, the platform is capable of node-to-node a hardware application live migration, while operating a cluster of heterogeneous FPGAs. Besides, the proposed software stack ensures backward compatibility when introducing a new overlay architecture. This paper also introduces accurate cost models for the early estimation of the reconfiguration time overhead. This approach that has been demonstrated in DASIP international conference is evaluated in this paper on both the Xilinx Artix-7 and Altera Cyclone V C9 FPGAs.

FPGA design space exploration for scientific HPC applications using a fast and accurate cost model based on roofline analysis

High-performance computing on heterogeneous platforms in general and those with FPGAs in particular presents a significant programming challenge. We contend that compiler technology has to evolve to automatically optimize applications by transforming a given original program. We are developing a novel methodology based on type transformations on a functional description of a given scientific kernel, for generating correct-by-construction design variants. An associated lightweight costing mechanism for evaluating these variants is a cornerstone of our methodology, and the focus of this paper. We discuss our use of the roofline model to work with our optimizing compiler to enable us to quickly derive accurate estimates of performance from the design’s representation in our custom intermediate language. We show results confirming the accuracy of our cost model by validating it on different scientific kernels. A case study is presented to demonstrate that a solution created from our optimizing framework outperforms commercial high-level synthesis tools both in terms of throughput and power efficiency.

Highly-Flexible and Optimized, Area: and Energy-Efficient Matrix Decomposition Accelerators based on Givens Algorithms and CORDIC Rotations

Matrix decomposition accelerators are attractive SoC components for many applications with a wide range of specifications. In this work, a new family of highly area- and energy-efficient modular matrix decomposition architectures based on the givens-algorithms and CORDIC rotations are elaborated. Accurate algebraic cost models enable for early cost estimation as well as for cross-level optimization over architecture, micro-architecture and circuit-level using a rich set of parameters. Quantitative results for an exemplary QRD application, implemented in 40-nm CMOS technology, are presented.