Performance Trade-off Analysis Research Articles

Extending SysML to Integrate Cost Analysis Into Model-Based Systems Engineering

Model-based systems design (MBSD), a current trend adopted by INCOSE, employs the systems modeling language (SysML), a standard introduced by OMG and INCOSE. Though there are numerous works on integrating performance exploitation in SysML, cost is not sufficiently explored as a driving design parameter. By integrating cost analysis in a popular modeling language like SysML, the proposed approach may be applied to any system designed using standardized languages and tools. In this work, we integrate cost analysis within SysML models at a generic level to explore design alternatives under specific cost and performance restrictions and perform tradeoff analysis. The proposed SysML extensions provide: 1) cost-related entities to encode cost aspects, such as capital and operating expenses, and 2) functions that enable the automatic computation of costs; these extensions are contained in a custom SysML cost profile. The feasibility and benefits of the approach are explored in two distinct real-world case studies with different purpose and characteristics. 1) Configuring a remote elderly monitoring system, taking into consideration patients' budgetary and operational concerns regarding the equipment installed in their homes. In this case, patients had the opportunity to evaluate and prioritize their concerns prior to using the system. 2) Exploring the improvement of the passengers' comfort as a level of service indicator in the Athens Metro railway system, taking into account operational cost constraints. In this case, the operator obtained forecasts of performance and cost of the metro system operation in order to choose between different operational policies.

System-Level Analysis of Energy and Performance Trade-Offs in mmWave 5G NR Systems

Energy efficiency and service reliability are two critical requirements for 5G New Radio cellular access. To address the latter, 3GPP has proposed multiconnectivity operation allowing user equipment (UE) to maintain active links to more than a single base station. However, the use of this technique compromises the energy efficiency of UE. In this paper, we develop a mathematical model capturing key energy and performance indicators as a function of system and environmental conditions. Then, we apply it to investigate the trade-offs between user performance and energy efficiency as well as the effect of scaling of discontinuous reception (DRX) timers. For a considered set of system parameters, our results reveal that for low micromobility speed ≤ 0.1° /s and blockers density, ≤ 0.1 bl./m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> two simultaneously supported links with minimal DRX timers lead to optimal performance. For higher blockers density more than two links are needed to optimize energy efficiency while for high micromobility speed multiconnectivity does not allow to improve energy efficiency at all. Thus, the optimal degree of multiconnectivity and DRX timer scaling coefficients depend on the environmental characteristics including both micromobility speed and density of blockers and need to be dynamically updated during UE operation.

Methodology for Early Design Phase Cost and Performance Trade-Off Analysis of a New Variant in a Product Family

AbstractGiven the Industry 4.0 trend towards smaller lot sizes and large product families, there is a need to increase the design efficiency in terms of cost and lead time. Design decisions taken in the early or conceptual design phases are shown to have the greatest impact on the final product cost, however the choices and trade-offs are often made in an ad-hoc way. This paper presents a structured methodology for formalizing early stage product design knowledge, which is key for designing and evaluating new variants in a product family in the early stages of the design process.

Sustainable Supplier Selection in Agri-Food Supply Chain Management

In recent times, sustainability of supply chains becomes the most interested subject by customers, firms, government bodies, academia, and suppliers as the environmental resources reveal as the most valuable and limited asset for human beings. One of the most important elements determining the performance of sustainable agri-food supply chain business is supplier’s commitment. A mainly inspiring feature in this domain is the wide range of factors influencing supplier selection decision-making process in Triple Bottom Line context must be analyzed. However, it is not easy to completely integrate and measure majority of these factors in a simple optimization model. Therefore, this paper starts with a comprehensive review of literature about supplier selection for sustainable supply chains (SSCs) development. Afterwards, a new two-steps hybrid solution method is suggested. In initial step, a set of criteria for supplier selection process is analyzed built on Fuzzy Analytic Network Process (FANP). The result of the first step is fed in to the second step of the analysis to design a model with multiple objectives for selection process of supplier using fuzzy VIKOR. All three dimensions of sustainability; specifically, factors such as weather, water and soil footprint, energy and material consumption, employment, social services and finally financial performance of supply chain are considered concurrently. The suggested approach generates a comprehensive model to help supply chain managers in supplier selection process. Numerical analysis is completed via exploiting data from a Turkish agri-food company case for validating effectiveness, also and success of the proposed solution. Final analytical results of the analysis provide valuable business policy oriented, operational and practical insights on the influence of economic, environmental, and social sustainability for enhanced supplier relationship management module in sustainable supply chain structure. Moreover, by tracking environmental impact indicators such as, emission and pollution rates, footprint rates, against social and cost indicators it would be possible to perform tradeoff analysis for giving more sustainable decisions on new supply chain investments.

End-to-End Instrument Performance Simulation System (EIPS) Framework: Application to Satellite Microwave Atmospheric Sounding Systems

This article presents a generic flexible framework for an End-to-end Instrument Performance Simulation System (EIPS) for satellite atmospheric remote sensing instruments. A systematic process for developing an end-to-end simulation system based on Rodgers’ atmospheric observing system design process has been visualised. The EIPS has been developed to support the quantitative evaluation of new satellite instrument concepts in terms of performance simulations, design optimisation, and trade-off analysis. Important features of this framework include: fast radiative transfer simulation capabilities (fast computation and line-by-line like simulations), applicability across the whole electromagnetic (EM) spectrum and a number of integrated retrieval diagnostics. Because of its applicability across the whole EM spectrum, the framework can be usefully applied to synergistic atmospheric retrieval studies. The framework is continually developing and evolving, and finding applications to support and evaluate emerging instrument and mission concepts. To demonstrate the framework’s flexibility in relation to advanced sensor technologies in the microwave range, a novel superconducting transition edge sensor (TES) -based multi-spectral microwave instrument has been presented as an example. As a case study, the performance of existing multi-spectral-type microwave instruments and a TES-technology-based multi-spectral microwave instrument has been simulated and compared using the developed end-to-end simulation framework.

Performance Modeling and Directives Optimization for High-Level Synthesis on FPGA

High-level synthesis (HLS) relies on the use of synthesis directives to generate digital designs meeting a set of specifications. However, the selection of directives depends largely on designer experience and knowledge of the target architecture and digital design. Existing automated methods of directive selection are very limited in scope and capability to analyze complex design descriptions in high-level languages to be synthesized using HLS. This paper proposes a comprehensive model-based analysis (COMBA) framework which is capable of analyzing the effects of a multitude of directives related to functions, loops and arrays in the design description using pluggable analytical models, a recursive data collector and a metric-guided design space exploration (DSE) algorithm. COMBA reports a small average error in estimating performance when compared with HLS tools like Vivado HLS, and finds a high-performance configuration of synthesis directives within minutes. Given different resource constraints, COMBA finds configurations with higher speed-ups, compared with the state-of-the-art. Moreover, COMBA can guide the performance and area trade-off analysis. Experiments show that our DSE algorithm outperforms the conventional genetic algorithm, and COMBA efficiently finds a near-optimal configuration, which proves the efficiency of our tool for optimizing the practical HLS based designs.

FlexCL: A Model of Performance and Power for OpenCL Workloads on FPGAs

Hardware acceleration is a promising trend for the energy and thermally constrained systems. The programmable nature of FPGAs allows it to deliver high performance and energy efficient solution. Unfortunately, the traditional RTL-based synthesis flow of FPGAs prevents its wide adoption. In response, recent adoption of OpenCL programming model has raised the possibility to program FPGAs in a software manner. To harness the power of FPGAs using OpenCL programming model, it is advantageous to design an analytical model for performance analysis, design space exploration and provide insights into the performance bottlenecks. To this end, this paper presents FlexCL, an analytical performance and power model for OpenCL workloads on FPGAs. FlexCL leverages static analysis to analyze the OpenCL kernels. As for the performance estimation, it first develops systematic computation models for processing elements, compute units and kernels by modeling the operation scheduling, work-item and work-group scheduling, and the resource constraints. Then, it models different global memory access patterns. Finally, FlexCL estimates the overall performance by tightly coupling the memory and computation models based on the communication mode. FlexCL can be also used to guide performance and power trade-off analysis. Experiments demonstrate that the average performance and power estimation errors of FlexCL are 9.5 and 12.6 percent for the Rodinia suite, respectively. The OpenCL model on FPGAs also exposes a rich optimization design space. With FlexCL, we can enable rapid exploration of the design space with respect to both performance and power within seconds instead of hours or days.

A Fast Algorithm on Minimum-Time Scheduling of an Autonomous Ground Vehicle Using a Traveling Salesman Framework

Manufacturing automation, especially through implementation of autonomous ground vehicle (AGV) technology, has been under intensive study due to increased productivity and reduced variations. The objective of this paper is to present an algorithm on scheduling of an AGV that traverses desired locations on a manufacturing floor. Although many algorithms have been developed to achieve this objective, most of them rely on exhaustive search, which is time-consuming. A novel two-step algorithm that generates “good,” but not necessarily optimal, solutions for relatively large data sets (≈1000 points) is proposed, taking into account time constraints. A tradeoff analysis of computational expense versus algorithm performance is discussed. The algorithm enables the AGV to find a tour, which is as good as possible within the time constraint, using which it can travel through all given coordinates before returning to the starting location or a specified end point. Compared to exhaustive search methods, this algorithm generates results within a stipulated computation time of 30 s on a laptop personal computer.

Package Design Optimization for Intel SoC Xeon-D

Xeon-D brings the high performance of Xeon processors into a dense, low-power system-on-chip. This paper addresses the importance of cost–performance tradeoff analysis for the Xeon-D package. It describes how to determine the low-cost package factors (size, footprint, pin map, and layer count) without compromising the performance of the system. The 10-GbE signal integrity design and high-speed differential cost-performance optimization are discussed. Low-power architecture and package power delivery features including fully integrated voltage regulator are presented in this paper as well.

Performance Tradeoff in a Unified Passive Radar and Communications System

Although radar and communication systems so far have been considered separately, recent advances in passive radar systems have motivated us to propose a unified system, capable of fulfilling the requirements of both radar and communications. In this paper, we provide performance tradeoff analysis for a system consisting of a transmitter, a passive radar receiver, and a communication receiver (CR). The total power is allocated for transmitting the radar waveforms and information signals in such a way that the probability of detection (PD) is maximized, while satisfying the information rate requirement of the CR. An exact closed-form expression for the probability of false alarm (PFA) is derived, whereas PD is approximated by assuming that the signal-to-noise ratio corresponding to the reference channel is often much larger than that corresponding to the surveillance channel. The performance tradeoff between the radar and communication subsystems is then characterized by the boundaries of the PFA-rate and PD-rate regions.

A systematic literature review on methods that handle multiple quality attributes in architecture-based self-adaptive systems

ContextHandling multiple quality attributes (QAs) in the domain of self-adaptive systems is an understudied research area. One well-known approach to engineer adaptive software systems and fulfill QAs of the system is architecture-based self-adaptation. In order to develop models that capture the required knowledge of the QAs of interest, and to investigate how these models can be employed at runtime to handle multiple quality attributes, we need to first examine current architecture-based self-adaptive methods. ObjectiveIn this paper we review the state-of-the-art of architecture-based methods for handling multiple QAs in self-adaptive systems. We also provide a descriptive analysis of the collected data from the literature. MethodWe conducted a systematic literature review by performing an automatic search on 28 selected venues and books in the domain of self-adaptive systems. As a result, we selected 54 primary studies which we used for data extraction and analysis. ResultsPerformance and cost are the most frequently addressed set of QAs. Current self-adaptive systems dealing with multiple QAs mostly belong to the domain of robotics and web-based systems paradigm. The most widely used mechanisms/models to measure and quantify QAs sets are QA data variables. After QA data variables, utility functions and Markov chain models are the most common models which are also used for decision making process and selection of the best solution in presence of many alternatives. The most widely used tools to deal with multiple QAs are PRISM and IBM's autonomic computing toolkit. KLAPER is the only language that has been specifically developed to deal with quality properties analysis. ConclusionsOur results help researchers to understand the current state of research regarding architecture-based methods for handling multiple QAs in self-adaptive systems, and to identity areas for improvement in the future. To summarize, further research is required to improve existing methods performing tradeoff analysis and preemption, and in particular, new methods may be proposed to make use of models to handle multiple QAs and to enhance and facilitate the tradeoffs analysis and decision making mechanism at runtime.

Vision-Based Front and Rear Surround Understanding Using Embedded Processors

Vision-based driver assistance systems involve a range of data-intensive operations, which pose challenges in implementing them as robust and real-time systems on resource constrained embedded computing platforms. In order to achieve both high accuracy and real-time performance, the constituent algorithms need to be designed and optimized such that they lend well for embedded realization. In this paper, we present a novel two-camera-based embedded driver assistance framework that analyzes the dynamics of vehicles in the front and rear surround views of the host vehicle (ego-vehicle). In order to do this, we propose a set of integrated techniques that combine contextual cues and lane information to detect vehicles that pose high threat to the ego-vehicle. The threat analysis is then used for generating a safe maneuver zone by the proposed system, which is implemented by using two Snapdragon 810 embedded CPUs. A detailed performance evaluation and tradeoff analysis is presented using a novel multiperspective dataset (DualCam Dataset) that is released as part of this paper. In terms of accuracy, the detailed evaluations show high robustness with true positive rates greater than 95% with less than 6% false alarm rate. The proposed embedded system operates at real-time frame rates in our testbed under real-world highway driving conditions. The proposed framework was presented as a live demonstration at the 2016 Consumer Electronics Show.

Analysis and Evaluation of Performance Gains and Tradeoffs for Massive MIMO Systems

Massive MIMO technique offers significant performance gains for the future of wireless communications via improving the spectral efficiency, energy efficiency and the channel quality with simple linear processing such as maximum-ratio transmission (MRT) or zero-forcing (ZF) by providing each user a large degree of freedom. In this paper, the system performance gains are studied in a multi-cell downlink massive MIMO system under the main considerations such as perfect channel estimation, imperfect channel estimation and the effect of interference among cells due to pilot sequences contamination. Then, mathematical expressions are derived for these gains i.e., spatial multiplexing gain, array gain and spatial diversity gain. After that, essential tradeoffs among these gains are considered under the effect of non-orthogonal interference, these tradeoffs are: spatial diversity gain vs. spatial multiplexing gain and array gain vs. spatial multiplexing gain. Simulation results show that the unbounded number of base station antennas boosts the array gain through concentrating the energy to spatial directions where users are sited, hence diminishing loss in array gain due to pilot contamination. The simulation results reveal also that massive MIMO strengthens the spatial multiplexing gain through increasing the number of served users via the same system resources in spite the effect of inter-cell interference. Finally, the spatial diversity gain is measured in term of outage probability and the simulation results show that raising the number of antennas will improve the outage probability. Meanwhile increasing the number of served users will lead to degrade the outage probability per user due to non-orthogonal interference from other cells.

WCET-Aware Energy-Efficient Data Allocation on Scratchpad Memory for Real-Time Embedded Systems

Scratchpad memory (SPM) is a software-managed, small, on-chip form of memory. For real-time embedded systems, worst case execution time (WCET) is more important than average-case performance. We address the problem of allocating program data variables between main memory and SPM to minimize the energy consumption due to data variable accesses, while respecting a given upper bound on a program’s WCET. We present an optimal branch-and-bound algorithm; and an efficient heuristic algorithm. Our approach provides a flexible framework for the designer to perform tradeoff analysis between the program WCET and the energy consumption based on application requirements.

A method to optimize the scope of a software product platform based on end-user features

ContextDue to increased competition and the advent of mass customization, many software firms are utilizing product families – groups of related products derived from a product platform – to provide product variety in a cost-effective manner. The key to designing a successful software product family is the product platform, so it is important to determine the most appropriate product platform scope related to business objectives, for product line development. AimThis paper proposes a novel method to find the optimized scope of a software product platform based on end-user features. MethodThe proposed method, PPSMS (Product Platform Scoping Method for Software Product Lines), mathematically formulates the product platform scope selection as an optimization problem. The problem formulation targets identification of an optimized product platform scope that will maximize life cycle cost savings and the amount of commonality, while meeting the goals and needs of the envisioned customers’ segments. A simulated annealing based algorithm that can solve problems heuristically is then used to help the decision maker in selecting a scope for the product platform, by performing tradeoff analysis of the commonality and cost savings objectives. ResultsIn a case study, PPSMS helped in identifying 5 non-dominated solutions considered to be of highest preference for decision making, taking into account both cost savings and commonality objectives. A quantitative and qualitative analysis indicated that human experts perceived value in adopting the method in practice, and that it was effective in identifying appropriate product platform scope.

Faceted classification of manufacturing processes for sustainability performance evaluation

Sustainability characterization of manufacturing processes is key to the inventory data development for life cycle analysis of products and processes in manufacturing industries. The ability of an organization to compare and contrast this sustainability performance in a variety of manufacturing process categories is crucial for purposes of evaluating and improving the process performance or doing tradeoff analysis for dealing with suboptimal performance. In suchasituation,itisnecessarytoabstractthis informationinto broader, aggregated, and consistent viewpoints such that sim- ilarities and contrasts in sustainability performance are visu- ally deliberated. For developing such a visual system, the abstracted viewpoint should be transformed to a process clas- sification scheme. A faceted approach to manufacturing pro- cess classifications has been proposed in this work for this purpose. We developed a visual interface to navigate the process classification scheme. We also provide a means by which dynamic hierarchical taxonomies can be generated by ranking the abstracted viewpoints called facets and classifiers.

Enhancing Secrecy With Multi-Antenna Transmission in Wireless Ad Hoc Networks

We study physical-layer security in wireless ad hoc networks and investigate two types of multi-antenna transmission schemes for providing secrecy enhancements. To establish secure transmission against malicious eavesdroppers, we consider the generation of artificial noise with either sectoring or beamforming. For both approaches, we provide a statistical characterization and tradeoff analysis of the outage performance of the legitimate communication and the eavesdropping links. We then investigate the network-wide secrecy throughput performance of both schemes in terms of the secrecy transmission capacity, and study the optimal power allocation between the information signal and the artificial noise. Our analysis indicates that, under transmit power optimization, the beamforming scheme outperforms the sectoring scheme, except for the case where the number of transmit antennas are sufficiently large. Our study also reveals some interesting differences between the optimal power allocation for the sectoring and beamforming schemes.

CODE TRANSFORMATIONS FOR ENHANCING THE PERFORMANCE OF SPECULATIVELY PARALLEL THREADS

As technology advances, microprocessors that integrate multiple cores on a single chip are becoming increasingly common. How to use these processors to improve the performance of a single program has been a challenge. For general-purpose applications, it is especially difficult to create efficient parallel execution due to the complex control flow and ambiguous data dependences. Thread-level speculation and transactional memory provide two hardware mechanisms that are able to optimistically parallelize potentially dependent threads. However, a compiler that performs detailed performance trade-off analysis is essential for generating efficient parallel programs for these hardwares. This compiler must be able to take into consideration the cost of intra-thread as well as inter-thread value communication. On the other hand, the ubiquitous existence of complex, input-dependent control flow and data dependence patterns in general-purpose applications makes it impossible to have one technique optimize all program patterns. In this paper, we propose three optimization techniques to improve the thread performance: (i) scheduling instruction and generating recovery code to reduce the critical forwarding path introduced by synchronizing memory resident values; (ii) identifying reduction variables and transforming the code the minimize the serializing execution; and (iii) dynamically merging consecutive iterations of a loop to avoid stalls due to unbalanced workload. Detailed evaluation of the proposed mechanism shows that each optimization technique improves a subset but none improve all of the SPEC2000 benchmarks. On average, the proposed optimizations improve the performance by 7% for the set of the SPEC2000 benchmarks that have already been optimized for register-resident value communication.

Collaborative Engineering Environment for 21st Century avionics

Collaborative engineering and virtual prototyping is the application of advanced distributed modeling and simulation and engineering tools in an integrated environment to support technology development, system design, performance, cost, and producibility trade-off analyses throughout the entire product and system engineering life-cycle. The Sensors and Information Directorates, Air Force Research Laboratory (AFRL) have initiated a major effort to implement a Collaborative Engineering Environment (CEE) to provide the infrastructure and development methodology required for affordable and timely avionics for the 21st Century warfighter. The CEE concept is a major cultural change in the technology development process that involves applying state-of-the-art simulation and information sharing technology to the way we do business. CEE enables partnerships among the laboratories, industry, and the warfighter to accelerate the development and transition of leading edge technology to operational weapon systems.

Integrated Ecological Modeling and Decision Analysis Within the Everglades Landscape

Planning for complex ecosystem restoration projects involves integrating ecological modeling with analysis of performance trade-offs among restoration alternatives. The authors used the Everglades Landscape Model and Multi-Criteria Decision Analysis to explore the effect of simulated ecosystem performance, risk preferences, and criteria weights on the ranking of three alternatives to restoring overland sheet flow in the Everglades. The ecological model outputs included both hydrologic and water quality criteria. Results were scored in the decision analysis framework, highlighting the trade-offs between hydrologic restoration and water quality constraints. Given equal weighting of performance measures, the alternative with more homogenous sheet flow was preferred over other alternatives, despite evidence of some localized eutrophication risk.