Two analytical models for evaluating performance of Gigabit Ethernet hosts with finite buffer

Summary of United States Human Proteome Organisation (HUPO) Symposium Entitled “Standardized Clinical Proteomics Platforms”

HTTP video streaming employs the TCP protocol as its transport protocol, which provides reliability on one hand, but delay or delay jitter on the other hand. IEEE 802.11 MAC protocol is assumed to support wireless networks, whose channel access efficiency impacts the performance of the upper layer, i.e., the data rate and the delay of TCP. In this paper, an analytical model for Quality of Experience (QoE) of HTTP video streaming over single-hop wireless networks is proposed. Particularly, two QoE performance metrics are evaluated, which are underflow times and fluency of playout, defined as the buffer empty times during playout, and the ratio of ideal watching time to the real watching time. The impacts of system parameters, such as the length of a video file, the playout threshold, the playout rate, and the arriving rate of a TCP streaming, on the system performance metrics are analyzed in detail. It is found that when both the TCP congestion window and the MAC contention window are set to one, the arriving rate of a TCP streaming can achieve its maximum, which results in the optimal QoE performance. Moreover, the optimal setting for the MAC contention window is also obtained for the case that the TCP congestion window is larger than one. The accuracy of the proposed analytical model is verified by extensive simulations.

Optimizing directly the system performance metric is an important method for correcting wave-front distortions in adaptive optics (AO) systems. In extended object imaging, some classical image quality metrics are often used as system performance evaluation function, which is the optimized object of the control algorithm. But those metrics do not consider the existence of imaging noise. Practically, the observed object images are degraded not only by the atmospheric turbulence but also imaging system noise. The noise in image will affect the value of image quality criteria and further affect the correction capability of AO system. An AO system with Stochastic Parallel Gradient Descent (SPGD) algorithm and a 61-element deformable mirror is simulated to restore the image of a turbulence-degraded extended object and the gray level variance function acted the optimized object by control algorithm. The analysis results show that the correction capability is affected severely when the turbulence strength is relatively big (D/r0<10). This paper presents a method that the observed image is processed by the bilateral filter before the system performance metric is calculated, so that the effect of noise on the system performance metric can be mitigated. Numerical simulation results verify the method is effective and the image quality based on AO technique and the bilateral filter is much better than that of only using AO technique.

Bottleneck identification and mitigation is one of the most important problems in production system research and practice. In the current literature, the bottleneck is often defined with respect to a system's long term performance. The corresponding mitigation approach is typically to prioritize the improvement of that particular machine. This applies when the improvement cannot be flexibly adjusted or reallocated to other machines in the system and the improvement is to be fixed to a machine for a sufficiently long period of time. In this paper, we attempt to redefine the bottleneck in a dynamic environment. A control-theoretic approach is used to formulate the bottleneck identification and mitigation problem as a state-based feedback control problem with the objective being maximization of steady state throughput. In this framework, we study serial production lines with finite buffers and Bernoulli reliability machines. Computation formulas and procedures are developed to calculate the performance metrics of such systems with dynamic bottleneck control and properties of the optimal control policy are investigated.

Many storage systems are undergoing a significant shift from dedicated appliance-based model to software-defined storage (SDS) because the latter is flexible, scalable and cost-effective for modern workloads. However, it is challenging to provide a reliable guarantee of end-to-end performance in SDS due to complex software stack, time-varying workload and performance interference among tenants. Therefore, modeling and monitoring the performance of storage systems is critical for ensuring reliable QoS guarantees. Existing approaches such as performance benchmarking and analytical modeling are inadequate because they are not efficient in exploring large configuration space, and cannot support elastic operations and diverse storage services in SDS. This paper presents Inside-Out, an automatic model building tool that creates accurate performance models for distributed storage services. Inside-Out is a black-box approach. It builds high-level performance models by applying machine learning techniques to low-level system performance metrics collected from individual components of the distributed SDS system. Inside-Out uses a two-level learning method that combines two machine learning models to automatically filter irrelevant features, boost prediction accuracy and yield consistent prediction. Our in-depth evaluation shows that Inside-Out is a robust solution that enables SDS to predict end-to-end performance even in challenging conditions, e.g., changes in workload, storage configuration, available cloud resources, size of the distributed storage service, and amount of interference due to multi-tenants. Our experiments show that Inside-Out can predict end-to-end performance with 91.1% accuracy on average. Its prediction accuracy is consistent across diverse storage environments.

A system of systems (SOS) is a coalition of independently operated and managed systems that function together as one unit to efficiently and effectively meet a specific need. Combining disparate systems can produce challenges across the entire system life-cycle. Specifically, system of systems introduces complications in performance management because of the autonomy of their individual system components and the complexity of their interactions as they function as a single unit. This paper examines the ways in which key performance metrics for the system's performance are affected by the autonomy of individual member systems. It assesses the impact the individual systems have on the system-of-systems. Included is a mapping of the system performance metrics to the system-of-system measures of effectiveness, and the development of an algorithm based on that mapping to determine the overall system effectiveness. The nonlinear objective function seeks to find the optimal system configuration while assessing all component systems simultaneously.

An M/G/c/c state dependent network is a quantitative model for replicating and analyzing the behavior of occupants travelling through a network. The model, however, assumes that its inter-arrival times are independent and exponentially distributed, which inadequately reflects the dynamic and high-stakes nature in emergency situations. This paper simulates the M/G/c/c model for emergency evacuations using Erlang-k distributions—representing the real-world arrival patterns of evacuees in a more controlled and less random arrivals—and correlated inter-arrival times—common during evacuations as occupants moving in clusters due to panic or structural flow patterns. To achieve this, an M/G/c/c simulation model was first developed using Arena simulation software. The model was used to analyze the impact of various arrival rates on system performance metrics. Consequently, the performance metrics were compared with those obtained by replacing exponential inter-arrival times with Erlang-k distributions and independent arrivals with correlated cluster arrivals. The results show that Erlang-k distributions lead to better performance and smoother flow since the arrival is more controlled, while correlated arrivals increase congestion.

Mobile data traffic is currently growing exponentially and these rapid increases have caused the backhaul data rate requirements to become the major bottleneck to reducing costs and raising revenue for operators. To address this problem, caching techniques have attracted significant attention since they can effectively reduce the backhaul traffic by eliminating duplicate data transmission that carries popular content. In addition, other system performance metrics can also be improved through caching techniques, e.g., spectrum efficiency, energy efficiency, and transmission delay. In this paper, we provide a systematical survey of the state-of-the-art caching techniques that were recently developed in cellular networks, including macro-cellular networks, heterogeneous networks, device-to-device networks, cloud-radio access networks, and fog-radio access networks. In particular, we give a tutorial on the fundamental caching techniques and introduce caching algorithms from three aspects, i.e., content placement, content delivery, and joint placement and delivery. We provide comprehensive comparisons among different algorithms in terms of different performance metrics, including throughput, backhaul cost, power consumption, and network delay. Finally, we summarize the main research achievements in different networks, and highlight main challenges and potential research directions.

Uncertainty propagation is an important step in technology forecasting. Uncertainty in technology impacts and other system variables must be propagated into the system performance metrics that will be used to judge competing technology portfolios. Monte Carlo simulation and Evidence Theory are two uncertainty propagation techniques commonly used for this purpose. This paper explores the differences between these two techniques when applied to a representative aircraft engine technology forecasting problem. As an example problem, uncertainties in compressor, turbine, and combustor efficiencies are propagated into seven aircraft engine performance metrics. A surrogate model built from NPSS and WATE++ data provides modeling and simulation of the system. A representative set of expert opinion derived uncertainty intervals drives this process. The MATLAB IPP Toolbox is used to conduct Evidence Theory uncertainty propagation on the example problem. The results presented show the impact of sample size selection (number of cases), method selection (Monte Carlo or Evidence Theory), and mixing rule (Dempster’s rule or weighted mixing) on the resulting CDFs. The increase in runtime with sample size for both methods is shown, as well.

Since Internet of Things (IoT) is suggested as the fundamental platform to adapt massive connections and secure transmission, we study physical-layer authentication in the point-to-point wireless systems relying on reconfigurable intelligent surfaces (RIS) technique. Due to lack of direct link from IoT devices (both legal and illegal devices) to the access point, we benefit from RIS by considering two main secure performance metrics. As main goal, we examine the secrecy performance of a RIS-aided wireless communication systems which show secure performance in the presence of an eavesdropping IoT devices. In this circumstance, RIS is placed between the access point and the legitimate devices and is designed to enhance the link security. To specify secure system performance metrics, we firstly present analytical results for the secrecy outage probability. Then, secrecy rate is further examined. Interestingly, we are to control both the average signal-to-noise ratio at the source and the number of metasurface elements of the RIS to achieve improved system performance. We verify derived expressions by matching Monte-Carlo simulation and analytical results.

We investigate optimization of system performance in the below scenario: capturing and transmitting videos by single or multiple video sensors using distributed video coding (DVC) over ad hoc networks. There is an intrinsic contradiction in this scenario that could affect the system performance: the contradiction between the decoding quality and network lifetime. In this paper, we propose a joint optimization between the decoding quality and network lifetime using a quantitative metric of system performance, which is defined as the amount of collected visual information during the operational time of the video sensor. Based on the proposed metric, an optimal encoding rate is determined, which results in an optimal system performance. The simulation results show that the optimal encoding rate can be determined to achieve the optimal system performance.KeywordsDistributed video codingad hoc networksenergyoptimization

Opportunities for emerging PV technologiesTo achieve the lowest possible levelized cost of electricity (LCOE) in a given location, a PV system should have a low upfront cost ($ W -1 ), long lifetime (years), high energy yield (kWh kW -1 per year), and low cost of capital (%) (Jones-Albertus et al 2016).These economic metrics directly drive the technology metrics and primary goals for PV research and development.The upfront system cost is composed of module and non-module (balance-ofsystem, or BOS) costs.Many BOS cost components-including the cost of inverters, grid interconnection, inspection, permitting, taxes, and business overhead, among others-cannot be directly mitigated by improving PV technology.However, PV researchers can still help reduce both module and selected non-module costs.To reduce module costs, one can use low-cost materials, reduce raw materials usage (g W -1 ), and reduce module manufacturing time, labor, equipment, and energy requirements ($ m -2 or $ W -1 ).To reduce non-module costs, one can increase the module solar-to-electric power conversion efficiency (% or W m -2 ) and simplify transportation and installation.The system lifetime depends on the rate at which system performance degrades and informs projections of the system's practical operating lifetime.To extend the module and system lifetime-specified as T80, the time to reach 80% of the initial power output-PV technology research aims to reduce cell and module degradation rates (%/year).Performance stability must be evaluated under different environmental and operating conditions, such as elevated humidity and temperature, temperature and voltage cycling, and varying illumination levels.The energy yield represents the real-world system performance, and is directly related to system performance metrics such as capacity factor and performance ratio (Jean et al 2015).To maximize energy yield, one can use solar tracking or develop strategies to improve cell and module power output under low light, off-angle illumination, spectral variations, high temperature, and other non-ideal conditions -ideally without increasing module costs substantially.

This paper investigates the feasibility of using a massive multiple-input multiple-output (MIMO) technology to enable integrated sensing and communication (ISAC) at the base-station (BS) and simultaneous energy harvesting at the users. A superimposed waveform is adopted to implement ISAC at the BS, and a power splitting protocol is employed at the users to implement simultaneous wireless information and power transfer (SWIPT). An efficient max-min based transmit power optimization algorithm is also proposed to maximize the data rate of the weakest user while ensuring a predefined signal-to-noise ratio to perform sensing tasks. The system performance in the presence of statistical channel state information is evaluated through the achievable sum rate, average harvested energy, and localization accuracy. Our numerical results unveil that the system performance metrics can be boosted subject to fundamental trade-offs among sensing, communication, and energy harvesting by using massive MIMO BSs.

In this paper we consider cooperative control of n dynamic agents to optimize an overall system performance metric. Due to limited communication resources, there exist structured interconnections among the agents and the interest is placed in synthesizing a suitably distributed control law to provide a given performance level. Based on a Youla-Kucera parameterization approach, the problem of designing a distributed controller to deliver given performance levels for different network topologies is convex in the Youla-Kucera parameter Q. Furthermore, if in addition to structured interconnections packet drops exist in the transmission of information, we provide convex conditions to guarantee mean square stability and system performance. The proposed method is also extended to deal with systems of triangular structures.

With increasing automation, the ‘human’ element in industrial systems is gradually being reduced, often for the sake of standardization. Complete automation, however, might not be optimal in complex, uncertain environments due to the dynamic and unstructured nature of interactions. Leveraging human perception and cognition can prove fruitful in making automated systems robust and sustainable. “Human-in-the-loop” (HITL) systems are systems which incorporate meaningful human interactions into the workflow. Agricultural Robotic Systems (ARS), developed for the timely detection and prevention of diseases in agricultural crops, are an example of cyber-physical systems where HITL augmentation can provide improved detection capabilities and system performance. Humans can apply their domain knowledge and diagnostic skills to fill in the knowledge gaps present in agricultural robotics and make them more resilient to variability. Owing to the multi-agent nature of ARS, HUB-CI, a collaborative platform for the optimization of interactions between agents is emulated to direct workflow logic. The challenge remains in designing and integrating human roles and tasks in the automated loop. This article explains the development of a HITL simulation for ARS, by first realistically modeling human agents, and exploring two different modes by which they can be integrated into the loop: Sequential, and Shared Integration. System performance metrics such as costs, number of tasks, and classification accuracy are measured and compared for different collaboration protocols. The results show the statistically significant advantages of HUB-CI protocols over the traditional protocols for each integration, while also discussing the competitive factors of both integration modes. Strengthening human modeling and expanding the range of human activities within the loop can help improve the practicality and accuracy of the simulation in replicating a HITL-ARS.