Saturation Estimation In Interactive Computer Systems

We study a mechanism design problem under the assumption that renegotiation cannot be prevented. We investigate what kind of equilibria of which mechanisms are renegotiation-proof under a variety of renegotiation procedures, and which social choice functions can be implemented in a way that is renegotiation-proof. In complete information environments, we show that the set of ex post renegotiation-proof implementable social choice functions contains all ex post efficient allocations when there at least three agents, but only budget balanced Groves allocations when there are two agents. In incomplete information environments with correlated beliefs and at least three agents, every ex post efficient social choice function can be implemented in the presence of ex post renegotiation, but with independent private values only social choice functions that are given by budget balanced “Groves in expectations” mechanisms are implementable in such a way. We further show that the requirement of interim renegotiation-proofness does not impose additional restrictions on implementable social choice functions under complete information, but is likely to impose additional restrictions under incomplete information.

This paper presents the results of applying time series forecasting techniques to the forecasting of maintenance work load. In particular, this paper discusses several models developed to forecast the electronics maintenance work load for a weather forecasting system located in Alaska. The maintenance work load for this system typically increases during the winter season. This is mostly due to the system's remote geographic locations and the additional travel time required to reach these locations during the harsh Alaskan winter. Several models were developed and evaluated on the basis of their data fit and forecasting accuracy of seasonal and non-seasonal electronics maintenance work load. In the first part of the analysis, a regression model that uses a serial autocorrelated error correction procedure was developed to model the non-seasonal components of the work load. Seasonal work load components were modeled using seasonal and cyclical indicator variables. The cyclical indicator variables were effective in modeling this system's seasonal work load behavior. A model that uses a combination of seasonal and cyclical indicator variables was also effective in this respect. In the second part of the analysis, seasonal autoregressive integrated moving average (ARIMA) techniques were used to model and forecast maintenance work load. A brief description of these forecasting methods and the procedures used to identify an optimal work load forecasting model are provided. Two seasonal ARIMA models were developed: The first model used only maintenance predictor variables; the second model used a combination of maintenance predictor variables and cyclical indicator variables. All of the models were evaluated on the basis of their goodness-of-fit and forecasting accuracy. A seasonal ARIMA model that uses a combination of maintenance predictor variables and cyclical indicator variables had the best goodness-of-fit and provided the most accurate maintenance work load forecast. Cyclical indicator variables were found to be extremely effective in modeling the seasonal behavior of the maintenance work load in both the causal and stochastic models.

An integrated module-based reasoning and axiomatic design approach for new product design under incomplete information environment

Dear SCPE Reader, We present a selection of papers which are extensions of papers presented at the 7-th International Symposium on Parallel and Distributed Computing, 1–5 July 2008, in Krakow, Poland. The motivation for publishing the selection in the SCPE Journal was, on the one hand, to present the flavour of the research reported at the conference and on the other hand to present some of the most relevant topics currently focused on the research on parallel and distributed computing in general. The selection contains only 6 papers out of about 60 presented at the conference, and thus, is far from covering all relevant topics represented at the ISPDC 2008. This is because not all of the invited authors were patient enough to accept a fairly long paper publishing process. Nevertheless, we hope that the presented papers will bring you closer to the research covered by the ISPDC conferences and will encourage you to participate in future ISPDC editions. The first paper ``The Impact of Workload Variability on Load Balancing Algorithms'' is by Marta Beltran and Antonio Guzman from King Juan Carlos University in Spain. It concerns an important topic of load balancing in cluster systems, namely adaptativity of the load balancing algorithms to changes of the workload in the system. Adequate accounting for additional load in the hosting system is of great relevance for correct optimization effects. The paper presents a thorough formal analysis of the workload variability metrics and their influence on the quality of load balancing algorithms. Four basic activities appearing in load balancing algorithms are identified, and based on them some algorithmic solutions are proposed to correctly deal with workload variability in system load balancing. The problem of dynamic load balancing algorithms robustness has been discussed. Two different robustness metrics sensitive to the applied type of opimization: local task-oriented or a global one enable selecting task remote execution or migration as load balancing operations. The proposed approach is illustrated with experiments. The second paper ``Model-Driven Engineering and Formal Validation of High-Performance Embedded Systems'' is by Abdoulaye Gamatie, Eric Rutten, Huafeng Yu, Pierre Boulet, Jean-Luc Dekeyser, from University of Lille and INRIA in France. The paper is concerned with a very advanced methodology of designing correct parallel embedded systems for intensive data-parallel computing. In their previous research, the authors of the paper designed the GASPARD embedded system design framework. It is based on the hardware/software co-design approach through model-driven engineering. The framework is based on an UML-like model specification language in which hardware and software elements are modelled using a component approach with special mechanisms for repetitive structures. This paper tries to combine the modelling framework of GASPARD with the mechanisms of synchronous languages to achieve design verifiability provided for such languages. The paper shows how GASPARD models can be translated into synchronous models based on data flow equations in order to formally check their correctness. The proposed approach is illustrated with an example of a video processing system. The third paper ``Relations Between Several Parallel Computational Models'' is by Stephan Bruda and Yuanqiao Zhang from Bishop’s University in Canada. The paper is concerned with theoretical aspects of shared memory systems described by the parallel random access machine PRAM model and aims in studying performance properties of different types of PRAM systems. The attention is focussed on analysing the computational power of two more sophisticated PRAM models (Combining CRCW and Broadcast Selective Reduction), which include data reduction in case of concurrent writes. The paper shows that these two models have equivalent computational power, which is a new result comparing the existing literature. The performance of both models applied to reconfigurable multiple bus machines was studied as a possible architectural solution for current VLSI processor implementations. It was shown that in such systems under reasonable assumptions concurrent-write does not enhance performance comparing the exclusive-write model. Another result important for the VLSI technology is that the Combining CRCW PRAM model (in which data of concurrent writes are arthmetically or logically combined before write) and the exclusive-write on directed reconfigurable busses perform in equivalent way under strong real-time requirements. The fourth paper ``Experiences with Mesh-Like Computations Using Prediction Binary Trees'' is by Gennaro Cordasco, Biagio Cosenza, Rosario de Chiara, Ugo Erra and Vittorio Scarano from the University ``degli Studi'' of Salerno and the University ``degli Studi della Biasilicata'' of Potenza in Italy. The paper concerns optimization methods for mesh-like computations in clusters of processors. The computations are perfomed assuming a phase-like program execution control using a tiling approach which reduces inter-processor communication. A temporal coherence is also assumed, which means that task sizes provide similar execution times in consecutive phases. Temporary coherent computations are structured in a Prediction Binary Tree, in which leaves represent computing tiles to be mapped to processors. A phase-by-phase semi-static load balancing is introduced to the scheduling algorithm. The scheduling algorithm is equipped with a predictor, which estimates the computation time of next phase tiles based on previous execution times and modifies the tiles to achieve balanced execution in phases. For this, two heuristics are used to leverage on data locality in processors. The proposed approach is illustrated by the example of interactive rendering with Parallel Ray Tracing algorithm. The fifth paper ``The Influence of the IBM pSeries Servers Virtualization Mechanism on Dynamic Resource Allocation in AIX 5L'' is by Maciej Mlynski from ASpartner Limited in Poland. The paper concerns a very up-to-date problem of system virtualization and presents the results of research carried on IBM pSeries servers. IBM is strongly developing the virtualization technique especially on IBM pSeries servers enabling an improved and flexible sharing of system resources between applications. The paper investigates novel facilities for dynamic resource management such as micro-partitioning and partition load manager. They enable dynamic creation of workload logical partitions of system resources and their dynamic mangement. It includes run-time resource re-alocation between logical partitions including setting of sharing specifications as well as run-time adding/removing/setting parameters of resources in the system. It remains an open question how to properly tune parameters of the operating system using the provided virtualization facilities to obtain the best efficiency for a given application program. The paper presents the results of experiments which study the effects of tuning the disk subsystem parameters under the IBM AIX 5L operating system with the use of the provided virtualization facilities on the resulting application execution performance. The results show that even small deterioration in the resource pool status requires an immediate adaptation of the operating system parameters to maintain the required performance. The sixth paper ``HeteroPBLAS: A Set of Parallel Basic Linear Algebra Subprograms Optimized for Heterogeneous Computational Clusters'' is by Ravi Reddy, Alexey Lastovetsky and Pedro Alonso from University College Dublin in Ireland and Polytechnic University of Valencia in Spain. The paper concerns the methodology for parallelization of linear algebra computations for execution in heterogeneous cluster environments. The design of the HeteroPBLAS library (Parallel Basic Linear Algebra Subprograms) for heterogeneous computational clusters is presented. The main contribution of the paper is the automation of the parallelization and optimization of the PBLAS, which is done by means of a special user interface and the underlying set of functions. An important element is here a performance model that is based on program code instrumentation, which determines parameters of the application and the executive heterogeneous platform relevant for execution performance of parallel code. The parameter values specified for or returned by execution of the performance model functions are next used for generation and optimal mapping of the parallel code of the library subroutines. The proposed approach is illustrated by experimental results of execution of optimized HeteroPBLAS programs on homogeneous and heterogeneous computing clusters. Marek Tudruj

Fifty subjects were given fatiguing tests at two work loads on a horizontal arm-crank friction ergometer.2 The initial speed was 120 rpm; the test continued for 10 min. Both fatigue curves were S-shaped—the rate of work dropped off slowly at first, then went through a rapid drop-off phase followed by a slow drop-off phase that approached an asymptotic steady state. The mathematical form was a two-component exponential equation which fitted the observed data very closely. Using a 2-k. work load, there was 22 percent decrement at the end of the test. Using a 3.45-k. work load (1 week later), the decrement was 48 percent. Test-retest reliability of individual differences in drop-off was moderately high (r = .86 and .85 for the two work loads). However, the correlation between drop-offs for the two work loads was only r = .61 and even after correction for attenuation was only .71. Thus only 50 percent of the individual difference variance in fatigability was common to the two work loads, while 50 percent was specific to a particular work load.

Exploitation vs. exploration: choosing a supplier in an environment of incomplete information

The aim of this paper is to show that strategic interaction provides a fruitful way to understand China’s decision-making behavior. States are actors in global politics and frequently have to make choices that are strategic. Credibility and uncertainty are central issues in this environment of incomplete information. This raises the question how China signals its motivations along this strategic chessboard. Strategic interaction assumes purposeful behavior from China in the African security environment. Signaling allows China to consider the problem of unknown motivations, making choices based in part, on what other actors are likely to do in the future. Knowing the choices of other African actors can help China judge what likely responses are.

Chapter 9 - Nanotechnology Risk Management: An Insurance Industry Perspective

The Gittins scheduling policy minimizes the mean response in the single-server M/G/1 queue in a wide variety of settings. Most famously, Gittins is optimal when preemption is allowed and service requirements are unknown but drawn from a known distribution. Gittins is also optimal much more generally, adapting to any amount of available information and any preemption restrictions. However, scheduling to minimize mean response time in a multiserver setting, specifically the central-queue M/G/k, is a much more difficult problem. In this work we give the first general analysis of Gittins in the M/G/k. Specifically, we show that under extremely general conditions, Gittins's mean response time in the M/G/k is at most its mean response time in the M/G/1 plus an O(log(1/(1 - ρ))) additive term, where ρ is the system load. A consequence of this result is that Gittins is heavy-traffic optimal in the M/G/k if the service requirement distribution S satisfies E[S2(log S)+] < ∞. This is the most general result on minimizing mean response time in the M/G/k to date. To prove our results, we combine properties of the Gittins policy and Palm calculus in a novel way. Notably, our technique overcomes the limitations of tagged job methods that were used in prior scheduling analyses.

Mobile healthcare applications offer new opportunities to prevent long-term health damage due to increased mental workload by continuously monitoring physiological signs related to prolonged high workload and providing just-in-time feedback. In order to achieve a day-by-day quantification of mental load, first different load levels which occur during a workday have to be discriminated. This work goes one step towards this goal: we present our experiment design and preliminary results in discriminating different levels of mental workload based on heart rate features obtained from a mobile ECG system. Based on the subjective ratings of the participants under study, we show that all participants perceived the induced load levels as intended from the experiment design. The heart rate variability (HRV) features under investigation could be classified into two distinct groups. Features in the first group, representing markers associated with parasympathetic nervous activity, show a decrease in their values with increased workload. Features in the second group, representing markers associated with sympathetic nervous activity, show an increase of their values with increased workload. These results provide evidence that a mobile ECG system is suited to discriminate different levels of mental workload. This would enable the development of mobile applications to monitor mental workload levels and to prevent long-term damage by giving early warning signs in case of prolonged high workload.

Multi-tiered storage, where each tier comprises one type of storage device, e.g., SSD, HDD, is a commonly used approach to achieve both high performance and cost efficiency in large-scale systems that need to store data with vastly different access characteristics. By aligning the access characteristics of the data to the characteristics of the storage devices, higher performance can be achieved for any given cost. This article presents ExaPlan, a method to determine both the data-to-tier assignment and the number of devices in each tier that minimize the system's mean response time for a given budget and workload. In contrast to other methods that constrain or minimize the system load, ExaPlan directly minimizes the system's mean response time estimated by a queueing model. Minimizing the mean response time is typically intractable as the resulting optimization problem is both non-convex and combinatorial in nature. ExaPlan circumvents this intractability by introducing a parameterized data-placement approach that makes it a highly scalable method that can be easily applied to exascale systems. Through experiments that use parameters from real-world storage systems, such as CERN and LOFAR, it is demonstrated that ExaPlan provides solutions that yield lower mean response times than previous works. It is also capable of determining a data-to-tier assignment both at the level of files and at the level of fixed-size extents. For some of the workloads evaluated, file-level placement exhibited a significant performance improvement over extent-level placement.

The article investigates the impact of modern cloud technologies on the efficiency of DevOps processes, which are key to the automation, flexibility and reliability of software development. With the rapid development of information technology, cloud platforms play an important role in accelerating software product development, testing and deployment. Implementing DevOps in the cloud environment reduces the time to market, increases the stability of software systems and optimizes the use of computing resources, which is critical for modern companies. This paper discusses DevOps teams' main challenges, including the need to scale infrastructure rapidly, ensure continuous integration and delivery (CI/CD), automate testing, monitor performance, and optimize costs. Particular attention is paid to security problems, configuration management and the human factor minimization when implementing program code changes. A comparative analysis of the three leading cloud platforms - Amazon Web Services, Google Cloud Platform, and Microsoft Azure – is carried out in the context of their impact on DevOps processes. The possibilities of automation, support for container technologies, infrastructure scalability, monitoring tools, and integration with CI/CD pipelines are evaluated. AWS was found to offer the broadest set of DevOps tools with a high level of automation, making it an attractive choice for organizations focused on complex enterprise solutions. Google Cloud Platform demonstrates the best support for Kubernetes and containerization, an essential factor for teams working with microservices architecture. Microsoft Azure provides the most profound integration with the Microsoft ecosystem and is the best choice for companies that use Windows and related products. An experimental study has shown that the choice of a cloud platform significantly impacts the speed of release cycles, the stability of the deployed infrastructure, the ability to respond quickly to changes in load, and the productivity of DevOps teams in general. Recommendations for choosing the optimal cloud platform are proposed depending on the specifics of the project, the scale of the organization, the level of system load, and automation requirements.

A bi-level multi-objective environmental economic dispatch model with wind farms is established based on decomposition coordination principle and users’ side resources. Considering the level of system load and users’ satisfaction degree, a multi-objective optimization mathematical model was established to determine fitting load of the system and time sharing price. Considering the randomness of wind power output, a multi-objective environmental economic dispatch model is established to determine the generation plan. A hybrid universal gravitation algorithm and tabu search algorithm is proposed to solve the model. The optimal scheduling scheme is determined by the method of approximate ideal solution based on entropy weight. Simulation results show that: Considering the influence of resources of source side in the scheduling process not only can improve load level of the system operation, the satisfaction of using electricity and absorptive capacity of wind power, but also can effectively in reduce the generation costs and pollution emissions.

Despite the physiological response of the body to heat has been modeled and described well, the effects of heat stress on cognitive ability are still not well understood. This study aims at measuring mental work load and specifically the effects of hot and humid weather on the level of mental work load among managers and supervisors working on a project of South Pars phases in Bushier (one of the province in south of Iran) . This study is descriptive---analytic and was carried out in two phases: the first phase in winter (February and March 2012) and the second one in summer (July and August 2013). A total of 98 managers and supervisors completed the questionnaires. Method of implementing was the same in both phases, and the same sample took part in both phases. The temperature and humidity information were obtained from synoptic stations. Results of this study showed that there is a direct relationship between the prevailing weather conditions in summer and mental work load. With increasing temperature and relative humidity and in sultry conditions, mental work load of managers and supervisors increased. Based on the results obtained, mental work load of managers and supervisors in South Pars phase projects is high. Nowadays, along with new jobs, work environments are getting more complex and require higher mental activities. Therefore, in addition to assessing physical work load, evaluating mental work load is very important. Also, to reduce mental work load, identifying its causes and factors affecting it is essential. Climate is a potential factor influencing mental work load.

A fuzzy set approach to the problem of sustainability