Real-time Performance Constraints Research Articles

Dynamic Resource Provisioning in Massively Multiplayer Online Games

Today's Massively Multiplayer Online Games (MMOGs) can include millions of concurrent players spread across the world and interacting with each other within a single session. Faced with high resource demand variability and with misfit resource renting policies, the current industry practice is to overprovision for each game tens of self-owned data centers, making the market entry affordable only for big companies. Focusing on the reduction of entry and operational costs, we investigate a new dynamic resource provisioning method for MMOG operation using external data centers as low-cost resource providers. First, we identify in the various types of player interaction a source of short-term load variability, which complements the long-term load variability due to the size of the player population. Then, we introduce a combined MMOG processor, network, and memory load model that takes into account both the player interaction type and the population size. Our model is best used for estimating the MMOG resource demand dynamically, and thus, for dynamic resource provisioning based on the game world entity distribution. We evaluate several classes of online predictors for MMOG entity distribution and propose and tune a neural network-based predictor to deliver good accuracy consistently under real-time performance constraints. We assess using trace-based simulation the impact of the data center policies on the quality of resource provisioning. We find that the dynamic resource provisioning can be much more efficient than its static alternative even when the external data centers are busy, and that data centers with policies unsuitable for MMOGs are penalized by our dynamic resource provisioning method. Finally, we present experimental results showing the real-time parallelization and load balancing of a real game prototype using data center resources provisioned using our method and show its advantage against a rudimentary client threshold approach.

Active Multicamera Networks: From Rendering to Surveillance

Active multicamera networks have a large and growing application base. On one end of the spectrum, active camera networks are being used to enhance the rendering or modeling of a single scene. Here, the many simultaneous views can be used to render a synthetic real time view even in a dynamically changing environment. The technical challenges include depth estimation within the scene, image correlation between multiple camera views, and manipulating the camera nodes in order to improve the rendered image. Active multicamera networks are also being used to enhance surveillance applications for which a large area needs to be monitored. In these systems, a primary focus is tracking objects both within a single video feed as well as throughout a collection of video feeds. Active components are used to monitor larger areas and provide more continuous coverage of moving targets. Regardless of the application, real time processing constraints and bandwidth limitations constitute a significant problem with large networked camera systems.

Model-based mapping of reconfigurable image registration on FPGA platforms

Image registration is a computationally intensive application in the medical imaging domain that places stringent requirements on performance and memory management efficiency. This paper develops techniques for mapping rigid image registration applications onto configurable hardware under real-time performance constraints. Building on the framework of homogeneous parameterized dataflow, which provides an effective formal model of design and analysis of hardware and software for signal processing applications, we develop novel methods for representing and exploring the hardware design space when mapping image registration algorithms onto configurable hardware. Our techniques result in an efficient framework for trading off performance and configurable hardware resource usage based on the constraints of a given application. Based on trends that we have observed when applying these techniques, we also present a novel architecture that enables dynamically-reconfigurable image registration. This proposed architecture has the ability to tune its parallel processing structure adaptively based on relevant characteristics of the input images.

A COGNITIVE MODEL OF IMITATIVE DEVELOPMENT IN HUMANS AND MACHINES

Several algorithms and models have recently been proposed for imitation learning in humans and robots. However, few proposals offer a framework for imitation learning in noisy stochastic environments where the imitator must learn and act under real-time performance constraints. We present a novel probabilistic framework for imitation learning in stochastic environments with unreliable sensors. Bayesian algorithms, based on Meltzoff and Moore's AIM hypothesis for action imitation, implement the core of an imitation learning framework. Our algorithms are computationally efficient, allowing real-time learning and imitation in an active stereo vision robotic head and on a humanoid robot. We present simulated and real-world robotics results demonstrating the viability of our approach. We conclude by advocating a research agenda that promotes interaction between cognitive and robotic studies of imitation.

Optimal Dynamic Voltage Scaling in Energy-Limited Nonpreemptive Systems with Real-Time Constraints

Dynamic voltage scaling is used in energy-limited systems as a means of conserving energy and prolonging their life. We consider a setting in which the tasks performed by such a system are nonpreemptive and aperiodic. Our objective is to control the processing rate over different tasks so as to minimize energy subject to hard real-time processing constraints. Under any given task scheduling policy, we prove that the optimal solution to the offline version of the problem can be efficiently obtained by exploiting the structure of optimal sample paths, leading to a new dynamic voltage scaling algorithm termed the critical task decomposition algorithm (CTDA). The efficiency of the algorithm rests on the existence of a set of critical tasks that decompose the optimal sample path into decoupled segments within which optimal processing times are easily determined. The algorithm is readily extended to an online version of the problem as well. Its worst-case complexity of both offline and online problems is O(N <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> )

Low-complexity iterative multiuser detection and decoding for real-time applications

This paper presents a low-complexity multiuser decoding technique that can be implemented in real time for a convolutionally coded direct sequence code division multiple access (DS-CDMA) system. The main contribution, denoted here as the iterative prior update (IPU), consists of iterative interference cancellation and prior updates on sequences of coded bits combined with M-algorithm and list decoding. We illustrate performance gains over other low-complexity sequence detection and decoding strategies and argue that the algorithm converges within a few iterations and requires only a small size buffer for keeping track of the priors along iterations. The fact that the we can use existing available architectures for Viterbi decoding with slight modifications and can meet the real-time processing constraints makes the IPU algorithm an attractive alternative for cellular systems.

Adaptable thermal compensation system for strain gage sensors based on programmable chip

This paper presents a new design of an adaptable system for strain gage sensors based on a programmable chip. The system is designed, in particular, to meet the requirement of flexibility and complex computations in the field of thermal compensation algorithms of strain gages. To satisfy the real-time processing constraints and system parameterization, parts of the processing algorithm are implemented in hardware and others are implemented in software. This new architecture, consisting of hardware and software functionalities, is implemented on a Field programmable gate array (FPGA) including a core processor. FPGA is used as a hardware target, thanks to its high flexibility and reconfiguration. A description of the methodology developed for the thermal compensation of the system is reported. Tradeoffs between time consumption and efficiency are studied through several methodologies. Experimental results and an analysis for three different sensors in the temperature scale −30 to +150 °C are presented.

Parallel Implementation of a Class of Adaptive Signal Processing Applications

Recently, High Performance Computing (HPC) platforms have been employed to realize many computationally demanding applications in signal and image processing. These applications require real-time performance constraints to be met. These constraints include latency as well as throughput. In order to meet these performance requirements, efficient parallel algorithms are needed. These algorithms must be engineered to exploit the computational characteristics of such applications. In this paper we present a methodology for mapping a class of adaptive signal processing applications onto HPC platforms such that the throughput performance is optimized. We first define a new task model using the salient computational characteristics of a class of adaptive signal processing applications. Based on this task model, we propose a new execution model. In the earlier linear pipelined execution model, the task mapping choices were restricted. The new model permits flexible task mapping choices, leading to improved throughput performance compared with the previous model. Using the new model, a three-step task mapping methodology is developed. It consists of (1) a data remapping step, (2) a coarse resource allocation step, and (3) a fine performance tuning step. The methodology is demonstrated by designing parallel algorithms for modern radar and sonar signal processing applications. These are implemented on IBM SP2 and Cray T3E, state-of-the-art HPC platforms, to show the effectiveness of our approach. Experimental results show significant performance improvement over those obtained by previous approaches. Our code is written using C and the Message Passing Interface (MPI). Thus, it is portable across various HPC platforms.

Echo Cancellation By Deforming Sound Waves Through Inverse Convolution

This study concerns the mathematical modelling of speech related acoustical hearing discomforts in medium to large environments (e.g. auditoriums and concert halls). Discomforts arise from both phase distortion (echo) and perceived frequency transformations (Comb filters) Bonwick[1], Ando[3]. These distortions are caused by the physical constraints imposed on sound transmission within large environments. Deconvolution was applied to the measured signals to recover the original sound from the environmentally distorted sound, thus separating it from the transformation system function (Room impulse response). Several samples were used for different positions within the hall. The different methods investigated for the deconvolution process were cross-correlation, cepstrum and adaptive filter techniques, Oppenheim[2]. Upon separation the inverse of the system transfer function was determined and used in the latter part of the study to pre-deform the original sound. Only a part of the system response was used. The first 20 - 30 ms constructively contribute to the intelligibility of speech and masked towards the end of the impulse response by the background noise at 40 dBA, Borwick[1]. The critical part between 0.020 and 2 sec was then used as the basis of the filter algorithm design. Because of the length of this part of thc impulse response as well as the real time processing constraints a FIR filter could not be implemented, Ando[3], Tohyama[4]. Instead the filter was designed in the frequency domain. During transmission the environment now acting on the pre-deformed (filtered) sound renders better quality speech that is easier to understand, effectively removing part of the deformation. Finally the psychoacoustics of the total system were evaluated by a panel of listeners in the auditorium (concert hall) and the system was found to be effective for some targeted arcas within the particular hall. The impact of these results for addressing acoustical problems will be discussed.