14201193
Adaptive time-critical resource management using time/utility functions: past, present, and future
Time/utility function time constraints (or TUFs) and utility accrual (UA) scheduling optimality criteria, constitute, arguably, the most effective and broadest approach for adaptive, dynamic time-critical resource management. A TUF, which is a generalization of the classical deadline constraint, specifies the utility of completing an application activity as an application-or situation-specific function of that activity's completion time. With TUF time constraints, timeliness optimality criteria can be specified in terms of accrued (e.g., summed) activity utilities. This work overviews past and recent advances on adaptive resource management for dynamic time-critical systems using UA algorithms. Emerging challenges and new research directions are also identified.
14712997
Proactive QoS negotiation in asynchronous real-time distributed systems
We present a fast, proactive, quality of service (QoS) negotiation algorithm called Best Effort Negotiation (or BEN), for asynchronous real-time distributed systems. BEN considers an application model where trans-node application timeliness and fault-tolerance requirements are expressed using benefit functions, and anticipated workload and system failure rates during future time intervals are expressed using adaptation functions and reliability functions, respectively. Furthermore, BEN considers an adaptation model where subtasks of application tasks are replicated at run-time for tolerating failures as well as for sharing workload increases. Given such models, the objective of the algorithm is to maximize the sum of aggregate real-time and fault-tolerance benefits during the time window of adaptation functions. Since determining the optimal solution is computationally intractable, BEN heuristically computer sub-optimal resource allocations in polynomial-time. To determine how well BEN performs, we describe another algorithm called HLC, that is inspired by the well-known Hill Climbing heuristic. We show that HLC is significantly slower than BEN. However, our experimental studies reveal that the performance of BEN, in general, is as good as that of HLC.
13992284
CPU scheduling for statistically-assured real-time performance and improved energy efficiency
We present a CPU scheduling algorithm, called Energy-efficient Utility Accrual Algorithm (or EUA), for battery-powered, embedded real-time systems. We consider an embedded software application model where repeatedly occurring application activities are subject to deadline constraints specified using step time/utility functions. For battery-powered embedded systems, system-level energy consumption is also a primary concern. We consider CPU scheduling that (1) provides assurances on individual and collective application timeliness behaviors and (2) maximizes system-level timeliness and energy efficiency. Since the scheduling problem is intractable, EUA heuristically computes CPU schedules with a polynomial-time cost. Several properties of EUA are analytically established, including timeliness optimality during under-load situations and statistical assurances on timeliness behavior. Further, our simulation results confirm EUA's superior performance.
16507795
DPR, LPR: proactive resource allocation algorithms for asynchronous real-time distributed systems
We present two proactive resource allocation algorithms, called DPR and LPR, for satisfying the timeliness requirements of real-time tasks in asynchronous real-time distributed systems. The algorithms are proactive in the sense that they allow application-specified and user-triggered resource allocation by allowing anticipated task workloads to be specified for future time intervals. When proactively triggered, the algorithms allocate resources to maximize the aggregate deadline-satisfied ratio for the future time interval under the anticipated workload. While DPR uses the earliest deadline first scheduling algorithm as the underlying algorithm for process scheduling and packet scheduling, LPR uses a modified least laxity first scheduling algorithm. We show that LPR is computationally more expensive than DPR. Further, our experimental studies reveal that LPR yields a higher deadline-satisfied ratio than DPR.
6241696
A formally verified application-level framework for real-time scheduling on POSIX real-time operating systems
We present a framework, called meta scheduler, for implementing real-time scheduling algorithms. The meta scheduler is a portable middleware layer component designed for implementations over POSIX-compliant operating systems. It accommodates pluggable real-time scheduling algorithms while offering the flexibility of platform independence - the singular underlying OS requirement is the now nearly ubiquitous POSIX compliance. The versatility of pluggable schedulers positions the meta scheduler for deployment in an interoperable heterogeneous real-time environment. We present the design of the meta scheduler and outline its implementation. Furthermore, we present a mechanized correctness verification using the UPPAAL model checker. Prototype implementation of the meta scheduler over QNX Neutrino real-time operating system demonstrates high performance and a small footprint.
51883995
Energy-efficient, utility accrual scheduling under resource constraints for mobile embedded systems
We present an energy-efficient real-time scheduling algorithm called the Resource-constrained Energy-Efficient Utility Accrual Algorithm (or ReUA). ReUA considers an application model where activities are subject to time/utility function-time constraints, resource dependencies including mutual exclusion constraints, and statistical performance requirements including probabilistically satisfied, activity (timeliness) utility bounds. Further, ReUA targets mobile embedded systems where system-level energy consumption is a major concern. For such a model, we consider the scheduling objectives of (1) satisfying statistical performance requirements, and (2) maximizing system-level energy efficiency, while respecting resource dependencies. Since the problem is NP-hard, ReUA allocates resources using statistical properties of application cycle demands and heuristically computes schedules with a polynomial-time cost. We analytically establish several timeliness and non-timeliness properties of the algorithm. Further, our simulation experiments illustrate ReUA's effectiveness.
12395366
Image Cues Fusion for Object Tracking Based on Particle Filter
Particle filter is a powerful algorithm to deal with non-linear and non-Gaussian tracking problems. However the algorithm relying only upon one image cue often fails in challenging scenarios. To overcome this, the paper first presents a color likelihood to capture color distribution of the object based on Bhattacharry coefficient, and a structure likelihood representing high level knowledge regarding the object. Together with the widely used edge likelihood, the paper further proposes a straightforward image cues fusion for object tracking in the framework of particle filter, under assumption that the visual measurement of each image cue is independent of each other. The experiments on real image sequences have shown that the method is effective, robust to illumination changes, pose variations and complex background.
6463215
Design and implementation of parallel video encoding strategies using divisible load analysis
The processing time needed for motion estimation usually accounts for a significant part of the overall processing time of the video encoder. To improve the video encoding speed, reducing the execution time for motion estimation process is essential. Parallel implementation of video encoding systems using either the software or the hardware approach has attracted much attention in the area of real time video coding. In this paper, we attempt to implement a video encoder on a bus network. Usually, for such a parallel system, the key concern is associated with partitioning and balancing of the computational load among the processors such that the overall processing time of the video encoder is minimized. With the use of the divisible load theory (DLT) paradigm, a strip-wise load partitioning/balancing scheme, a load distribution strategy, two implementation strategies are developed to exploit the data parallelism inherent in the video encoding process. The striking feature of our design is that,both the granularity of the load partitions and all the associated overheads caused during parallel video encoding process can be explicitly considered. This significantly contributes to the minimization of the overall processing time of the video encoder. Extensive experimental studies are carried out to test the effectiveness of the proposed strategies. The performance of the parallel video encoder is quantified using the metrics speedup and performance gain, respectively. The experimental results show that our strategies are effective for exploiting the available parallelism inherent in the video encoding process and provide a theoretical insight on how to analytically quantify and minimize the overall processing time of a parallel system. The proposed strategies can be easily extended and applied to improve other existing parallel systems.
9166431
Practical information flow control in Web-based information systems
This paper presents a practical application of language-based information-flow control, namely, a domain-specific Web scripting language designed for interfacing with databases. The primary goal is to provide strong enforcement of confidentiality and integrity policies: confidential data can be released only in permitted ways and trustworthy data must result from expected computations or conform to expected patterns. Such security policies are specified in the database layer and statically enforced for the rest of the system in an end-to-end fashion. In contrast with existing Web-scripting languages, which provide only ad hoc mechanisms for information security, the scripting language described here uses principles based on the well-studied techniques in information-flow type systems. However, because Web scripts often need to downgrade confidential data and manipulated untrusted user input, they require practical and convenient ways of downgrading secure data. To achieve this goal, the language allows safe downgrading according to downgrading policies specified by the programmer. This novel, pattern-based approach provides a practical instance of recent work on delimited release and relaxed noninterference and extends that work by accounting for integrity policies.
9618917
Towards A Robust and Real-time Face Detection and Tracking Framework
Traditional research and development in face detection and tracking are mostly focused on either face detection in still images, or face tracking in image streams, without remarkable combination of both. As a result, few impressive face detection and tracking systems can be seen nowadays. The paper contributes to effectively incorporation of two modules, detection module and tracking module, into one integrated and coupled framework. In the detection module we construct AdaBoost detectors for face detection in video stream, which are able to detect multi-view faces and are robust to illumination changes in video stream. The tracking module is based on the improved mean shift tracking, which has advantage of target model adaption. The detection module, triggered every ten frames, is not only responsible for initiating a tracking thread, but also, together with tracking module, contributes to target model update. The system is able to benefit from the strengths of different techniques and overcome their respective limitations. Experiments on real image sequences show that the system is robust and real-time in realistic environment.
42009299
Probabilistic Object Tracking Based on Machine Learning and Importance Sampling
The paper presents a novel particle filtering framework for visual object tracking. One of the contributions is the development of a likelihood function based on one of machine learning algorithm–AdaBoost algorithm. The likelihood function can capture the structure characteristics of one class of objects, and is thus robust to clutters and noise in the complex background. The other contribution is the adoption of mean shift iteration as a proposal distribution, which can steer discrete samples towards regions which most likely contain the targets, and is therefore leading to computational efficiency in the algorithm. The effectiveness of such a framework is demonstrated with a particular class of objects–human faces.