34096561
Distributed Evolutionary Algorithm Search for Multiobjective Spanning Tree Problem
The problem of computing spanning trees along with specific constraints is mostly NP-hard. Many approximation and stochastic algorithms which yield a single solution, have been proposed. Essentially, the problem is multi-objective in nature, and a major challenge to solve the problem is to capture possibly all the (representative) equivalent and diverse solutions at convergence. In this paper, we formulate the generic multi-objective spanning tree (MOST) problem, and attempt to solve, in a novel way, with evolutionary algorithm (EA). We consider, without loss of generality, edge-cost and diameter as the two objectives. Since the problem is hard, and the Pareto-front is unknown, the main issue in such problem-instances is how to assess the convergence. We use a multiobjective evolutionary algorithm (MOEA) that produces diverse solutions without needing a priori knowledge of the solution space. We employ a distributed version of the algorithm and generate solutions from multiple tribes in order to have some approximation of the movement of the solution front. Since no experimental results are available for MOST, we consider two well known diameter-constrained spanning tree algorithms and modify them, for fair comparison, to yield a near-optimal solution-front. We observe that EA could provide superior solutions in the entire-range of the Pareto-front, which none of the existing algorithms could do.
6342150
Frame based fair multiprocessor scheduler: a fast fair algorithm for real-time embedded systems
This paper presents frame based fair multiprocessor scheduler (FBFMS) which provides accurate real-time proportional fair scheduling for a set of dynamic tasks on a symmetric multiprocessor environment with O(1) scheduling overhead. FBFMS meets these seemingly contradictory goals by applying the benefits of virtual time round-robin scheduling mechanism along with a frame based scheduling approach. Simulation results show that the algorithm provides 2.5 to 26 times speedup (over O(lg n) complexity schedulers) with distortion in fairness less than 1% in most cases, and less than 15% in the worst case.
24684227
Adaptive Control of Anytime Algorithm Parameters
39741624
An Efficient Technique for Dynamic Slicing of Concurrent Java Programs
Program slice has many applications such as program debugging, testing, maintenance and complexity measurement. We propose a new dynamic program slicing technique for concurrent Java programs that is more efficient than the related algorithms. We introduce the notion of Concurrent Program Dependence Graph (CPDG). Our algorithm uses CPDG as the intermediate representation and is based on marking and unmarking the edges in the CPDG as and when the dependencies arise and cease during run-time. Our approach eliminates the use of trace files and is more efficient than the existing algorithms.
537233
Evolutionary Multimodal Optimization Revisited
We revisit a class of multimodal function optimizations using evolutionary algorithms reformulated into a multiobjective framework where previous implementations have needed niching/sharing to ensure diversity. In this paper, we use a steady-state multiobjective algorithm which preserves diversity without niching to produce diverse sampling of the Pareto-front with significantly lower computational effort.
5790802
Object-oriented symbol management in syntax-directed compiler systems
Each symbol in a source program has a series of attributes that govern the operations performed on it. In this paper, we explore object-oriented techniques for representing the attributes of symbols and translating the operations performed on them. This approach is shown superior in memory and run-time speed to the procedural style traditionally used for writing compilers. We demonstrate object-oriented symbol management in a compiler for an arithmetic subset of C. We have used C++ --- a single-dispatching language --- to implement the same, but we emphasize the need for multiple dispatching to develop programs in a heavily-object-oriented style.
13974246
Decentralized failure diagnosis of discrete event systems
By decentralized diagnosis we mean diagnosis using multiple diagnosers, each possessing its own set of sensors, without involving any communication among diagnosers or to any coordinators. The notion of decentralized diagnosis is formalized by introducing the notion of codiagnosability that requires that a failure be detected by one of the diagnosers within a bounded delay. Algorithms of complexity polynomial in the size of the system and the nonfault specification are provided for: 1) testing codiagnosability, 2) computing the bound in delay of diagnosis, 3) offline synthesis of individual diagnosers, and 4) online diagnosis using them. The notion of codiagnosability and the above algorithms are initially presented in a setting of a specification language (violation of which represents a fault) and are later specialized to the case where faults are modeled as the occurrences of certain events. The notion of strong codiagnosability is also introduced to capture the ability of being certain about both the failure as well as the nonfailure conditions in a system within a bounded delay.
15931484
Optimal slope bin classification in gradient adjusted predictor for lossless compression of medical images
Gradient adjusted predictor (GAP) uses seven fixed range of slope quantization bins and different predictors associated with each bin, for prediction of pixels of all kinds of images. Criteria for range of slope in the bins and associated predictors are not reported in the literature. This paper presents a technique for slope quantization bins which are optimum for a given set of images. It also presents a technique for finding a statistically optimal predictor for a given range of slope bin. Simulation results, for medical images, using optimal slope bins and associated predictors show a significant better compression performance as compared to the other methods such as GAP and edge-directed prediction (EDP) method. The proposed method and GAP has same order of computational complexity while EDP is computationally much expensive.
14410544
Fluid modeling of pollution proliferation in P2P networks
P2P systems are highly vulnerable to pollution attacks in which attackers inject multiple versions of corrupted content into the system, which is then further proliferated by unsuspecting users. However, to our knowledge, there are no closed-form solutions that describe this phenomenon, nor are there models that describe how the injection of multiple versions of corrupted content impacts a clients' ability to receive a valid copy. In this paper we develop a suite of fluid models that model pollution proliferation in P2P systems. These fluid models lead to systems of non-linear differential equations. We obtain closed-form solutions for the differential equations; for the remaining models, we efficiently solve the differential equations numerically. The models capture a variety of user behaviors, including propensity for popular versions, abandonment after repeated failure to obtain a good version, freeloading, and local version blacklisting. Our analysis reveals intelligent strategies for attackers as well as strategies for clients seeking to recover non-polluted content within large-scale P2P networks.
8733679
Stochastic Fluid Theory for P2P Streaming Systems
We develop a simple stochastic fluid model that seeks to expose the fundamental characteristics and limitations of P2P streaming systems. This model accounts for many of the essential features of a P2P streaming system, including the peers' realtime demand for content, peer churn (peers joining and leaving), peers with heterogeneous upload capacity, limited infrastructure capacity, and peer buffering and playback delay. The model is tractable, providing closed-form expressions which can be used to shed insight on the fundamental behavior of P2P streaming systems. The model shows that performance is largely determined by a critical value. When the system is of moderate-to-large size, if a certain ratio of traffic loads exceeds the critical value, the system performs well; otherwise, the system performs poorly. Furthermore, large systems have better performance than small systems since they are more resilient to bandwidth fluctuations caused by peer churn. Finally, buffering can dramatically improve performance in the critical region, for both small and large systems. In particular, buffering can bring more improvement than can additional infrastructure bandwidth.
9633354
Real-time personalized sports video filtering and summarization
We demonstrate a real-time fully automated software system for filtering important events in sports video. Events represent occurrences of actions or state changes in video content. In the current prototype, we demonstrate detection of pitching in baseball and serving in tennis. For wireless video applications, we propose and apply a unique notion of content-based adaptive streaming,in which video encoding rate and media modality is dynamically varied according to the event filtering results. Our system includes an event detection module, an adaptive encoding module, and a buffer management module for adaptive streaming. We achieve the real-time performance by exploring compressed-domain techniques and multi-stage multi-resolution content-analysis processes.