12472730
Frame-Based Proportional Round-Robin
All known real-time proportional fair scheduling mechanisms either have high scheduling overheads (O(lg n) per time-slot) or do not efficiently handle dynamic task sets. This paper presents frame-based proportional round-robin (FBPRR), a real-time fair scheduler providing high and bounded proportional fairness accuracy and O(1) scheduling overhead with the ability to efficiently handle a set of dynamic tasks. FBPRR achieves this by applying the benefits of virtual-time round-robin (VTRR) scheduling mechanism within a frame-based scheduling approach. Simulation results show that the algorithm gains a speedup of 5 to 20 times (over O(lg n) complexity schedulers) with fairly high fairness
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.
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.
24684227
Adaptive Control of Anytime Algorithm Parameters
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.
16795084
Analysis of a Multiobjective Evolutionary Algorithm on the 0-1 knapsack problem
Multiobjective Evolutionary Algorithms (MOEAs) are increasingly being used for effectively solving many real-world problems, and many empirical results are available. However, theoretical analysis is limited to a few simple toy functions. In this work, we select the well-known knapsack problem for the analysis. The multiobjective knapsack problem in its general form is NP-complete. Moreover, the size of the set of Pareto-optimal solutions can grow exponentially with the number of items in the knapsack. Thus, we formalize a (1 + e)-approximate set of the knapsack problem and attempt to present a rigorous running time analysis of a MOEA to obtain the formalized set. The algorithm used in the paper is based on a restricted mating pool with a separate archive to store the remaining population; we call the algorithm a Restricted Evolutionary Multiobjective Optimizer (REMO). We also analyze the running time of REMO on a special bi-objective linear function, known as LOTZ (Leading Ones : Trailing Zeros), whose Pareto set is shown to be a subset of the knapsack. An extension of the analysis to the Simple Evolutionary Multiobjective Optimizer (SEMO) is also presented. A strategy based on partitioning of the decision space into fitness layers is used for the analysis.
7841699
Dynamic data fusion for future sensor networks
DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.
3134095
Supervisory Control of Real-Time Systems Using Prioritized Synchronization
The theory of supervisory control of discrete event systems is extended to the real-time setting. The real-time behavior of a system is represented by the set of all possible timed traces of the system. This is alternatively specified using timed automata where each transition is associated with an event occurrence time set during which time the transition can occur. Our model for time is more general in that the time advances continuously as compared to a model where time advances discretely. We extend the notion of prioritized synchronous composition to the real-time setting to use it as the control mechanism. It is shown that a suitable extension of the controllability condition to the real-time setting yields a condition for the existence of a supervisor achieving a desired timed behavior. Although the real-time controllability is similar in form to its untimed counterpart, they are different in the sense that one does not imply the other and vice-versa.
262403
Collaboration and multimedia authoring on mobile devices
This paper introduces adaptation-aware editing and progressive update propagation, two novel mechanisms that enable authoring multimedia content and collaborative work on mobile devices. Adaptation-aware editing enables editing content that was adapted to reduce download time to the mobile device. Progressive update propagation reduces the time for propagating content generated at the mobile device by transmitting either a fraction of the modifications or transcoded versions thereof.With application-aware editing and progressive update propagation, an object present at a mobile device is characterized not only by a particular version, as in conventional replication, but also by a particular fidelity. We demonstrate that replication models can be extended to account for fidelity independently of the mechanisms used for concurrency control and consistency maintenance. As a result, the two techniques described in this paper can easily be added to any replication protocol, whether optimistic or pessimistic.We report on our experience implementing adaptation-aware editing and progressive update propagation. Experiments with two multimedia applications, an email reader and a presentation software package, show that both mechanisms can be added with modest programming effort and achieve substantial reductions in upload and download latencies.
1898402
DFuse: a framework for distributed data fusion
Simple in-network data aggregation (or fusion) techniques for sensor networks have been the focus of several recent research efforts, but they are insufficient to support advanced fusion applications. We extend these techniques to future sensor networks and ask two related questions: (a) what is the appropriate set of data fusion techniques, and (b) how do we dynamically assign aggregation roles to the nodes of a sensor network. We have developed an architectural framework, DFuse, for answering these two questions. It consists of a data fusion API and a distributed algorithm for energy-aware role assignment. The fusion API enables an application to be specified as a coarse-grained dataflow graph, and eases application development and deployment. The role assignment algorithm maps the graph onto the network, and optimally adapts the mapping at run-time using role migration. Experiments on an iPAQ farm show that, the fusion API has low-overhead, and the role assignment algorithm with role migration significantly increases the network lifetime compared to any static assignment.
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.