6228281
AWA* - A Window Constrained Anytime Heuristic Search Algorithm
This work presents an iterative anytime heuristic search algorithm called Anytime Window A* (AWA*) where node expansion is localized within a sliding window comprising of levels of the search tree/graph. The search starts in depth-first mode and gradually proceeds towards A* by incrementing the window size. An analysis on a uniform tree model provides some very useful properties of this algorithm. A modification of AWA* is presented to guarantee bounded optimal solutions at each iteration. Experimental results on the 0/1 Knapsack problem and TSP demonstrate the efficacy of the proposed techniques over some existing anytime search methods.
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.
11255660
A flexible contention resolution scheme for QoS provisioning in optical burst switching networks
Contention resolution is a major issue in bufferless optical burst switching (OBS) networks. The existing contention resolution schemes consider priority and arrival time to resolve contention. For most multimedia applications priority and delay are key parameters for QoS provisioning. In this paper, we propose a new signaling protocol for reducing contention in OBS networks and consider three parameters, namely, priority of the burst, number of hops traversed, and burst size into account to resolve contention. The source node in the proposed protocol can be informed of the contention up to halfway along the path of the burst, and thus, can reschedule the burst accordingly. The scheme is adaptable to both prioritized and delay constrained traffic. We call the scheme OBS-Flex. For selecting a data channel, we propose three channel selection algorithms, namely, Least Recently Used (LRU), First Fit (FF), and Priority Set (PS). We simulate OBS-Flex and compare with preemptive priority just-enough-time (PPJET) contention resolution scheme. We show that OBS-Flex outperforms PPJET in terms of burst loss rates. For simulation, we have considered Poisson and bursty traffic models.
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.
2076832
Iterative adaptation for mobile clients using existing APIs
Iterative adaptation is a novel approach to adaptation for resource-limited mobile and wireless environments that supports powerful application-specific adaptations without requiring modifications to the application's source code. Common productivity applications, such as browsers, word processors, and presentation tools, export APIs that allow external applications to control their operation. The novel premise in iterative adaptation is that these APIs are sufficient to support a wide range of adaptation policies for applications running on resource-limited devices. In addition to allowing adaptation without having to change the application's source code, this approach has a unique combination of advantages. First, it supports centralized management of resources across multiple applications. Second, it makes it possible to modify application behavior after the application has been deployed. This paper evaluates the extent to which existing APIs can be used for the purposes of adapting document-based applications to run on bandwidth-limited devices. In particular, we implement a large number of bandwidth adaptations for applications from the Microsoft Office and the OpenOffice productivity suites and for Internet Explorer. Although we find limitations in their APIs, we are able to implement many adaptation policies without much complexity and with good performance. Moreover, iterative adaptation achieves performance similar to an approach that implements adaptation by modifying the application, while requiring only a fraction of the coding effort.
2162080
Harbor: software-based memory protection for sensor nodes
Many sensor nodes contain resource constrained microcontrollers where user level applications, operating system components, and device drivers share a single address space with no form of hardware memory protection. Programming errors in one application can easily corrupt the state of the operating system or other applications. In this paper, we propose Harbor, a memory protection system that prevents many forms of memory corruption. We use software based fault isolation ("sandboxing") to restrict application memory accesses and control flow to protection domains within the address space. A flexible and efficient memory map data structure records ownership and layout information for memory regions; writes are validated using the memory map. Control flow integrity is preserved by maintaining a safe stack that stores return addresses in a protected memory region. Run-time checks validate computed control flow instructions. Cross domain calls perform low-overhead control transfers between domains. Checks are introduced by rewriting an application's compiled binary. The sand-boxed result is verified on the sensor node before it is admitted for execution. Harbor's fault isolation properties depend only on the correctness of this verifier and the Harbor runtime. We have implemented and tested Harbor on the SOS operating system. Harbor detected and prevented memory corruption caused by programming errors in application modules that had been in use for several months. Harbor's overhead, though high, is less than that of application-specific virtual machines, and reasonable for typical sensor workloads.
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.
7339641
Efficient beacon placement for network tomography
Recent interest in using tomography for network monitoring has raised the fundamental issue of whether it is possible to use only a small number of probing nodes (beacons) for monitoring all edges of a network in the presence of dynamic routing. Past work has shown that minimizing the number of beacons is NP-hard, and has provided approximate solutions that may be fairly suboptimal. In this paper, we use a two-pronged approach to compute an efficient beacon set: (i) we formulate the need for, and design algorithms for, computing the set of edges that can be monitored by a beacon under all possible routing states; and (ii) we minimize the number of beacons used to monitor all network edges. We show that the latter problem is NP-complete and use an approximate placement algorithm that yields beacon sets of sizes within 1+<i>ln</i>(|<i>E</i>|) of the optimal solution, where E is the set of edges to be monitored. Beacon set computations for several Rocketfuel ISP topologies indicate that our algorithm may reduce the number of beacons yielded by past solutions by more than 50%.
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.