12872505
Value-Based Notification Conditions in Large-Scale Publish/Subscribe Systems
We address the problem of providing scalable support for subscriptions with personalized value-based notification conditions in wide-area publish/subscribe systems. Notification conditions can be fine-tuned by subscribers, allowing precise and flexible control of when events are delivered to the subscribers. For example, a user may specify that she should be notified if and only if the price of a particular stock moves outside a "radius" around her last notified value. Naive techniques for handling notification conditions are not scalable. It is challenging to share subscription processing and notification dissemination of subscriptions with personalized value-based notification conditions, because two subscriptions may see two completely different sequences of notifications even if they specify the same radius. We develop and experimentally evaluate scalable processing and dissemination techniques for these subscriptions. Our approach uses standard network substrates for notification dissemination, and avoids pushing complex application processing into the network. Compared with other alternatives, our approach generates orders of magnitude lower network traffic, and incurs lower server processing cost.
154630
Dual Labeling: Answering Graph Reachability Queries in Constant Time
Graph reachability is fundamental to a wide range of applications, including XML indexing, geographic navigation, Internet routing, ontology queries based on RDF/OWL, etc. Many applications involve huge graphs and require fast answering of reachability queries. Several reachability labeling methods have been proposed for this purpose. They assign labels to the vertices, such that the reachability between any two vertices may be decided using their labels only. For sparse graphs, 2-hop based reachability labeling schemes answer reachability queries efficiently using relatively small label space. However, the labeling process itself is often too time consuming to be practical for large graphs. In this paper, we propose a novel labeling scheme for sparse graphs. Our scheme ensures that graph reachability queries can be answered in constant time. Furthermore, for sparse graphs, the complexity of the labeling process is almost linear, which makes our algorithm applicable to massive datasets. Analytical and experimental results show that our approach is much more efficient than stateof- the-art approaches. Furthermore, our labeling method also provides an alternative scheme to tradeoff query time for label space, which further benefits applications that use tree-like graphs.
7776820
Scalable continuous query processing by tracking hotspots
This paper considers the problem of scalably processing a large number of continuous queries. We propose a flexible framework with novel data structures and algorithms for group-processing and indexing continuous queries by exploiting potential overlaps in query predicates. Our approach partitions the collection of continuous queries into groups based on the clustering patterns of the query ranges, and then applies specialized processing strategies to those heavily-clustered groups (or hotspots). To maintain the partition dynamically, we present efficient algorithms that maintain a nearly optimal partition in nearly amortized logarithmic time. We show how to use the hotspots to scalably process large numbers of continuous select-join and band-join queries, which are much more challenging than simple range selection queries. Experiments demonstrate that this approach can improve the processing throughput by orders of magnitude. As another application of hotspots, we show how to use them to build a high-quality histogram for intervals in linear time.
26384016
A new evolutionary approach to developing neural autonomous agents
This paper explores the use of neural networks to control robots in tasks requiring sequential and learning behavior. We propose a family competition evolutionary algorithm (FCEA) to evolve networks that can integrate these different types of behavior in a smooth and continuous manner. The approach integrates self-adaptive Gaussian mutation, self-adaptive Cauchy mutation, decreasing-based Gaussian mutation, and family competition. In order to illustrate the power of the approach, we apply this approach to two different task domains: the "artificial ant" problem and a sequential behavior problem - an agent learns to play football. From the experimental results, we find our approach performs much better than other evolutionary algorithms in these two tasks. Based on the results from our experiments, it is shown that our approach can evolve neural networks to provide a means of integrating, sequencing and learning within a single control system.
207007399
Integrating adaptive mutations and family competition into genetic algorithms as function optimizer
Abstract In this paper, we propose a robust evolutionary algorithm, called adaptive mutations genetic algorithm, for function optimization problems. Our main contribution is robustly optimizing problems whose number of variables from 2 to 200. In order to have a fair comparison, we propose the criteria for constructing a testing bed and for classifying these problems into different complexity degrees. The proposed approach, based on the family competition and multiple adaptive rules, successfully integrates the decreasing-based Gaussian mutation and self-adaptive Cauchy mutation to balance the exploitation and exploration. It is implemented and applied to widely used test functions and several nonseparable multimodal functions. Experimental results indicate that our approach is more robust than ten evolutionary algorithms.
1138265
A Fault-Tolerant Dynamic Channel Allocation Scheme for Enhancing QoS in Cellular Networks
In cellular networks. it is vital to allocate communica­ tion channels efficiently because the bandwidth allocated for cellular communication is limited. When mobile hosts move from one cell to another cell. to provide uninterrupted service. the new cell should have enough channels to sup­ pan the ongoing communication of the mobile hosts that moved into the cell. If channels are statically allocated. as is the case in current cellular networks. a cell may run out of channels when large number of mobile hosts move to a cell. thus degrading the quality of service. To over­ come this problem. dynamic channel allocation approaches have been proposed. Under dynamic channel allocation. channels are allocated to cells on demand. thus increasing channel utilization and hence improving the quality of ser­ vice. Such channel allocation approaches fall under two categories. namely. centralized and distributed. Central­ ized approaches are neither scalable nor reliable. while dis­ lributed approaches have the potential to be both reliable and scalable. In this paper, we present a distributed dy­ namic channel allocation scheme for cellular networks that is fault tolerant. Our approach can tolerate the failure of mobile nodes as well as static nodes and enhance the qual­ ity of service by making efficient reuse of channels.
20784712
A fault-tolerant distributed channel allocation scheme for cellular networks
In cellular networks, it is vital to allocate communication channels efficiently because the bandwidth allocated for cellular communication is limited. If channels are statically allocated, as is the case in current cellular networks, a cell may run out of channels when a large number of mobile hosts move to the cell. To overcome this problem, dynamic channel allocation approaches have been proposed. Under dynamic channel allocation, channels are allocated to cells on demand, thus increasing channel utilization. Such channel allocation approaches fall under two categories, namely, centralized and distributed. Centralized approaches are neither scalable nor reliable, while distributed approaches have the potential to be both reliable and scalable. In the distributed approaches, a mobile service station is responsible for allocating channels to mobile hosts in the same cell. In cellular networks, mobile service stations may fail. In addition, the network may encounter intermittent network congestion and/or link failures. It is desirable for a channel allocation algorithm to work well, even in the presence of network congestion, link failures, and/or mobile service station failures. In this paper, we present a distributed dynamic channel allocation scheme for cellular networks that is fault-tolerant. Our approach can tolerate the failure of mobile service stations, link failure, and network congestion, and make efficient reuse of channels. We also provide results of the performance evaluation of our algorithm.
3829528
Dynamically inferring temporal properties
Model checking requires a specification of the target system's desirable properties, some of which are temporal. Formulating a temporal property of the system based on either its abstract model or implementation requires a deep understanding of its behavior and sophisticated knowledge of the chosen formalism. This has been a major impediment to documenting and verifying temporal properties. We propose a dynamic approach to automatically infer a program's temporal properties based on a set of property pattern templates. We describe a preliminary implementation of this approach, and report on our experience using it to discover interesting temporal properties of a small program.