5924956
On joining and caching stochastic streams
We consider the problem of joining data streams using limited cache memory, with the goal of producing as many result tuples as possible from the cache. Many cache replacement heuristics have been proposed in the past. Their performance often relies on implicit assumptions about the input streams, e.g., that the join attribute values follow a relatively stationary distribution. However, in general and in practice, streams often exhibit more complex behaviors, such as increasing trends and random walks, rendering these "hardwired" heuristics inadequate.In this paper, we propose a framework that is able to exploit known or observed statistical properties of input streams to make cache replacement decisions aimed at maximizing the expected number of result tuples. To illustrate the complexity of the solution space, we show that even an algorithm that considers, at every time step, all possible sequences of future replacement decisions may not be optimal. We then identify a condition between two candidate tuples under which an optimal algorithm would always choose one tuple over the other to replace. We develop a heuristic that behaves consistently with an optimal algorithm whenever this condition is satisfied. We show through experiments that our heuristic outperforms previous ones.As another evidence of the generality of our framework, we show that the classic caching/paging problem for static objects can be reduced to a stream join problem and analyzed under our framework, yielding results that agree with or extend classic ones.
14448318
On Suspending and Resuming Dataflows
Consider a long-running, resource-intensive query Q running on a database management system (DBMS). Suppose another task T with much higher priority arrives, and we need to process T as quickly as possible and with all available resources. Ideally, the system should suspend the execution of Q, quickly release all resources held by Q, and start T using all resources. Query Q can be resumed once T finishes execution, ideally without losing any significant fraction of the work that Q had done prior to suspend. Beyond supporting mixed-priority DBMS workloads, suspend/resume of queries, or dataflows in general, is important in many other settings: Utility and grid: Dataflows now run frequently on computational utilities and grids composed of autonomous resources. When the owner of resources wants to use them, dataflows running on these resources must release control quickly, and migrate to other resources. Software rejuvenation: Benefits of software rejuvenation, the practice of rebooting enterprise computing systems regularly, are now recognized widely. Reboot is critical when performance degrades due to resource exhaustion caused by resource leaks. Suspend and resume is important in this setting because (1) the challenge of predicting completion times accurately makes it difficult to schedule task completion to match a rejuvenation schedule; (2) when performance degrades, it may not be cost-effective to wait for all dataflows to complete before rebooting.
2510058
Constraint chaining: on energy-efficient continuous monitoring in sensor networks
Wireless sensor networks have created new opportunities for data collection in a variety of scenarios, such as environmental and industrial, where we expect data to be temporally and spatially correlated. Researchers may want to continuously collect all sensor data from the network for later analysis. Suppression, both temporal and spatial, provides opportunities for reducing the energy cost of sensor data collection. We demonstrate how both types can be combined for maximal benefit. We frame the problem as one of monitoring node and edge constraints. A monitored node triggers a report if its value changes. A monitored edge triggers a report if the difference between its nodes' values changes. The set of reports collected at the base station is used to derive all node values. We fully exploit the potential of this global inference in our algorithm, CONCH, short for constraint chaining. Constraint chaining builds a network of constraints that are maintained locally, but allow a global view of values to be maintained with minimal cost. Network failure complicates the use of suppression, since either causes an absence of reports. We add enhancements to CONCH to build in redundant constraints and provide a method to interpret the resulting reports in case of uncertainty. Using simulation we experimentally evaluate CONCH's effectiveness against competing schemes in a number of interesting scenarios.
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.
214792426
TIP: a temporal extension to Informix
Commercial relational database systems today provide only limited temporal support. To address the needs of applications requiring rich temporal data and queries, we have built TIP (Temporal Information Processor), a temporal extension to the Informix database system based on its DataBlade technology. Our TIP DataBlade extends Informix with a rich set of datatypes and routines that facilitate temporal modeling and querying. TIP provides both C and Java libraries for client applications to access a TIP-enabled database, and provides end-users with a GUI interface for querying and browsing temporal data.
2070842
Distributed Network Querying with Bounded Approximate Caching
As networks continue to grow in size and complexity, distributed network monitoring and resource querying are becoming increasingly difficult. Our aim is to design, build, and evaluate a scalable infrastructure for answering queries over distributed measurements, at reduced costs (in terms of both network traffic and query latency) while maintaining required precision. In this infrastructure, each network node owns a set of numerical measurements and actively maintains bounds on these values cached at other nodes. We can answer queries approximately, using bounds from nearby caches to avoid contacting the owners directly. We focus on developing efficient and scalable techniques to place, locate, and manage bounded approximate caches across a large network. We have developed two approaches: One uses a recursive partitioning of the network space to place caches in a static, controlled manner, while the other uses a locality-aware distributed hash table to place caches in a dynamic and decentralized manner. In this paper, we focus on the latter approach. Experiments over a large-scale emulated network show that our techniques are very effective in reducing query costs while generating an acceptable amount of background traffic; they are also able to exploit various forms of locality that are naturally present in queries, and adapt to volatility of measurements.
11796157
Functional Brain Image Analysis Using Joint Function-Structure Priors
We propose a new method for context-driven analysis of functional magnetic resonance images (fMRI) that incorporates spatial relationships between functional parameter clusters and anatomical structure directly for the first time. We design a parametric scheme that relates functional and structural spatially-compact regions in a single unified manner. Our method is motivated by the fact that the fMRI and anatomical MRI (aMRI) have consistent relations that provide configurations and context that aid in fMRI analysis. We develop a statistical decision-making strategy to estimate new fMRI parameter images (based on a General Linear Model-GLM) and spatially-clustered zones within these images. The analysis is based on the time-series data and contextual information related to appropriate spatial grouping of parameters in the functional data and the relationship of this grouping to relevant gray matter structure from the anatomical data. We introduce a representation for the joint prior of the functional and structural information, and define a joint probability distribution over the variations of functional clusters and the related structure contained in a set of training images. We estimate the Maximum A Posteriori (MAP) functional parameters, formulating the function-structure model in terms of level set functions. Results from 3D fMRI and aMRI show that this context-driven analysis potentially extracts more meaningful information than the standard GLM approach.
19364030
The evidential reasoning approach for multiple attribute decision analysis using interval belief degrees
Abstract Multiple attribute decision analysis (MADA) problems having both quantitative and qualitative attributes under uncertainty can be modeled using the evidential reasoning (ER) approach. Several types of uncertainties such as ignorance and fuzziness can be modeled in the ER framework. In this paper, the ER approach will be extended to model new types of uncertainties including interval belief degrees and interval data that could be incurred in decision situations such as group decision making. The Dempster–Shafer (D–S) theory of evidence is first extended, which is one of the bases of the ER approach. The analytical ER algorithm is used to combine all evidence simultaneously. Two pairs of nonlinear optimization models are constructed to estimate the upper and lower bounds of the combined belief degrees and to compute the maximum and the minimum expected utilities of each alternative, respectively. Interval data are equivalently transformed to interval belief degrees and are incorporated into the nonlinear optimization models. A cargo ship selection problem is examined to show the implementation process of the proposed approach.
15187378
An efficient fault-tolerant distributed channel allocation algorithm for cellular networks
A channel allocation algorithm in a cellular network consists of two parts: a channel acquisition algorithm and a channel selection algorithm. Some of the previous works in this field focused on centralized approaches to allocating channels. But, centralized approaches are neither scalable nor reliable. Recently, distributed dynamic channel allocation algorithms have been proposed, and they have gained a lot of attention due to their high reliability and scalability. But, in most of the algorithms, the cell that wants to borrow a channel has to wait for replies from all its interference neighbors and, hence, is not fault-tolerant. In this paper, we propose a new algorithm that is fault-tolerant and makes full use of the available channels. It can tolerate the failure of mobile nodes as well as static nodes without any significant degradation in service.
5708746
On the Robust Single Machine Scheduling Problem
The single machine scheduling problem with sum of completion times criterion (SS) can be solved easily by the Shortest Processing Time (SPT) rule. In the case of significant uncertainty of the processing times, a robustness approach is appropriate. In this paper, we show that the robust version of the (SS) problem is NP-complete even for very restricted cases. We present an algorithm for finding optimal solutions for the robust (SS) problem using dynamic programming. We also provide two polynomial time heuristics and demonstrate their effectiveness.
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.