3419561
A Dynamically Adaptive Distributed System for Processing Complex Continuous Queries
Recent years have witnessed rapidly growing research attention on continuous query processing over streams [2, 3]. A continuous query system can easily run out of resources in case of large amount of input stream data. Distributed continuous query processing over a shared nothing architecture, i.e., a cluster of machines, has been recognized as a scalable method to solve this problem [2, 8, 9]. Due to the lack of initial cost information and the fluctuating nature of the streaming data, uneven workload among machines may occur and this may impair the benefits of distributed processing. Thus dynamic adaptation techniques are crucial for a distributed continuous query system.
14837083
A Transactional Approach to Parallel Data Warehouse Maintenance
Data Warehousing is becoming an increasingly important technology for information integration and data analysis. Given the dynamic nature of modern distributed environments, both source data and schema changes are likely to occur autonomously and even concurrently in different sources. We have thus developed a comprehensive solution approach, called TxnWrap, that successfully maintains the warehouse views under any type of concurrent source updates. In this work, we now overcome TxnWrap's restriction that the maintenance is processed one by one for each source update, since that limits the performance. To overcome this limitation, we exploit the transactional approach of TxnWrap to achieve parallel data warehouse maintenance. For this, we first identify the read/write conflicts among the different warehouse maintenance processes. We then propose a parallel maintenance scheduler (PMS) that generates legal schedules that resolve these conflicts. PMS has been implemented and incorporated into our TxnWrap system. The experimental results confirm that our parallel maintenance scheduler significantly improves the performance of data warehouse maintenance.
7704531
A TCAM-based distributed parallel IP lookup scheme and performance analysis
Using ternary content addressable memory (TCAM) for high-speed IP address lookup has been gaining popularity due to its deterministic high performance. However, restricted by the slow improvement of memory accessing speed, the route lookup engines for next-generation terabit routers demand exploiting parallelism among multiple TCAM chips. Traditional parallel methods always incur excessive redundancy and high power consumption. We propose in this paper an original TCAM-based IP lookup scheme that achieves both ultra-high lookup throughput and optimal utilization of the memory while being power-efficient. In our multi-chip scheme, we devise a load-balanced TCAM table construction algorithm together with an adaptive load balancing mechanism. The power efficiency is well controlled by decreasing the number of TCAM entries triggered in each lookup operation. Using four 133 MHz TCAM chips and given 25% more TCAM entries than the original route table, the proposed scheme achieves a lookup throughput of up to 533 MPPS while remains simple for ASIC implementation
1001399
DS-PPS: A Practical Framework to Guarantee Differentiated QoS in Terabit Routers with Parallel Packet Switch
Parallel Packet Switch (PPS) is used intensively in today's terabit router to construct the switching fabric. Basic PPS equally deals with all of the traffic in order to achieve uniform load-balancing and high throughput, but it fails to support differentiated QoS. With the recent blooming of delay- sensitive Internet traffic, such as the peer-to-peer live streaming and IPTV, differentiated QoS is becoming an urgent demand. In this paper, we propose a novel and practical framework, the Differentiated Service Parallel Packet Switch (DS-PPS), which supports three fundamental QoS features: guaranteed-delay (GD), guaranteed-bandwidth (GB) and best-effort (BE). By adaptively adjusting the number of switching planes offered to each QoS class, DS-PPS precisely controls the delay bounds of GD traffic and the drop precedence of GB traffic. We evaluate DS-PPS by extensive theoretical analyses and comprehensive simulations. Experimental results on a prototype implementation of the framework show that DS-PPS outperforms the basic PPS in three main aspects. First, the average delay of TCP short packet under full load is reduced by more than 94%. Second, the average delay of real-time traffic under full load is reduced by more than 82%. And third, the GB traffic of low drop precedence is guaranteed of nearly three times the throughput of high drop precedence at the hotspots. Significantly, our proposed DS-PPS framework is universal and scalable to support various kinds of emerging QoS-sensitive applications in multi-service terabit routers without any extra overhead.
16051355
DPPC-RE: TCAM-based distributed parallel packet classification with range encoding
Packet classification has been a critical data path function for many emerging networking applications. An interesting approach is the use of ternary content addressable memory (TCAM) to achieve deterministic, high-speed packet classification performance. However, apart from high cost and power consumption, due to slow growing clock rate for memory technology, in general, the traditional single TCAM-based solution has difficulty to keep up with fast growing line rates. Moreover, the TCAM storage efficiency is largely affected by the need to support rules with ranges or range matching. In this paper, a distributed TCAM scheme that exploits chip-level-parallelism is proposed to greatly improve the throughput performance. This scheme seamlessly integrates with a range encoding scheme which not only solves the range matching problem, but also ensures a balanced high throughput performance. A thorough theoretical worst-case analysis of throughput, processing delay, and power consumption, as well as the experimental results show that the proposed solution can achieve scalable throughput performance matching up to OC768 line rate or higher. The added TCAM storage overhead is found to be reasonably small for the five real-world classifiers studied
3143012
TCAM-based distributed parallel packet classification algorithm with range-matching solution
Packet classification (PC) has been a critical data path function for many emerging networking applications. An interesting approach is the use of TCAM to achieve deterministic, high speed PC However, apart from high cost and power consumption, due to slow growing clock rate for memory technology in general, PC based on the traditional single TCAM solution has difficulty to keep up with fast growing line rates. Moreover, the TCAM storage efficiency is largely affected by the need to support rules with ranges, or range matching. In this paper, a distributed TCAM scheme that exploits chip-level-parallelism is proposed to greatly improve the PC throughput. This scheme seamlessly integrates with a range encoding scheme, which not only solves the range matching problem but also ensures a balanced high throughput performance. Using commercially available TCAM chips, the proposed scheme achieves PC performance of more than 100 million packets per second (Mpps), matching OC768 (40 Gbps) line rate.
10886416
Prediction and indexing of moving objects with unknown motion patterns
Existing methods for peediction spatio-temporal databases assume that objects move according to linear functions. This severely limits their applicability, since in practice movement is more complex, and individual objects may follow drastically diffferent motion patterns. In order to overcome these problems, we first introduce a general framework for monitoring and indexing moving objects, where (i) each boject computes individually the function that accurately captures its movement and (ii) a server indexes the object locations at a coarse level and processes queries using a filter-refinement mechanism. Our second contribution is a novel recursive motion function that supports a broad class of non-linear motion patterns. The function does not presume any a-priori movement but can postulate the particular motion of each object by examining its locations at recent timestamps. Finally. we propse an efficient indexing scheme that faciliates the processing of predicitive queries without false misses.