RASC: Dynamic Rate Allocation for Distributed Stream Processing Applications

Drougas, Yannis; Kalogeraki, Vana

doi:10.1109/ipdps.2007.370302

Cited by 5 publications

(12 citation statements)

References 22 publications

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“…Constraints (5) express that the optimal solution should only maximize one or more δ q 's only in the case when this optimization will have no impact on the rest of the δ q 's. In what follows, we will usually need to refer only to the unit vectors that have the same direction with δ and δ eq .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Let us consider the feasible region of Example 1 ( Figure 4). Given an input rate combination p = (r 1 , r 2 ), maximizing δ 1 + δ 2 without violating constraints (2), (4) and (5), would result in a component rate assignment that is equivalent to the combination of application input rates p ′ = (r 1 + δ 1 , r 2 + δ 2 ), as shown in Figure 6. On the other hand, if a burst results in the application input rate combination given in Figure 7, we would prefer a plan that provisions for an application input rate combination p 2 = (r 1 + δ 1 , r 2 + δ ′ 2 ) rather than one that provisions for p ′ = (r 1 + δ 1 , r 2 + δ 2 ), since increasing δ 2 to δ ′ 2 will have no impact on δ 1 .…”

Section: Problem Formulationmentioning

confidence: 99%

“…Recent efforts have proposed distributed stream processing infrastructures and have investigated composition and placement algorithms. The majority of these (including our previous work [5], [6], [7]) focus on composition and placement techniques making the assumption that resource availability and application rate requirements are constant.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Accommodating bursts in distributed stream processing systems

Drougas

Kalogeraki

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

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Section: Problem Formulationmentioning

confidence: 99%

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Accommodating bursts in distributed stream processing systems

Drougas

Kalogeraki

2009

2009 IEEE International Symposium on Parallel &Amp; Distributed Processing

Self Cite

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“…[5] prioritizes data based on the error rates of the data flows. While some scheduling algorithms have been designed and tested on simulators [3] [13] [12], to the best of our knowledge, Tiny-DWFQ is the first implementation of a prioritized scheduling algorithm on a real wireless sensor network.…”

Section: Related Workmentioning

confidence: 99%

TinyOS-Based Quality of Service Management in Wireless Sensor Networks

Peterson

Anusuya-Rangappa

Shirazi

et al. 2009

2009 42nd Hawaii International Conference on System Sciences

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Previously the cost and extremely limited capabilities of sensors prohibited Quality of Service (QoS) implementations in wireless sensor networks. With advances in technology, sensors are becoming significantly less expensive and the increases in computational and storage capabilities are opening the door for new, sophisticated algorithms to be implemented. Newer sensor network applications require higher data rates with more stringent priority requirements. We introduce a dynamic scheduling algorithm to improve bandwidth for high priority data in sensor networks, called Tiny-DWFQ. Our Tiny-Dynamic Weighted Fair Queuing scheduling algorithm allows for dynamic QoS for prioritized communications by continually adjusting the treatment of communication packages according to their priorities and the current level of network congestion. For performance evaluation, we tested Tiny-DWFQ, Tiny-WFQ (traditional WFQ algorithm implemented in TinyOS), and FIFO queues on an Imote2-based wireless sensor network and report their throughput and packet loss. Our results show that Tiny-DWFQ performs better in all test cases.

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“…One of the salient characteristics of this class of applications is the demanding compute and network resource requirements [8]. Huge volume of data generated at a high rate need to be processed within real-time constraints.…”

Section: Introductionmentioning

confidence: 99%

Using Dedicated and Opportunistic Networks in Synergy for a Cost-Effective Distributed Stream Processing Platform

Asaduzzaman

Maheswaran

2008

2008 14th IEEE International Conference on Parallel and Distributed Systems

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This paper presents a case for exploiting the synergy of dedicated and opportunistic network resources in a distributed hosting platform for data stream processing applications. Our previous studies have demonstrated the benefits of combining dedicated reliable resources with opportunistic resources in case of high-throughput computing applications, where timely allocation of the processing units is the primary concern. Since distributed stream processing applications demand large volume of data transmission between the processing sites at a consistent rate, adequate control over the network resources is important here to assure a steady flow of processing. In this paper, we propose a system model for the hybrid hosting platform where stream processing servers installed at distributed sites are interconnected with a combination of dedicated links and public Internet. Decentralized algorithms have been developed for allocation of the two classes of network resources among the competing tasks with an objective towards higher task throughput and better utilization of expensive dedicated resources. Results from extensive simulation study show that with proper management, systems exploiting the synergy of dedicated and opportunistic resources yield considerably higher task throughput and thus, higher return on investment over the systems solely using expensive dedicated resources.

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RASC: Dynamic Rate Allocation for Distributed Stream Processing Applications

Cited by 5 publications

References 22 publications

Accommodating bursts in distributed stream processing systems

Accommodating bursts in distributed stream processing systems

TinyOS-Based Quality of Service Management in Wireless Sensor Networks

Using Dedicated and Opportunistic Networks in Synergy for a Cost-Effective Distributed Stream Processing Platform

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