Power-aware operator placement and broadcasting of continuous query results

Neophytou, Panayiotis; Sharaf, Mohamed A.; Chrysanthis, Panos K.; Labrinidis, Alexandros

doi:10.1145/1850822.1850831

Cited by 6 publications

(8 citation statements)

References 21 publications

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“…To address this NP-hardness, our proposed heuristic solution called Single Objective Optimization (SOO) algorithm is inspired from BOSe [21]. SOO first applies a parallel search to generate a solution that attempts to minimize the overall resource usage cost RU by minimizing independently RUj for each data stream Sj.…”

Section: Heuristic Solutionmentioning

confidence: 99%

Efficient Scheduling of Streaming Operators for IoT Edge Analytics

Ntumba

Georgantas

Christophides

2021

2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)

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Data stream processing and analytics (DSPA) applications are widely used to process the ever increasing amounts of data streams produced by highly geographical distributed data sources such as fixed and mobile IoT devices in order to extract valuable information in a timely manner for real-time actuation. To efficiently handle this ever increasing amount of data streams, the emerging Edge/Fog computing paradigms is used as the middle-tier between the Cloud and the IoT devices to process data streams closer to their sources and to reduce the network resource usage and network delay to reach the Cloud. In this paper, we account for the fact that both network resources and computational resources can be limited and shareable among multiple DSPA applications in the Edge-Fog-Cloud architecture, hence it is necessary to ensure their efficient usage. In this respect, we propose a resource-aware and time-efficient heuristic called SOO that identifies a good DSPA operator placement on the Edge-Fog-Cloud architecture towards optimizing the trade-off between the computational and network resource usage. Via thorough simulation experiments, we show that the solution provided by SOO is very close to the optimal one while the execution time is considerably reduced.

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Section: Heuristic Solutionmentioning

confidence: 99%

Efficient Scheduling of Streaming Operators for IoT Edge Analytics

Ntumba

Georgantas

Christophides

2021

2021 Sixth International Conference on Fog and Mobile Edge Computing (FMEC)

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“…Considering the interaction between front-end and server, Neophytou et al proposed three power-aware query operator placement algorithms that determine which part of a continuous query plan is executed at the stream management server and which part is executed at the users' wireless devices [29]. Gredik et al also presented a distributed real-time approach to monitor moving object.…”

Section: Mobile Sidementioning

confidence: 99%

A survey on energy-efficient data management

et al. 2011

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Energy management has now become a critical and urgent issue in green computing. A lot of efforts have been made on energy-efficiency computing at various levels from individual hardware components, system software, to applications. In this paper, we describe the energyefficiency computing problem, as well as possible strategies to tackle the problem. We survey some recently developed energy-saving data management techniques. Benchmarks and power models are described in the end for the evaluation of energy-efficiency solutions.

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“…a) DataMinCut [3]: minimizes the tuning energy expended by the clients by labeling every edge with the average data size flowing through and discovering the MinCut of the graph. Broadcasting the data flowing through the MinCut of the graph guarantees that the broadcast size is minimal.…”

Section: Operator Placement Algorithmsmentioning

confidence: 99%

“…Broadcasting the data flowing through the MinCut of the graph guarantees that the broadcast size is minimal. b) PowerMinCut [3]: attempts to minimize the overall energy by choosing the edges that result in smallest total energy (tuning and processing) for the client. It augments the edge labels with the client processing energy cost of all the operators downstream of each edge.…”

Section: Operator Placement Algorithmsmentioning

confidence: 99%

“…The results of CQs are also in the form of continuous data streams that need to be continuously disseminated to the mobile end-users. Our goal in [3], [4] was to design operator placement algorithms that work together with the broadcast organization to minimize the total energy consumption on the hand-held devices. The approach was based on our experimental observations that the energy costs for receiving data are typically much higher than the energy costs for processing said data, an observation that has also motivated in-network processing in wireless sensor networks (e.g., [5], [6]).…”

Section: Introductionmentioning

confidence: 99%

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Visualization of Energy Consumption of Continuous Query Processing with Mobile Clients

Szwedko

Neophytou

Chrysanthis

et al. 2011

2011 IEEE 12th International Conference on Mobile Data Management

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Complex event detection over data streams has become ubiquitous through the widespread use of sensors, wireless connectivity and the wide variety of end-user mobile devices. Typically, event detection is carried out by a central server executing continuous queries. In this demonstration, we focus on the case where users with mobile devices submit continuous queries (for event detection) to a data stream management server which disseminates the results to the users over a shared broadcast medium. In order to minimize the overall energy consumption of the mobile devices (clients), we have proposed operator placement algorithms that split the processing of each continuous query between the centralized server and the requesting mobile clients, thus trading off energy consumption for communication energy consumption for computation. Specifically, in this demonstration, we present an interactive graphical interface to the inner workings of our three proposed operator placement algorithms, whereby attendees are able to investigate various query plans and the decisions that the algorithms make, as well as visualize the results of these algorithms in terms of client power consumption and response time. Besides being able to step through an algorithm's execution as it considers various operator placement decisions, attendees are able to experiment with different scenarios by customizing the parameters of the query workloads (e.g., changing the selectivities and projectivities of the operators) or the client's profile (e.g., power consumed per unit of time of processing) and examine the impact.

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Power-aware operator placement and broadcasting of continuous query results

Cited by 6 publications

References 21 publications

Efficient Scheduling of Streaming Operators for IoT Edge Analytics

Efficient Scheduling of Streaming Operators for IoT Edge Analytics

A survey on energy-efficient data management

Visualization of Energy Consumption of Continuous Query Processing with Mobile Clients

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