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2019
Conference article
Open Access
Performance analysis of WebRTC-based video streaming over power constrained platforms
Bacco M., Catena M., De Cola T., Gotta A., Tonellotto N.
This work analyses the use of the Web Real-Time Communications (WebRTC) framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of the so-called Size, Weight and Power (SWaP) requirements, such as the use case of Unmanned Aerial Vehicles (UAVs) delivering real-time video streams over WebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality.Source: Globecom 2018 - 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 9-13 December 2018
DOI: 10.1109/glocom.2018.8647375
DOI: 10.5281/zenodo.2705728
DOI: 10.5281/zenodo.2705727
Project(s): BigDataGrapes
Metrics:
Bacco M., Catena M., De Cola T., Gotta A., Tonellotto N.
This work analyses the use of the Web Real-Time Communications (WebRTC) framework on resource-constrained platforms. WebRTC is a consolidated solution for real-time video streaming, and it is an appealing solution in a wide range of application scenarios. We focus our attention on those in which power consumption, size and weight are of paramount importance because of the so-called Size, Weight and Power (SWaP) requirements, such as the use case of Unmanned Aerial Vehicles (UAVs) delivering real-time video streams over WebRTC to peers on the ground. The testbed described in this work shows that the power consumption can be reduced by changing WebRTC default settings while maintaining comparable video quality.Source: Globecom 2018 - 2018 IEEE Global Communications Conference, Abu Dhabi, United Arab Emirates, 9-13 December 2018
DOI: 10.1109/glocom.2018.8647375
DOI: 10.5281/zenodo.2705728
DOI: 10.5281/zenodo.2705727
Project(s): BigDataGrapes
Metrics:
See at:
ZENODO 
| ZENODO | ISTI Repository | zenodo.org | zenodo.org | doi.org 
| ieeexplore.ieee.org | CNR ExploRA
2019
Conference article
Closed Access
Enhanced news retrieval: passages lead the way!
Catena M., Nardini F. M., Frieder O., Perego R., Muntean C. I., Tonellotto N.
We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles. Our experimentation, conducted using three publicly available news datasets, demonstrates that BM25P markedly outperforms BM25 in term of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1.Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1269–1272, Parigi, Francia, 21-25 July 2019
DOI: 10.1145/3331184.3331373
Metrics:
Catena M., Nardini F. M., Frieder O., Perego R., Muntean C. I., Tonellotto N.
We observe that most relevant terms in unstructured news articles are primarily concentrated towards the beginning and the end of the document. Exploiting this observation, we propose a novel version of the classical BM25 weighting model, called BM25 Passage (BM25P), which scores query results by computing a linear combination of term statistics in the different portions of news articles. Our experimentation, conducted using three publicly available news datasets, demonstrates that BM25P markedly outperforms BM25 in term of effectiveness by up to 17.44% in NDCG@5 and 85% in NDCG@1.Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1269–1272, Parigi, Francia, 21-25 July 2019
DOI: 10.1145/3331184.3331373
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2019
Conference article
Closed Access
Multiple query processing via logic function factoring
Catena M., Tonellotto N.
Some extensions to search systems require support for multiple query processing. This is the case with query variations, i.e., different query formulations of the same information need. The results of their processing can be fused together to improve effectiveness, but this requires to traverse more than once the query terms' posting lists, thus prolonging the multiple query processing time. In this work, we propose an approach to optimize the processing of query variations to reduce their overall response time. Similarly to the standard Boolean model, we firstly represent a group of query variations as a logic function where Boolean variables represent query terms. We then apply factoring to such function, in order to produce a more compact but logically equivalent representation. The factored form is used to process the query variations in a single pass over the inverted index. We experimentally show that our approach can improve by up to 1.95× the mean processing time of a multiple query with no statistically significant degradation in terms of NDCG@10.Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 937–940, Parigi, Francia, 21-25 July, 2019
DOI: 10.1145/3331184.3331297
Project(s): BigDataGrapes
Metrics:
Catena M., Tonellotto N.
Some extensions to search systems require support for multiple query processing. This is the case with query variations, i.e., different query formulations of the same information need. The results of their processing can be fused together to improve effectiveness, but this requires to traverse more than once the query terms' posting lists, thus prolonging the multiple query processing time. In this work, we propose an approach to optimize the processing of query variations to reduce their overall response time. Similarly to the standard Boolean model, we firstly represent a group of query variations as a logic function where Boolean variables represent query terms. We then apply factoring to such function, in order to produce a more compact but logically equivalent representation. The factored form is used to process the query variations in a single pass over the inverted index. We experimentally show that our approach can improve by up to 1.95× the mean processing time of a multiple query with no statistically significant degradation in terms of NDCG@10.Source: 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 937–940, Parigi, Francia, 21-25 July, 2019
DOI: 10.1145/3331184.3331297
Project(s): BigDataGrapes
Metrics:
See at:
dl.acm.org | doi.org | CNR ExploRA
2018
Conference article
Open Access
Efficient energy management in distributed web search
Catena M., Frieder O., Tonellotto N.
Distributed Web search engines (WSEs) require warehouse-scale computers to deal with the ever-increasing size of the Web and the large amount of user queries they daily receive. The energy consumption of this infrastructure has a major impact on the economic profitability of WSEs. Recently several approaches to reduce the energy consumption of WSEs have been proposed. Such solutions leverage dynamic voltage and frequency scaling techniques in modern CPUs to adapt the WSEs' query processing to the incoming query traffic without negative impacts on latencies. A state-of-the-art research approach is the PESOS (Predictive Energy Saving Online Scheduling) algorithm, which can reduce the energy consumption of a WSE' single server by up to 50%. We evaluate PESOS on a simulated distributed WSE composed of a thousand of servers, and we compare its performance w.r.t. an industry-level baseline, called PEGASUS. Our results show that PESOS can reduce the CPU energy consumption of a distributed WSE by up to 18% with respect to PEGASUS, while providing query response times which are in line with user expectations.Source: 27th ACM International Conference on Information and Knowledge Management, pp. 1555–1558, Torino, Italia, 22-26/10/2018
DOI: 10.1145/3269206.3269263
DOI: 10.5281/zenodo.2710864
DOI: 10.5281/zenodo.2710863
Project(s): BigDataGrapes
Metrics:
Catena M., Frieder O., Tonellotto N.
Distributed Web search engines (WSEs) require warehouse-scale computers to deal with the ever-increasing size of the Web and the large amount of user queries they daily receive. The energy consumption of this infrastructure has a major impact on the economic profitability of WSEs. Recently several approaches to reduce the energy consumption of WSEs have been proposed. Such solutions leverage dynamic voltage and frequency scaling techniques in modern CPUs to adapt the WSEs' query processing to the incoming query traffic without negative impacts on latencies. A state-of-the-art research approach is the PESOS (Predictive Energy Saving Online Scheduling) algorithm, which can reduce the energy consumption of a WSE' single server by up to 50%. We evaluate PESOS on a simulated distributed WSE composed of a thousand of servers, and we compare its performance w.r.t. an industry-level baseline, called PEGASUS. Our results show that PESOS can reduce the CPU energy consumption of a distributed WSE by up to 18% with respect to PEGASUS, while providing query response times which are in line with user expectations.Source: 27th ACM International Conference on Information and Knowledge Management, pp. 1555–1558, Torino, Italia, 22-26/10/2018
DOI: 10.1145/3269206.3269263
DOI: 10.5281/zenodo.2710864
DOI: 10.5281/zenodo.2710863
Project(s): BigDataGrapes
Metrics:
See at:
ZENODO | ZENODO | ISTI Repository | zenodo.org | dl.acm.org | doi.org | CNR ExploRA
2017
Journal article
Restricted
Energy-efficient query processing in web search engines
Catena M., Tonellotto N.
Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs, but they are necessary to ensure low latencies, since users expect sub-second response times (e.g., 500 ms). However, users can hardly notice response times that are faster than their expectations. Hence, we propose the Predictive Energy Saving Online Scheduling Algorithm ( PESOS) to select the most appropriate CPU frequency to process a query on a per-core basis. PESOS aims at process queries by their deadlines, and leverage high-level scheduling information to reduce the CPU energy consumption of a query processing node. PESOS bases its decision on query efficiency predictors, estimating the processing volume and processing time of a query. We experimentally evaluate PESOS upon the TREC ClueWeb09B collection and the MSN2006 query log. Results show that PESOS can reduce the CPU energy consumption of a query processing node up to similar to 48 percent compared to a system running at maximum CPU core frequency. PESOS outperforms also the best state-of-the-art competitor with a similar to 20 percent energy saving, while the competitor requires a fine parameter tuning and it may incurs in uncontrollable latency violations.Source: IEEE transactions on knowledge and data engineering (Print) 29 (2017): 1412–1425. doi:10.1109/TKDE.2017.2681279
DOI: 10.1109/tkde.2017.2681279
Metrics:
Catena M., Tonellotto N.
Web search engines are composed by thousands of query processing nodes, i.e., servers dedicated to process user queries. Such many servers consume a significant amount of energy, mostly accountable to their CPUs, but they are necessary to ensure low latencies, since users expect sub-second response times (e.g., 500 ms). However, users can hardly notice response times that are faster than their expectations. Hence, we propose the Predictive Energy Saving Online Scheduling Algorithm ( PESOS) to select the most appropriate CPU frequency to process a query on a per-core basis. PESOS aims at process queries by their deadlines, and leverage high-level scheduling information to reduce the CPU energy consumption of a query processing node. PESOS bases its decision on query efficiency predictors, estimating the processing volume and processing time of a query. We experimentally evaluate PESOS upon the TREC ClueWeb09B collection and the MSN2006 query log. Results show that PESOS can reduce the CPU energy consumption of a query processing node up to similar to 48 percent compared to a system running at maximum CPU core frequency. PESOS outperforms also the best state-of-the-art competitor with a similar to 20 percent energy saving, while the competitor requires a fine parameter tuning and it may incurs in uncontrollable latency violations.Source: IEEE transactions on knowledge and data engineering (Print) 29 (2017): 1412–1425. doi:10.1109/TKDE.2017.2681279
DOI: 10.1109/tkde.2017.2681279
Metrics:
See at:
IEEE Transactions on Knowledge and Data Engineering | ieeexplore.ieee.org | CNR ExploRA
2017
Conference article
Open Access
Recent advances in energy efficient query processing
Catena M., Tonellotto N.
Web search companies distribute their infrastructures and operations across several, geographically distant data centers. This distributed architecture facilitates high performance query processing, which is fundamental for the success of a Web search engine. At the same time, data centers require an huge amount of electricity to operate their computing resources. In this extended abstract, we briefly discuss our recent works for improving the energy effciency of query processing systems. Firstly, we introduce a novel query forwarding algorithm which exploits green energy sources to reduce the electricity expenditure and carbon footprint of Web search engines. Then, we propose to delegate the CPU power management from a server' operative system directly to the query processing application, to reduce the energy consumption of a search engine's servers. Finally, we introduce PESOS, a scheduling algorithm which manages the CPU power consumption on a per-query basis while considering query latency constraints.Source: IIR 2017 - 8th Italian Information Retrieval Workshop, pp. 125–128, Lugano, Switzerland, 05-07 June, 2017
Catena M., Tonellotto N.
Web search companies distribute their infrastructures and operations across several, geographically distant data centers. This distributed architecture facilitates high performance query processing, which is fundamental for the success of a Web search engine. At the same time, data centers require an huge amount of electricity to operate their computing resources. In this extended abstract, we briefly discuss our recent works for improving the energy effciency of query processing systems. Firstly, we introduce a novel query forwarding algorithm which exploits green energy sources to reduce the electricity expenditure and carbon footprint of Web search engines. Then, we propose to delegate the CPU power management from a server' operative system directly to the query processing application, to reduce the energy consumption of a search engine's servers. Finally, we introduce PESOS, a scheduling algorithm which manages the CPU power consumption on a per-query basis while considering query latency constraints.Source: IIR 2017 - 8th Italian Information Retrieval Workshop, pp. 125–128, Lugano, Switzerland, 05-07 June, 2017
See at:
ceur-ws.org | ISTI Repository | CNR ExploRA
2015
Contribution to conference
Restricted
Energy Efficiency in Web Search Engines
Catena M.
Today, Web search is a frequent action in the everyday life of many people. To perform it on a large scale, Web companies need energy-hungry data center, which raise environmental and economical challenges. For these reasons, Green Information Retrieval promotes energy and energy-cost awareness in contemporary Web search engines. In this document, we propose to further the research on Green Information Retrieval, which is still at its early stage. Moreover, we illustrate our first results in evaluating and improving the energy efficiency of search servers.Source: Sixth BCS-IRSG Symposium on Future Directions in Information Access, Salonicco, 31/08/2015-04/09/2015
Catena M.
Today, Web search is a frequent action in the everyday life of many people. To perform it on a large scale, Web companies need energy-hungry data center, which raise environmental and economical challenges. For these reasons, Green Information Retrieval promotes energy and energy-cost awareness in contemporary Web search engines. In this document, we propose to further the research on Green Information Retrieval, which is still at its early stage. Moreover, we illustrate our first results in evaluating and improving the energy efficiency of search servers.Source: Sixth BCS-IRSG Symposium on Future Directions in Information Access, Salonicco, 31/08/2015-04/09/2015
See at:
ewic.bcs.org | CNR ExploRA