A generic and adaptive aggregation service for large-scale decentralized networks

Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial fo...
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Autor*in:	Pournaras, Evangelos [verfasserIn] Warnier, Martijn Brazier, Frances MT

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Aggregation Adaptation Agent Bloom filter Consistency

Anmerkung:	© Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Übergeordnetes Werk:	Enthalten in: Complex Adaptive Systems Modeling - Berlin : Springer, 2013, 1(2013), 1 vom: 08. Nov.
Übergeordnetes Werk:	volume:1 ; year:2013 ; number:1 ; day:08 ; month:11

Links:	Volltext

DOI / URN:	10.1186/2194-3206-1-19

Katalog-ID:	SPR036417378

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520			\|a Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service.
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allfields	10.1186/2194-3206-1-19 doi (DE-627)SPR036417378 (SPR)2194-3206-1-19-e DE-627 ger DE-627 rakwb eng Pournaras, Evangelos verfasserin aut A generic and adaptive aggregation service for large-scale decentralized networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. Aggregation (dpeaa)DE-He213 Adaptation (dpeaa)DE-He213 Agent (dpeaa)DE-He213 Bloom filter (dpeaa)DE-He213 Consistency (dpeaa)DE-He213 Warnier, Martijn aut Brazier, Frances MT aut Enthalten in Complex Adaptive Systems Modeling Berlin : Springer, 2013 1(2013), 1 vom: 08. Nov. (DE-627)768576091 (DE-600)2733680-3 2194-3206 nnns volume:1 year:2013 number:1 day:08 month:11 https://dx.doi.org/10.1186/2194-3206-1-19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2013 1 08 11
spelling	10.1186/2194-3206-1-19 doi (DE-627)SPR036417378 (SPR)2194-3206-1-19-e DE-627 ger DE-627 rakwb eng Pournaras, Evangelos verfasserin aut A generic and adaptive aggregation service for large-scale decentralized networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. Aggregation (dpeaa)DE-He213 Adaptation (dpeaa)DE-He213 Agent (dpeaa)DE-He213 Bloom filter (dpeaa)DE-He213 Consistency (dpeaa)DE-He213 Warnier, Martijn aut Brazier, Frances MT aut Enthalten in Complex Adaptive Systems Modeling Berlin : Springer, 2013 1(2013), 1 vom: 08. Nov. (DE-627)768576091 (DE-600)2733680-3 2194-3206 nnns volume:1 year:2013 number:1 day:08 month:11 https://dx.doi.org/10.1186/2194-3206-1-19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2013 1 08 11
allfields_unstemmed	10.1186/2194-3206-1-19 doi (DE-627)SPR036417378 (SPR)2194-3206-1-19-e DE-627 ger DE-627 rakwb eng Pournaras, Evangelos verfasserin aut A generic and adaptive aggregation service for large-scale decentralized networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. Aggregation (dpeaa)DE-He213 Adaptation (dpeaa)DE-He213 Agent (dpeaa)DE-He213 Bloom filter (dpeaa)DE-He213 Consistency (dpeaa)DE-He213 Warnier, Martijn aut Brazier, Frances MT aut Enthalten in Complex Adaptive Systems Modeling Berlin : Springer, 2013 1(2013), 1 vom: 08. Nov. (DE-627)768576091 (DE-600)2733680-3 2194-3206 nnns volume:1 year:2013 number:1 day:08 month:11 https://dx.doi.org/10.1186/2194-3206-1-19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2013 1 08 11
allfieldsGer	10.1186/2194-3206-1-19 doi (DE-627)SPR036417378 (SPR)2194-3206-1-19-e DE-627 ger DE-627 rakwb eng Pournaras, Evangelos verfasserin aut A generic and adaptive aggregation service for large-scale decentralized networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. Aggregation (dpeaa)DE-He213 Adaptation (dpeaa)DE-He213 Agent (dpeaa)DE-He213 Bloom filter (dpeaa)DE-He213 Consistency (dpeaa)DE-He213 Warnier, Martijn aut Brazier, Frances MT aut Enthalten in Complex Adaptive Systems Modeling Berlin : Springer, 2013 1(2013), 1 vom: 08. Nov. (DE-627)768576091 (DE-600)2733680-3 2194-3206 nnns volume:1 year:2013 number:1 day:08 month:11 https://dx.doi.org/10.1186/2194-3206-1-19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2013 1 08 11
allfieldsSound	10.1186/2194-3206-1-19 doi (DE-627)SPR036417378 (SPR)2194-3206-1-19-e DE-627 ger DE-627 rakwb eng Pournaras, Evangelos verfasserin aut A generic and adaptive aggregation service for large-scale decentralized networks 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. Aggregation (dpeaa)DE-He213 Adaptation (dpeaa)DE-He213 Agent (dpeaa)DE-He213 Bloom filter (dpeaa)DE-He213 Consistency (dpeaa)DE-He213 Warnier, Martijn aut Brazier, Frances MT aut Enthalten in Complex Adaptive Systems Modeling Berlin : Springer, 2013 1(2013), 1 vom: 08. Nov. (DE-627)768576091 (DE-600)2733680-3 2194-3206 nnns volume:1 year:2013 number:1 day:08 month:11 https://dx.doi.org/10.1186/2194-3206-1-19 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2014 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 1 2013 1 08 11
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This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. 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spellingShingle	Pournaras, Evangelos misc Aggregation misc Adaptation misc Agent misc Bloom filter misc Consistency A generic and adaptive aggregation service for large-scale decentralized networks
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title_sort	generic and adaptive aggregation service for large-scale decentralized networks
title_auth	A generic and adaptive aggregation service for large-scale decentralized networks
abstract	Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstractGer	Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
abstract_unstemmed	Purpose Aggregation functions are used in distributed environments to make system-wide information locally available in the nodes of a network. The computation of different aggregation functions, e.g., summation, average, maximum etc., in large-scale distributed systems is challenging and crucial for a wide range of applications. This is especially the case when the input values of these functions dynamically change during system runtime. Related approaches of decentralized aggregation are function-dependent, interaction-dependent, assume static values or cannot always tolerate duplicates and continuously changing information. Methods This paper introduces DIAS, the Dynamic Intelligent Aggregation Service. DIAS is an agent-based middleware that addresses these issues with a holistic approach: an efficient availability of the distributed information in every node of the network that enables the simultaneous computation of almost any aggregation function. Such an abstraction initially requires a significant communication and storage cost and has a rather large overhead. These issues are resolved by introducing an implicit local representation and storage of the explicit distributed information: aggregation memberships in bloom filters. Results The performance impact of bloom filters in DIAS is critical for its applicability as it compensates and reduces the initial high communication and storage required for such an abstraction. Conclusions Experimental evaluation under various aggregation and resource-constrained settings shows that DIAS is an efficient and accurate decentralized aggregation service. © Pournaras et al.; licensee Springer. 2013. This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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A generic and adaptive aggregation service for large-scale decentralized networks

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