Publications_old

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.

Keywords

Aggregation Adaptation Agent Bloom filter Consistency

Basically filling the space here. So this is copied text from above. 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.

Keywords

Aggregation Adaptation Agent Bloom filter Consistency

Basically filling the space here. So this is copied text from above. 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.

Keywords

Aggregation Adaptation Agent Bloom filter Consistency

Basically filling the space here. So this is copied text from above. 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.

Keywords

Aggregation Adaptation Agent Bloom filter Consistency

Basically filling the space here. So this is copied text from above. 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.

Keywords

Aggregation Adaptation Agent Bloom filter Consistency