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University of Lbeck
Why we think this paper is great for you:
This paper directly addresses optimizing throughput and handling faults in complex multi-agent systems, offering valuable insights into managing performance bottlenecks. It provides a framework highly relevant to your focus on robust, high-performance distributed environments.
This paper directly addresses optimizing throughput and handling faults in complex multi-agent systems, offering valuable insights into managing performance bottlenecks. It provides a framework highly relevant to your focus on robust, high-performance distributed environments.
Abstract
In recent years, the research of multi-agent systems has taken a direction to explore larger and more complex models to fulfill sophisticated tasks. We point out two possible pitfalls that might be caused by increasing complexity; susceptibilities to faults, and performance bottlenecks. To prevent the former threat, we propose a transaction-based framework to design very complex multi-agent systems (VCMAS). To address the second threat, we offer to integrate transaction scheduling into the proposed framework. We implemented both of these ideas to develop the OptiMA framework and show that it is able to facilitate the execution of VCMAS with more than a hundred agents. We also demonstrate the effect of transaction scheduling on such a system by showing improvements up to more than 16\%. Furthermore, we also performed a theoretical analysis on the transaction scheduling problem and provided practical tools that can be used for future research on it.
In recent years, the research of multi-agent systems has taken a direction to explore larger and more complex models to fulfill sophisticated tasks. We point out two possible pitfalls that might be caused by increasing complexity; susceptibilities to faults, and performance bottlenecks. To prevent the former threat, we propose a transaction-based framework to design very complex multi-agent systems (VCMAS). To address the second threat, we offer to integrate transaction scheduling into the proposed framework. We implemented both of these ideas to develop the OptiMA framework and show that it is able to facilitate the execution of VCMAS with more than a hundred agents. We also demonstrate the effect of transaction scheduling on such a system by showing improvements up to more than 16\%. Furthermore, we also performed a theoretical analysis on the transaction scheduling problem and provided practical tools that can be used for future research on it.
AI Summary
- OptiMA introduces a novel transaction-based framework to design Very Complex Multi-Agent Systems (VCMAS), addressing susceptibility to faults by ensuring ACID properties for agent operations. [2]
- The framework integrates transaction scheduling to optimize throughput in VCMAS, demonstrating performance improvements of up to more than 16% for systems with over a hundred agents. [2]
- OptiMA employs a variant of Rigorous Conservative 2-Phase Locking (RC2PL) for concurrency control, which acquires all locks before transaction start and releases them only after commit/abort, minimizing transaction aborts for 'real actions' and ensuring high isolation. [2]
- The paper provides a comprehensive theoretical analysis of the Transaction Scheduling Problem (TxnSP), proving its NP-Hard complexity and investigating how its solvability varies with conflict parity (cp). [2]
- A software library for TxnSP is developed, offering tools for creating, analyzing, and solving TxnSP instances using methods like exhaustive search, mixed-integer programming, dynamic programming, and simulated annealing. [2]
- OptiMA supports dynamic VCMAS architectures with features like supervisor-subordinate relationships, plugin access control, and inter-agent communication permissions, all enforced through consistency constraints. [2]
- Very Complex Multi-Agent Systems (VCMAS): Multi-agent systems designed to accomplish sophisticated tasks, often involving LLM-based agents, characterized by complex agent roles, superior-subordinate relationships, plugin usage, and dynamic state alteration. [2]
- OptiMA Framework: A transaction-based framework that provides an environment to design and execute VCMAS safely and efficiently, integrating fault tolerance through ACID transactions and throughput optimization via transaction scheduling. [2]
- Transaction Scheduling Problem (TxnSP): The problem of scheduling 'n' jobs (transactions) on 'm' identical parallel machines, where some job pairs cannot be processed concurrently, with the objective of minimizing the makespan. [2]
- Conflict Parity (cp): A metric representing the ratio of conflicting job pairs to the total number of possible pairs in a TxnSP instance, ranging from 0 (no conflicts) to 1 (all jobs conflict). [2]
University of Applied Sci
Why we think this paper is great for you:
This work on lightweight latency prediction for real-time edge applications is highly relevant, as it directly tackles the challenge of achieving low latency in distributed computing. It provides methods for ensuring reliable task offloading in time-sensitive scenarios.
This work on lightweight latency prediction for real-time edge applications is highly relevant, as it directly tackles the challenge of achieving low latency in distributed computing. It provides methods for ensuring reliable task offloading in time-sensitive scenarios.
Abstract
Accurately predicting end-to-end network latency is essential for enabling reliable task offloading in real-time edge computing applications. This paper introduces a lightweight latency prediction scheme based on rational modelling that uses features such as frame size, arrival rate, and link utilization, eliminating the need for intrusive active probing. The model achieves state-of-the-art prediction accuracy through extensive experiments and 5-fold cross-validation (MAE = 0.0115, R$^2$ = 0.9847) with competitive inference time, offering a substantial trade-off between precision and efficiency compared to traditional regressors and neural networks.
Accurately predicting end-to-end network latency is essential for enabling reliable task offloading in real-time edge computing applications. This paper introduces a lightweight latency prediction scheme based on rational modelling that uses features such as frame size, arrival rate, and link utilization, eliminating the need for intrusive active probing. The model achieves state-of-the-art prediction accuracy through extensive experiments and 5-fold cross-validation (MAE = 0.0115, R$^2$ = 0.9847) with competitive inference time, offering a substantial trade-off between precision and efficiency compared to traditional regressors and neural networks.
Washington State Universt
Why we think this paper is great for you:
This paper's focus on quantifying and modeling resilience in power systems, particularly against extreme events and outages, is a direct match for your needs. It offers methods to understand and improve system robustness against disruptions.
This paper's focus on quantifying and modeling resilience in power systems, particularly against extreme events and outages, is a direct match for your needs. It offers methods to understand and improve system robustness against disruptions.
Abstract
The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learning approaches to quantitatively model the relationship between weather parameters and power system resilience metrics. By leveraging real-world publicly available outage and weather data, we identify key weather variables of wind speed, temperature, and precipitation influencing a particular region's resilience metrics. A case study of Cook County, Illinois, and Miami-Dade County, Florida, reveals that these weather parameters are critical factors in resiliency analysis and risk assessment. Additionally, we find that these weather variables have combined effects when studied jointly compared to their effects in isolation. This framework provides valuable insights for understanding how weather events affect power distribution system performance, supporting decision-makers in developing more effective strategies for risk mitigation, resource allocation, and adaptation to changing climatic conditions.
The increasing frequency and intensity of extreme weather events is significantly affecting the power grid, causing large-scale outages and impacting power system resilience. Yet limited work has been done on systematically modeling the impacts of weather parameters to quantify resilience. This study presents a framework using statistical and Bayesian learning approaches to quantitatively model the relationship between weather parameters and power system resilience metrics. By leveraging real-world publicly available outage and weather data, we identify key weather variables of wind speed, temperature, and precipitation influencing a particular region's resilience metrics. A case study of Cook County, Illinois, and Miami-Dade County, Florida, reveals that these weather parameters are critical factors in resiliency analysis and risk assessment. Additionally, we find that these weather variables have combined effects when studied jointly compared to their effects in isolation. This framework provides valuable insights for understanding how weather events affect power distribution system performance, supporting decision-makers in developing more effective strategies for risk mitigation, resource allocation, and adaptation to changing climatic conditions.
Purdue University, IN 479
Why we think this paper is great for you:
This paper explores performance optimization in multi-user systems, which can offer transferable insights into managing and optimizing resource placement for overall system efficiency. It touches on system performance aspects that might interest you.
This paper explores performance optimization in multi-user systems, which can offer transferable insights into managing and optimizing resource placement for overall system efficiency. It touches on system performance aspects that might interest you.
Abstract
It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works consider either simple coverage maximization or simultaneous optimization of the placement of the RIS along with the beamforming and reflection coefficients, most of which assume that the location of the RIS, base station (BS), and users are known. However, in practice, only the spatial variation of user density and obstacle configuration are likely to be known prior to deployment of the system. Thus, we formulate a non-convex problem that optimizes the position of the RIS over the expected minimum signal-to-interference-plus-noise ratio (SINR) of the system with user randomness, assuming that the system employs joint beamforming after deployment. To solve this problem, we propose a recursive coarse-to-fine methodology that constructs a set of candidate locations for RIS placement based on the obstacle configuration and evaluates them over multiple instantiations from the user distribution. The search is recursively refined within the optimal region identified in each stage to determine the final optimal region for RIS deployment. Numerical results are presented to corroborate our findings.
It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works consider either simple coverage maximization or simultaneous optimization of the placement of the RIS along with the beamforming and reflection coefficients, most of which assume that the location of the RIS, base station (BS), and users are known. However, in practice, only the spatial variation of user density and obstacle configuration are likely to be known prior to deployment of the system. Thus, we formulate a non-convex problem that optimizes the position of the RIS over the expected minimum signal-to-interference-plus-noise ratio (SINR) of the system with user randomness, assuming that the system employs joint beamforming after deployment. To solve this problem, we propose a recursive coarse-to-fine methodology that constructs a set of candidate locations for RIS placement based on the obstacle configuration and evaluates them over multiple instantiations from the user distribution. The search is recursively refined within the optimal region identified in each stage to determine the final optimal region for RIS deployment. Numerical results are presented to corroborate our findings.
Center for Gravitation
Why we think this paper is great for you:
While not directly aligned with your core interests, this paper discusses complex system analysis for achieving high sensitivity, which might offer abstract parallels in optimizing performance in highly precise systems. It delves into intricate system design for specific performance goals.
While not directly aligned with your core interests, this paper discusses complex system analysis for achieving high sensitivity, which might offer abstract parallels in optimizing performance in highly precise systems. It delves into intricate system design for specific performance goals.
Abstract
Forthcoming space-based gravitational-wave (GW) detectors will employ second-generation time-delay interferometry (TDI) to suppress laser frequency noise and achieve the sensitivity required for GW detection. We introduce an inverse light-path operator $\mathcal{P}_{i_{1}i_{2}i_{3}\ldots i_{n-1}i_{n}}$, which enables simple representation of second-generation TDI combinations and a concise description of light propagation. Analytical expressions and high-accuracy approximate formulas are derived for the sky- and polarization-averaged response functions, noise power spectral densities (PSDs), and sensitivity curves of TDI Michelson, ($\alpha,\beta,\gamma$), Monitor, Beacon, Relay, and Sagnac combinations, as well as their orthogonal $A, E, T$ channels. Our results show that: (i) second-generation TDIs have the same sensitivities as their first-generation counterparts; (ii) the $A, E, T$ sensitivities and the optimal sensitivity are independent of the TDI generation and specific combination; (iii) the $A$ and $E$ channels have equal averaged responses, noise PSDs, and sensitivities, while the $T$ channel has much weaker response and sensitivity at low frequencies ($2\pi fL/c\lesssim3$); (iv) except for the $(\alpha,\beta,\gamma)$ and $\zeta$ combinations and the $T$ channel, all sensitivity curves exhibit a flat section in the range $f_{n}
Forthcoming space-based gravitational-wave (GW) detectors will employ second-generation time-delay interferometry (TDI) to suppress laser frequency noise and achieve the sensitivity required for GW detection. We introduce an inverse light-path operator $\mathcal{P}_{i_{1}i_{2}i_{3}\ldots i_{n-1}i_{n}}$, which enables simple representation of second-generation TDI combinations and a concise description of light propagation. Analytical expressions and high-accuracy approximate formulas are derived for the sky- and polarization-averaged response functions, noise power spectral densities (PSDs), and sensitivity curves of TDI Michelson, ($\alpha,\beta,\gamma$), Monitor, Beacon, Relay, and Sagnac combinations, as well as their orthogonal $A, E, T$ channels. Our results show that: (i) second-generation TDIs have the same sensitivities as their first-generation counterparts; (ii) the $A, E, T$ sensitivities and the optimal sensitivity are independent of the TDI generation and specific combination; (iii) the $A$ and $E$ channels have equal averaged responses, noise PSDs, and sensitivities, while the $T$ channel has much weaker response and sensitivity at low frequencies ($2\pi fL/c\lesssim3$); (iv) except for the $(\alpha,\beta,\gamma)$ and $\zeta$ combinations and the $T$ channel, all sensitivity curves exhibit a flat section in the range $f_{n}
Purdue University, IN 479
Why we think this paper is great for you:
This paper explores performance optimization in multi-user systems, which can offer transferable insights into managing and optimizing resource placement for overall system efficiency. It touches on system performance aspects that might interest you.
This paper explores performance optimization in multi-user systems, which can offer transferable insights into managing and optimizing resource placement for overall system efficiency. It touches on system performance aspects that might interest you.
Abstract
It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works consider either simple coverage maximization or simultaneous optimization of the placement of the RIS along with the beamforming and reflection coefficients, most of which assume that the location of the RIS, base station (BS), and users are known. However, in practice, only the spatial variation of user density and obstacle configuration are likely to be known prior to deployment of the system. Thus, we formulate a non-convex problem that optimizes the position of the RIS over the expected minimum signal-to-interference-plus-noise ratio (SINR) of the system with user randomness, assuming that the system employs joint beamforming after deployment. To solve this problem, we propose a recursive coarse-to-fine methodology that constructs a set of candidate locations for RIS placement based on the obstacle configuration and evaluates them over multiple instantiations from the user distribution. The search is recursively refined within the optimal region identified in each stage to determine the final optimal region for RIS deployment. Numerical results are presented to corroborate our findings.
It is well established that the performance of reconfigurable intelligent surface (RIS)-assisted systems critically depends on the optimal placement of the RIS. Previous works consider either simple coverage maximization or simultaneous optimization of the placement of the RIS along with the beamforming and reflection coefficients, most of which assume that the location of the RIS, base station (BS), and users are known. However, in practice, only the spatial variation of user density and obstacle configuration are likely to be known prior to deployment of the system. Thus, we formulate a non-convex problem that optimizes the position of the RIS over the expected minimum signal-to-interference-plus-noise ratio (SINR) of the system with user randomness, assuming that the system employs joint beamforming after deployment. To solve this problem, we propose a recursive coarse-to-fine methodology that constructs a set of candidate locations for RIS placement based on the obstacle configuration and evaluates them over multiple instantiations from the user distribution. The search is recursively refined within the optimal region identified in each stage to determine the final optimal region for RIS deployment. Numerical results are presented to corroborate our findings.
Center for Gravitation
Why we think this paper is great for you:
While not directly aligned with your core interests, this paper discusses complex system analysis for achieving high sensitivity, which might offer abstract parallels in optimizing performance in highly precise systems. It delves into intricate system design for specific performance goals.
While not directly aligned with your core interests, this paper discusses complex system analysis for achieving high sensitivity, which might offer abstract parallels in optimizing performance in highly precise systems. It delves into intricate system design for specific performance goals.
Abstract
Forthcoming space-based gravitational-wave (GW) detectors will employ second-generation time-delay interferometry (TDI) to suppress laser frequency noise and achieve the sensitivity required for GW detection. We introduce an inverse light-path operator $\mathcal{P}_{i_{1}i_{2}i_{3}\ldots i_{n-1}i_{n}}$, which enables simple representation of second-generation TDI combinations and a concise description of light propagation. Analytical expressions and high-accuracy approximate formulas are derived for the sky- and polarization-averaged response functions, noise power spectral densities (PSDs), and sensitivity curves of TDI Michelson, ($\alpha,\beta,\gamma$), Monitor, Beacon, Relay, and Sagnac combinations, as well as their orthogonal $A, E, T$ channels. Our results show that: (i) second-generation TDIs have the same sensitivities as their first-generation counterparts; (ii) the $A, E, T$ sensitivities and the optimal sensitivity are independent of the TDI generation and specific combination; (iii) the $A$ and $E$ channels have equal averaged responses, noise PSDs, and sensitivities, while the $T$ channel has much weaker response and sensitivity at low frequencies ($2\pi fL/c\lesssim3$); (iv) except for the $(\alpha,\beta,\gamma)$ and $\zeta$ combinations and the $T$ channel, all sensitivity curves exhibit a flat section in the range $f_{n}
Forthcoming space-based gravitational-wave (GW) detectors will employ second-generation time-delay interferometry (TDI) to suppress laser frequency noise and achieve the sensitivity required for GW detection. We introduce an inverse light-path operator $\mathcal{P}_{i_{1}i_{2}i_{3}\ldots i_{n-1}i_{n}}$, which enables simple representation of second-generation TDI combinations and a concise description of light propagation. Analytical expressions and high-accuracy approximate formulas are derived for the sky- and polarization-averaged response functions, noise power spectral densities (PSDs), and sensitivity curves of TDI Michelson, ($\alpha,\beta,\gamma$), Monitor, Beacon, Relay, and Sagnac combinations, as well as their orthogonal $A, E, T$ channels. Our results show that: (i) second-generation TDIs have the same sensitivities as their first-generation counterparts; (ii) the $A, E, T$ sensitivities and the optimal sensitivity are independent of the TDI generation and specific combination; (iii) the $A$ and $E$ channels have equal averaged responses, noise PSDs, and sensitivities, while the $T$ channel has much weaker response and sensitivity at low frequencies ($2\pi fL/c\lesssim3$); (iv) except for the $(\alpha,\beta,\gamma)$ and $\zeta$ combinations and the $T$ channel, all sensitivity curves exhibit a flat section in the range $f_{n}
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