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Schahram Dustdar

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4 papers

2 author rows

TAAS Journal 2025 Journal Article

MemIndex: Agentic Event-based Distributed Memory Management for Multi-agent Systems

Alaa Saleh
Sasu Tarkoma
Anders Lindgren
Praveen Kumar Donta
Schahram Dustdar
Susanna Pirttikangas
Lauri Lovén

Interactive applications are latency-sensitive systems that enable dynamic responses to user inputs in domains such as robotics, industrial automation, and autonomous control. These applications require efficient application protocols for communication, with the pub/sub model being one of the most promising approaches. However, existing pub/sub systems are architecturally constrained, particularly by limited memory capacity and inefficiencies in dynamic environments. Addressing these challenges requires effective distributed memory management, yet this aspect has received limited attention in existing research. This paper addresses the gap by proposing MemIndex, an adaptive and autonomous distributed memory-management framework with an intent-indexed bipartite graph architecture. It is designed for an LM-based multi-agent pub/sub systems, enabling agents to autonomously negotiate memory operations in real time through dynamic index spaces for efficient reasoning. We evaluate our proposed MemIndex using diverse models against two baselines. Experimental results show MemIndex outperforms both baselines across storage, retrieval, update, and deletion operations, achieving average reductions of about 34% and 56% in elapsed time, 57% and 75% in CPU utilization, 23% and 76% in memory usage. Scalability tests further demonstrate that MemIndex maintains low end-to-end delay as submissions and agents grow, confirming that its negotiation-driven offloading enables efficient distributed memory management in interactive applications.

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IS Journal 2025 Journal Article

SNNL: A Programming Language for SNN Development

Qinghui Xing
Zirun Li
Ying Li
Schahram Dustdar
Xin Du
Gang Pan
Shuiguang Deng

Spiking Neural Networks (SNNs) are gaining attention for biological plausibility and energy efficiency. Advances in neuromorphic systems—integrating hardware and software tools—accelerate SNN implementation. Yet, deploying SNNs on such platforms remains challenging due to model complexity and system heterogeneity, requiring flexible frameworks. Existing tools (e. g. , PyNN, Brian2) show limited expressiveness for neuromorphic applications or poor cross-platform support. This paper proposes SNNL, a flexible domain-specific language for SNN development and deployment on neuromorphic hardware. SNNL decouples neuronal dynamics modeling from network topology specification: equation-based representations handle diverse neuron/synapse models, while hierarchical constructs define complex connectivity patterns. We present a Darwin3-targeted compiler with efficient code generation. Evaluations confirm SNNL achieves precise neuronal dynamic descriptions and flexible network configurations. This work bridges algorithm-hardware gaps in neuromorphic computing by enhancing programmability. Experimental results have demonstrated the feasibility of SNNL in developing SNNs for neuromorphic systems.

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ICLR Conference 2022 Conference Paper

Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting

Shizhan Liu
Hang Yu 0002
Cong Liao
Jianguo Li
Weiyao Lin
Alex X. Liu
Schahram Dustdar

Accurate prediction of the future given the past based on time series data is of paramount importance, since it opens the door for decision making and risk management ahead of time. In practice, the challenge is to build a flexible but parsimonious model that can capture a wide range of temporal dependencies. In this paper, we propose Pyraformer by exploring the multiresolution representation of the time series. Specifically, we introduce the pyramidal attention module (PAM) in which the inter-scale tree structure summarizes features at different resolutions and the intra-scale neighboring connections model the temporal dependencies of different ranges. Under mild conditions, the maximum length of the signal traversing path in Pyraformer is a constant (i.e., $\mathcal O(1)$) with regard to the sequence length $L$, while its time and space complexity scale linearly with $L$. Extensive numerical results show that Pyraformer typically achieves the highest prediction accuracy in both single-step and long-range forecasting tasks with the least amount of time and memory consumption, especially when the sequence is long.

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KER Journal 2014 Journal Article

Quality of Context: models and applications for context-aware systems in pervasive environments

Atif Manzoor
Hong-Linh Truong
Schahram Dustdar

Abstract Limitations of sensors and the situation of a specific measurement can affect the quality of context information that is implicitly collected in pervasive environments. The lack of information about Quality of Context (QoC) can result in degraded performance of context-aware systems in pervasive environments, without knowing the actual problem. Context-aware systems can take advantage of QoC if context producers also provide QoC metrics along with context information. In this paper, we analyze QoC and present our model for processing QoC metrics. We evaluate QoC metrics considering the capabilities of sensors, circumstances of specific measurement, requirements of context consumer, and the situation of the use of context information. We also illustrate how QoC metrics can facilitate in enhancing the effectiveness and efficiency of different tasks performed by a system to provide context information in pervasive environments.

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