Towards an Artificial Hormone System for Self-organizing Real-Time Task Allocation

Brinkschulte, Uwe; Pacher, Mathias; Renteln, Alexander von

doi:10.1007/978-3-540-75664-4_34

Cited by 27 publications

(25 citation statements)

References 2 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Similarly, the technique proposed in this paper does not involve task migration and hence remapping does not require moving a tasks state from one processor to another. Bio-inspired task management techniques have been explored by Brinkschulte et al [8], where they introduce an artificial hormone system for task mapping on heterogeneous processing elements, inspired by the hormone system of animals. Initial task allocation is found by exchanging different hormone signals and optimization is done by periodic re-allocation They also show how to guarantee an upper bound for self-configuration time, which is beneficial for real-time applications.…”

Section: B Dynamic Task Remappingmentioning

confidence: 99%

“…[7]) to overcome the limitations of centralised systems by partitioning the system resources and employing multiple cluster managers. Despite these efforts, the complexity of dynamic applications and large-scale multiprocessor, distributed systems of the future have given reason to investigate fully-distributed, autonomous self-organising/optimising mechanisms [8]- [10]. Such systems should be able to adapt or optimize itself to changing workload and internal conditions and to recover from faults.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Mendis

Indrusiak

Audsley

2015

2015 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)

View full text Add to dashboard Cite

Centralised management of distributed systems require a significant amount of monitoring traffic to maintain an accurate view of the system global state. The communication overhead of these systems becomes a bottleneck as the number of processing elements in the network and workload increase. State-of-the art in decentralised resource management techniques address this issue by allowing individual or clusters of nodes to make decisions at runtime to manage the dynamic workload. The primary contribution of this paper is using a bio-inspired, distributed, task remapping technique to manage dynamic multiple video stream decoding workloads. Our proposed technique has a low-communication overhead and is used to reduce the cumulative job lateness of the video streams. Secondary contributions include, several improvements to an existing clusterbased resource management approach to introduce awareness of task blocking and relocation distance. We evaluate these two remapping methods by comparing the improvement of job lateness, communication overhead and distribution of utilisation via simulation of several workload patterns.

show abstract

Section: B Dynamic Task Remappingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Mendis

Indrusiak

Audsley

2015

2015 13th IEEE Symposium on Embedded Systems for Real-Time Multimedia (ESTIMedia)

View full text Add to dashboard Cite

show abstract

“…Cluster based resource management techniques have been introduced (e.g., [69]) to overcome the 95 96 Swarm Intelligence Algorithms for Dynamic Task Reallocation limitations of centralised systems by partitioning the system resources and employing multiple cluster managers. Despite those efforts, the complexity of dynamic applications and the avaliability of distributed large-scale multiprocessor systems motivate the investigation of fully-distributed, autonomous self-organising/optimising mechanisms [21,97,145]. Such systems should be able to adapt or optimize itself to changing workload and internal conditions and to recover from faults.…”

Section: Swarm Intelligence Algorithms For Dynamic Task Reallocationmentioning

confidence: 99%

“…Task conditional admittance (lines [14][15][16][17][18][19][20][21][22][23][24][25]: If the controller output value is below threshold −Υ, the processor performance is not the highest possible and the previous change of P-State was done early enough (line 14), P-State is decreased (line 15) and the current time is substituted to γ (line 16). Similarly, provided the controller output value is above threshold +Υ, the processor performance is not the lowest possible (P-State is different from P max , the highest P-State available in the processor) and the previous change of P-State was done early enough (line 18), the processor P-State is increased (line 19) and the current time is assigned to γ (line 20).…”

mentioning

confidence: 99%

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

Indrusiak

Dziurzański

Singh

2016

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

View full text Add to dashboard Cite

The "River Publishers Series in Information Science and Technology" covers research which ushers the 21st Century into an Internet and multimedia era. Multimedia means the theory and application of filtering, coding, estimating, analyzing, detecting and recognizing, synthesizing, classifying, recording, and reproducing signals by digital and/or analog devices or techniques, while the scope of "signal" includes audio, video, speech, image, musical, multimedia, data/content, geophysical, sonar/radar, bio/medical, sensation, etc. Networking suggests transportation of such multimedia contents among nodes in communication and/or computer networks, to facilitate the ultimate Internet.Theory, technologies, protocols and standards, applications/services, practice and implementation of wired/wireless networking are all within the scope of this series. Based on network and communication science, we further extend the scope for 21st Century life through the knowledge in robotics, machine learning, embedded systems, cognitive science, pattern recognition, quantum/biological/molecular computation and information processing, biology, ecology, social science and economics, user behaviors and interface, and applications to health and society advance.Books published in the series include research monographs, edited volumes, handbooks and textbooks. The books provide professionals, researchers, educators, and advanced students in the field with an invaluable insight into the latest research and developments.

show abstract

“…In recent years, artificial endocrine systems have been widely used in human-machine communication of emotions [27][28][29], multiprocessor system control [30][31][32], decoupling control [33][34][35], management systems in autonomic networks [36], and real-time task allocation in heterogeneous processing systems [37][38][39].…”

Section: Introductionmentioning

confidence: 99%

Lattice-based artificial endocrine system model and its application in robotic swarms

Wang

2011

Sci. China Inf. Sci.

View full text Add to dashboard Cite

To solve the problem of controlling robot swarms in a distributed manner, we propose a novel latticebased artificial endocrine system (LAES) model, inspired by modern endocrinology theory. Based on a latticed environment, relying on cell intelligentization, connected by cumulative hormones, and directed by target cells, the LAES model can finally adapt to the continuous volatility of the external environment and maintain the relevant stability of the internal dynamics of the system, thus exhibiting the self-organizing and self-repairing features of the biological endocrine system. Experiments show that the LAES model enables a robotic swarm to search an unfamiliar space and seize multiple targets automatically without using unique global identifiers or a centralized control strategy for the individual robots. This demonstrates that the model can reliably be used to simulate large scale swarm behavior through wireless communication. CitationXu Q Z, Wang L. Lattice-based artificial endocrine system model and its application in robotic swarms.

show abstract

Towards an Artificial Hormone System for Self-organizing Real-Time Task Allocation

Cited by 27 publications

References 2 publications

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Bio-inspired distributed task remapping for multiple video stream decoding on homogeneous NoCs

Dynamic Resource Allocation in Embedded, High-Performance and Cloud Computing

Lattice-based artificial endocrine system model and its application in robotic swarms

Contact Info

Product

Resources

About