Related papers: Intelligent Management of Mobile Systems through C…
This paper addresses the challenges of high resource dynamism and scheduling complexity in cloud-native database systems. It proposes an adaptive resource orchestration method based on multi-agent reinforcement learning. The method…
Intermittent renewable energy resources like wind and solar pose great uncertainty of multiple time scales, from minutes to years, on the design and operation of power systems. Energy system optimization models have been developed to find…
The paper faces the problem of scheduling from a new perspective, trying to bridge the gap between classical heuristic approaches and system identification and control strategies. To this aim, a complete mathematical formulation of a…
Job submissions of parallel applications to production supercomputer systems will have to be carefully tuned in terms of the job submission parameters to obtain minimum response times. In this work, we have developed an end-to-end resource…
Uncertainty is a pervasive challenge in decision and risk management and it is usually studied by quantification and modeling. Interestingly, engineers and other decision makers usually manage uncertainty with strategies such as…
To accurately make adaptation decisions, a self-adaptive system needs precise means to analyze itself at runtime. To this end, runtime verification can be used in the feedback loop to check that the managed system satisfies its requirements…
Maintaining an acceptable level of quality of service in modern complex systems is challenging, particularly in the presence of various forms of uncertainty caused by changing execution context, unpredicted events, etc. Although…
The wide adoption of wireless devices in the Internet of Things requires controllers that are able to operate with limited resources, such as battery life. Operating these devices robustly in an uncertain environment, while managing…
With increasing complexity and heterogeneity of computing devices, it has become crucial for system to be autonomous, adaptive to dynamic environment, robust, flexible, and having so called self-*properties. These autonomous systems are…
Humanoid robots are designed to operate in human centered environments. They face changing, dynamic environments in which they need to fulfill a multitude of challenging tasks. Such tasks differ in complexity, resource requirements, and…
Conventional theoretical machine learning studies generally assume explicitly or implicitly that there are enough or even infinitely supplied computational resources. In real practice, however, computational resources are usually limited,…
Due to the diversity and implicit redundancy in terms of processing units and compute kernels, off-the-shelf heterogeneous systems offer the opportunity to detect and tolerate faults during task execution in hardware as well as in software.…
The ever increasing adoption of mobile devices with limited energy storage capacity, on the one hand, and more awareness of the environmental impact of massive data centres and server pools, on the other hand, have both led to an increased…
Resource pooling in ad hoc networks deals with accumulating computing and network resources to implement network control schemes such as routing, congestion, traffic management, and so on. Pooling of resources can be accomplished using the…
We propose novel iterative learning control algorithms to track a reference trajectory in resource-constrained control systems. In many applications, there are constraints on the number of control actions, delivered to the actuator from the…
The effective usages of computational resources are a primary concern of up-to-date distributed applications. In this paper, we present a methodology to reason about resource usages (acquisition, release, revision, ...), and therefore the…
Multi-agent systems powered by large language models have emerged as a promising paradigm for solving complex reasoning tasks through collaborative intelligence. However, efficiently deploying these systems on serverless GPU platforms…
Testing and code reviews are known techniques to improve the quality and robustness of software. Unfortunately, the complexity of modern software systems makes it impossible to anticipate all possible problems that can occur at runtime,…
This paper addresses the challenges of rapid resource variation and highly uncertain task loads in cloud computing environments. It proposes an optimization method for elastic cloud resource scaling based on a multi-agent system. The method…
We use ideas from distributed computing to study dynamic environments in which computational nodes, or decision makers, follow adaptive heuristics (Hart 2005), i.e., simple and unsophisticated rules of behavior, e.g., repeatedly "best…