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ACM Transactions on Autonomous and Adaptive Systems (TAAS), Volume 12 Issue 2, May 2017

Budget-Driven Scheduling of Scientific Workflows in IaaS Clouds with Fine-Grained Billing Periods
Maria A. Rodriguez, Rajkumar Buyya
Article No.: 5
DOI: 10.1145/3041036

With the advent of cloud computing and the availability of data collected from increasingly powerful scientific instruments, workflows have become a prevailing mean to achieve significant scientific advances at an increased pace. Scheduling...

On Service Migrations in the Cloud for Mobile Accesses: A Distributed Approach
Yang Wang, Bharadwaj Veeravalli, Chen-Khong Tham, Shuibing He, Chengzhong Xu
Article No.: 6
DOI: 10.1145/3050438

We study the problem of dynamically migrating a service in the cloud to satisfy an online sequence of mobile batch-request demands in a cost-effective way. The service may have single or multiple replicas, each running on a virtual machine. As the...

Autonomous Mobile Sensor Placement in Complex Environments
Novella Bartolini, Tiziana Calamoneri, Stefano Ciavarella, Thomas La Porta, Simone Silvestri
Article No.: 7
DOI: 10.1145/3050439

In this article, we address the problem of autonomously deploying mobile sensors in an unknown complex environment. In such a scenario, mobile sensors may encounter obstacles or environmental sources of noise, so that movement and sensing...

Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition
Hongbign Wang, Xin Chen, Qin Wu, Qi Yu, Xingguo Hu, Zibin Zheng, Athman Bouguettaya
Article No.: 8
DOI: 10.1145/3058592

Service-oriented architecture is a widely used software engineering paradigm to cope with complexity and dynamics in enterprise applications. Service composition, which provides a cost-effective way to implement software systems, has attracted...

Prediction-Based Multi-Agent Reinforcement Learning in Inherently Non-Stationary Environments
Andrei Marinescu, Ivana Dusparic, Siobhán Clarke
Article No.: 9
DOI: 10.1145/3070861

Multi-agent reinforcement learning (MARL) is a widely researched technique for decentralised control in complex large-scale autonomous systems. Such systems often operate in environments that are continuously evolving and where agents’...

Feature Construction for Controlling Swarms by Visual Demonstration
Karan K. Budhraja, John Winder, Tim Oates
Article No.: 10
DOI: 10.1145/3084541

Agent-based modeling is a paradigm of modeling dynamic systems of interacting agents that are individually governed by specified behavioral rules. Training a model of such agents to produce an emergent behavior by specification of the emergent (as...