ACM Transactions on

Autonomous and Adaptive Systems (TAAS)

Latest Articles

SOD: Making Smartphone Smart on Demand with Radio Interface Management

A major concern for today’s smartphones is their much faster battery drain than traditional feature phones, despite their greater battery capacities. The difference is mainly contributed by those more powerful but also much more power-consuming smartphone components, such as the multi-core application processor and the high-definition (HD)... (more)

Improving Data-Analytics Performance Via Autonomic Control of Concurrency and Resource Units

Many big-data processing jobs use data-analytics frameworks such as Apache Hadoop (currently also known as YARN). Such frameworks have tunable... (more)

Probabilistic Policy Reuse for Safe Reinforcement Learning

This work introduces Policy Reuse for Safe Reinforcement Learning, an algorithm that combines Probabilistic Policy Reuse and teacher advice for safe... (more)

Adaptive Behavior Modeling in Logistic Systems with Agents and Dynamic Graphs

Inside a logistic system, actors of the logistics have to interact to manage a coherent flow of goods. They also must deal with the constraints of... (more)


New EiC

ACM Transactions on Autonomous and Adaptive Systems Names Bashar Nuseibeh as EiC

ACM Transactions on Autonomous and Adaptive Systems (TAAS) has named Bashar Nuseibeh as Editor-in-Chief, for the term October 1, 2017 to September 30, 2020. Bashar is a Professor of Computing at The Open University, UK,  and a Professor of Software Engineering at Lero - The Irish Software Research Centre. He is also Visiting Professor at University College London and at the National Institute of Informatics in Japan.

TSLAM: A Trust-enabled Self-Learning Agent Model for Service Matching in the Cloud Market

With the rapid development of cloud computing, various types of cloud services emerge. However, it remains a big challenge for cloud users to find suitable and cost-effective services for two major reasons. One is that it can be a possibility that providers dont provide services in accordance with their declared Service Level Agreements(SLA). The other is that it is difficult for customers to describe their requirements accurately. In order to help users select cloud services efficiently, this paper presents a Trust enabled Self-Learning Agent Model for service Matching (TSLAM). In the model, Agents represent cloud entities to take actions and the learning module embedded in broker agents help to capture the implicit demands and find the real service preferences of users. Along with the transactions, under the effect of the learning mechanism, services are classified into different types, and each type is managed by different broker agents, due to the randomness of customers needs faced by different brokers. Moreover, trust is introduced into the process to improve the security of cloud market against malicious behaviors. Extensive experiments prove that TSLAM is able to optimize the service market so that services can be matched to customers requirements efficiently.

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