ACM Transactions on Autonomous and Adaptive Systems (TAAS)最新文献

{"title":"An Innovative Approach for Ad Hoc Network Establishment in Disaster Environments by the Deployment of Wireless Mobile Agents","authors":"Xing Su, Minjie Zhang, Q. Bai","doi":"10.1145/3337795","DOIUrl":"https://doi.org/10.1145/3337795","url":null,"abstract":"In disasters, many stationary tasks, such as saving survivors in debris, extinguishing fire of buildings, and so on, need first responders to complete on site. In such circumstances, wireless mobile robots are usually employed to search for tasks and establish ad hoc networks to assist first responders. Due to the unknown and complexity of environments and limited capabilities of wireless mobile robots, searching and establishing ad hoc networks in disaster environments is a challenging issue in both theory and practice. To this end, a task-based wireless mobile robot deployment approach is proposed in this article. The proposed approach consists of a search process and a deployment process. The search process can guide wireless mobile robots to efficiently find tasks in unknown and complex environments. The deployment process can find suitable deployment locations for wireless mobile robots to establish ad hoc networks. The established ad hoc networks can ensure the communication of wireless mobile robots in the network and can cover the maximum number of task locations and the maximum areas in a disaster environment. Experimental results demonstrate that based on the proposed approach, wireless mobile robots have better performance in terms of search and ad hoc network establishment in disaster environments.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121018253","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Behavior Modeling in Logistic Systems with Agents and Dynamic Graphs","authors":"Thibaut Démare, C. Bertelle, Antoine Dutot, D. Fournier","doi":"10.1145/3313799","DOIUrl":"https://doi.org/10.1145/3313799","url":null,"abstract":"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 their environment. The article’s first goal is to study how macro properties (such as global performance) emerge from the dynamic and local behaviors of actors and the structure of the territory. The second goal is to understand which local parameters affect these macro properties. A multi-scale approach made of an agent-based model coupled with dynamic graphs describes the system’s components, including actors and the transportation network. Adaptive behaviors are implemented in this model (with data about the Seine axis) to highlight the system’s dynamics. Agent strategies are evolving according to traffic dynamics and disruptions. This logistic system simulator has the capacity to exhibit large-scale evolution of territorial behavior and efficiency face to various scenarios of local agent behaviors.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115770606","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Probabilistic Policy Reuse for Safe Reinforcement Learning","authors":"Javier García, F. Fernández","doi":"10.1145/3310090","DOIUrl":"https://doi.org/10.1145/3310090","url":null,"abstract":"This work introduces Policy Reuse for Safe Reinforcement Learning, an algorithm that combines Probabilistic Policy Reuse and teacher advice for safe exploration in dangerous and continuous state and action reinforcement learning problems in which the dynamic behavior is reasonably smooth and the space is Euclidean. The algorithm uses a continuously increasing monotonic risk function that allows for the identification of the probability to end up in failure from a given state. Such a risk function is defined in terms of how far such a state is from the state space known by the learning agent. Probabilistic Policy Reuse is used to safely balance the exploitation of actual learned knowledge, the exploration of new actions, and the request of teacher advice in parts of the state space considered dangerous. Specifically, the π-reuse exploration strategy is used. Using experiments in the helicopter hover task and a business management problem, we show that the π-reuse exploration strategy can be used to completely avoid the visit to undesirable situations while maintaining the performance (in terms of the classical long-term accumulated reward) of the final policy achieved.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"69 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127275812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Data-Analytics Performance Via Autonomic Control of Concurrency and Resource Units","authors":"Gil Jae Lee, J. Fortes","doi":"10.1145/3309539","DOIUrl":"https://doi.org/10.1145/3309539","url":null,"abstract":"Many big-data processing jobs use data-analytics frameworks such as Apache Hadoop (currently also known as YARN). Such frameworks have tunable configuration parameters set by experienced system administrators and/or job developers. However, tuning parameters manually can be hard and time-consuming because it requires domain-specific knowledge and understanding of complex inter-dependencies among parameters. Most of the frameworks seek efficient resource management by assigning resource units to jobs, the maximum number of units allowed in a system being part of the static configuration of the system. This static resource management has limited effectiveness in coping with job diversity and workload dynamics, even in the case of a single job. The work reported in this article seeks to improve performance (e.g., multiple-jobs makespan and job completion time) without modification of either the framework or the applications and avoiding problems of previous self-tuning approaches based on performance models or resource usage. These problems include (1) the need for time-consuming training, typically offline and (2) unsuitability for multi-jobs/tenant environments. This article proposes a hierarchical self-tuning approach using (1) a fuzzy-logic controller to dynamically adjust the maximum number of concurrent jobs and (2) additional controllers (one for each cluster node) to adjust the maximum number of resource units assigned to jobs on each node. The fuzzy-logic controller uses fuzzy rules based on a concave-downward relationship between aggregate CPU usage and the number of concurrent jobs. The other controllers use a heuristic algorithm to adjust the number of resource units on the basis of both CPU and disk IO usage by jobs. To manage the maximum number of available resource units in each node, the controllers also take resource usage by other processes (e.g., system processes) into account. A prototype of our approach was implemented for Apache Hadoop on a cluster running at CloudLab. The proposed approach was demonstrated and evaluated with workloads composed of jobs with similar resource usage patterns as well as other realistic mixed-pattern workloads synthesized by SWIM, a statistical workload injector for MapReduce. The evaluation shows that the proposed approach yields up to a 48% reduction of the jobs makespan that results from using Hadoop-default settings.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130627751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Decentralized Collective Learning for Self-managed Sharing Economies","authors":"Evangelos Pournaras, Peter Pilgerstorfer, Thomas Asikis","doi":"10.1145/3277668","DOIUrl":"https://doi.org/10.1145/3277668","url":null,"abstract":"The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133263661","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SDN Flow Entry Management Using Reinforcement Learning","authors":"Ting-Yu Mu, Ala Al-Fuqaha, K. Shuaib, F. Sallabi, Junaid Qadir","doi":"10.1145/3281032","DOIUrl":"https://doi.org/10.1145/3281032","url":null,"abstract":"Modern information technology services largely depend on cloud infrastructures to provide their services. These cloud infrastructures are built on top of Datacenter Networks (DCNs) constructed with high-speed links, fast switching gear, and redundancy to offer better flexibility and resiliency. In this environment, network traffic includes long-lived (elephant) and short-lived (mice) flows with partitioned/aggregated traffic patterns. Although SDN-based approaches can efficiently allocate networking resources for such flows, the overhead due to network reconfiguration can be significant. With limited capacity of Ternary Content-Addressable Memory (TCAM) deployed in an OpenFlow enabled switch, it is crucial to determine which forwarding rules should remain in the flow table and which rules should be processed by the SDN controller in case of a table-miss on the SDN switch. This is needed in order to obtain the flow entries that satisfy the goal of reducing the long-term control plane overhead introduced between the controller and the switches. To achieve this goal, we propose a machine learning technique that utilizes two variations of Reinforcement Learning (RL) algorithms—the first of which is a traditional RL-based algorithm, while the other is deep reinforcement learning-based. Emulation results using the RL algorithm show around 60% improvement in reducing the long-term control plane overhead and around 14% improvement in the table-hit ratio compared to the Multiple Bloom Filters (MBF) method, given a fixed size flow table of 4KB.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114438141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Crowdsourcing Systems from a Multiagent Perspective and Approach","authors":"Jiuchuan Jiang, Bo An, Yichuan Jiang, Donghui Lin, Zhan Bu, Jie Cao, Zhifeng Hao","doi":"10.1145/3226028","DOIUrl":"https://doi.org/10.1145/3226028","url":null,"abstract":"Crowdsourcing has recently been significantly explored. Although related surveys have been conducted regarding this subject, each has mainly consisted of a review of a single aspect of crowdsourcing systems or on the application of crowdsourcing in a specific application domain. A crowdsourcing system is a comprehensive set of multiple entities, including various elements and processes. Multiagent computing has already been widely envisioned as a powerful paradigm for modeling autonomous multi-entity systems with adaptation to dynamic environments. Therefore, this article presents a novel multiagent perspective and approach to understanding crowdsourcing systems, which can be used to correlate the research on crowdsourcing and multiagent systems and inspire possible interdisciplinary research between the two areas. This article mainly discusses the following two aspects: (1) The multiagent perspective can be used for conducting a comprehensive survey on the state of the art of crowdsourcing, and (2) the multiagent approach can bring about concrete enhancements for crowdsourcing technology and inspire future research directions that enable crowdsourcing research to overcome the typical challenges in crowdsourcing technology. Finally, this article discusses the advantages and disadvantages of the multiagent perspective by comparing it with two other popular perspectives on crowdsourcing: the business perspective and the technical perspective.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130165354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Process Migrations in Coupled Applications for Exchanging Data in Local File Cache","authors":"Jianwei Liao, Zhigang Cai, François Trahay, J. Zhou, G. Xiao","doi":"10.1145/3226027","DOIUrl":"https://doi.org/10.1145/3226027","url":null,"abstract":"Many problems in science and engineering are usually emulated as a set of mutually interacting models, resulting in a coupled or multiphysics application. These component models show challenges originating from their interdisciplinary nature and from their computational and algorithmic complexities. In general, these models are independently developed and maintained, so that they commonly employ the global file system for exchanging their data in the coupled application. To effectively use the local file cache on the compute node for exchanging the data among the processes of such applications, and consequently boosting I/O performance, this article presents a novel mechanism to migrate a process from one compute node to another node on the basis of block I/O dependency. In this newly proposed mechanism, the block I/O dependency between two involved processes running on the different nodes is profiled as block access similarity by taking advantage of the Cohen’s kappa statistic. Then, the process is supposed to be dynamically migrated from its source node to the destination node, on which there is another process having heavy block I/O dependency. As a result, both processes can exchange their data by utilizing the local file cache instead of the global file system to reduce I/O time. The experimental results demonstrate that the I/O performance can be significantly improved, and the time required for executing the application can be resultantly decreased, as expected.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132965556","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive Opportunistic Airborne Sensor Sharing","authors":"J. Beal, K. Usbeck, J. Loyall, Mason Rowe, J. Metzler","doi":"10.1145/3179994","DOIUrl":"https://doi.org/10.1145/3179994","url":null,"abstract":"Airborne sensor platforms are becoming increasingly significant for both civilian and military operations; yet, at present, their sensors are typically idle for much of their flight time, e.g., while the sensor-equipped platform is in transit to and from the locations of sensing tasks. The sensing needs of many other potential information consumers might thus be served by sharing such sensors, thereby allowing other information consumers to opportunistically task them during their otherwise unscheduled time, as well as enabling other improvements, such as decreasing the number of platforms needed to achieve a goal and increasing the resilience of sensor tasks through duplication. We have implemented a prototype system realizing these goals in Mission-Driven Tasking of Information Producers (MTIP), which leverages an agent-based representation of tasks and sensors to enable fast, effective, and adaptive opportunistic sharing of airborne sensors. Using a simulated large-scale disaster-response scenario populated with publicly available Geographic Information System (GIS) datasets, we demonstrate that correlations in task location are likely to lead to a high degree of potential for sensor-sharing. We then validate that our implementation of MTIP can successfully carry out such sharing, showing that it increases the number of sensor tasks served, reduces the number of platforms required to serve a given set of sensor tasks, and adapts well to radical changes in flight path.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121027878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Engineering Self-Adaptive Software Systems","authors":"Konstantinos Angelopoulos, A. Papadopoulos, V. Souza, J. Mylopoulos","doi":"10.1145/3105748","DOIUrl":"https://doi.org/10.1145/3105748","url":null,"abstract":"Self-adaptive software systems monitor their operation and adapt when their requirements fail due to unexpected phenomena in their environment. This article examines the case where the environment changes dynamically over time and the chosen adaptation has to take into account such changes. In control theory, this type of adaptation is known as Model Predictive Control and comes with a well-developed theory and myriad successful applications. The article focuses on modeling the dynamic relationship between requirements and possible adaptations. It then proposes a controller that exploits this relationship to optimize the satisfaction of requirements relative to a cost function. This is accomplished through a model-based framework for designing self-adaptive software systems that can guarantee a certain level of requirements satisfaction over time by dynamically composing adaptation strategies when necessary. The proposed framework is illustrated and evaluated through two simulated systems, namely, the Meeting-Scheduling exemplar and an E-Shop.","PeriodicalId":377078,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems (TAAS)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123631830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}