Sajib Kundu, R. Rangaswami, Ming Zhao, Ajay Gulati, K. Dutta
The increasing VM density in cloud hosting services makes careful management of physical resources such as CPU, memory, and I/O bandwidth within individual virtualized servers a priority. To maximize cost-efficiency, resource management needs to be coupled with the revenue generating mechanisms of cloud hosting: the service level agreements (SLAs) of hosted client applications. In this paper, we develop a server resource management framework that reduces data center resource management complexity substantially. Our solution implements revenue-driven dynamic resource allocation which continuously steers the resource distribution across hosted VMs within a server such as to maximize the SLA-generated revenue from the server. Our experimental evaluation for a VMware ESX hyper visor highlights the importance of both resource isolation and resource sharing across VMs. The empirical data shows a 7%-54% increase in total revenue generated for a mix of 10-25 VMs hosting either similar or diverse workloads when compared to using the currently available resource distribution mechanisms in ESX.
{"title":"Revenue Driven Resource Allocation for Virtualized Data Centers","authors":"Sajib Kundu, R. Rangaswami, Ming Zhao, Ajay Gulati, K. Dutta","doi":"10.1109/ICAC.2015.40","DOIUrl":"https://doi.org/10.1109/ICAC.2015.40","url":null,"abstract":"The increasing VM density in cloud hosting services makes careful management of physical resources such as CPU, memory, and I/O bandwidth within individual virtualized servers a priority. To maximize cost-efficiency, resource management needs to be coupled with the revenue generating mechanisms of cloud hosting: the service level agreements (SLAs) of hosted client applications. In this paper, we develop a server resource management framework that reduces data center resource management complexity substantially. Our solution implements revenue-driven dynamic resource allocation which continuously steers the resource distribution across hosted VMs within a server such as to maximize the SLA-generated revenue from the server. Our experimental evaluation for a VMware ESX hyper visor highlights the importance of both resource isolation and resource sharing across VMs. The empirical data shows a 7%-54% increase in total revenue generated for a mix of 10-25 VMs hosting either similar or diverse workloads when compared to using the currently available resource distribution mechanisms in ESX.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"36 1","pages":"197-206"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80746638","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}
Kazuo Hashimoto, Keiji Yamada, K. Tabata, Michio Oda, T. Suganuma, A. Biswas, P. Vlacheas, V. Stavroulaki, Dimitris Kelaidonis, A. Georgakopoulos
Intelligent Knowledge as a Service (iKaaS) is an ambitious project aiming at integrating sensor management using Internet of Things (IoT) and cloud services by employing sensor data. The platform design covers self-healing functions based on self-awareness as well as basic functions such as inter-cloud, security/privacy management, and devices and data management. From the viewpoint of application development, ontology sharing is the most important to integrate services. This paper, the first step towards ontology sharing, defines the iKaaS data model as one that integrates data models used in all applications in the project. The data defined in the iKaaS data model is converted into RDF format and stored in the RDF database. The reasoning mechanism in semantic web allows the semantic integration of data and applications. The iKaaS project is developing a prototype community service, town management and healthcare, in Tagonishi's Smart City. Presenting the iKaaS data model for these said services, this paper emphasizes the necessity of higher contextual awareness to achieve the goal of a better-fitted personalization for the individual.
{"title":"iKaaS Data Modeling: A Data Model for Community Services and Environment Monitoring in Smart City","authors":"Kazuo Hashimoto, Keiji Yamada, K. Tabata, Michio Oda, T. Suganuma, A. Biswas, P. Vlacheas, V. Stavroulaki, Dimitris Kelaidonis, A. Georgakopoulos","doi":"10.1109/ICAC.2015.64","DOIUrl":"https://doi.org/10.1109/ICAC.2015.64","url":null,"abstract":"Intelligent Knowledge as a Service (iKaaS) is an ambitious project aiming at integrating sensor management using Internet of Things (IoT) and cloud services by employing sensor data. The platform design covers self-healing functions based on self-awareness as well as basic functions such as inter-cloud, security/privacy management, and devices and data management. From the viewpoint of application development, ontology sharing is the most important to integrate services. This paper, the first step towards ontology sharing, defines the iKaaS data model as one that integrates data models used in all applications in the project. The data defined in the iKaaS data model is converted into RDF format and stored in the RDF database. The reasoning mechanism in semantic web allows the semantic integration of data and applications. The iKaaS project is developing a prototype community service, town management and healthcare, in Tagonishi's Smart City. Presenting the iKaaS data model for these said services, this paper emphasizes the necessity of higher contextual awareness to achieve the goal of a better-fitted personalization for the individual.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"7 1","pages":"301-306"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73446086","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}
Monitoring message workflow transmission is a very challenging problem, especially in pervasive environments, mainly because of the wide range of unexpected events (e.g. Human and material resources unavailability) and context changes (e.g. Source and target localizations) that may occur at run-time. In this paper, we propose an information system and services orchestration framework enabling intelligent message routing policy adaptation. Our objective is to build a reliable routing strategy that can autonomously and intelligently adapt its own behavior and decisions according to source and target context changes as well as to controlled message status (e.g. Exceeded deadlines for message reception). We present a solution that emphasizes some cutting-edge characteristics that we believe are crucial for enhancing the quality of message communication, such as intelligence, controllability, scalability, adaptivity and personalization. The routing decisions can be adapted at different levels of decision-making such as message itinerary, delay for message treatment, etc., by means of advanced AI methods that we detail for some of the most sensitive self-adaptive services.
{"title":"Framework for Intelligent Message Routing Policy Adaptation","authors":"N. Guizani, J. Fayn","doi":"10.1109/ICAC.2015.35","DOIUrl":"https://doi.org/10.1109/ICAC.2015.35","url":null,"abstract":"Monitoring message workflow transmission is a very challenging problem, especially in pervasive environments, mainly because of the wide range of unexpected events (e.g. Human and material resources unavailability) and context changes (e.g. Source and target localizations) that may occur at run-time. In this paper, we propose an information system and services orchestration framework enabling intelligent message routing policy adaptation. Our objective is to build a reliable routing strategy that can autonomously and intelligently adapt its own behavior and decisions according to source and target context changes as well as to controlled message status (e.g. Exceeded deadlines for message reception). We present a solution that emphasizes some cutting-edge characteristics that we believe are crucial for enhancing the quality of message communication, such as intelligence, controllability, scalability, adaptivity and personalization. The routing decisions can be adapted at different levels of decision-making such as message itinerary, delay for message treatment, etc., by means of advanced AI methods that we detail for some of the most sensitive self-adaptive services.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"43 1","pages":"235-238"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86473965","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}
Offloading is a technique that utilises the powerful computation resource of Clouds by migrating heavy computations from thin clients like mobile devices to a remote server. Although task completion in the cloud is usually fast, an unreliable network connection often causes delays or interruptions which level the advantages of powerful resources off. Restart is an efficient method that can under certain conditions reduce the task completion in computer and network systems. In this paper we introduce an automated restart scheme. It aims first at completing the job using restart with offloading. Once the number of offloading attempts exceeds a threshold, the job is completed locally. A key challenge is to identify the optimal limit for offloading attempts as to minimise the task completion time. To address this problem we mathematically derive the expected task completion time under different thresholds and compare results of our analysis.
{"title":"Automated Adaptive Restart for Accelerating Task Completion in Cloud Offloading Systems","authors":"Qiushi Wang, K. Wolter","doi":"10.1109/ICAC.2015.11","DOIUrl":"https://doi.org/10.1109/ICAC.2015.11","url":null,"abstract":"Offloading is a technique that utilises the powerful computation resource of Clouds by migrating heavy computations from thin clients like mobile devices to a remote server. Although task completion in the cloud is usually fast, an unreliable network connection often causes delays or interruptions which level the advantages of powerful resources off. Restart is an efficient method that can under certain conditions reduce the task completion in computer and network systems. In this paper we introduce an automated restart scheme. It aims first at completing the job using restart with offloading. Once the number of offloading attempts exceeds a threshold, the job is completed locally. A key challenge is to identify the optimal limit for offloading attempts as to minimise the task completion time. To address this problem we mathematically derive the expected task completion time under different thresholds and compare results of our analysis.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"114 1","pages":"157-158"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80583385","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}
Md. Tanvir Al Amin, Shen Li, Muntasir Raihan Rahman, P. Seetharamu, Shiguang Wang, T. Abdelzaher, Indranil Gupta, M. Srivatsa, R. Ganti, Reaz Ahmed, H. Le
The increasing availability of smartphones, cameras, and wearables with instant data sharing capabilities, and the exploitation of social networks for information broadcast, heralds a future of real-time information overload. With the growing excess of worldwide streaming data, such as images, geotags, text annotations, and sensory measurements, an increasingly common service will become one of data summarization. The objective of such a service will be to obtain a representative sampling of large data streams at a configurable granularity, in real-time, for subsequent consumption by a range of data-centric applications. This paper describes a general-purpose self-summarizing storage service, called Social Trove, for social sensing applications. The service summarizes data streams from human sources, or sensors in their possession, by hierarchically clustering received information in accordance with an application-specific distance metric. It then serves a sampling of produced clusters at a configurable granularity in response to application queries. While Social Trove is a general service, we illustrate its functionality and evaluate it in the specific context of workloads collected from Twitter. Results show that Social Trove supports a high query throughput, while maintaining a low access latency to the produced real-time application-specific data summaries. As a specific application case-study, we implement a fact-finding service on top of Social Trove.
{"title":"Social Trove: A Self-Summarizing Storage Service for Social Sensing","authors":"Md. Tanvir Al Amin, Shen Li, Muntasir Raihan Rahman, P. Seetharamu, Shiguang Wang, T. Abdelzaher, Indranil Gupta, M. Srivatsa, R. Ganti, Reaz Ahmed, H. Le","doi":"10.1109/ICAC.2015.47","DOIUrl":"https://doi.org/10.1109/ICAC.2015.47","url":null,"abstract":"The increasing availability of smartphones, cameras, and wearables with instant data sharing capabilities, and the exploitation of social networks for information broadcast, heralds a future of real-time information overload. With the growing excess of worldwide streaming data, such as images, geotags, text annotations, and sensory measurements, an increasingly common service will become one of data summarization. The objective of such a service will be to obtain a representative sampling of large data streams at a configurable granularity, in real-time, for subsequent consumption by a range of data-centric applications. This paper describes a general-purpose self-summarizing storage service, called Social Trove, for social sensing applications. The service summarizes data streams from human sources, or sensors in their possession, by hierarchically clustering received information in accordance with an application-specific distance metric. It then serves a sampling of produced clusters at a configurable granularity in response to application queries. While Social Trove is a general service, we illustrate its functionality and evaluate it in the specific context of workloads collected from Twitter. Results show that Social Trove supports a high query throughput, while maintaining a low access latency to the produced real-time application-specific data summaries. As a specific application case-study, we implement a fact-finding service on top of Social Trove.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"12 1","pages":"41-50"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89920700","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}
Symbiotic Cognitive Systems (SCS) are multi-agent systems comprising both human and software agents that are designed to collectively perform cognitive tasks such as decision-making better than humans or software agents can unaided. Autonomic Computing Systems (ACS) are multi-agent systems that manage applications as well as software and hardware resources in accordance with goals specified by human administrators and users. SCS and ACS share some key characteristics. First, both are designed to extend human intellectual capabilities, and as such they require effective means by which humans can communicate their objectives to the computing system. Second, their natural architecture is a multi-agent system in which dozens, hundreds or even more semi-autonomous entities interact. In both SCS and ACS, issues of inter-agent communication and coordination come to the fore. We report our experience with a moderate-scale SCS prototype that helps human experts make decisions with financial impacts ranging from millions to even billions of US: corporate mergers and acquisitions. Taking advantage of the commonalities, we translate this experience into insights that may benefit future research on ACS, and recommend a stronger focus on agent-human communication and building realistic system prototypes.
{"title":"A Symbiotic Cognitive Computing Perspective on Autonomic Computing","authors":"J. Kephart, J. Lenchner","doi":"10.1109/ICAC.2015.16","DOIUrl":"https://doi.org/10.1109/ICAC.2015.16","url":null,"abstract":"Symbiotic Cognitive Systems (SCS) are multi-agent systems comprising both human and software agents that are designed to collectively perform cognitive tasks such as decision-making better than humans or software agents can unaided. Autonomic Computing Systems (ACS) are multi-agent systems that manage applications as well as software and hardware resources in accordance with goals specified by human administrators and users. SCS and ACS share some key characteristics. First, both are designed to extend human intellectual capabilities, and as such they require effective means by which humans can communicate their objectives to the computing system. Second, their natural architecture is a multi-agent system in which dozens, hundreds or even more semi-autonomous entities interact. In both SCS and ACS, issues of inter-agent communication and coordination come to the fore. We report our experience with a moderate-scale SCS prototype that helps human experts make decisions with financial impacts ranging from millions to even billions of US: corporate mergers and acquisitions. Taking advantage of the commonalities, we translate this experience into insights that may benefit future research on ACS, and recommend a stronger focus on agent-human communication and building realistic system prototypes.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"6 1","pages":"109-114"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73232297","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}
Jan Kantert, H. Spiegelberg, Sven Tomforde, J. Hähner, C. Müller-Schloer
Grid systems are an ideal basis to parallelise computationally intensive tasks that efficiently can be split into parts. One possible application domain for such systems is rendering of films. Since small companies and underground film producers do not have the possibility to maintain appropriate computing environments for their own films, grid-based approaches can be used to build a self-organised and autonomic computing infrastructure. In order to avoid such systems from being exploited by malicious agents, we present a novel approach introducing technical trust which results in the Trusted Desktop Grid. In this paper, we demonstrate that the system is able to automatically isolate malicious agents and support an efficient utilisation for benevolent agents -- resulting in a self-protecting and self-healing system.
{"title":"Distributed Rendering in an Open Self-Organised Trusted Desktop Grid","authors":"Jan Kantert, H. Spiegelberg, Sven Tomforde, J. Hähner, C. Müller-Schloer","doi":"10.1109/ICAC.2015.66","DOIUrl":"https://doi.org/10.1109/ICAC.2015.66","url":null,"abstract":"Grid systems are an ideal basis to parallelise computationally intensive tasks that efficiently can be split into parts. One possible application domain for such systems is rendering of films. Since small companies and underground film producers do not have the possibility to maintain appropriate computing environments for their own films, grid-based approaches can be used to build a self-organised and autonomic computing infrastructure. In order to avoid such systems from being exploited by malicious agents, we present a novel approach introducing technical trust which results in the Trusted Desktop Grid. In this paper, we demonstrate that the system is able to automatically isolate malicious agents and support an efficient utilisation for benevolent agents -- resulting in a self-protecting and self-healing system.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"28 1","pages":"267-272"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77002541","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}
The adoption of low power, small footprint systems such as Hewlett Packard Moons hot cartridge servers massively increases the number of servers in cloud/farms implementations. Understanding problems, bottlenecks, and scaling of distributed applications running on such clusters requires the ability to replay the exhaustive data collected by monitoring systems. Current monitoring solutions make compromises, simplify (i.e. Destroy) the data over time or do not scale. Moreover, in the cloud model, server roles and assignments often change, making it mandatory to correlate monitoring data with higher level information such as task assignments known by scheduling software. We present an optimized and fast process to store and retrieve monitoring data, allowing access to all samples collected without any granularity loss and, at the same time, a generic mechanism to correlate with information from orchestrators.
{"title":"Optimized Storage and Fast Retrieval of Large Monitoring Datasets without Compromising Granularity","authors":"Sebastien Cabaniols, Nathalie Viollet, Clement Poulain","doi":"10.1109/ICAC.2015.53","DOIUrl":"https://doi.org/10.1109/ICAC.2015.53","url":null,"abstract":"The adoption of low power, small footprint systems such as Hewlett Packard Moons hot cartridge servers massively increases the number of servers in cloud/farms implementations. Understanding problems, bottlenecks, and scaling of distributed applications running on such clusters requires the ability to replay the exhaustive data collected by monitoring systems. Current monitoring solutions make compromises, simplify (i.e. Destroy) the data over time or do not scale. Moreover, in the cloud model, server roles and assignments often change, making it mandatory to correlate monitoring data with higher level information such as task assignments known by scheduling software. We present an optimized and fast process to store and retrieve monitoring data, allowing access to all samples collected without any granularity loss and, at the same time, a generic mechanism to correlate with information from orchestrators.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"18 1","pages":"135-136"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80304778","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}
Self-adaptive systems, which are highly related to Autonomic Computing, are a response to the increasing complexity and size of information systems. They are able to adapt their behavior to changes in the environment or system resources. A self-adaptive system consists of managed resources that realize functionality and an adaptation logic that controls the adaptations. So far, many research has been performed on adapting the managed resources. However, only few works cover adapting the adaptation logic, which might be necessary in several cases, e.g., When the architecture of the managed resources changes. This work adresses why adaptation of the adaptation logic might be beneficial, how it can be achieved, and what challenges arise.
{"title":"Runtime Evolution of the Adaptation Logic in Self-Adaptive Systems","authors":"F. Roth, Christian Krupitzer, C. Becker","doi":"10.1109/ICAC.2015.20","DOIUrl":"https://doi.org/10.1109/ICAC.2015.20","url":null,"abstract":"Self-adaptive systems, which are highly related to Autonomic Computing, are a response to the increasing complexity and size of information systems. They are able to adapt their behavior to changes in the environment or system resources. A self-adaptive system consists of managed resources that realize functionality and an adaptation logic that controls the adaptations. So far, many research has been performed on adapting the managed resources. However, only few works cover adapting the adaptation logic, which might be necessary in several cases, e.g., When the architecture of the managed resources changes. This work adresses why adaptation of the adaptation logic might be beneficial, how it can be achieved, and what challenges arise.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"24 1","pages":"141-142"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82036343","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}
The Internet of Things paradigm has gained ground both in the industry and in research worlds, and it is considered to revolution the way the physical and virtual worlds are connected by 2025. Fault tolerance, resilience, and self-healing research face new challenges in the Internet of Things context due to its promise of connecting billions of devices in an Internet-like structure. Objects in the Internet of Things must function autonomously, with minimum requirement for human intervention, specially when they are part of complex and critical applications. The goal of this paper is to: (i) motivate and show the relevance of the need of self-healing applications in the Internet of Things, (ii) introduce the responsible objects concept and the requirements for building a responsible object API, and (ii) set future research directions regarding self-healing Internet of Things applications.
{"title":"Responsible Objects: Towards Self-Healing Internet of Things Applications","authors":"Rafael Angarita","doi":"10.1109/ICAC.2015.60","DOIUrl":"https://doi.org/10.1109/ICAC.2015.60","url":null,"abstract":"The Internet of Things paradigm has gained ground both in the industry and in research worlds, and it is considered to revolution the way the physical and virtual worlds are connected by 2025. Fault tolerance, resilience, and self-healing research face new challenges in the Internet of Things context due to its promise of connecting billions of devices in an Internet-like structure. Objects in the Internet of Things must function autonomously, with minimum requirement for human intervention, specially when they are part of complex and critical applications. The goal of this paper is to: (i) motivate and show the relevance of the need of self-healing applications in the Internet of Things, (ii) introduce the responsible objects concept and the requirements for building a responsible object API, and (ii) set future research directions regarding self-healing Internet of Things applications.","PeriodicalId":6643,"journal":{"name":"2015 IEEE International Conference on Autonomic Computing","volume":"220 1","pages":"307-312"},"PeriodicalIF":0.0,"publicationDate":"2015-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87020727","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}
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