Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed “Inter...
{"title":"Human-centric Data Dissemination in the IoP","authors":"MordacchiniMatteo, ContiMarco, PassarellaAndrea, BrunoRaffaele","doi":"10.1145/3366372","DOIUrl":"https://doi.org/10.1145/3366372","url":null,"abstract":"Data management using Device-to-Device (D2D) communications and opportunistic networks (ONs) is one of the main focuses of human-centric pervasive Internet services. In the recently proposed “Inter...","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"25 1","pages":"1-25"},"PeriodicalIF":2.7,"publicationDate":"2020-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79517866","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Toosi, K. Vanmechelen, Farzad Khodadadi, R. Buyya
Dynamic forms of resource pricing have recently been introduced by cloud providers that offer Infrastructure as a Service (IaaS) capabilities in order to maximize profits and balance resource supply and demand. The design of a mechanism that efficiently prices perishable cloud resources in line with a provider’s profit maximization goal remains an open research challenge, however. In this article, we propose the Online Extended Consensus Revenue Estimate mechanism in the setting of a recurrent, multiunit and single price auction for IaaS cloud resources. The mechanism is envy-free, has a high probability of being truthful, and generates a near optimal profit for the provider. We combine the proposed auction design with a scheme for dynamically calculating reserve prices based on data center Power Usage Effectiveness (PUE) and electricity costs. Our simulation-based evaluation of the mechanism demonstrates its effectiveness under a broad variety of market conditions. In particular, we show how it improves on the classical uniform price auction, and we investigate the value of prior knowledge on the execution time of virtual machines for maximizing profit. We also developed a system prototype and conducted a small-scale experimental study with a group of 10 users that confirms the truthfulness property of the mechanism in a real test environment.
{"title":"An Auction Mechanism for Cloud Spot Markets","authors":"A. Toosi, K. Vanmechelen, Farzad Khodadadi, R. Buyya","doi":"10.1145/2843945","DOIUrl":"https://doi.org/10.1145/2843945","url":null,"abstract":"Dynamic forms of resource pricing have recently been introduced by cloud providers that offer Infrastructure as a Service (IaaS) capabilities in order to maximize profits and balance resource supply and demand. The design of a mechanism that efficiently prices perishable cloud resources in line with a provider’s profit maximization goal remains an open research challenge, however. In this article, we propose the Online Extended Consensus Revenue Estimate mechanism in the setting of a recurrent, multiunit and single price auction for IaaS cloud resources. The mechanism is envy-free, has a high probability of being truthful, and generates a near optimal profit for the provider. We combine the proposed auction design with a scheme for dynamically calculating reserve prices based on data center Power Usage Effectiveness (PUE) and electricity costs. Our simulation-based evaluation of the mechanism demonstrates its effectiveness under a broad variety of market conditions. In particular, we show how it improves on the classical uniform price auction, and we investigate the value of prior knowledge on the execution time of virtual machines for maximizing profit. We also developed a system prototype and conducted a small-scale experimental study with a group of 10 users that confirms the truthfulness property of the mechanism in a real test environment.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"27 1","pages":"2:1-2:33"},"PeriodicalIF":2.7,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73349967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The article presents an approach to learn collaborative strategies among multiple agents via imitation. Imitation-based learning involves learning from an expert by observing the demonstration of a task and then replicating it. This mechanism makes it convenient for a knowledge engineer to transfer knowledge to a software agent. This article applies imitation to learn not only the strategy of an individual agent, but also the collaborative strategy of a team of agents to achieve a common goal. The article presents an imitation-based solution that learns a weighted naïve Bayes structure, whereas the weights of the model are optimized using Artificial Immune Systems. The learned model is then used by agents to act autonomously. The applicability of the presented approach is assessed in the RoboCup Soccer 3D Simulation environment, which is a promising platform to address many complex real-world problems. The performance of the trained agents is benchmarked against other RoboCup Soccer 3D Simulation teams. In addition to performance characteristics, the research also analyzes the behavioral traits of the imitating team to assess how closely they are imitating the demonstrating team.
{"title":"Using Imitation to Build Collaborative Agents","authors":"Saleha Raza, Sajjad Haider","doi":"10.1145/2831237","DOIUrl":"https://doi.org/10.1145/2831237","url":null,"abstract":"The article presents an approach to learn collaborative strategies among multiple agents via imitation. Imitation-based learning involves learning from an expert by observing the demonstration of a task and then replicating it. This mechanism makes it convenient for a knowledge engineer to transfer knowledge to a software agent. This article applies imitation to learn not only the strategy of an individual agent, but also the collaborative strategy of a team of agents to achieve a common goal. The article presents an imitation-based solution that learns a weighted naïve Bayes structure, whereas the weights of the model are optimized using Artificial Immune Systems. The learned model is then used by agents to act autonomously. The applicability of the presented approach is assessed in the RoboCup Soccer 3D Simulation environment, which is a promising platform to address many complex real-world problems. The performance of the trained agents is benchmarked against other RoboCup Soccer 3D Simulation teams. In addition to performance characteristics, the research also analyzes the behavioral traits of the imitating team to assess how closely they are imitating the demonstrating team.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"104 1","pages":"3:1-3:21"},"PeriodicalIF":2.7,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85984607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
As climate change has become a global concern and server energy demand continues to soar, many IT companies have started to explore server clusters running on various renewable energy sources. Existing green data center designs often yield suboptimal performance as they only look at a certain specific type of energy source. This article explores data centers powered by hybrid renewable energy systems. We propose GreenWorks, a framework for HPC data centers running on a renewable energy mix. Specifically, GreenWorks features a cross-layer power management scheme tailored to the timing behaviors and capacity constraints of different energy sources. Using realistic workload traces and renewable energy data, we show that GreenWorks could provide a near-optimal workload performance (within 3% difference) on average. It can also reduce the worst-case performance degradation by 43% compared to the state-of-the-art design. Moreover, the performance improvements are based on carbon-neutral operations and are not at the cost of significant efficiency degradation and reduced battery lifecycle. Our technique becomes more efficient when servers become more energy proportional and can effectively handle the ever-increasing depth of renewable power penetration in green data centers.
{"title":"Managing Server Clusters on Renewable Energy Mix","authors":"Chao Li, Rui Wang, D. Qian, Tao Li","doi":"10.1145/2845085","DOIUrl":"https://doi.org/10.1145/2845085","url":null,"abstract":"As climate change has become a global concern and server energy demand continues to soar, many IT companies have started to explore server clusters running on various renewable energy sources. Existing green data center designs often yield suboptimal performance as they only look at a certain specific type of energy source. This article explores data centers powered by hybrid renewable energy systems. We propose GreenWorks, a framework for HPC data centers running on a renewable energy mix. Specifically, GreenWorks features a cross-layer power management scheme tailored to the timing behaviors and capacity constraints of different energy sources. Using realistic workload traces and renewable energy data, we show that GreenWorks could provide a near-optimal workload performance (within 3% difference) on average. It can also reduce the worst-case performance degradation by 43% compared to the state-of-the-art design. Moreover, the performance improvements are based on carbon-neutral operations and are not at the cost of significant efficiency degradation and reduced battery lifecycle. Our technique becomes more efficient when servers become more energy proportional and can effectively handle the ever-increasing depth of renewable power penetration in green data centers.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"129 1","pages":"1:1-1:24"},"PeriodicalIF":2.7,"publicationDate":"2016-04-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77081794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Peter Vrancx, Pasquale Gurzi, Abdel Rodríguez, K. Steenhaut, A. Nowé
In today’s Internet, the commercial aspects of routing are gaining importance. Current technology allows Internet Service Providers (ISPs) to renegotiate contracts online to maximize profits. Changing link prices will influence interdomain routing policies that are now driven by monetary aspects as well as global resource and performance optimization. In this article, we consider an interdomain routing game in which the ISP’s action is to set the price for its transit links. Assuming a cheapest path routing scheme, the optimal action is the price setting that yields the highest utility (i.e., profit) and depends both on the network load and the actions of other ISPs. We adapt a continuous and a discrete action learning automaton (LA) to operate in this framework as a tool that can be used by ISP operators to learn optimal price setting. In our model, agents representing different ISPs learn only on the basis of local information and do not need any central coordination or sensitive information exchange. Simulation results show that a single ISP employing LAs is able to learn the optimal price in a stationary environment. By introducing a selective exploration rule, LAs are also able to operate in nonstationary environments. When two ISPs employ LAs, we show that they converge to stable and fair equilibrium strategies.
{"title":"A Reinforcement Learning Approach for Interdomain Routing with Link Prices","authors":"Peter Vrancx, Pasquale Gurzi, Abdel Rodríguez, K. Steenhaut, A. Nowé","doi":"10.1145/2719648","DOIUrl":"https://doi.org/10.1145/2719648","url":null,"abstract":"In today’s Internet, the commercial aspects of routing are gaining importance. Current technology allows Internet Service Providers (ISPs) to renegotiate contracts online to maximize profits. Changing link prices will influence interdomain routing policies that are now driven by monetary aspects as well as global resource and performance optimization. In this article, we consider an interdomain routing game in which the ISP’s action is to set the price for its transit links. Assuming a cheapest path routing scheme, the optimal action is the price setting that yields the highest utility (i.e., profit) and depends both on the network load and the actions of other ISPs. We adapt a continuous and a discrete action learning automaton (LA) to operate in this framework as a tool that can be used by ISP operators to learn optimal price setting. In our model, agents representing different ISPs learn only on the basis of local information and do not need any central coordination or sensitive information exchange. Simulation results show that a single ISP employing LAs is able to learn the optimal price in a stationary environment. By introducing a selective exploration rule, LAs are also able to operate in nonstationary environments. When two ISPs employ LAs, we show that they converge to stable and fair equilibrium strategies.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"18 1","pages":"5:1-5:26"},"PeriodicalIF":2.7,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87242898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In multiplex networks, agents are connected by multiple types of links; a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources; thus, the task’s access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks; the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks.
{"title":"Reliable Task Allocation with Load Balancing in Multiplex Networks","authors":"Yichuan Jiang, Yifeng Zhou, Yunpeng Li","doi":"10.1145/2700327","DOIUrl":"https://doi.org/10.1145/2700327","url":null,"abstract":"In multiplex networks, agents are connected by multiple types of links; a multiplex network can be split into more than one network layer that is composed of the same type of links and involved agents. Each network link type has a bias for communicating different types of resources; thus, the task’s access to the required resources in multiplex networks is strongly related to the network link types. However, traditional task allocation and load balancing methods only considered the situations of agents themselves and did not address the effects of network link types in multiplex networks. To solve this problem, this article considers both link types and agents, and substantially extends the existing work by highlighting the effect of network layers on task allocation and load balancing. Two multiplex network-adapted models of task allocation with load balancing are presented: network layer-oriented allocation and agent-oriented allocation. This article also addresses the unreliability in multiplex networks, which includes the unreliable links and agents, and implements a reliable task allocation based on a negotiation reputation and reward mechanism. Our findings show that both of our presented models can effectively and robustly satisfy the task allocation objectives in unreliable multiplex networks; the experiments prove that they can significantly reduce the time costs and improve the success rate of tasks for multiplex networks over the traditional simplex network-adapted task allocation model. Lastly, we find that our presented network layer-oriented allocation performs much better in terms of reliability and allocation time compared to our presented agent-oriented allocation, which further explains the importance of network layers in multiplex networks.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"31 1","pages":"3:1-3:32"},"PeriodicalIF":2.7,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81463500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Emerging pervasive computing services will typically involve a large number of devices and service components cooperating together in an open and dynamic environment. This calls for suitable models and infrastructures promoting spontaneous, situated, and self-adaptive interactions between components. SAPERE (Self-Aware Pervasive Service Ecosystems) is a general coordination framework aimed at facilitating the decentralized and situated execution of self-organizing and self-adaptive pervasive computing services. SAPERE adopts a nature-inspired approach, in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services and devices, all of which interact in accord to a limited set of coordination laws, or eco-laws. In this article, we present the overall rationale underlying SAPERE and its reference architecture. We introduce the eco-laws--based coordination model and show how it can be used to express and easily enforce general-purpose self-organizing coordination patterns. The middleware infrastructure supporting the SAPERE model is presented and evaluated, and the overall advantages of SAPERE are discussed in the context of exemplary use cases.
{"title":"Engineering Pervasive Service Ecosystems: The SAPERE Approach","authors":"G. Castelli, M. Mamei, A. Rosi, F. Zambonelli","doi":"10.1145/2700321","DOIUrl":"https://doi.org/10.1145/2700321","url":null,"abstract":"Emerging pervasive computing services will typically involve a large number of devices and service components cooperating together in an open and dynamic environment. This calls for suitable models and infrastructures promoting spontaneous, situated, and self-adaptive interactions between components. SAPERE (Self-Aware Pervasive Service Ecosystems) is a general coordination framework aimed at facilitating the decentralized and situated execution of self-organizing and self-adaptive pervasive computing services. SAPERE adopts a nature-inspired approach, in which pervasive services are modeled and deployed as autonomous individuals in an ecosystem of other services and devices, all of which interact in accord to a limited set of coordination laws, or eco-laws. In this article, we present the overall rationale underlying SAPERE and its reference architecture. We introduce the eco-laws--based coordination model and show how it can be used to express and easily enforce general-purpose self-organizing coordination patterns. The middleware infrastructure supporting the SAPERE model is presented and evaluated, and the overall advantages of SAPERE are discussed in the context of exemplary use cases.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"95 1","pages":"1:1-1:27"},"PeriodicalIF":2.7,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85388145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Performance improvement and energy efficiency are two important goals in provisioning Internet services in datacenter servers. In this article, we propose and develop a self-tuning request batching mechanism to simultaneously achieve the two correlated goals. The batching mechanism increases the cache hit rate at the front-tier Web server, which provides the opportunity to improve an application’s performance and the energy efficiency of the server system. The core of the batching mechanism is a novel and practical two-layer control system that adaptively adjusts the batching interval and frequency states of CPUs according to the service level agreement and the workload characteristics. The batching control adopts a self-tuning fuzzy model predictive control approach for application performance improvement. The power control dynamically adjusts the frequency of Central Processing Units (CPUs) with Dynamic Voltage and Frequency Scaling (DVFS) in response to workload fluctuations for energy efficiency. A coordinator between the two control loops achieves the desired performance and energy efficiency. We further extend the self-tuning batching with DVFS approach from a single-server system to a multiserver system. It relies on a MIMO expert fuzzy control to adjust the CPU frequencies of multiple servers and coordinate the frequency states of CPUs at different tiers. We implement the mechanism in a test bed. Experimental results demonstrate that the new approach significantly improves the application performance in terms of the system throughput and average response time. At the same time, the results also illustrate the mechanism can reduce the energy consumption of a single-server system by 13% and a multiserver system by 11%, respectively.
{"title":"Self-Tuning Batching with DVFS for Performance Improvement and Energy Efficiency in Internet Servers","authors":"Dazhao Cheng, Yanfei Guo, Changjun Jiang, Xiaobo Zhou","doi":"10.1145/2720023","DOIUrl":"https://doi.org/10.1145/2720023","url":null,"abstract":"Performance improvement and energy efficiency are two important goals in provisioning Internet services in datacenter servers. In this article, we propose and develop a self-tuning request batching mechanism to simultaneously achieve the two correlated goals. The batching mechanism increases the cache hit rate at the front-tier Web server, which provides the opportunity to improve an application’s performance and the energy efficiency of the server system. The core of the batching mechanism is a novel and practical two-layer control system that adaptively adjusts the batching interval and frequency states of CPUs according to the service level agreement and the workload characteristics. The batching control adopts a self-tuning fuzzy model predictive control approach for application performance improvement. The power control dynamically adjusts the frequency of Central Processing Units (CPUs) with Dynamic Voltage and Frequency Scaling (DVFS) in response to workload fluctuations for energy efficiency. A coordinator between the two control loops achieves the desired performance and energy efficiency. We further extend the self-tuning batching with DVFS approach from a single-server system to a multiserver system. It relies on a MIMO expert fuzzy control to adjust the CPU frequencies of multiple servers and coordinate the frequency states of CPUs at different tiers. We implement the mechanism in a test bed. Experimental results demonstrate that the new approach significantly improves the application performance in terms of the system throughput and average response time. At the same time, the results also illustrate the mechanism can reduce the energy consumption of a single-server system by 13% and a multiserver system by 11%, respectively.","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"32 1","pages":"6:1-6:32"},"PeriodicalIF":2.7,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84892693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The complexity of systems is considered an obstacle to the progress of the IT industry. Autonomic computing is presented as the alternative to cope with the growing complexity. It is a holistic approach, in which the systems are able to configure, heal, optimize, and protect by themselves. Web-based applications are an example of systems where the complexity is high. The number of components, their interoperability, and workload variations are factors that may lead to performance failures or unavailability scenarios. The occurrence of these scenarios affects the revenue and reputation of businesses that rely on these types of applications. In this article, we present a self-healing framework for Web-based applications (SHõWA). SHõWA is composed by several modules, which monitor the application, analyze the data to detect and pinpoint anomalies, and execute recovery actions autonomously. The monitoring is done by a small aspect-oriented programming agent. This agent does not require changes to the application source code and includes adaptive and selective algorithms to regulate the level of monitoring. The anomalies are detected and pinpointed by means of statistical correlation. The data analysis detects changes in the server response time and analyzes if those changes are correlated with the workload or are due to a performance anomaly. In the presence of performance anomalies, the data analysis pinpoints the anomaly. Upon the pinpointing of anomalies, SHõWA executes a recovery procedure. We also present a study about the detection and localization of anomalies, the accuracy of the data analysis, and the performance impact induced by SHõWA. Two benchmarking applications, exercised through dynamic workloads, and different types of anomaly were considered in the study. The results reveal that (1) the capacity of SHõWA to detect and pinpoint anomalies while the number of end users affected is low; (2) SHõWA was able to detect anomalies without raising any false alarm; and (3) SHõWA does not induce a significant performance overhead (throughput was affected in less than 1%, and the response time delay was no more than 2 milliseconds).
{"title":"SHõWA: A Self-Healing Framework for Web-Based Applications","authors":"J. Magalhães, L. Silva","doi":"10.1145/2700325","DOIUrl":"https://doi.org/10.1145/2700325","url":null,"abstract":"The complexity of systems is considered an obstacle to the progress of the IT industry. Autonomic computing is presented as the alternative to cope with the growing complexity. It is a holistic approach, in which the systems are able to configure, heal, optimize, and protect by themselves. Web-based applications are an example of systems where the complexity is high. The number of components, their interoperability, and workload variations are factors that may lead to performance failures or unavailability scenarios. The occurrence of these scenarios affects the revenue and reputation of businesses that rely on these types of applications.\u0000 In this article, we present a self-healing framework for Web-based applications (SHõWA). SHõWA is composed by several modules, which monitor the application, analyze the data to detect and pinpoint anomalies, and execute recovery actions autonomously. The monitoring is done by a small aspect-oriented programming agent. This agent does not require changes to the application source code and includes adaptive and selective algorithms to regulate the level of monitoring. The anomalies are detected and pinpointed by means of statistical correlation. The data analysis detects changes in the server response time and analyzes if those changes are correlated with the workload or are due to a performance anomaly. In the presence of performance anomalies, the data analysis pinpoints the anomaly. Upon the pinpointing of anomalies, SHõWA executes a recovery procedure. We also present a study about the detection and localization of anomalies, the accuracy of the data analysis, and the performance impact induced by SHõWA. Two benchmarking applications, exercised through dynamic workloads, and different types of anomaly were considered in the study. The results reveal that (1) the capacity of SHõWA to detect and pinpoint anomalies while the number of end users affected is low; (2) SHõWA was able to detect anomalies without raising any false alarm; and (3) SHõWA does not induce a significant performance overhead (throughput was affected in less than 1%, and the response time delay was no more than 2 milliseconds).","PeriodicalId":50919,"journal":{"name":"ACM Transactions on Autonomous and Adaptive Systems","volume":"145 1","pages":"4:1-4:28"},"PeriodicalIF":2.7,"publicationDate":"2015-03-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88086653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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