Computing and Information Systems - Research Publications
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ItemExtracting Audio Summaries to Support Effective Spoken Document Search(WILEY, 2017-09)We address the challenge of extracting query biased audio summaries from podcasts to support users in making relevance decisions in spoken document search via an audio‐only communication channel. We performed a crowdsourced experiment that demonstrates that transcripts of spoken documents created using Automated Speech Recognition (ASR), even with significant errors, are effective sources of document summaries or “snippets” for supporting users in making relevance judgments against a query. In particular, the results show that summaries generated from ASR transcripts are comparable, in utility and user‐judged preference, to spoken summaries generated from error‐free manual transcripts of the same collection. We also observed that content‐based audio summaries are at least as preferred as synthesized summaries obtained from manually curated metadata, such as title and description. We describe a methodology for constructing a new test collection, which we have made publicly available.
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ItemiFogSim: A toolkit for modeling and simulation of resource management techniques in the Internet of Things, Edge and Fog computing environments(WILEY, 2017-09)Summary Internet of Things (IoT) aims to bring every object (eg, smart cameras, wearable, environmental sensors, home appliances, and vehicles) online, hence generating massive volume of data that can overwhelm storage systems and data analytics applications. Cloud computing offers services at the infrastructure level that can scale to IoT storage and processing requirements. However, there are applications such as health monitoring and emergency response that require low latency, and delay that is caused by transferring data to the cloud and then back to the application can seriously impact their performances. To overcome this limitation, Fog computing paradigm has been proposed, where cloud services are extended to the edge of the network to decrease the latency and network congestion. To realize the full potential of Fog and IoT paradigms for real‐time analytics, several challenges need to be addressed. The first and most critical problem is designing resource management techniques that determine which modules of analytics applications are pushed to each edge device to minimize the latency and maximize the throughput. To this end, we need an evaluation platform that enables the quantification of performance of resource management policies on an IoT or Fog computing infrastructure in a repeatable manner. In this paper we propose a simulator, called iFogSim, to model IoT and Fog environments and measure the impact of resource management techniques in latency, network congestion, energy consumption, and cost. We describe two case studies to demonstrate modeling of an IoT environment and comparison of resource management policies. Moreover, scalability of the simulation toolkit of RAM consumption and execution time is verified under different circumstances.
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ItemOn the effectiveness of isolation-based anomaly detection in cloud data centers(WILEY, 2017-09-25)Summary The high volume of monitoring information generated by large‐scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource‐intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large‐scale environment such as clouds. Isolation‐based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation‐based anomaly detection method, to detect anomalies in large‐scale cloud data centers. We propose a method to code time‐series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time‐series and to be applied online and in real‐time.
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ItemOnline virtual machine migration for renewable energy usage maximization in geographically distributed cloud datacenters(WILEY, 2017-09-25)Summary Energy consumption and its associated costs represent a huge part of cloud providers' operational costs. In this study, we explore how much energy cost savings can be made knowing the future level of renewable energy (solar/wind) available in data centers. Since renewable energy sources have intermittent nature, we take advantage of migrating virtual machines to the nearby data centers with excess renewable energy. In particular, we first devise an optimal offline algorithm with full future knowledge of renewable level in the system. Since in practice, accessing long‐term and exact future knowledge of renewable energy level is not feasible, we propose two online deterministic algorithms, one with no future knowledge called deterministic and one with limited knowledge of the future renewable availability called future‐aware. We show that the deterministic and future‐aware algorithms are 1+1/s and 1+1/s−ω/s.Tm competitive in comparison to the optimal offline algorithm, respectively, where s is the network to the brown energy cost, ω is the look‐ahead window‐size, and Tm is the migration time. The effectiveness of the proposed algorithms is analyzed through extensive simulation studies using real‐world traces of meteorological data and Google cluster workload.
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ItemA model linking video gaming, sleep quality, sweet drinks consumption and obesity among children and youth(WILEY, 2017-08)There is a growing need to curb paediatric obesity. The aim of this study is to untangle associations between video-game-use attributes and obesity as a first step towards identifying and examining possible interventions. Cross-sectional time-lagged cohort study was employed using parent-child surveys (t1) and objective physical activity and physiological measures (t2) from 125 children/adolescents (mean age = 13.06, 9-17-year-olds) who play video games, recruited from two clinics at a Canadian academic children's hospital. Structural equation modelling and analysis of covariance were employed for inference. The results of the study are as follows: (i) self-reported video-game play duration in the 4-h window before bedtime is related to greater abdominal adiposity (waist-to-height ratio) and this association may be mediated through reduced sleep quality (measured with the Pittsburgh Sleep Quality Index); and (ii) self-reported average video-game session duration is associated with greater abdominal adiposity and this association may be mediated through higher self-reported sweet drinks consumption while playing video games and reduced sleep quality. Video-game play duration in the 4-h window before bedtime, typical video-game session duration, sweet drinks consumption while playing video games and poor sleep quality have aversive associations with abdominal adiposity. Paediatricians and researchers should further explore how these factors can be altered through behavioural or pharmacological interventions as a means to reduce paediatric obesity.
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ItemMitigating impact of short-term overload on multi-cloud web applications through geographical load balancing(WILEY, 2017-06-25)Summary Managed by an auto‐scaler in the clouds, applications may still be overloaded by sudden flash crowds or resource failures as the auto‐scaler takes time to make scaling decisions and provision resources. With more cloud providers building geographically dispersed data centers, applications are commonly deployed in multiple data centers to better serve customers worldwide. In this case, instead of sufficiently over‐provisioning each data center to prepare for occasional overloads, it is more cost‐efficient to over‐provision each data center a small amount of capacity and to balance the extra load among them when resources in any data center are suddenly saturated. In this paper, we present a decentralized system that timely detects short‐term overload situations and autonomously handles them using geographical load balancing and admission control to minimize the resulted performance degradation. Our approach also includes a new algorithm that optimally distributes the excessive load to remote data centers causing minimum increase of overall response times. We developed a prototype and evaluated it on Amazon Web Services. The results show that our approach is able to maintain acceptable quality of service while greatly increase the number of requests served during overloading periods.
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ItemA survey on load balancing algorithms for virtual machines placement in cloud computing(WILEY, 2017-06-25)Summary The emergence of cloud computing based on virtualization technologies brings huge opportunities to host virtual resource at low cost without the need of owning any infrastructure. Virtualization technologies enable users to acquire, configure, and be charged on pay‐per‐use basis. However, cloud data centers mostly comprise heterogeneous commodity servers hosting multiple virtual machines (VMs) with potential various specifications and fluctuating resource usages, which may cause imbalanced resource utilization within servers that may lead to performance degradation and service level agreements violations. So as to achieve efficient scheduling, these challenges should be addressed and solved by using load balancing strategies, which have been proved to be nondeterministic polynomial time (NP)‐hard problem. From multiple perspectives, this work identifies the challenges and analyzes existing algorithms for allocating VMs to hosts in infrastructure clouds, especially focuses on load balancing. A detailed classification targeting load balancing algorithms for VM placement in cloud data centers is investigated, and the surveyed algorithms are classified according to the classification. The goal of this paper is to provide a comprehensive and comparative understanding of existing literature and aid researchers by providing an insight for potential future enhancements.
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ItemExponentially Weighted Ellipsoidal Model for Anomaly Detection(WILEY, 2017-09)
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