Person: ARI, Ismail
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Ismail
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ARI
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ArticlePublication Open Access Multivariate sensor data analysis for oil refineries and multi-mode identification of system behavior in real-time(IEEE, 2018) Khodabakhsh, Athar; Arı, İsmail; Bakır, M.; Ercan, Ali Özer; Electrical & Electronics Engineering; Computer Science; ARI, Ismail; ERCAN, Ali Özer; Khodabakhsh, AtharLarge-scale oil refineries are equipped with mission-critical heavy machinery (boilers, engines, turbines, and so on) and are continuously monitored by thousands of sensors for process efficiency, environmental safety, and predictive maintenance purposes. However, sensors themselves are also prone to errors and failure. The quality of data received from these sensors should be verified before being used in system modeling. There is a need for reliable methods and systems that can provide data validation and reconciliation in real-time with high accuracy. In this paper, we develop a novel method for real-time data validation, gross error detection and classification over multivariate sensor data streams. The validated and high-quality data obtained from these processes is used for pattern analysis and modeling of industrial plants. We obtain sensor data from the power and petrochemical plants of an oil refinery and analyze them using various time-series modeling and data mining techniques that we integrate into a complex event processing engine. Next, we study the computational performance implications of the proposed methods and uncover regimes where they are sustainable over fast streams of sensor data. Finally, we detect shifts among steady-states of data, which represent systems' multiple operating modes and identify the time when a model reconstruction is required using DBSCAN clustering algorithm.
ArticlePublication Metadata only Democratization of runtime verification for internet of things(Elsevier, 2018-05) İnçki, Koray; Arı, İsmail; Computer Science; ARI, Ismail; İnçki, KorayInternet of Things (IoT) devices have gained more prevalence in ambient assisted living (AAL) systems. Reliability of AAL systems is critical especially in assuring the safety and well-being of elderly people. Runtime verification (RV) is described as checking whether the observed behavior of a system conforms to its expected behavior. RV techniques generally involve heavy formal methods; thus, it is poorly utilized in the industry. Therefore, we propose a democratization of RV for IoT systems by presenting a model-based testing (MBT) approach. To enable modeling expected behaviors of an IoT system, we first describe an extension to a UML profile. Then, we capture the expected behavior of an interaction that is modeled on a Sequence Diagram (SD). Later, the expected behaviors are translated into runtime monitor statements expressed in Event-Processing Language (EPL), which are executed at the edge of the IoT network. We further demonstrate our contributions on a sample AAL system.ArticlePublication Metadata only MaLeFICE: Machine learning support for continuous performance improvement in computational engineering(Wiley, 2022-04-25) Sönmezer, Hasan Berk; Muhtaroğlu, Nitel; Arı, İsmail; Gökçin, Deniz; Computer Science; ARI, Ismail; Sönmezer, Hasan Berk; Muhtaroğlu, Nitel; Gökçin, DenizComputer aided engineering (CAE) practices improved drastically within the last decade due to ease of access to computing resources and open-source software. However, increasing complexity of hardware and software settings and the scarcity of multiskilled personnel rendered the practice inefficient and infeasible again. In this article, we present a method for continuous performance improvement in computational engineering that combines online performance profiling with machine learning (ML). To test the viability of this method, we provide a detailed analysis for solution time estimation of finite element analysis (FEA) jobs based on multidimensional models. These models combine numerous matrix features (matrix size, density, bandwidth, etc.), solver features (direct-iterative, preconditioning, tolerance), and hardware features (core count, virtual–physical). We repeat our analysis over different machines as well as docker containers to demonstrate applicability over different platforms. Next, we train supervised and unsupervised ML algorithms over commonly used, realistic FEA benchmarks and compare accuracy of different models. Finally, we design two new ML-based online batch schedulers called shortest predicted time first (SPTF) and shortest cluster time first (SCTF), which are comparable in performance to the optimal, but offline shortest job first (SJF) scheduler. We find that ML-based profiling and scheduling can reduce the average turnaround times by 2x –5x over other alternatives.Conference ObjectPublication Metadata only Testing performance of application containers in the cloud with hpc loads(Civil-Comp, 2017) Muhtaroglu, Nitel; Kolcu, Birkan; Arı, İsmail; Computer Science; Iványi, P.; Topping, B.H.V.; Várady, G.; ARI, Ismail; Muhtaroglu, Nitel; Kolcu, BirkanIn this paper, we evaluate HPC application deployment using container technology and examine performance metrics on a public cloud. The research focuses on the ease of deployment, performance and isolation issues related to application containers (Docker) and HPC software, which is OpenFOAM in our case. We find that performance of Docker is comparable to Physical or Virtual HPC alternatives with negligible overheads (5%), whereas its savings in setup and deployment times are drastically better (10-15x).Conference ObjectPublication Metadata only Towards low cost and smart load testing as a service using containers(Springer, 2021) Baransel, Berrak Alara; Peker, Alper; Balkıs, Hilmi Ömer; Arı, İsmail; Computer Science; ARI, Ismail; Baransel, Berrak Alara; Peker, Alper; Balkıs, Hilmi ÖmerProviding end-users with high quality e-commerce, online communication, education services requires careful performance monitoring, tuning and prediction under heavy traffic loads. To address this issue, we propose and evaluate a novel methodology using Docker containers for load testing. Our experience over several benchmarks, local machines vs. Cloud, and web servers suggest that load testing as a service requires a multi-dimensional optimization over slave counts, network latencies, bandwidth, and traffic patterns and there are opportunities for learning these parameters that can later be modelled into a smart load testing algorithm, with machine learning at the driver seat. Beyond the ease and speed of deployment, containers and cloud also provide a low cost alternative to load testing; we completed our cloud experiments by spending only $10. The only disadvantage of public clouds can be their centralized nature and distance to real customer bases.Conference ObjectPublication Open Access Model-based runtime monitoring of smart city systems(Elsevier, 2018) İnçki, Koray; Arı, İsmail; Computer Science; ARI, Ismail; İnçki, KorayThe pace of proliferation for smart systems in city wide applications is unmatched. The introduction of Internet of Things (IoT), an enabler of smart city phenomenon, has incubated a productive environment for such innovations. Smart things equipped with IoT capabilities, allow for developing smart city applications at such large scale that each application can be represented as a system of systems (SoS). Nevertheless, the complexity of engineering such SoS has been a major challenge in developing and maintaining smart city applications. One of the engineering challenges that industry face today is the verification of a SoS smart city application at runtime. We introduce utilization of a model-based runtime monitoring approach for providing reliable service. We propose to use message sequence charts for representing a smart city application, later allow the practitioners to express expected behavior of an application in terms of complex-event processing patterns. We demonstrate the fidelity of our approach on a sample smart parking system. Our approach is one of its kind in enabling a non-intrusive monitoring of IoT behavior at runtime (online).Conference ObjectPublication Metadata only Realtime healthcare services via nested complex event processing technology(The ACM Digital Library, 2012) Liu, M.; Ray, M.; Zhang, D.; Rundensteiner, E.; Dougherty, D. J.; Gupta, C.; Wang, S.; Arı, İsmail; Computer Science; ARI, IsmailComplex Event Processing (CEP) over event streams has become increasingly important for real-time applications ranging from healthcare to supply chain management. In such applications, arbitrarily complex sequence patterns as well as non existence of such complex situations must be detected in real time. To assure real-time responsiveness for detection of such complex pattern over high volume high-speed streams, efficient processing techniques must be designed. Unfortunately the efficient processing of complex sequence queries with negations remains a largely open problem to date. To tackle this shortcoming, we designed optimized strategies for handling nested CEP query. In this demonstration, we propose to showcase these techniques for processing and optimizing nested pattern queries on streams. In particular our demonstration showcases a platform for specifying complex nested queries, and selecting one of the alternative optimized techniques including sub-expression sharing and intermediate result caching to process them. We demonstrate the efficiency of our optimized strategies by graphically comparing the execution time of the optimized solution against that of the default processing strategy of nested CEP queries. We also demonstrate the usage of the proposed technology in several healthcare services.Conference ObjectPublication Metadata only Remote dbugging for containerized applications in edge computing environments(IEEE, 2019) Özcan, M. O.; Odacı, F.; Arı, İsmail; Computer Science; ARI, IsmailEdge Computing (EC) became popular again with the rise of IoT, Cloud Computing, and Industry 4.0. In this paper, difficulties of application development in the EC environment are discussed and a container-based solution using remote debugging at the edge is proposed. This container allows application developers to write code in the production environment. Our implementation increases the development speed and facilitates in-place debugging for EC environments.Conference ObjectPublication Metadata only Stream analytics and adaptive windows for operational mode identification of time-varying industrial systems(IEEE, 2018-09-07) Khodabakhsh, Athar; Arı, İsmail; Bakır, M.; Alagoz, S. M.; Computer Science; ARI, Ismail; Khodabakhsh, AtharIt is necessary to develop accurate, yet simple and efficient models that can be used with high-speed industrial data streams. In this paper, we develop a mode identification technique using stream analytics and show that it may be more effective than batch models, especially for time-varying systems. These industrial systems continuously monitor hundreds of sensors, but the relationships among variables change over time, which are identified as different operational modes. To detect drifts among modes, predictive modeling techniques such as regression analysis, K-means and DBSCAN clustering are used over sensor data streams from an oil refinery and models are updated in real-time using window-based analysis. Finally, an adaptive window size tuning approach based on the TCP congestion control algorithm is discussed, which reduces model update costs as well as prediction errors.Conference ObjectPublication Metadata only NEEL: The nested complex event language for real-time event analytics(Springer International Publishing, 2011) Liu, M.; Rundensteiner, E. A.; Dougherty, D.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, IsmailComplex event processing (CEP) over event streams has become increasingly important for real-time applications ranging from health care, supply chain management to business intelligence. These monitoring applications submit complex event queries to track sequences of events that match a given pattern. As these systems mature the need for increasingly complex nested sequence query support arises, while the state-of-art CEP systems mostly support the execution of only flat sequence queries. In this paper, we introduce our nested CEP query language NEEL for expressing nested queries composed of sequence, negation, AND and OR operators. Thereafter, we also define its formal semantics. Subtle issues with negation and predicates within the nested sequence context are discussed. An E-Analytics system for processing nested CEP queries expressed in the NEEL language has been developed. Lastly, we demonstrate the utility of this technology by describing a case study of applying this technology to a real-world application in health care.