Person: ARI, Ismail
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Ismail
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ARI
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Conference ObjectPublication Metadata only E-Cube: multi-dimensional event sequence processing using concept and pattern hierarchies(IEEE, 2010) Liu, M.; Rundensteiner, E. A.; Greenfield, K.; Gupta, C.; Wang, S.; Arı, İsmail; Mehta, A.; Computer Science; ARI, IsmailMany modern applications including tag based mass transit systems, RFID-based supply chain management systems and online financial feeds require special purpose event stream processing technology to analyze vast amounts of sequential multi-dimensional data available in real-time data feeds. Traditional online analytical processing (OLAP) systems are not designed for real-time pattern-based operations, while Complex Event Processing (CEP) systems are designed for sequence detection and do not support OLAP operations. We will demonstrate a novel E-Cube model that combines CEP and OLAP techniques for multi-dimensional event pattern analysis at different abstraction levels. A London transit scenario will be given to demonstrate the utility and performance of this proposed technology.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 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 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 Observing interoperability of IoT systems through model-based testing(Springer Nature, 2018) İnçki, Koray; Arı, İsmail; Computer Science; Fortino, G.; Palau, C. E.; Guerrieri, A.; Cuppens, N.; Cuppens, F.; Cuppens, H.; Gabillon, A.; ARI, Ismail; İnçki, KorayInternet of Things (IoT) has drastically modified the industrial services provided through autonomous machine-to-machine interactions. Such systems comprise of devices manufactured by various suppliers. Verification is a challenge due to high heterogeneity of composing devices. In this paper, we present initial results of model-based interoperability testing for IoT systems to facilitate automatic test case generation. We utilize messaging model of Constrained Application Protocol so as to deduce complex relations between participating devices. We use Complex-Event Processing (CEP) techniques in order to streamline the verification process after generating proper runtime monitors from sequence diagrams. We demonstrate our solution on a fictitious healthcare 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 Democratization of HPC cloud services with automated parallel solvers and application containers(Wiley, 2018-11-10) Muhtaroğlu, Nitel; Arı, İsmail; Kolcu, Birkan; Computer Science; ARI, Ismail; Kolcu, Birkan; Muhtaroğlu, NitelIn this paper, we investigate several design choices for HPC services at different layers of the cloud computing architecture to simplify and broaden its use cases. We start with the platform-as-a-service (PaaS) layer and compare direct and iterative parallel linear equation solvers. We observe that several matrix properties that can be identified before starting long-running solvers can help HPC services automatically select the amount of computing resources per job, such that the job latency is minimized and the overall job throughput is maximized. As a proof of concept, we use classical problems in structural mechanics and mesh these problems with increasing granularities leading to various matrix sizes, ie, largest having 1 billion non-zero elements. In addition to matrix size, we take into account matrix condition numbers, preconditioning effects, and solver types and execute these finite element analysis (FEA) over an IBM HPC cluster. Next, we focus on the infrastructure-as-a-service (IaaS) layer and explore HPC application performance, load isolation, and deployment issues using application containers (Docker) while also comparing them to physical and virtual machines (VM) over a public cloud.Conference ObjectPublication Metadata only Finecloud: Fine-grained cloud service advisory using machine learning(IEEE, 2022) Orhun, Yasemin; İstanbullu, Yiğit; Arı, İsmail; Computer Science; ARI, Ismail; Orhun, Yasemin; İstanbullu, YiğitMotivated by real customer problems, we investigated utilization of cloud services at different layers including infrastructure (IaaS), application services (PaaS) and databases (DaaS). We found several issues such as forgetting about unused resources, bursty workloads and service dependencies causing under-utilization (a.k.a. over- provisioning) problem. Cloud advisory tools offered by the public providers either lack the fine-grained analysis needed for actionable recommendations or can't see the correlations among services that are used by the same customers' resource groups. We proposed an automated, near real-time advisor that utilizes historical usage data and machine learning (ML) models to recommend cost saving opportunities. We demonstrated significant cost savings averaging around 20%, which can accumulate as thousands of Dollars for large and active systems. Since our advisory models depend on time-series data, we compared several forecasting algorithms including ARIMA, LSTM and Prophet. We found LSTM model to deliver the most accurate results for our workloads.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.Book PartPublication Metadata only Hybrid job scheduling for improved cluster utilization(Springer Science+Business Media, 2014) Arı, İsmail; Kocak, Uğur; Computer Science; ARI, Ismail; Kocak, UğurIn this paper, we investigate the models and issues as well as performance benefits of hybrid job scheduling over shared physical clusters. Clustering technologies that are currently supported include MPI, Hadoop-MapReduce and NoSQL systems. Our proposed scheduling model is above the cluster-specific middleware and OS-level schedulers and it is complementary to them. First, we demonstrate that we can effectively schedule MPI, Hadoop, NoSQL jobs together by profiling them and then co-scheduling. Second, we find that it is better to schedule cluster jobs with different job characteristics together (CPU vs. I/O intensive) rather than two CPU-intensive jobs. Third, we use the learning outcome of this principle to design of a greedy sort-merge scheduler. Up to 37% savings in total job completion times are demonstrated. These savings are directly proportional to the cluster utilization improvements.