Browsing by Author "Zeydan, Engin"

Now showing 1 - 11 of 11

Cloud2hdd: Large-Scale Hdd Data Analysis on Cloud for Cloud Datacenters
(IEEE, 2020) Zeydan, Engin; Arslan, Şefik Şuayb
The main focus of this paper is to develop a distributed large scale data analysis platform for the opensource data of Backblaze cloud datacenter which consists of operational hard disk drive (HDD) information collected over an observable period of 2272 days (over 74 months). To carefully analyze the intrinsic characteristics of the hard disk behavior, we have exploited a large bolume of data and the benefits of Hadoop ecosystem as our big data processing engine. In other words, we have utilized a special distributed scheme on cloud for cloud HDD data, which is termed as Cloud2HDD. To classify the remaining lifetime of hard disk drives based on health indicators such as in-built S.M.A.R.T (Self-Monitoring, Analysis, and Reporting Technology) features, we used some of the state-of-the-art classification algorithms and compared their accuracy, precision, and recall rates simultaneously. In addition, importance of various S.M.A.R.T. features in predicting the true remaining lifetime of HDDs are identified. For instance, our analysis results indicate that Random Forest Classifier (RFC) can yield up to 94% accuracy with the highest precision and recall at a reasonable time by classifying the remaining lifetime of drives into one of three different classes, namely critical, high and low ideal states in comparison to other classification approaches based on a specific subset of S.M.A.R.T. features.
Experimental Performance Analysis for Mobile Data Offloading in Heterogeneous Wireless Networks
(2016) Akpolat, Gamze; Zeydan, Engin; Tan, A. Serdar
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The Impact of D2d Connections on Network-Assisted Mobile Data Offloading
(IEEE, 2018) Tan, Ahmet Serdar; Zeydan, Engin
The exponential increase of mobile data traffic pushes mobile operators to seek more efficient heterogeneous communication techniques. In this study, multi-user extension methods for multiple attribute decision making algorithms for network-assisted data offloading in heterogeneous wireless networks are developed and performance evaluations are performed in the presence of Device-to-Device (D2D) connections. Evaluations are carried out using simulations to point out the metrics and factors influencing data offloading in heterogeneous networks. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions. Additionally, up to 67% increase in user satisfaction can be achieved when D2D density is kept 68% under a heavy load scenario. The simulation results also indicate the existence of optimal D2D densities in heterogeneous networks depending on the total number of users and available network capacity.
Integration and Management of Wi-Fi Offloading in Service Provider Infrastructures
(2016) Zeydan, Engin; Tan, A. Serdar
Integration of offloading technologies into mobile network operator's infrastructures that provide heterogeneous access services is a challenging task for mobile operators. A connectivity management platform is a key element for heterogeneous mobile network operators in order to enable optimal offloading. In this study, development and integration of a connectivity management platform that uses a novel multiple attribute decision making algorithms for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. The proposed platform collects several terminal and network level attributes via infrastructure and client Application Programming Interfaces (APIs) and decides the best network access technology to connect for requested users. Through experimentation, we provide details on the platform integration with service provider's network and sensitivity analysis of the multiple attribute decision making algorithm.
On the Distribution Modeling of Heavy-Tailed Disk Failure Lifetime in Big Data Centers
(IEEE, 2021) Arslan, Şuayb Şefik; Zeydan, Engin
It has become commonplace to observe frequent multiple disk failures in big data centers in which thousands of drives operate simultaneously. Disks are typically protected by replication or erasure coding to guarantee a predetermined reliability. However, in order to optimize data protection, real life disk failure trends need to be modeled appropriately. The classical approach to modeling is to estimate the probability density function of failures using nonparametric estimation techniques such as kernel density estimation (KDE). However, these techniques are suboptimal in the absence of the true underlying density function. Moreover, insufficient data may lead to overfitting. In this article, we propose to use a set of transformations to the collected failure data for almost perfect regression in the transform domain. Then, by inverse transformation, we analytically estimated the failure density through the efficient computation of moment generating functions, and hence, the density functions. Moreover, we developed a visualization platform to extract useful statistical information such as model-based mean time to failure. Our results indicate that for other heavy-tailed data, the complex Gaussian hypergeometric distribution and classical KDE approach can perform best if the overfitting problem can be avoided and the complexity burden is overtaken. On the other hand, we show that the failure distribution exhibits less complex Argus-like distribution after performing the Box–Cox transformation up to appropriate scaling and shifting operations.
An Overview of Blockchain Technologies: Principles, Opportunities and Challenges
(IEEE, 2018) Arslan, Şuayb Şefik; Mermer, Gültekin Berahan; Zeydan, Engin
Blokzincir, toplumumuzun birbiriyle iletişim kurma ve ticaret yapma biçiminde devrim yapma potansiyeline sahip, yakın zamanda ortaya çıkmış olan bir teknolojidir. Bu teknolojinin sağladığı en önemli avantaj aracı gerektiren bir oluşumda güvenilir bir merkezi kuruma ihtiyaç duymadan değer taşıyan işlemleri değiş tokuş edebilmesidir. Ayrıca, veri bütünlüğü, dahili orijinallik ve kullanıcı şeffaflığı sağlayabilir. Blokzincir, birçok yenilikçi uygulamanın temel alınacağı yeni internet olarak görülebilir. Bu çalışmada, genel çalışma prensibi, oluşan fırsatlar ve ileride karşılaşılabilecek zorlukları içerecek şekilde güncel blokzincir teknolojilerinin genel bir görünümünü sunmaktayız.
Performance Maximization of Network Assisted Mobile Data Offloading With Opportunistic Device-To Communications
(2018) Zeydan, Engin; Tan, A. Serdar
Mobile data traffic inside mobile operator's infrastructure is increasing exponentially every year. This increasing demand forces mobile network operators (MNOs) to seek for alternative communication methods in order to relieve the traffic load in base stations, especially in highly populated and crowded environments. Network assisted data offload and Device-to-Device(D2D) communications are two prominent methods to help MNOs solve this problem. In this study, a data offload framework is developed that incorporates network assisted multiple attribute decision making (MADM) for best network selection and D2D communications for exploiting user proximity in crowded environments. The performance of the framework is evaluated with simulation results as well as analytic solutions and performance bounds. The simulation results indicate the superiority of incorporating network-based information besides user-based information in offloading decisions and demonstrates the significant benefits of D2D communications when the density of D2D users is properly adjusted. The simulation results depict that up to 168% and 200% increase in user satisfaction and throughput can be achieved under high network load scenarios at optimal D2D density. (C) 2018 Elsevier B.V. All rights reserved.
Quality-Aware Wi-Fi Offload: Analysis, Design and Integration Perspectives
(2018) Mester, Yavuz; Buyruk, Hasan; Zeydan, Engin; Tan, A. Serdar
The rapid spread of smart wireless devices and expansion of mobile data traffic have increased the interest for efficient traffic offloading techniques in next-generation communication technologies. Wi-Fi offloading uses ubiquitous Wi-Fi technology in order to satisfy the ever increasing demand for mobile bandwidth and therefore is an appropriate methodology for mobile operators. As a matter of fact, design and integration of an offloading technology inside mobile network operators' infrastructures is a challenging task due to convergence issues between the The 3rd Generation Partnership Project (3GPP) and non-3GPP networks. Therefore, a connectivity management platform is a key element for integrated heterogeneous mobile network operators in order to enable smart and effective offloading. In this paper, analysis, design and integration of a connectivity management platform that uses a Multiple Attribute Decision Making (MADM) algorithm for efficient Wi-Fi Offloading in heterogeneous wireless networks is presented. In order to enhance the end-user's quality-of-experience (QoE), we have especially concentrated on the benefits that can be achieved by exploiting the presence of integrated service provider platform. Hence, the proposed platform can collect several User Equipment (UE) and network-based attributes via infrastructure and client Application Programming Interfaces (APIs) and decides on the best network access technology (i.e. 3GPP and non-3GPP) to connect to for requested users. We have also proposed multi-user extensions of the MADM algorithms for offloading. Through simulations and experiments, we provide details of the comparisons of the proposed algorithms as well as the sensitivity analysis of the MADM algorithm through an experimental set-up.
A Ran/Sdn Controller Based Connectivity Management Platform for Mobile Service Providers
(Institute of Electrical and Electronics Engineers Inc., 2017) Ayhan, Gökhan; Koca, Melih; Zeydan, Engin; Tan, A. Serdar
In this demo, we demonstrate the integration of radio access network (RAN)/Software-Defined Networking (SDN) controller with a connectivity management platform designed for mobile wireless networks. This is an architecture designed throughout the EU Celtic-Plus project SIGMONA1. OpenDaylight based RAN/SDN controller and the application server are capable of collecting infrastructure and client related parameters from OpenFlow enabled switches and Android based phones respectively. The decision on the best access network selection is computed at the application server using a Multiple Attribute Decision Making (MADM) algorithm and instructed back to Android-based mobile client for execution of access network selection. © 2017 IFIP.
Service-Aware Multi-Resource Allocation in Software-Defined Next Generation Cellular Networks
(2018) Arslan, Şuayb Şefik; Zeydan, Engin; Narmanloğlu, Ömer
Network slicing is one of the major solutions needed to meet the requirements of next generation cellular networks, under one common network infrastructure, in supporting multiple vertical services provided by mobile network operators. Network slicing makes one shared physical network infrastructure appear as multiple logically isolated virtual networks dedicated to different service types where each Network Slice (NS) benefits from on-demand allocated resources. Typically, the available resources distributed among NSs are correlated and one needs to allocate them judiciously in order to guarantee the service, MNO, and overall system qualities. In this paper, we consider a joint resource allocation strategy that weights the significance of the resources per a given NS by leveraging the correlation structure of different quality-of-service (QoS) requirements of the services. After defining the joint resource allocation problem including the correlation structure, we propose three novel scheduling mechanisms that allocate available network resources to the generated NSs based on different type of services with different QoS requirements. Performance of the proposed schedulers are then investigated through Monte-Carlo simulations and compared with each other as well as against a traditional max-min fairness algorithm benchmark. The results reveal that our schedulers, which have different complexities, outperform the benchmark traditional method in terms of service-based and overall satisfaction ratios, while achieving different fairness index levels.
A Visualization Platfom for Disk Failure Analysis
(IEEE, 2018) Arslan, Şuayb Şefik; Yiğit, İbrahim Onuralp; Zeydan, Engin
It has become a norm rather than an exception to observe multiple disks malfunctioning or whole disk failures in places like big data centers where thousands of drives operate simultaneously. Data that resides on these devices is typically protected by replication or erasure coding for long-term durable storage. However, to be able to optimize data protection methods, real life disk failure trends need to be modeled. Modelling helps us build insights while in the design phase and properly optimize protection methods for a given application. In this study, we developed a visualization platform in light of disk failure data provided by BackBlaze, and extracted useful statistical information such as failure rate and model-based time to failure distributions. Finally, simple modeling is performed for disk failure predictions to alarm and take necessary system-wide precautions.