Browsing by Author "Rathnayake, S"

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    item: Conference-Abstract
    Detection of false sharing using machine learning
    (2014-06-25) Jayasena, VSD; Amarasinghe, S; Abeyweera, A; Amarasinghe, G; De Silva, H; Rathnayake, S; Meng, X; Liu, Y
    False sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine learning. We develop a set of mini-programs in which false sharing can be turned on and off. We then run the mini-programs both with and without false sharing, collect a set of hardware performance event counts and use the collected data to train a classifier. We can use the trained classifier to analyze data from arbitrary programs for detection of false sharing. Experiments with the PARSEC and Phoenix benchmarks show that our approach is indeed effective. We detect published false sharing regions in the benchmarks with zero false positives. Our performance penalty is less than 2%. Thus, we believe that this is an effective and practical method for detecting false sharing.
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    Detection of false sharing using machine learning
    (2015-06-19) Jayasena, S; Amarasinghe, S; Abeyweera, A; Amarasinghe, G; De Silva, H; Rathnayake, S; Meng, X; Liu, Y
    False sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine learning. We develop a set of mini-programs in which false sharing can be turned on and off. We then run the mini-programs both with and without false sharing, collect a set of hardware performance event counts and use the collected data to train a classifier. We can use the trained classifier to analyze data from arbitrary programs for detection of false haring. Experiments with the PARSEC and Phoenix benchmarks show that our approach is indeed effective. We detect published false sharing regions in the benchmarks with zero false positives. Our performance penalty is less than 2%. Thus, we believe that this is an effective and practical method for detecting false sharing.
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    item: Conference-Extended-Abstract
    Interactive learning facility for PLCs, and simulations using factory IO
    (Engineering Research Unit, 2023-12) Adhikari, M; Kalansooriya, G; Rathnayake, S; Jayasekara, AGBP
    To improve efficiency, productivity, and worker safety, industries are implementing automation facilities into their processes to take advantage of the rapid development of technologies [1]. Programmable Logic Controllers (PLCs) are one of the major parts of automatic systems in the industry [2]. Learning PLCs and their functions is one major component in learning industrial automation, and hence laboratories pay much attention to instruct students about PLCs and their functions. On the other hand, there is much research being done on intelligent learning platforms such as ALESK, and BYJU’s learning, where they implement student knowledge assessment methods and adaptive teaching methods [3]. This is one part of an ongoing project working on developing an intelligent learning platform for students to learn PLCs, its decentralized connections and simulations using factory IO. With an attempt to identify the hardware components needed to build a ‘Palletizer’, this work tries, • to develop a simulation in Factory IO for students to learn about PLCs, ladder programming and input/output devices, • to develop a web platform for students to refer to lab materials, data sheets and tutorials. As outcomes of this work, a simulation and the ladder logic program of a palletizer was built, and the components needed to build a physical palletizer and their specifications were identified. Moreover, a web interface was designed for students to learn about the lab practical.
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    item: Thesis-Abstract
    Oblivious multi-cloud file storage
    (2023) Pushpakumara, ERTD; Rathnayake, S
    Cloud storage facilities are now predominantly used to store outgrowing data. Information availability, improved performance and the trustworthiness are the key factors that the data owners mainly focus on, in storing data with a third party. With the multi-tenant concept on cloud computing, security threats have been evolved, as the trustworthiness of the neighbors has become a doubt. A malicious user could monitor the traffic between the client and the CSP. By analyzing the traffic attacker can get a clear picture regarding what kind of data has been passed or retrieved by the client and these questions the privacy level of stored data. Critical, highly Sensitive and Personally Identifiable Information (PII) used in government organizations such as Defense Ministry, Person’s Registration, Motor Traffic Department, Immigration and Emigration systems, among others, require data privacy, integrity and confidentiality which demotivate them in storing these highly sensitive data on cloud storage. But these organizations handle thousands of data records and adding more day by day and the physical storage expansion has become a huge challenge with the investments on infrastructure. The proposed solution would address both these challenges. The major security concerns the proposed solution focuses on is the data privacy, integrity, and confidentiality. In this research we propose a novel approach to obfuscate the data distribution patterns in a multi cloud environment. The solution is to be implemented at the client side based on the systems’ business requirements. So that a unified interface could be provided in storing/retrieving data in several cloud platforms. The uploaded file is encrypted with a public key, calculated the hash value, and divided into several small file chunks. Then the file chunks are scattered across several Storage accounts created on several CSPs randomly and hence, the confidentiality, integrity and privacy of data also can be achieved. The proposed solution consists of a central component through which all the communication between the client and the CSPs take place. Technology which is used within the central component is related to the ORAM concept. Further this facilitates dynamical scaling up of cloud storages.
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    item: Article-Full-text
    Path towards a sustainable bioeconomy : conversion of locally available rice straw to nanocellulose
    (2021) Ratnakumar, A; Samarasekara, B; Amarasinghe, S; Rathnayake, S; Karunanayake, L
    The Sri Lankan agriculture sector has the potential to support a national bioeconomy. Rice straw is a key by-product generated from paddy cultivation. While it is traditionally treated as a waste matter, straw can be a valuable resource in producing biomass fibers in the green composite production due to properties such as recyclability, biodegradability, renewability, nontoxicity, and high functionality.