Browsing by Subject "Cloud computing"

Now showing 1 - 14 of 14

Open Access
A utilization based genetic algorithm for virtual machine placement in cloud systems
(2024-01-15) Çavdar, Mustafa Can; Körpeoğlu, İbrahim; Ulusoy, Özgür
Due to the increasing demand for cloud computing and related services, cloud providers need to come up with methods and mechanisms that increase the performance, availability and reliability of data centers and cloud systems. Server virtualization is a key component to achieve this, which enables sharing of resources of a single physical machine among multiple virtual machines in a totally isolated manner. Optimizing virtualization has a very significant effect on the overall performance of a cloud computing system. This requires efficient and effective placement of virtual machines into physical machines. Since this is an optimization problem that involves multiple constraints and objectives, we propose a method based on genetic algorithms to place virtual machines into physical servers of a data center. By considering the utilization of machines and node distances, our method, called Utilization Based Genetic Algorithm (UBGA), aims at reducing resource waste, network load, and energy consumption at the same time. We compared our method against several other placement methods in terms of utilization achieved, networking bandwidth consumed, and energy costs incurred, using an open-source, publicly available CloudSim simulator. The results show that our method provides better performance compared to other placement approaches.
Open Access
Architecture framework for mapping parallel algorithms to parallel computing platforms
(CEUR-WS, 2013) Tekinerdogan, Bedir; Arkin, E.
Mapping parallel algorithms to parallel computing platforms requires several activities such as the analysis of the parallel algorithm, the definition of the logical configuration of the platform, and the mapping of the algorithm to the logical configuration platform. Unfortunately, in current parallel computing approaches there does not seem to be precise modeling approaches for supporting the mapping process. The lack of a clear and precise modeling approach for parallel computing impedes the communication and analysis of the decisions for supporting the mapping of parallel algorithms to parallel computing platforms. In this paper we present an architecture framework for modeling the various views that are related to the mapping process. An architectural framework organizes and structures the proposed architectural viewpoints. We propose five coherent set of viewpoints for supporting the mapping of parallel algorithms to parallel computing platforms. We illustrate the architecture framework for the mapping of array increment algorithm to the parallel computing platform. Copyright © 2013 for the individual papers by the papers' authors.
Open Access
A comparative study of deep learning architectures for multivariate cloud workload prediction
(2022-06) Gözen, Derya
Cloud computing and use of cloud data centers are in high demand due to their benefits to customers including but not limited to low cost, high availability, reliability, robustness and scalability. Cloud service providers are obliged to fulfill service level agreements that promise high quality of service to their customers. This brings out the need for effective and efficient utilization of data center resources, especially the resources of the compute servers. To achieve proactive and effective resource allocation and scaling policies, accurate prediction of workloads in cloud computing environments plays a critical role. Cloud workload prediction is a challenging task due to high dimensionality, variance and complexity of the workload data. In addition, workload prediction models are expected to work with sufficient amount of past observations to correctly learn workload patterns, at the same time, handle longer forecast horizons accurately. In order to tackle this problem and address the challenges, we investigated and compared five deep learning-based schemes for multivariate time series forecasting to predict the CPU utilization of virtual machines in cloud data centers. The performance of the deep learning schemes is analyzed and compared by using two real-world data sets: Alibaba cluster trace and Bitbrains trace. Our study reveals the relative strengths and weaknesses of the compared schemes for cloud workload prediction. We also observed that, among the compared schemes, Encoder-Decoder LSTM Network with Attention is a more effective solution for workload prediction in cloud computing.
Open Access
Factors affecting the adoption of cloud for software development: A case from Turkey
(World Scientific Publishing Co. Pte. Ltd., 2023-07-04) Pisirir, E.; Chouseinoglou, Oumout; Sevgi, Cüneyt; Uçar, Erkan
Cloud-based solutions for software development activities have been emerging in the last decade. This study aims to develop a hybrid technology adoption model for cloud use in software development activities. It is based on Technology Acceptance Model (TAM), Technology–Organization–Environment (TOE) framework, and the proposed extension Personal–Organization–Project (POP) structure. The methodology selected is a questionnaire-based survey and data are collected through personally administered questionnaire sessions with developers and managers, resulting in 268 responses regarding 84 software development projects from 30 organizations in Turkey, selected by considering company and project sizes and geographical proximity to allow face-to-face response collection. Structural Equation Modeling (SEM) is used for statistical evaluation and hypothesis testing. The final model was reached upon modifications and it was found to explain the intention to adopt and use the cloud for software development meaningfully. To the best of our knowledge, this is the first study to identify and understand factors that affect the intention of developing software on the cloud. The developed hybrid model was validated to be used in further technology adoption studies. Upon modifying the conceptual model and discovering new relations, a novel model is proposed to draw the relationships between the identified factors and the actual use, intention to use and perceived suitability. Practical and social implications are drawn from the results to help organizations and individuals make decisions on cloud adoption for software development.
Open Access
Fog-Based Data Distribution Service (F-DAD) for Internet of Things (IoT) applications
(Elsevier, 2019) Karataş, Fırat; Körpeoğlu, İbrahim
With advances in technology, devices, machines, and appliances get smarter, more capable and connected to each other. This defines a new era called Internet of Things (IoT), consisting of a huge number of connected devices producing and consuming large amounts of data that may be needed by multiple IoT applications. At the same time, cloud computing and its extension to the network edge, fog computing, become an important way of storing and processing large amounts of data. Then, an important issue is how to transport, place, store, and process this huge amount of IoT data in an efficient and effective manner. In this paper, we propose a geographically distributed hierarchical cloud and fog computing based IoT architecture, and propose techniques for placing IoT data into the components, i.e., cloud and fog data centers, of the proposed architecture. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we model the data placement problem as an optimization problem and propose algorithms for efficient and effective placement of data generated and consumed by geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is efficiently accessed by applications needing that type of data. We perform extensive simulation experiments to evaluate our proposal and the results show that our architecture and placement techniques can place and store data efficiently while providing good performance for applications and network in terms of access latency and bandwidth consumed.
Open Access
Generic resource allocation metrics and methods for heterogeneous cloud infrastructures
(Elsevier, 2019) Mergenci, Cem; Körpeoğlu, İbrahim
With the advent of cloud computing, computation has become a commodity used by customers to access computing resources with no up-front investment, but as an on-demand and pay-as-you-go basis. Cloud providers make their infrastructure available to public so that anyone can obtain a virtual machine (VM) instance that can be remotely configured and managed. The cloud infrastructure is a large resource pool, allocated to VM instances on demand. In a multi-resource heterogeneous cloud, allocation state of the data center needs to be captured in metrics that can be used by allocation algorithms to make proper assignments of virtual machines to servers. In this paper, we propose two novel metrics reflecting the current state of VM allocation. These metrics can be used by online and offline VM placement algorithms in judging which placement would be better. We also propose multi-dimensional resource allocation heuristic algorithms showing how metrics can be used. We studied the performance of proposed methods and compared them with the methods from the literature. Results show that our metrics perform significantly better than the others and can be used to efficiently place virtual machines with high success rate.
Open Access
Hybrid fog-cloud based data distribution for internet of things applications
(2019-09) Karataş, Fırat
Technological advancements keep making machines, devices, and appliances faster, more capable, and more connected to each other. The network of all interconnected smart devices is called Internet of Things (IoT). It is envisioned that there will be billions of interconnected IoT devices producing and consuming petabytes of data that may be needed by multiple IoT applications. This brings challenges to store and process such a large amount of data in an efficient and effective way. Cloud computing and its extension to the network edge, fog computing, emerge as new technology alternatives to tackle some of these challenges in transporting, storing, and processing petabytes of IoT data in an efficient and effective manner. In this thesis, we propose a geographically distributed hierarchical cloud and fog computing based IoT storage and processing architecture, and propose techniques for placing IoT data into its components, i.e., cloud and fog data centers. Data is considered in different types and each type of data may be needed by multiple applications. Considering this fact, we generate feasible and realistic network models for a large-scale distributed storage architecture, and propose algorithms for efficient and effective placement of data generated and consumed by large number of geographically distributed IoT nodes. Data used by multiple applications is stored only once in a location that is easily accessed by applications needing that type of data. We performed extensive simulation experiments to evaluate our proposal. The results show that our network architecture and placement techniques can be used to store IoT data efficiently while providing reduced latency for IoT applications without increasing network bandwidth consumed.
Open Access
Model-driven transformations for mapping parallel algorithms on parallel computing platforms
(MDHPCL, 2013) Arkin, E.; Tekinerdoğan, Bedir
One of the important problems in parallel computing is the mapping of the parallel algorithm to the parallel computing platform. Hereby, for each parallel node the corresponding code for the parallel nodes must be implemented. For platforms with a limited number of processing nodes this can be done manually. However, in case the parallel computing platform consists of hundreds of thousands of processing nodes then the manual coding of the parallel algorithms becomes intractable and error-prone. Moreover, a change of the parallel computing platform requires considerable effort and time of coding. In this paper we present a model-driven approach for generating the code of selected parallel algorithms to be mapped on parallel computing platforms. We describe the required platform independent metamodel, and the model-to-model and the model-to-text transformation patterns. We illustrate our approach for the parallel matrix multiplication algorithm. Copyright © 2013 for the individual papers by the papers' authors.
Open Access
Network-aware virtual machine placement in cloud data centers with multiple traffic-intensive components
(Elsevier BV, 2015) Ilkhechi, A. R.; Korpeoglu, I.; Ulusoy, Özgür
Following a shift from computing as a purchasable product to computing as a deliverable service to consumers over the Internet, cloud computing has emerged as a novel paradigm with an unprecedented success in turning utility computing into a reality. Like any emerging technology, with its advent, it also brought new challenges to be addressed. This work studies network and traffic aware virtual machine (VM) placement in a special cloud computing scenario from a provider's perspective, where certain infrastructure components have a predisposition to be the endpoints of a large number of intensive flows whose other endpoints are VMs located in physical machines (PMs). In the scenarios of interest, the performance of any VM is strictly dependent on the infrastructure's ability to meet their intensive traffic demands. We first introduce and attempt to maximize the total value of a metric named "satisfaction" that reflects the performance of a VM when placed on a particular PM. The problem of finding a perfect assignment for a set of given VMs is NP-hard and there is no polynomial time algorithm that can yield optimal solutions for large problems. Therefore, we introduce several off-line heuristic-based algorithms that yield nearly optimal solutions given the communication pattern and flow demand profiles of subject VMs. With extensive simulation experiments we evaluate and compare the effectiveness of our proposed algorithms against each other and also against naïve approaches.
Unknown
Server and wireless network resource allocation strategies in heterogeneous cloud data centers
(2020-08) Mergenci, Cem
Resource allocation is one of the most important challenges in operating a data center. We investigate allocation of two main types of resources: servers and network links. Server resource allocation problem is the problem of how to allocate virtual machines (VMs) to physical machines (PMs). By modeling server resources (CPU, memory, storage, IO, etc.) as a multidimensional vector space, we present design criteria for metrics that measure the fitness of an allocation of VMs into PMs. We propose two novel metrics that conform to these design criteria. We also propose VM allocation methods that use these metrics to compare allocation alternatives when allocating a set of VMs into a set of PMs. We compare performances of our proposed metrics to the ones from the literature using vector bin packing with heterogeneous bins (VBPHB) benchmark. Results show that our methods find feasible solutions to a greater number of allocation problems than the others. Network resource allocation problem is examined in hybrid wireless data centers. We propose a system model in which each top-of-the-rack (ToR) switch is equipped with two radios operating in 60-GHz band using 3-channel 802.11ad. Given traffic flows between servers, we allocate wireless links between ToR switches so that the traffic carried over the wireless network is maximized. We also present a method to randomly generate traffic based on a real data center traffic pattern. We evaluate the performance of our proposed traffic allocation methods using randomly generated traffic. Results show that our methods can offload significant amount of traffic from wired to wireless network, while achieving low latency, high throughput, and high bandwidth utilization.
Unknown
Structural equation modeling in cloud computing studies: a systematic literature review
(Emerald, 2019) Pişirir, E.; Uçar, Erkan; Chouseinoglou, O.; Sevgi, Cüneyt
Purpose – This study aims to examine the current state of literature on structural equation modeling (SEM) studies in “cloud computing” domain with respect to study domains of research studies, theories and frameworks they use and SEM models they design. Design/methodology/approach – Systematic literature review (SLR) protocol is followed. In total, 96 cloud computing studies from 2009 to June 2018 that used SEM obtained from four databases are selected, and relevant data are extracted to answer the research questions. Findings – A trend of increasing SEM usage over years in cloud studies is observed, where technology adoption studies are found to be more common than the use studies. Articles appear under four main domains, namely, business, personal use, education and health care. Technology acceptance model (TAM) is found to be the most commonly used theory. Adoption, intention to use and actual usage are the most common selections for dependent variables in SEM models, whereas security and privacy concerns, costs, ease of use, risks and usefulness are the most common selections for causal factors. Originality/value – Previous cloud computing SLR studies did not focus on statistical analysis method used in primary studies. This review will display the current state of SEM studies in cloud domain for all future academics and practical professionals.
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Understanding the tendency of software development teams to develop software over the cloud
(CEUR-WS, 2016) Çoban, S.; Uçar, Erkan; Chouseinoglou, Oumout; Sevgi, C.; Testik, Murat Caner
Today, Cloud Computing offers attractive and effective solutions for organizations which enable them to decrease IT costs, provide flexibility to ser-vices and make it easier to access IT services -Therefore enable faster market entries. For an organization that decides to make use of Cloud services, there are various factors to evaluate - similar to outsourcing. In this paper, we studied these factors through the literature and then we tried to understand the viewpoints of software developers regarding the existing and possible future usage of Cloud in software development processes. In this context, we prepared a questionnaire based on the findings in the literature and applied it to software development team members working in technoparks in Turkey. We used the dataset which is obtained from this questionnaire to observe the relationship between the tendency of using Cloud in software development processes and the factors effecting them. This research is performed as the first phase of a study with a larger scope, de-signed to forecast the Cloud needs of software developing organizations and it provides important findings. The questionnaire findings also describe the current demographics of software development organizations in Turkish technoparks to-gether with their perception of Cloud services.
Unknown
A utilization based genetic algorithm for virtual machine placement in cloud computing systems
(2016-09) Çavdar, Mustafa Can
Due to increasing demand for cloud computing and related services, cloud providers need to come up with methods and mechanisms that increase performance, availability and reliability of datacenters and cloud computing systems. Server virtualization is a key component to achieve this, which enables sharing of resources of a physical machine among multiple virtual machines in a totally isolated manner. Optimizing virtualization has a very signi cant e ect on the overall performance of cloud computing systems. This requires e cient and effective placement of virtual machines into physical machines. Since this is an optimization problem that involves multiple constraints and objectives, we propose a method based on genetic algorithms to place virtual machines. By considering utilization of machines and node distances, our method aims at reducing resource waste, network load, and energy consumption at the same time. We compared our method with several other methods in terms of utilization achieved, networking bandwidth consumed, and energy costs incurred, using the publicly available CloudSim simulation platform. The results show that our approach provides improved performance compared to other similar approaches.
Unknown
The virtual design studio on the cloud: a blended and distributed approach for technology-mediated design education
(Taylor and Francis Ltd., 2015) Pektaş, Ş. T.
The studio is widely accepted as the core in design education because it aims to integrate many curricular topics within its scope. However, learning environments in studio teaching have not been explored and exploited as a response to developing technology and changing socio-cultural context, yet. In order to alleviate the problem, this paper presents an innovative model for a virtual design studio which utilizes social networking media and cloud computing. The virtual design studio is conceptualized as a socio-technical system where intelligence is distributed across people and tools. The study proposes several means of augmenting intelligence in such a studio. The application of the theoretical framework is demonstrated in a real-life case study. The results of an empirical survey show that the proposed model was well accepted by the students. In the paper, the opportunities and challenges of this approach are discussed and suggestions are made for further studies.