Browsing by Author "Shishira, S.R."

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A comprehensive survey on federated cloud computing and its future research directions
(Springer Science and Business Media Deutschland GmbH, 2021) Shishira, S.R.; Kandasamy, A.
The cloud computing paradigm is popular due to its pay-as-you-go model. Due to its increasing demand for service, the user has a huge advantage in paying for the service currently needed. In a federated cloud environment, there is one or more number of cloud service providers who share their servers to service the user request. It improves minimizing cost, utilization of services and improves performance. Clients will get benefited as there is a Service Level Agreement between both. In the present paper, survey is provided on the benefits of the federated environment, its architecture, provision of resources and future research directions. Paper also gives the comparative study on the above aspects. © Springer Nature Singapore Pte Ltd 2021.
A Conceptual Framework for Intelligent Management of Workloads in Cloud Environment
(Institute of Electrical and Electronics Engineers Inc., 2020) Shishira, S.R.; Kandasamy, A.
Cloud computing is an important paradigm for processing, computation, storage, and network bandwidth. Workloads are the amount of data given to the hardware resource for processing. Its behavior and properties play a major role in the efficient scheduling of requests to given resources. Also, it is very difficult to predict workloads nature if they are changing excessively. To address this issue, we propose a conceptual framework which can be used for efficient prediction and optimization of workloads in a cloud environment. Classification of optimization metrics based on the provider and consumer constraints are presented. In addition to this, some of the research gaps found during the study has been highlighted and also provided possible solutions in the cloud research domain. Â© 2020 IEEE.
A Novel Feature Extraction Model for Large-Scale Workload Prediction in Cloud Environment
(Springer, 2021) Shishira, S.R.; Kandasamy, A.
In an enterprise cloud environment, it is difficult to handle an extensive number of loads. Serving the request in very less time leads to resource allocation problem. It is better to have prior knowledge of the incoming loads to auto-scale the resources. A novel architecture is proposed for the better prediction of workloads in the cloud environment. The proposed feature extraction model considers three essentials for managing cloud resources, i.e., CPU, Disk, and Memory. The model with the very nominal error achieved an accuracy of 98.72%. The proposed model is contrasted with other conventional predictive models for validation. © 2021, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.
BeeM-NN: An efficient workload optimization using Bee Mutation Neural Network in federated cloud environment
(Springer Science and Business Media Deutschland GmbH, 2021) Shishira, S.R.; Kandasamy, A.
Cloud computing is an extensively implemented technique to handle enormous amount of data as it provides flexibility and scalability features. In an established cloud environment, users process their request to share the data that are stored in it. Under the dynamic cloud environment, multiple requests are processed in a short time, which leads to the problem of resource allocation. Virtual Machines or servers aid the cloud in maintaining the workflow active through proper distribution of resources. However, the accurate workload prediction model is necessary for effective resource management. In the present paper, a novel BeeM-NN framework is proposed through the integration of Workload Neural Network Algorithm (WNNA) and Novel Bee Mutation Optimization Algorithm (NBMOA) for optimized workload prediction in a cloud environment. The proposed model encloses the Fitness Feature Extraction Algorithm initially to extract the feature dataset from Azure public dataset and is provided to train the WNNA. The predicted workloads are optimized with the NBMOA in the cloud. The generated model is tested using the workload data traces from the federated cloud service provider and is evaluated and compared with the existing models. The outcome showed the prediction model achieved an accuracy of 99.98% better than the current models with optimum performance in the consumption of resources and cost. The future work is to use the predicted workloads for scheduling in the cloud. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Comparative study of simulation tools and challenging issues in cloud computing
(2018) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Resource Scheduling lays a key role in large-scale cloud applications. It is difficult for the developers to do an extensive research on all the issues in real time as it requires infrastructure which is beyond the control, also network condition cannot be predicted. Hence simulations are used which imitates the real time environment. There are various simulators developed for the research as it is difficult to maintain the infrastructure on premise. Thus to understand the tools in deep, we focused on five open source tools such as Cloudsim, CloudAnalyst, iCancloud, Greencloud and CloudSched. The above mentioned tools are compared based on the respective architecture, the process of simulation, structural elements and performance parameters. In the paper, we have also discussed some of the challenging issues among the tools for further research. � Springer Nature Singapore Pte Ltd. 2018.
Comparative study of simulation tools and challenging issues in cloud computing
(Springer Verlag service@springer.de, 2018) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Resource Scheduling lays a key role in large-scale cloud applications. It is difficult for the developers to do an extensive research on all the issues in real time as it requires infrastructure which is beyond the control, also network condition cannot be predicted. Hence simulations are used which imitates the real time environment. There are various simulators developed for the research as it is difficult to maintain the infrastructure on premise. Thus to understand the tools in deep, we focused on five open source tools such as Cloudsim, CloudAnalyst, iCancloud, Greencloud and CloudSched. The above mentioned tools are compared based on the respective architecture, the process of simulation, structural elements and performance parameters. In the paper, we have also discussed some of the challenging issues among the tools for further research. Â© Springer Nature Singapore Pte Ltd. 2018.
RSim: Routing simulator for analyzing the performance of routing algorithms in a network
(2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
It is necessary to select the best routing techniques to send the data from a network to the other efficiently through the subnet. Routing refers to the process of gaining and distributing the information in a network. So far none of the algorithms provided the best choice for all the cases. This paper presents a simulator which is a routing simulator tool that measures the performances of the given routing algorithms. We present the performance analysis of routing algorithms such as Distance Vector, Routing Information Protocol, Link state, Hot potato, Flooding, Source routing. Descriptions of the mentioned algorithms, with the details of the experiments conducted and their results are presented in the paper. � 2016 IEEE.
RSim: Routing simulator for analyzing the performance of routing algorithms in a network
(Institute of Electrical and Electronics Engineers Inc., 2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
It is necessary to select the best routing techniques to send the data from a network to the other efficiently through the subnet. Routing refers to the process of gaining and distributing the information in a network. So far none of the algorithms provided the best choice for all the cases. This paper presents a simulator which is a routing simulator tool that measures the performances of the given routing algorithms. We present the performance analysis of routing algorithms such as Distance Vector, Routing Information Protocol, Link state, Hot potato, Flooding, Source routing. Descriptions of the mentioned algorithms, with the details of the experiments conducted and their results are presented in the paper. Â© 2016 IEEE.
Survey on meta heuristic optimization techniques in cloud computing
(2016) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on demand access to shared resources over the Internet in a self-service, dynamically scalable and metered manner. To reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map the job to resources that optimize more than one objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes long time to find an optimal solution. In cloud environment, it is best to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey on optimization algorithms for cloud environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and a novel technique: League Championship Algorithm (LCA). � 2016 IEEE.
Survey on meta heuristic optimization techniques in cloud computing
(Institute of Electrical and Electronics Engineers Inc., 2016) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Cloud computing has become a buzzword in the area of high performance distributed computing as it provides on demand access to shared resources over the Internet in a self-service, dynamically scalable and metered manner. To reap its full benefits, much research is required across a broad array of topics. One of the important research issues which need to be focused for its efficient performance is scheduling. The goal of scheduling is to map the job to resources that optimize more than one objectives. Scheduling in cloud computing belongs to a category of problems known as NP-hard problem due to large solution space and thus it takes long time to find an optimal solution. In cloud environment, it is best to find suboptimal solution, but in short period of time. Metaheuristic based techniques have been proved to achieve near optimal solutions within reasonable time for such problems. In this paper, we provide an extensive survey on optimization algorithms for cloud environments based on three popular metaheuristic techniques: Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), Genetic Algorithm (GA) and a novel technique: League Championship Algorithm (LCA). Â© 2016 IEEE.
Workload Characterization: Survey of Current Approaches and Research Challenges
(2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Workload is a set of inputs given to a infrastructure for processing. Performance can be measured based on the efficient processing of the workloads. Different workloads has different set of characteristics. In this paper, we have mainly focused on cloud workloads. Understanding the characteristics of workloads is the key to make an optimal configuration decisions and improve the system performance. This paper describes various computing workloads and relates them to their resource utilization. Specifically, the paper concentrates on cloud workloads characterization. We have classified the workloads based on different aspects from the literature also we have provided the characteristic features of the workload to know the properties and make it more understandable for the researchers. � 2017 Association for Computing Machinery.
Workload Characterization: Survey of Current Approaches and Research Challenges
(Association for Computing Machinery acmhelp@acm.org, 2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Workload is a set of inputs given to a infrastructure for processing. Performance can be measured based on the efficient processing of the workloads. Different workloads has different set of characteristics. In this paper, we have mainly focused on cloud workloads. Understanding the characteristics of workloads is the key to make an optimal configuration decisions and improve the system performance. This paper describes various computing workloads and relates them to their resource utilization. Specifically, the paper concentrates on cloud workloads characterization. We have classified the workloads based on different aspects from the literature also we have provided the characteristic features of the workload to know the properties and make it more understandable for the researchers. Â© 2017 Association for Computing Machinery.
Workload scheduling in cloud: A comprehensive survey and future research directions
(2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Cloud Computing handles the computation and storage needs in an efficient and cost-effective manner. One of the emerging research area in the cloud computing is scheduling of workloads. In this paper, we present a comprehensive survey on workload scheduling in cloud computing environment. In particular, we focus on summarizing the scheduling methods, type of workloads and considered QoS parameters along with the comments on each selected studies. Furthermore, we have analyzed several workload scheduling approaches to find out the recent research trends. In addition, we have highlighted possible future research directions in this research area. One of the important finding of the survey is that, most of the approaches failed in providing adaptive scheduling policies. In future, context information such as cost, energy, workload pattern, network cost, etc can be considered to make workload scheduling policies more adaptive. � 2017 IEEE.
Workload scheduling in cloud: A comprehensive survey and future research directions
(Institute of Electrical and Electronics Engineers Inc., 2017) Shishira, S.R.; Kandasamy, A.; Chandrasekaran, K.
Cloud Computing handles the computation and storage needs in an efficient and cost-effective manner. One of the emerging research area in the cloud computing is scheduling of workloads. In this paper, we present a comprehensive survey on workload scheduling in cloud computing environment. In particular, we focus on summarizing the scheduling methods, type of workloads and considered QoS parameters along with the comments on each selected studies. Furthermore, we have analyzed several workload scheduling approaches to find out the recent research trends. In addition, we have highlighted possible future research directions in this research area. One of the important finding of the survey is that, most of the approaches failed in providing adaptive scheduling policies. In future, context information such as cost, energy, workload pattern, network cost, etc can be considered to make workload scheduling policies more adaptive. Â© 2017 IEEE.