Faculty Publications
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Publications by NITK Faculty
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Item Trust models in cloud: A survey on pros and cons(Springer Verlag service@springer.de, 2015) Divakarla, U.; Chandra Sekaran, K.Cloud is the recent emerging technology in all aspects. The basic concern with the usage of this cloud technology is security. Security poses a major drawback with data storage, resource utilization, virtualization, etc. In the highly competitive environment the assurances are insufficient for the customers to identify the trust worthy cloud service providers. In a nut shell all the entities in cloud and cloud computing environment should be trusted by each other and the entities that have communication should be trusted by each other. This paper throws light on different Trust Models developed and their drawback with respect to resource security. A strong Trust Model is recommended to enhance the security of the resources in Cloud. © Springer International Publishing Switzerland 2015.Item Dynamic partner selection in Cloud Federation for ensuring the quality of service for cloud consumers(World Scientific Publishing Co. Pte Ltd wspc@wspc.com.sg, 2017) Thomas, M.V.; Chandrasekaran, K.Cloud Computing has become the popular paradigm for accessing the various scalable and on-demand computing services over the internet. Nowadays, individual Cloud Service Providers (CSPs) offering specialized services to the customers collaborate to form the Cloud Federation, in order to reap the real benefits of Cloud Computing. By collaboration, the member CSPs of the federation achieve better resource utilization and Quality of Service (QoS), thereby increasing their business prospects. When a CSP runs out of resources in the Cloud Federation, in order to offload the customer requests for resources to other CSP(s), identifying a suitable partner is a challenging task due to the lack of global coordination among them. In this paper, we propose the design and implementation of an efficient partner selection mechanism in the Cloud Federation, using the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methods, and also considering the trust values of various CSPs in the federation. The AHP method is used to calculate the weights of the QoS parameters used in the TOPSIS method which is used to rank the various CSPs in the Cloud Federation according to the user requirements. Simulation results show the effectiveness of this approach in order to efficiently select the trustworthy partners in large scale federations to ensure the required QoS to the cloud consumers. © 2017 World Scientific Publishing Company.Item An efficient cost optimized scheduling for spot instances in heterogeneous cloud environment(Elsevier B.V., 2018) Domanal, S.; Guddeti, G.In this paper, we propose a novel efficient and cost optimized scheduling algorithm for a Bag of Tasks (BoT) on Virtual Machines (VMs). Further, in this paper, we use artificial Neural Network to predict the future values of Spot instances and then validate these predicted values with respect to the current (actual) values of Spot instances. On-Demand and Spot are the key instances which are procured by the cloud customers and hence, in this paper, we use these instances for the cost optimization. The key idea of our proposed algorithm is to efficiently utilize the cloud resources (mainly VMs instances, Central Processing Unit (CPU) and Memory) and also to optimize the cost of executing the BoT in the heterogeneous Infrastructure as a Service (IaaS) based cloud environment. Experimental results demonstrate that our proposed scheduling algorithm outperforms state-of-the-art benchmark algorithms (Round Robin, First Come First Serve, Ant Colony Optimization, Genetic Algorithm, etc.) in terms of Quality of Service (QoS) parameters (Reliability, Time and Cost) while executing the BoT in the heterogeneous cloud environment. Since the obtained results are in the form of ordinal, hence we carried out the statistical analysis on both predicted and actual Spot instances using the Spearman's Rho Test. © 2018 Elsevier B.V.Item Dynamic ranking-based MapReduce job scheduler to exploit heterogeneous performance in a virtualized environment(Springer New York LLC barbara.b.bertram@gsk.com, 2019) Jeyaraj, J.; Ananthanarayana, V.S.; Paul, A.“More data, more information.” Big data helps businesses and research communities to gain insights and increase productivity. Many public cloud service providers offer Hadoop MapReduce as a service based on pay-per-use via infrastructure as a service on clusters of virtual machines promising on-demand horizontal scaling. These clusters of virtual machines are launched in various physical machines across racks in cloud data centers. Such multi-tenancy negatively introduces performance heterogeneity for Hadoop virtual machines due to hardware heterogeneity and interference from co-located virtual machine. Performance heterogeneity largely affects MapReduce job latency and resource utilization of rented Hadoop virtual clusters. Default MapReduce schedulers assign map/reduce tasks assuming the hardware is homogeneous. Interference-aware schedulers perform by only observing the interference pattern generated by co-located virtual machines. These schedulers do not consider the heterogeneous performance of virtual machines.Therefore, we propose a dynamic ranking-based MapReduce job scheduler that places the map and reduces tasks based on a virtual machine’s performance rank to minimize job latency and improve resource utilization. Our proposed approach calculates the performance score for each virtual machine based on hardware heterogeneity and co-located virtual machine interference. Then, it ranks the virtual machines based on the map and reduce performance separately to place map and reduce tasks. To demonstrate our ideas, we have set a test bed with 29 virtual machines on eight physical machines with different configurations and capacities. We modify a default fair scheduler in Hadoop 2.x to incorporate our ideas and evaluate them with different workloads on the PUMA dataset. The proposed method is then compared against a default fair scheduler (resource-aware) and an interference-aware scheduler based on job latency and resource utilization. Finally, we argue in favor of our approach as it improves resource utilization by 30–65% and overall job latency by up to 30%. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.Item Improving MapReduce scheduler for heterogeneous workloads in a heterogeneous environment(John Wiley and Sons Ltd, 2020) Jeyaraj, R.; Ananthanarayana, V.S.; Paul, A.Big data is largely influencing business entities and research sectors to be more data-driven. Hadoop MapReduce is one of the cost-effective ways to process large scale datasets and offered as a service over the Internet. Even though cloud service providers promise an infinite amount of resources available on-demand, it is inevitable that some of the hired virtual resources for MapReduce are left unutilized and makespan is limited due to various heterogeneities that exist while offering MapReduce as a service. As MapReduce v2 allows users to define the size of containers for the map and reduce tasks, jobs in a batch become heterogeneous and behave differently. Also, the different capacity of virtual machines in the MapReduce virtual cluster accommodate a varying number of map/reduce tasks. These factors highly affect resource utilization in the virtual cluster and the makespan for a batch of MapReduce jobs. Default MapReduce job schedulers do not consider these heterogeneities that exist in a cloud environment. Moreover, virtual machines in MapReduce virtual cluster process an equal number of blocks regardless of their capacity, which affects the makespan. Therefore, we devised a heuristic-based MapReduce job scheduler that exploits virtual machine and MapReduce workload level heterogeneities to improve resource utilization and makespan. We proposed two methods to achieve this: (i) roulette wheel scheme based data block placement in heterogeneous virtual machines, and (ii) a constrained 2-dimensional bin packing to place heterogeneous map/reduce tasks. We compared heuristic-based MapReduce job scheduler against the classical fair scheduler in MapReduce v2. Experimental results showed that our proposed scheduler improved makespan and resource utilization by 45.6% and 47.9% over classical fair scheduler. © 2019 John Wiley & Sons, Ltd.