http://13.232.72.61:8080/jspui/handle/123456789/1000 2026-04-03T18:21:39Z 2026-04-03T18:21:39Z Manujakshi, B. C. Ramesh, K. B. http://13.232.72.61:8080/jspui/handle/123456789/1005 2020-02-28T14:13:48Z 2016-03-01T00:00:00Z

Title: Review of Techniques used for Sensory Data Analytics over Cloud. Authors: Manujakshi, B. C.; Ramesh, K. B. Abstract: In recent years, there is a high growth in wireless sensor network (WSNs) for secure communications or sensor network applications. It has been seen that sensor applications are revised with upcoming technologies in order to meet the demands. There is a requirement of efficient design and implementation of WSNs, due to the huge SNs to allow applications which connect the physical world to the virtual world. With a wide range of applications for SNs, some of the application areas are health, military, and security will create issues in data transmissions from one sensor network to another sensor network. As sensors gathers complex data, it is quite a difficult job to understand how far it can be valuable with respect to analysis. Owing to inherent complexities e.g. size, heterogeneity, un-structuredness, etc. it may pose serious problems in futuristic sensor data analytics over cloud. This paper discusses about the techniques used in managing sensor data over cloud to understand the existing system and its effectiveness.

2016-03-01T00:00:00Z Sadashiv, Naidila Dilip Kumar, S. M. Goudar, R. S. http://13.232.72.61:8080/jspui/handle/123456789/1003 2019-02-25T09:21:54Z 2017-02-01T00:00:00Z

Title: Cloud Capacity Planning and HSI based Optimal Resource Provisioning Authors: Sadashiv, Naidila; Dilip Kumar, S. M.; Goudar, R. S. Abstract: Cloud service providers offer spot instances through highest bidding plans that are at a very economical price compared to other pricing plans, namely on-demand and reservation. The usage of spot instance enables utilization of idle resources and provides service for cost sensitive tasks. However, this approach introduces the problem of cloud capacity allocation to different pricing plans that will have impact on the task completion time. To address these issues and improve the provider’s revenue, in this paper a capacity planning has been carried out based on the prediction of resource requirements for each of the different resource pricing pools. The paper also presents a solution to overcome the burden faced by the service provider due to the free issue of last hour at the time of out-of-bid situation. Simulation carried out based on capacity planning along with hybrid spot instance using Amazon EC2's price show that the resource utilization is improved across the different resource pricing pools with increased number of task completion and improved provider's revenue.

2017-02-01T00:00:00Z Naidila, Naidila Dilip Kumar, S. M. Goudar, R. S. http://13.232.72.61:8080/jspui/handle/123456789/1002 2019-02-25T09:21:11Z 2014-12-01T00:00:00Z

Title: Hybrid Spot Instance based Resource Provisioning Strategy in Dynamic Cloud Environment Authors: Naidila, Naidila; Dilip Kumar, S. M.; Goudar, R. S. Abstract: Utilization of resources to the maximum extent in large scale distributed cloud environment is a major challenge due to the nature of cloud. Spot Instances in the Amazon Elastic Compute Cloud (EC2) are provisioned based on highest bid with no guarantee of task completion but incurs the overhead of longer task execution time and price. The paper demonstrates the last partial hour and cost overhead that can be avoided by the proposed strategy of Hybrid Spot Instance. It aims to provide reliable service to the ongoing task so as to complete the execution without abruptly interrupting the long running tasks by redefining the bid price. The strategy also considers that on-demand resource services can be acquired when spot price crosses on-demand price and thereby availing high reliability. This will overcome the overhead involved during checkpointing, restarting and workload migration as in the existing system, leading to efficient resources usage for both the providers and users. Service providers revenue is carefully optimized by eliminating the free issue of last partial hour which is a taxing factor for the provider. Simulation carried out based on real time price of various instances considering heterogenous applications shows that the number of out-of-bid scenarios can be reduced largely which leads to the increased number of task completion. Checkpointing is also minimized maximally due to which the overhead associated with it is reduced. This resource provisioning strategy aims to provide preference to existing customers and the task which are nearing the execution completion.

2014-12-01T00:00:00Z Sadashiv, Naidila Dilip Kumar, S. M. Goudar, R. S. http://13.232.72.61:8080/jspui/handle/123456789/1001 2019-02-25T09:20:18Z 2014-09-01T00:00:00Z

Title: Priority Based Resource Allocation and Demand Based Pricing Model in Peer-to-Peer Clouds Authors: Sadashiv, Naidila; Dilip Kumar, S. M.; Goudar, R. S. Abstract: Management of resources in large scale distributed cloud environment is a major challenge due to the nature of cloud. On-demand resource provisioning allows the requests to be made on the fly. In order to provide QoS in accordance with the SLA in such a distributed environment, an effective resource handling scheme and pricing models that will benefit both the provider and cloud users is required. This paper aims to provide priority based resource allocation to the tasks by giving higher preference to the tasks that requests large amount of CPU. The tasks are classified into high, medium and low priority sets using the k-means algorithm. We also propose a dynamic pricing model where in the price is calculated based on the current demand for a resource and its availability. During high resource contention across the network, the resources are priced more than when there are surplus amount of resources. In such scenarios, the resources are discovered from the peer clouds through content addressable network for prioritized tasks. Simulation under different contention periods is carried out based on our priority based allocation. The results show that our algorithm provides better resource utilization ratio and throughput ratio when compared with non-prioritized tasks.

2014-09-01T00:00:00Z