Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item Virtual Machine Migration Triggering using Application Workload Prediction(Elsevier, 2015) Raghunath, B.R.; Annappa, B.Dynamic provisioning of physical resources to Virtual Machines (VMs) in virtualized environments can be achieved by (i) vertical scaling-adding/removing attached resources from existing virtual machine and (ii) horizontal scaling-adding a new virtual machine with additional resources. The live migration of virtual machines across different Physical Machines (PMs) is a vertical scaling technique which facilitates resource hot-spot mitigation, server consolidation, load balancing and system level maintenance. It takes significant amount of resources to iteratively copy memory pages. Hence during the migration there may be too much overload which can affect the performance of applications running on the VMs on the physical server. It is better to predict the future workload of applications running on physical server for early detection of overloads and trigger the migration at an appropriate point where sufficient number of resources are available for all the applications so that there will not be performance degradation. This paper presents an intelligent decision maker to trigger the migration by predicting the future workload and combining it with predicted performance parameters of migration process. Experimental results shows that migration is triggered at an appropriate point such that there are sufficient amount of resources available (15-20% more resources than high valued threshold method) and no application performance degradation exists as compared to properly chosen threshold method for triggering the migration. Prediction with support vector regression has got decent accuracy with MSE of 0.026. Also this system helps to improve resource utilization as compared to safer threshold value for triggering migration by removing unnecessary migrations. © 2015 The Authors.Item Prediction based dynamic resource provisioning in virtualized environments(Institute of Electrical and Electronics Engineers Inc., 2017) Raghunath, B.R.; Annappa, B.Dynamic provisioning to virtual machines (VMs) is one of the important requirements in the virtualized data centers to make effective utilization of resources. This can be achieved by vertical scaling or horizontal scaling of attached resources. Live virtual machine migration of virtual machines across physical machines is a vertical scaling technique which facilitates resource hotspot mitigation, server consolidation, load balancing and system level maintenance. As live migration is triggered during heavy workload (hotspot) and its procedure takes significant amount of resources to iteratively copy memory pages from source to destination, it affects the performance of other running VMs hosted on the source as well as destination physical machine (PM). Hence to avoid such performance interference effects it is necessary to trigger the migration procedure at such a point where sufficient amount of resources will be available to all the running VMs and to the migrating procedure. It is also important to select such a VM which will produce less performance interference at the source and destination. This paper presents an intelligent decision maker to trigger the migration in such a way that it avoids the said performance interference effects. It predicts the future workload for early detection of overloads and accordingly triggers the migration procedure. It also models the migration procedure to calculate performance parameters and interference parameters which are used in the decision of selection of a VM. Experimental results show that it is able to increase the performance by 45%-50% for network intensive workloads and 25%-30% for CPU, memory intensive workloads when compared with traditional method. It improves the performance by 35%-40% for network intensive workloads and 15%-20% for CPU, memory intensive workloads when compared with Sandpiper method. © 2017 IEEE.Item Dynamic Resource Allocation Using Fuzzy Prediction System(Institute of Electrical and Electronics Engineers Inc., 2018) Raghunath, B.R.; Annappa, B.Virtualization is the main technology in the large scale data centers with which resources are shared among different application running on different VMs. Virtualization through virtual machine monitor (VMM) like Xen only provides resource isolation among co-located VMs. However, it has been shown that resource isolation does not imply performance isolation between VMs. Hence it necessitates on-demand allocation of the physical shared resources to individual VM as per their dynamic requirements to satisfy the SLA between customer and cloud provider. To do this efficiently future resource utilization is predicted using fuzzy logic based prediction. To avoid underestimation prediction errors due to spikes in the workload, the predicted values are padded with proper value and immediately resource caps are raised. The resource conflict is resolved locally if resources are available otherwise migration is triggered. This scheme allocates resources efficiently and reduces the response time as compared to static allocation. The resource saving with proposed method is around 30-40% and around 10-20% performance improvement in terms of response time of an application. © 2018 IEEE.Item IoT based Smart Management of Poultry Farm and Electricity Generation(Institute of Electrical and Electronics Engineers Inc., 2018) Sitaram, K.A.; Ankush, K.R.; Anant, K.N.; Raghunath, B.R.Poultry is one of the most important growing economic segments of agricultural sector in India today. Nowadays because of standardized farming management and good manufacturing practices, chicken production in the world has been increasing gradually. In contemporary world automation plays a vital role and concept of Internet of Things (IOT) is also emerging very fast, there is an approach to convert traditional systems into automated systems. The paper focuses on automation of poultry farm using IoT technology to perform various management related things. The environmental factors which affect the health of chicken such as temperature, humidity, light and Ammonia gas are monitored and the manual jobs like food feeding, water supply system, cleanliness are managed. If all these parameters are maintained, the production and quality of chicken increases. Along with this, electricity is generated from the methane gas which is produced from the chicken manure and stored in battery. The management and monitoring of the farm can also be done through a web based system. Which keeps track of the management of poultry farm from anywhere and at any time. © 2018 IEEE.