Faculty Publications

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Publications by NITK Faculty

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Author: search.filters.author.Annappa, B.Subject: search.filters.subject.Cloud computing

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    Survey of dynamic resource management approaches in virtualized data centers
    (IEEE Computer Society help@computer.org, 2013) Bane, R.R.; Annappa, B.; Shet, K.C.
    Virtualization technology enabled hosting of applications and services in an isolated and resource guaranteed virtual machines (VMs). Typically single physical machine (PM) runs multiple virtual machines and application resource demands are changing with time. To achieve this, dynamic resource provisioning of physical machine resources to VMs in virtualized data center is necessary. Data center requires this provisioning should be elastic so that its cost can be minimized and service level objectives (SLO) can be met by allocating exact amount of resources. It invites two main challenges: (1) determining how many resources need to be allocated to the application where resource demand is dynamic and (2) prediction of the application resource need in advance so that resource allocation could be adjusted ahead of the actual need. In this paper we have given various ways of handling above mentioned challenges for dynamic resource management and their comparisons. © 2013 IEEE.
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    An efficient framework and access control scheme for cloud health care
    (Institute of Electrical and Electronics Engineers Inc., 2016) Saravana, N.; Rajya Lakshmi, G.V.; Annappa, B.
    Cloud computing is being a potential role in providing services for utilizing a huge data in various application, as it is ubiquitous. In emerging growth of Cloud services been focused on security issues and optimal data storage used by consumers. Eventually, the Cloud storage is the best way to keep essential business data secure and accessible. Along with that, there are few important feature been considered. i.e( file versioning, automatic sync,collaboration tools, security File Encryption). In our research article, the framework is designed for real-time Healthcare business application to be achieved all the essential features with Inter-Cloud data storage.To do additionally, has been implemented and tested by an efficient CP-ABE (Cipher Policy-Attribute Based Encryption) algorithm for secure data transmission. Outcomes were powerful in a such way that can be promised in a designed framework developed in Python 3 in Charm-Cryptography. © 2015 IEEE.
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    Cost aware service broker algorithm for load balancing geo-distrubuted data centers in cloud
    (Institute of Electrical and Electronics Engineers Inc., 2017) Kulkarni, A.K.; Annappa, B.
    With increased cloud adoption globally, the cloud service providers are setting up their data centers in various geographical location to cater the needs of diverse range of users across the globe. The cost of managing data center includes not only hardware, software costs but also the electricity costs prevailing at that location. The cost of electricity varies from location to location and it is mainly because of the uneven production & availability of resources, infrastructure to generate electricity at that part of the globe. It is important for data center owners to reduce the data center management cost without affecting the agreed SLA of service to its users. The paper proposes an algorithm for routing service requests to geo-distributed datacenters considering the electricity cost and response times to optimize the cost of datacenter management. The experimental results using cloud analyst show that our approach reduces the cost of data center management without any increase in the response time for users. © 2017 IEEE.
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    GPU-aware resource management in heterogeneous cloud data centers
    (Springer, 2021) Kulkarni, A.K.; Annappa, B.
    The power of rapid scalability and easy maintainability of cloud services is driving many high-performance computing applications from company server racks into cloud data centers. With the evolution of Graphics Processing Units, composing of an extensive array of parallel computing single-instruction-multiple-data processors are being considered as a platform for high-performance computing because of their high throughput. Many cloud providers have begun offering GPU-enabled services for the users where GPUs are essential (for high computational power) to meet the desired Quality-of-service. Virtual machine placement and load balancing the GPUs in the virtualized environments like the cloud is still an evolving area of research and it is of prime importance to achieve higher resource efficiency and also to save energy. The current VM placement techniques do not consider the impact of VM workload type and GPU memory status on the VM placement decisions. This paper discusses the current issues with the First Fit policy of virtual machine placement used in VMWare Horizon and proposes a GPU-aware VM placement technique for GPU-enabled virtualized environments like cloud data centers. The experiments conducted using the synthetic workloads indicate reduction in the energy consumption, reduction in search space of physical hosts, and the makespan of the system. It also presents a summary of the current challenges for GPU resource management in virtualized environments and specific issues in developing cloud applications targeting GPUs under the virtualization layer. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.