Browsing by Author "Kulkarni, A.K."

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Context Aware VM Placement Optimization Technique for Heterogeneous IaaS Cloud
(Institute of Electrical and Electronics Engineers Inc., 2019) Kulkarni, A.K.; Annappa, A.
Ever increasing demand for cloud adoption is prompting researchers and engineers around the world to make cloud computing more efficient and beneficial for cloud service providers and users. Cloud computing brings profits for all when the cloud infrastructure is used efficiently, and its services are made affordable to businesses of all scales. Managing cloud data center incurs a significant cost, which includes investing in IT infrastructure at the beginning and data center management costs for power, repair, space, and so on at later stages. The power costs are contributing to a significant share in overall data center management costs, and saving in power consumption can help reduce management costs for data center owners. This paper proposes an efficient context-aware adaptive heuristic-based solution for the virtual machine (VM) placement optimization in the heterogeneous cloud data centers. The proposed VM placement technique takes into the account of physical machine characteristics and load (peak and non-peak) conditions in the heterogeneous data centers to save power and also improve performance efficiency for data center owners. The experiments conducted with real cloud workloads and also synthetic workloads against a well-known adaptive heuristic-based technique indicate significant performance improvements and energy saving with our proposed solution. © 2013 IEEE.
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. Â© 2017 IEEE.
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.
Load balancing strategy for optimal peak hour performance in cloud datacenters
(2015) Kulkarni, A.K.; Annappa, B.
Cloud computing is a growing computing model that is influencing every other entity in the global business industry. Efficient load balancing techniques plays a major role in cloud computing by allocating requests to computing resources efficiently to prevent under/over-allocation of Virtual Machines (VMs) and improve the response time to clients. It is observed that during peak hours when request frequency is high, active VM load balancer (packaged in cloudAnalyst) over-allocates initial VMs and under-allocates later ones creating load imbalance. In this paper we propose a novel VM load balancing algorithm that ensures uniform allocation of requests to virtual machines even during peak hours when frequency of requests received in data center is very high to ensure faster response times to users. The simulations results suggest that our algorithm allocates requests to VM uniformly even during peak traffic situations. � 2015 IEEE.
Load balancing strategy for optimal peak hour performance in cloud datacenters
(Institute of Electrical and Electronics Engineers Inc., 2015) Kulkarni, A.K.; Annappa, B.
Cloud computing is a growing computing model that is influencing every other entity in the global business industry. Efficient load balancing techniques plays a major role in cloud computing by allocating requests to computing resources efficiently to prevent under/over-allocation of Virtual Machines (VMs) and improve the response time to clients. It is observed that during peak hours when request frequency is high, active VM load balancer (packaged in cloudAnalyst) over-allocates initial VMs and under-allocates later ones creating load imbalance. In this paper we propose a novel VM load balancing algorithm that ensures uniform allocation of requests to virtual machines even during peak hours when frequency of requests received in data center is very high to ensure faster response times to users. The simulations results suggest that our algorithm allocates requests to VM uniformly even during peak traffic situations. Â© 2015 IEEE.