BITS Faculty Publications
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Item Workflow Scheduling In Clouds Using Randomized Scheduling Algorithm(IJPAM, 2018) Jangiti, SaikishorThe provisioning of on-demand resources makes it optimal for executing scientific application workflows in cloud computing. An application starts the process with a small number of resources, and it allocates the resources when required. However, workflow scheduling belongs to NP-hard class of problems, so optimization techniques are preferred for the solution. This paper explores the effect of a Randomized scheduling algorithm in workflow scheduling for the scheduling problem. The use of Randomized scheduling algorithm in comparison with other scheduling algorithms increases the efficiency of workflow scheduling in various scientific workflows and simulators. The experimental result confirms that the Randomized scheduling algorithm well performed than other scheduling approaches and provides better scheduling with reduced makespan.Item Bulk-bin-packing based migration management of reserved virtual machine requests for green cloud computing(European Alliance for Innovation, 2019) Jangiti, SaikishorThe dynamic consolidation of Virtual Machines (VMs) into a minimum number of Physical Machines (PMs) is a key energy-efficient practice in a cloud data centre, to reduce the running PMs and save electricity costs. We proposed a migration based VM consolidation approach for reserved requests. Real Dataset EC2 was used in the simulation experiments. The proposed BBPMM has demonstrated the elastic capability of adjusting the running PMs and it reduced 38% of running PMs in a reservation transition period.Item The role of cloud computing infrastructure elasticity in energy efficient management of datacenters(IEEE, 2017) Jangiti, SaikishorCloud Computing is growing its customer base due to its pay per use model of leased computational resources as well as software services. Along with the rapid growth of cloud computing adoption, the energy consumed by cloud computing infrastructure is growing. There is an urgent need for the research on the energy efficiency of cloud computing infrastructure. We reviewed the key technologies and techniques which will support and enhance the energy efficiency of cloud computing infrastructure and makes cloud a sustainable model. Virtualization, elasticity, and energy-efficiency are three important attributes of cloud and its infrastructure; we studied the interdependencies of these techniques and addressed few questions related to their interdependencies.Item Scalable hybrid and ensemble heuristics for economic virtual resource allocation in cloud and fog cyber-physical systems(IOS, 2019-05) Jangiti, SaikishorWith the advent of cloud computing, a cost-effective and reliable choice to employ IT infrastructure, the cyber-physical systems (CPS) are transforming into loosely coupled cloud and fog CPS. The sensor information from physical processes at CPS is continuously processed by fog computing nodes and is forwarded for advanced data analytics offered as a service from the cloud. The computation offloaded by fog devices are initiated as Virtual Machines (VMs) in the cloud data center. The effective placement of these VMs into minimum Physical Machines (PMs) involves economic and environmental issues. Recent research works signify the use of First-Fit Decreasing (FFD) based heuristic techniques to address this NP-Hard problem as a vector bin-packing problem. In this research work, we present a set of hybrid heuristics and an ensemble heuristic to improve the solution quality. The simulation results show that the proposed heuristics are highly scalable and economical in comparison with the individual heuristic-based approaches.Item EMC2: Energy-efficient and multi-resource- fairness virtual machine consolidation in cloud data centres(Elsevier, 2020-09) Jangiti, SaikishorThe rapid rise in the cloud service adoption reflects the growth of Cloud Data Centers' (CDCs) number, size, energy consumption and eco-unfriendly carbon footprints. In CDCs, Virtual Machine Consolidation (VMC) plays a significant role in reducing their energy consumption and thereby reducing the carbon footprints. The state-of-the-art VMC heuristics based on First-Fit Decreasing (FFD) and Dominant Residual Resource (DRR) called DRR-FFD and DRR-BinFill are grouping the VMs based on single VM resource. We attempt to further reduce the energy consumption of CDCs through the proposed EMC2, an energy-efficient VMC framework that employs our multi-resource-fairness based VM selection heuristics, namely VMNeAR-H (Hierarchical), VM NeAR- D (Directed Hierarchical) and VM NeAR-E (Euclidean Distance). A dataset extracted from ENERGY STAR® containing the heterogeneous physical machine resource capacities and their estimated energy consumptions is utilised in the simulation experiments. The proposed EMC2-VMNeAR-D heuristic dominates the existing DRR heuristics in terms of total energy consumed by all the physical machines in the CDC (3.318 % energy savings on average of 40 schedules = 185107 kWh).Item Virtual Machine (VM) placement consolidates VMs into a minimum number of Physical Machines (PMs), which can be viewed as a Vector Bin-Packing (VBP) problem. Recent literature reveals the significance of first-fit-decreasing variants in solving VBP problems, however they suffer from reduced packing efficiency and delayed packing speed. This paper presents VM NeAR (VM Nearest and Available to Residual resource ratios of PM), a novel heuristic method to address the above said challenges in VBP. Further, we have developed Bulk-Bin-Packing based VM Placement (BBPVP) and Multi-Capacity Bulk VM Placement (MCBVP) as a solution for VBP. The simulation results on real-time Amazon EC2 dataset and synthetic datasets obtained from CISH, SASTRA shows that VM NeAR based MCVBP achieves about 1.6% reduction in the number of PMs and possess a packing speed which was found to be 24 times faster than exisiting state-of-the-art VBP heuristics.(Springer, 2018-08) Jangiti, SaikishorVirtual Machine (VM) placement in a cloud data center is a Vector Bin-Packing (VBP) problem to minimize the number of PMs used for hosting the given VM requests. First-Fit-Decreasing (FFD) variants are widely used for VM placement. In this paper, a novel FFD variant, Aggregated Rank in FFD (FFD-AR) is proposed for VM placement. Simulation experiments were carried out using two datasets: a dataset inspired by Amazon EC2 instances and another is a synthetic dataset. The packing efficiency of the proposed FFD-AR results is better as compared to all the other baseline FFD variants. We believe the proposed FFD-AR can be applied to wide applications of VBP like production planning and logistics.Item Scalable and direct vector bin-packing heuristic based on residual resource ratios for virtual machine placement in cloud data centers(Elsevier, 2018) Jangiti, SaikishorVirtual Machine (VM) placement consolidates VMs into a minimum number of Physical Machines (PMs), which can be viewed as a Vector Bin-Packing (VBP) problem. Recent literature reveals the significance of first-fit-decreasing variants in solving VBP problems, however they suffer from reduced packing efficiency and delayed packing speed. This paper presents VM NeAR (VM Nearest and Available to Residual resource ratios of PM), a novel heuristic method to address the above said challenges in VBP. Further, we have developed Bulk-Bin-Packing based VM Placement (BBPVP) and Multi-Capacity Bulk VM Placement (MCBVP) as a solution for VBP. The simulation results on real-time Amazon EC2 dataset and synthetic datasets obtained from CISH, SASTRA shows that VM NeAR based MCVBP achieves about 1.6% reduction in the number of PMs and possess a packing speed which was found to be 24 times faster than exisiting state-of-the-art VBP heuristics.