Papers by Jeyarani Rajarathinam
Cluster Computing, 2018
Cloud Computing distinguishes itself from other distributed computing paradigm through offering s... more Cloud Computing distinguishes itself from other distributed computing paradigm through offering services on-demand basis without any geographical restrictions. This revolutionizes the computing by offering services to wide array of customers starting from casual user to highly business oriented Industries. In spite of its capabilities, Cloud Computing still struggle with handling wide array of faults, this causes loss of credibility to Cloud Computing. Among those faults Byzantine faults offers serious challenge to fault tolerance mechanism, because it often go undetected at the initial stage and it can easily propagate to other VMs before a detection is made. Consequently some of the mission critical application such as air traffic control, online baking etc still staying away from the cloud for such reasons. However if a Byzantine faults is not detected and tolerated at initial stage then applications such as big data analytics can go completely wrong in spite of hours of computations performed by the entire cloud. Therefore in the previous work a foolproof Byzantine fault detection has been proposed, as a continuation this work designs a scheduling algorithm (WSSS) and checkpoint optimization algorithm (TCC) to tolerate and eliminate the Byzantine faults before it makes any impact. The WSSS algorithm keeps track of server performance which is part of Virtual Clusters to help allocate best performing server to mission critical application. WSSS therefore ranks the servers based on a counter which monitors every Virtual Nodes (VN) for time and performance failures. The TCC algorithm works to generalize the possible Byzantine error prone region through monitoring delay variation to start new VNs with previous checkpointing. Moreover it can stretch the state interval for performing and error free VNs in an effect to minimize the space, time and cost overheads caused by checkpointing. The analysis is performed with plotting state transition and CloudSim based simulation. The result shows TCC reduces fault tolerance overhead exponentially and the WSSS allots virtual resources effectively.
International Journal of Cloud Applications and Computing, 2012
This paper presents a novel Meta scheduler algorithm using Particle Swarm Optimization (PSO) for ... more This paper presents a novel Meta scheduler algorithm using Particle Swarm Optimization (PSO) for cloud computing environment that focuses on fulfilling deadline requirements of the resource consumers as well as energy conservation requirement of the resource provider contributing towards green IT. PSO is a population-based heuristic method which can be used to solve NP-hard problems. The nature of jobs is considered to be independent, non pre-emptive, parallel and time critical. In order to execute jobs in a cloud, primarily Virtual Machine (VM) instances are launched in appropriate physical servers available in a data-center. The number of VM instances to be created across different servers to complete the time critical jobs successfully, is identified using PSO by exploiting the idle resources in powered-on servers. The scheduler postpones the power-up/activation of new servers/hosts for launching enqueued VM requests, as long as it is possible to meet the deadline requirements of...
Power-Aware Meta Scheduler with Non-linear Workload Prediction for Adaptive Virtual Machine Provisioning
Lecture Notes in Computer Science, 2014
Infrastructure cloud typically involves provisioning of dynamically scalable and virtualized reso... more Infrastructure cloud typically involves provisioning of dynamically scalable and virtualized resources to cloud users. It is a fact that the resource demand in the cloud is highly dynamic in nature. To meet the dynamic demand from the cloud consumers, over-provisioning of resources is the common solution. This ultimately increases power consumption when the demand is normal or drops below average. On the contrary, under-provisioning of resources may lead to Service Level Agreement (SLA) violations. To balance between power conservation and performance issues, it has been realized that forecasting the demand for computing resources is essential in cloud environment. Hence we proposed a prediction based adaptive resource provisioning methodology incorporating statistical predictor in our earlier work. In order to improve prediction accuracy, we have proposed a recurrent neural network called Non-linear Auto Regressive network with eXogenous input (NARX) based prediction in this paper. The proposed NARX predictor makes near-accurate run time estimate of resource demand as it is able to learn hidden patterns and trends in the historical data representing resource demand and hence it helps to realize better power conservation. This paper shows that the proposed predictor integrated with adaptive provisioning resulted in 27.25 % more power saving compared to its statistical counterpart.
2009 International Conference on Advances in Recent Technologies in Communication and Computing, 2009
Cloud computing focuses on delivery of reliable, fault-tolerant and scalable infrastructure for h... more Cloud computing focuses on delivery of reliable, fault-tolerant and scalable infrastructure for hosting Internet based application services. This paper presents the implementation of an efficient Quality of Service (QoS) based Meta-Scheduler and Backfill strategy based light weight Virtual Machine Scheduler for dispatching jobs. The user centric meta-scheduler deals with selection of proper resources to execute high level jobs. The system centric Virtual Machine (VM) scheduler optimally dispatches the jobs to processors for better resource utilization. We also present our thoughts on scheduling heuristics that can be incorporated at data center level for selecting ideal host for VM creation. The implementation can be further extended at the host level, using Inter VM scheduler for adaptive load balancing in cloud environment.
International Journal of Intelligent Information Technologies, 2011
Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized servic... more Cloud Computing provides dynamic leasing of server capabilities as a scalable, virtualized service to end users. The discussed work focuses on Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data-center. The context of the environment is a large scale, heterogeneous and dynamic resource pool. Nonlinear variation in the availability of processing elements, memory size, storage capacity, and bandwidth causes resource dynamics apart from the sporadic nature of workload. The major challenge is to map a set of VM instances onto a set of servers from a dynamic resource pool so the total incremental power drawn upon the mapping is minimal and does not compromise the performance objectives. This paper proposes a novel Self Adaptive Particle Swarm Optimization (SAPSO) algorithm to solve the intractable nature of the above challenge. The proposed approach promptly detects and efficiently tracks the changing optimum...
Future Generation Computer Systems, 2012
Cloud computing aims at providing dynamic leasing of server capabilities as scalable, virtualized... more Cloud computing aims at providing dynamic leasing of server capabilities as scalable, virtualized services to end users. Our work focuses on the Infrastructure as a Service (IaaS) model where custom Virtual Machines (VM) are launched in appropriate servers available in a data center. The cloud data center taken into consideration is heterogeneous and large scale in nature. Such a resource pool is basically characterized by high resource dynamics caused by non-linear variation in the availability of processing elements, memory size, storage capacity, bandwidth and power drawn resulting from the sporadic nature of workload. Apart from the said resource dynamics, our proposed work also considers the processor transitions to various sleep states and their corresponding wake up latencies that are inherent in contemporary enterprise servers. The primary objective of the proposed metascheduler is to map efficiently a set of VM instances onto a set of servers from a highly dynamic resource pool by fulfilling resource requirements of maximum number of workloads. As the cloud data centers are overprovisioned to meet the unexpected workload surges, huge power consumption has become one of the major issues of concern. We have proposed a novel metascheduler called Adaptive Power-Aware Virtual Machine Provisioner (APA-VMP) that schedules the workload in such a way that the total incremental power drawn by the server pool is minimum without compromising the performance objectives. The APA-VMP makes use of swarm intelligence methodology to detect and track the changing optimal target servers for VM placement very efficiently. The scenario was experimented by novel Self-adaptive Particle Swarm Optimization (SAPSO) for VM provisioning, which makes best possible use of the power saving states of idle servers and instantaneous workload on the operational servers. It is evident from the results that there is a significant reduction in the power numbers against the existing strategies.
International Journal of Cloud Applications and Computing, 2011
Cloud Computing provides on-demand access to a shared pool of configurable computing resources. T... more Cloud Computing provides on-demand access to a shared pool of configurable computing resources. The major issue lies in managing extremely large agile data centers which are generally over provisioned to handle unexpected workload surges. This paper focuses on green computing by introducing Power-Aware Meta Scheduler, which provides right fit infrastructure for launching virtual machines onto host. The major challenge of the scheduler is to make a wise decision in transitioning state of the processor cores by exploiting various power saving states inherent in the recent microprocessor technology. This is done by dynamically predicting the utilization of the cloud data center. The authors have extended existing cloudsim toolkit to model power aware resource provisioning, which includes generation of dynamic workload patterns, workload prediction and adaptive provisioning, dynamic lifecycle management of random workload, and implementation of power aware allocation policies and chip a...
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Papers by Jeyarani Rajarathinam