Sudhansu Shekhar Patra

Performance analysis of cloud with queue-dependent virtual machines

Cloud computing provides a new way for industries to meet the emerging business need for agility. Many public clouds are available for developers to build web applications on cloud. The process of entering into the cloud is generally in the form of a queue, so that each user need to wait until the current user is being served. In the system, each Cloud Computing User (CCU) requests Cloud Computing Service Provider (CCSP) for use of resources. If CCU finds the server busy, then the user has to wait till the current user completes the job. This may result in increase of queue length as well as waiting time, which may lead to request drop. To handle this problem, CCSP needs to find ways to reduce waiting time. We propose a finite multiserver queueing model with queue dependent heterogeneous servers where the web applications are modeled as queues and the virtual machines are modeled as service providers. CCSPs can use multiple servers and the number of busy servers changes depending on the queue length for reducing queue length and waiting time. This helps us to dynamically create and remove virtual machines in order to scaling up and down. We develop a recursive method to obtain the system steady-state probabilities. Various performance measures of the proposed scheme have been described and evaluated. Computational experiences in the form of graphs are presented.

Dynamic Dedicated Server Allocation for Service Oriented Multi-Agent Data Intensive Architecture in Biomedical and Geospatial Cloud

Cloud computing has recently received considerable attention, as a promising approach for delivering Information and Communication Technologies (ICT) services as a utility. In the process of providing these services it is necessary to improve the utilization of data centre resources which are operating in most dynamic workload environments. Datacenters are integral parts of cloud computing. In the datacenter generally hundreds and thousands of virtual servers run at any instance of time, hosting many tasks and at the same time the cloud system keeps receiving the batches of task requests. It provides services and computing through the networks. Service Oriented Architecture (SOA) and agent frameworks renders tools for developing distributed and multi agent systems which can be used for the administration of cloud computing environments which supports the above characteristics. This paper presents a SOQM (Service Oriented QoS Assured and Multi Agent Cloud Computing) architecture which supports QoS assured cloud service provision and request. Biomedical and geospatial data on cloud can be analyzed through SOQM and has allowed the efficient management of the allocation of resources to the different system agents. It has proposed a finite heterogeneous multiple vm model which are dynamically allocated depending on the request from biomedical and geospatial stakeholders. Dynamic Dedicated Server Allocation for Service Oriented Multi-Agent Data Intensive Architecture in Biomedical and Geospatial Cloud

Performance Analysis of Cloud Computing Centers for Bulk Services

Cloud is a service oriented platform where all kinds of virtual resources are treated as services to users. Several cloud service providers have offered different capabilities for a variety of market segments over the past few years. The most important aspects of cloud computing are resource scheduling, performance measures, and user requests. Sluggish access to data, applications, and web pages spoils employees and customers alike, as well as cause application crashes and data losses. In this paper, the authors propose an analytical queuing model for performance evaluation of cloud server farms for processing bulk data. Some important performance measures such as mean number of tasks in the queue, blocking probability, and probability of immediate service, and waiting-time distribution in the system have also been discussed. Finally, a variety of numerical results showing the effect of model parameters on key performance measures are presented.

Dynamic Provisioning and Resource Management for Multi-Tier Cloud Based Applications

Abstract Dynamic capacity provisioning is a useful technique for handling the workload variations seen in cloud environment. In this paper, we propose a dynamic provisioning technique for multi-tier applications to allocate resources efficiently using queueing model. It dynamically increases the mean service rate of the virtual machines to avoid congestion in the multi-tier environments. An optimization model to minimize the total number of virtual machines for computing resources in each tier has been presented. Using the supplementary variable and the recursive techniques, we obtain the system-length distributions at pre-arrival and arbitrary epochs. Some important performance indicators such as blocking probability, request waiting time and number of tasks in the system and in the queue have also been investigated. Finally, computational results showing the effect of model parameters on key performance indicators are presented.

Energy-Efficient Task Consolidation for Cloud Data Center

Energy saving in a Cloud Computing environment is a multidimensional challenge, which can directlydecreasethein-usecostsandcarbondioxideemission,whileraisingthesystemconsistency. Theprocessofmaximizingthecloudcomputingresourceutilizationwhichbringsmanybenefits such as better use of resources, rationalization of maintenance, IT service customization, QoS andreliableservices,etc.,isknownastaskconsolidation.Thisarticlesuggeststheenergysaving with task consolidation, by minimizing the number of unused resources in a cloud computing environment. In thisarticle,various taskconsolidationalgorithmssuchasMinIncreaseinEnergy, MaxUtilECTC,NoIdleMachineECTC,andNoIdleMachineMaxUtilarepresentedaimstooptimize energyconsumptionofclouddatacenter.Theoutcomeshaveshownthatthesuggestedalgorithms surpasstheexistingECTCandFCFSMaxUtil,MaxMaxUtilalgorithmsintermsoftheCPUutilization andenergyconsumption. KEywoRDS Cloud Computing, Energy Efficiency, MaxUtilECTC, MinIncreaseinEnergy, NoIdleMachineECTC, NoIdleMachineMaxUtil

Performance Analysis of Cloud Systems with Load Dependent Virtual Machine Activation and Sleep Modes

Dueto theadvancements invirtualization technology, it isnowanupandcomingfieldandhas becomeamoreappealingareaofinternettechnology.Sincethereisarapidgrowthforthedemandof computationalpowerincreasesbyscientific,business,andweb-applications,itleadstothecreation oflarge-scaledatacenters.Thesedatacentersconsumeenormousamountsofelectricalpower.In thisarticle,theauthorsstudyenergysavingmethodsbyconsolidationandbyswitchingoffthose virtualmachineswhicharenotinuse.Accordingtothispolicy,cvirtualmachinescontinueserving thecustomeruntilthenumberofidleserverattainsthethresholdleveld;thendidleserverstake synchronousvacationsimultaneously,otherwisetheseserverswouldbeginservingthecustomers. Numerical results are provided to demonstrate the applicability of the proposed model for data centermanagementinparticular,toquantifythetradeofftheoreticallybetweentheconflictingaims ofenergyefficiencyandQoS. KEywoRDS Cloud Computing, Consolidation, Performance Modelling, Queueing, Vacation, Virtual Machines

Performance Evaluation of the Controller in Software-Defined Networking

Classical Internet architecture plays major obstacles in IPV6 deployment. There exits different reasons to extend classical approach to be more polished. Next generation of future Internet demands for routing not only within the same network domain but also outside the domain. Along with this, it offers many attributes such as network availability, end-to-end network connectivity, QoS management dynamically, and many more. The application area extends from small network size to big data center. To take in hand all these concerns, software-defined networking (SDN) has taken the major role in current situations. SDN separates the control plane and data plane to make the routing more versatile. We model the packet-in message processing of SDN controller as the queueing systems M / M / 1.

Performance analysis and optimal resource usage in finite population cloud environment

Cloud computing provides a new paradigm for industries to meet the emerging business needs by accessing distributed computing resources such as infrastructure, hardware and software applications on-demand over the internet as services. As the technology and the need is growing very fast, in future there may be multiple vendors offering different services with different Quality of Services (QoS) and at various prices. This would lead to development of new methods and tools for the performance evaluation of the system to meet the offerings and requirements. In this paper, we present an analytical finite population model for performance evaluation of a private cloud computing system. Various performance measures of the cloud system for finite population environment indicate that the proposed provisioning technique helps the cloud operators in tuning the resources accordingly to improve the QoS targets.

Optimal Management of Cloud Centers with Different Arrival Modes for Cloud Computing Environment

Cloud computing is a new computing paradigm in which information and computing services can be accessed from a Web browser by clients. Understanding of the characteristics of computer service performance has become critical for service applications in cloud computing. For the commercial success of this new computing paradigm, the ability to deliver guaranteed Quality of Services QoS is crucial. Based on the Service level agreement, the requests are processed in the cloud centers in different modes. This paper analyzes a finite-buffer multi-server queuing system where client requests have two arrival modes. It is assumed that each arrival mode is serviced by one or more Virtual machines, and both the modes have equal probabilities of receiving service. Various performance measures are obtained and optimal cost policy is presented with numerical results. The genetic algorithm is employed to search the optimal values of various parameters for the system.