Virtual Machines Overloaded In Cloud Computing Using Cloudsim

Cloud computing has a massive pool of resources. Cloud Computing is an Internet based computing. Cloud Computing provides shared computer processing resources and data to computers. Now-a-days most of the companies are working under the Cloud Computing. Cloud application has a different configuration,composition, and deployment. Cloud Computing provides three service models such as Infrastructure as a Service,Platform as a Service,Software as a Service. Based on the service model it classified as Public Cloud, Private Cloud, Hybrid Cloud, Community Cloud. Through this paper,. We suggests the execution of Private cloud system that provides Infrastructure as a Service using CloudSim. CloudSim is framework for modeling and simulation of Cloud Computing Infrastructures and services .CloudSim is a toolkit for Cloud computing that supports modeling and creation of one or more Virtual Machine on a parallel Nodes of a Datacenter, Jobs and their mapping to suitable VMs

MSL Framework: (Minimum Service Level Framework) for Cloud Providers and Users

Cloud Computing ensures parallel computing and emerged as an efficient technology to meet the challenges of rapid growth of data that we experienced in this internet age. Cloud computing is an emerging technology that offers subscription based services, and provide different models such as IaaS, PaaS and SaaS to cater the needs of different users groups. The technology has enormous benefits but there are serious concerns and challenges related to lack of uniform standards or nonexistence of minimum benchmark for level of services across the industry to provide an effective, uniform and reliable service to the cloud users. As the cloud computing is gaining popularity organizations and users are having problems to adopt the service due to lack of minimum service level framework which can act as a benchmark in the selection of the cloud provider and provide quality of services according to the users expectations. The situation becomes more critical due to distributed nature of the service...info:eu-repo/semantics/publishedVersio

Scientific High Performance Computing (HPC) Applications On The Azure Cloud Platform

Cloud computing is emerging as a promising platform for compute and data intensive scientific applications. Thanks to the on-demand elastic provisioning capabilities, cloud computing has instigated curiosity among researchers from a wide range of disciplines. However, even though many vendors have rolled out their commercial cloud infrastructures, the service offerings are usually only best-effort based without any performance guarantees. Utilization of these resources will be questionable if it can not meet the performance expectations of deployed applications. Additionally, the lack of the familiar development tools hamper the productivity of eScience developers to write robust scientific high performance computing (HPC) applications. There are no standard frameworks that are currently supported by any large set of vendors offering cloud computing services. Consequently, the application portability among different cloud platforms for scientific applications is hard. Among all clouds, the emerging Azure cloud from Microsoft in particular remains a challenge for HPC program development both due to lack of its support for traditional parallel programming support such as Message Passing Interface (MPI) and map-reduce and due to its evolving application programming interfaces (APIs). We have designed newer frameworks and runtime environments to help HPC application developers by providing them with easy to use tools similar to those known from traditional parallel and distributed computing environment set- ting, such as MPI, for scientific application development on the Azure cloud platform. It is challenging to create an efficient framework for any cloud platform, including the Windows Azure platform, as they are mostly offered to users as a black-box with a set of application programming interfaces (APIs) to access various service components. The primary contributions of this Ph.D. thesis are (i) creating a generic framework for bag-of-tasks HPC applications to serve as the basic building block for application development on the Azure cloud platform, (ii) creating a set of APIs for HPC application development over the Azure cloud platform, which is similar to message passing interface (MPI) from traditional parallel and distributed setting, and (iii) implementing Crayons using the proposed APIs as the first end-to-end parallel scientific application to parallelize the fundamental GIS operations

A component-based framework for certification of components in a cloud of HPC services

HPC Shelfis a proposal of a cloud computing platform to provide component-oriented services for High Performance Computing (HPC) applications. This paper presents a Verification-as-a-Service (VaaS) framework for component certification onHPC Shelf. Certification is aimed at providing higher confidence that components of parallel computing systems ofHPC Shelfbehave as expected according to one or more requirements expressed in their contracts. To this end, new abstractions are introduced, starting with certifier components. They are designed to inspect other components and verify them for different types of functional, non-functional and behavioral requirements. The certification framework is naturally based on parallel computing techniques to speed up verification tasks.NORTE-01-0145- FEDER-000037

Service Level Agreement Driven Adaptive Resource Management For Web Applications on Heterogeneous Compute Clouds

Cloud computing is an emerging topic in the field of parallel and distributed computing. Many IT giants such as IBM, Sun, Amazon, Google, and Microsoft are promoting and offering various storage and compute clouds. Clouds provide services such as high performance computing, storage, and application hosting. Cloud providers are expected to ensure Quality of Service (QoS) through a Service Level Agreement (SLA) between the provider and the consumer. In this research, I develop a heterogeneous testbed compute cloud and investigate adaptive management of resources for Web applications to satisfy a SLA that enforces specific response time requirements. I develop a system on top of EUCALYTPUS framework that actively monitors the response time of the compute resources assign to a Web application and dynamically allocates the resources required by the application to satisfy the specific response time requirements

Evolutionary Neural Network Based Energy Consumption Forecast for Cloud Computing

The success of Hadoop, an open-source framework for massively parallel and distributed computing, is expected to drive energy consumption of cloud data centers to new highs as service providers continue to add new infrastructure, services and capabilities to meet the market demands. While current research on data center airflow management, HVAC (Heating, Ventilation and Air Conditioning) system design, workload distribution and optimization, and energy efficient computing hardware and software are all contributing to improved energy efficiency, energy forecast in cloud computing remains a challenge. This paper reports an evolutionary computation based modeling and forecasting approach to this problem. In particular, an evolutionary neural network is developed and structurally optimized to forecast the energy load of a cloud data center. The results, both in terms of forecasting speed and accuracy, suggest that the evolutionary neural network approach to energy consumption forecasting for cloud computing is highly promising

A cost-effective cloud computing framework for accelerating multimedia communication simulations

Multimedia communication research and development often requires computationally intensive simulations in order to develop and investigate the performance of new optimization algorithms. Depending on the simulations, they may require even a few days to test an adequate set of conditions due to the complexity of the algorithms. The traditional approach to speed up this type of relatively small simulations, which require several develop-simulate-reconfigure cycles, is indeed to run them in parallel on a few computers and leaving them idle when developing the technique for the next simulation cycle. This work proposes a new cost-effective framework based on cloud computing for accelerating the development process, in which resources are obtained on demand and paid only for their actual usage. Issues are addressed both analytically and practically running actual test cases, i.e., simulations of video communications on a packet lossy network, using a commercial cloud computing service. A software framework has also been developed to simplify the management of the virtual machines in the cloud. Results show that it is economically convenient to use the considered cloud computing service, especially in terms of reduced development time and costs, with respect to a solution using dedicated computers, when the development time is longer than one hour. If more development time is needed between simulations, the economic advantage progressively reduces as the computational complexity of the simulation increases

SLA Driven Load Balancing For Web Applications in Cloud Computing Environment

Cloud computing is an emerging topic in the field of parallel and distributed computing. Many IT giants suchas IBM, Sun, Amazon, Google, and Microsoft are promoting and offering various storage and computeclouds. Clouds provide services such as high performance computing, storage, and application hosting.Cloud providers are expected to ensure Quality of Service (QoS) through a Service Level Agreement (SLA)between the provider and the consumer. In this research, we develop a heterogeneous test bed compute cloudand investigate adaptive management of resources for Web applications to satisfy a SLA that enforcesspecific response time requirements. We develop a system on top of EUCALYTPUS framework that activelymonitors the response time of the computed resources assign to a Web application and dynamicallyallocates the resources required by the application to satisfy the specific response time requirements.Keywords: Eucalyptus, SLA, QOS, Virtualization, Minimum Response Time

Dynamic Resource Allocation for Parallel Data Processing in Cloud Computing Environment

Dynamic resource allocation problem is one of the most challenging problems in the resource management problems. The dynamic resource allocation in cloud computing has attracted attention of the research community in the last few years. Many researchers around the world have come up with new ways of facing this challenge. Ad-hoc parallel data processing has emerged to be one of the killer applications for Infrastructure-as-a-Service (IaaS) cloud. Number of Cloud provider companies has started to include frameworks for parallel data processing in their product which making it easy for customers to access these services and to deploy their programs. The processing frameworks which are currently used have been designed for static and homogeneous cluster setups. So the allocated resources may be inadequate for large parts of the submitted tasks and unnecessarily increase processing cost and time. Again due to opaque nature of cloud, static allocation of resources is possible, but vice-versa in dynamic situations. The proposed new Generic data processing framework is intended to explicitly exploit the dynamic resource allocation in cloud for task scheduling and execution