Achieving Adaptation Through Live Virtual Machine Migration in Two-Tier Clouds

This thesis presents a model-driven approach for application deployment and management in two-tier heterogeneous cloud environments. For application deployment, we introduce the architecture, the services and the domain specific language that abstract common features of multi-cloud deployments. By leveraging the architecture and the language, application deployers author a deployment model that captures the high-level structure of the application. The deployment model is then translated into deployment workflows on specific clouds. As a use case, we introduce a live VM migration framework that maintains the application quality of services through VM migrations across two tier-clouds. The proposed framework can monitor the performance of the applications and their underlying infrastructure and plan and executes VM migrations to eliminate hotspots in a datacenter. We evaluate both the application deployment architecture and the live migration on public clouds

Energy efficient task scheduling in data center

First of all, I am thankful to God for his blessings and showing me the right direction. With His mercy, it has been made possible for me to reach so far. Foremost, I would like to express my sincere gratitude to my advisor Prof. Durga Prasad Mohapatra for the continuous support of my M.Tech study and research, for his patience, motivation, enthusiasm, and immense knowledge. I am thankful for her continual support, encouragement, and invaluable suggestion. His guidance helped me in all the time of research and writing of this thesis. I could not have imagined having a better advisor and mentor for my M.Tech study. Besides my advisor, I extend my thanks to our HOD, Prof. S. K. Rath and Prof. B. D. Sahoo for their valuable advices and encouragement. I express my gratitude to all the sta members of Computer Science and Engineering Department for providing me all the facilities required for the completion of my thesis work. I would like to say thanks to all my friends especially Dilip Kumar, Alok Pandey for their support. Last but not the least I am highly grateful to all my family members for their inspiration and ever encouraging moral support, which enables me to purse my studies

End-to-end service quality for cloud applications

This paper aims to highlight the importance of End-to-End (E2E) service quality for cloud scenarios, with focus on telecom carrier-grade services. In multi-tenant distributed and virtualized cloud infrastructures, enhanced resource sharing raises issues in terms of performance stability and reliability. Moreover, the heterogeneity of business entities responsible for the cloud service delivery, threatens the possibility of offering precise E2E service levels. Setting up proper Service-Level Agreements (SLAs) among the involved players, may become overly challenging. However, problems may be mitigated by a thoughtful intervention of standardization. The paper reviews some of the most important efforts in research and industry to tackle E2E service quality and concludes with some recommendations for additional research and/or standardization effort required to be able to deploy mission critical or interactive real-time services with high demands on service quality, reliability and predictability on cloud platforms. © 2013 Springer International Publishing

Holistic Resource Management for Sustainable and Reliable Cloud Computing:An Innovative Solution to Global Challenge

Minimizing the energy consumption of servers within cloud computing systems is of upmost importance to cloud providers towards reducing operational costs and enhancing service sustainability by consolidating services onto fewer active servers. Moreover, providers must also provision high levels of availability and reliability, hence cloud services are frequently replicated across servers that subsequently increases server energy consumption and resource overhead. These two objectives can present a potential conflict within cloud resource management decision making that must balance between service consolidation and replication to minimize energy consumption whilst maximizing server availability and reliability, respectively. In this paper, we propose a cuckoo optimization-based energy-reliability aware resource scheduling technique (CRUZE) for holistic management of cloud computing resources including servers, networks, storage, and cooling systems. CRUZE clusters and executes heterogeneous workloads on provisioned cloud resources and enhances the energy-efficiency and reduces the carbon footprint in datacenters without adversely affecting cloud service reliability. We evaluate the effectiveness of CRUZE against existing state-of-the-art solutions using the CloudSim toolkit. Results indicate that our proposed technique is capable of reducing energy consumption by 20.1% whilst improving reliability and CPU utilization by 17.1% and 15.7% respectively without affecting other Quality of Service parameters

Performance Improvement of Cloud Computing Data Centers Using Energy Efficient Task Scheduling Algorithms

Cloud computing is a technology that provides a platform for the sharing of resources such as software, infrastructure, application and other information. It brings a revolution in Information Technology industry by offering on-demand of resources. Clouds are basically virtualized datacenters and applications offered as services. Data center hosts hundreds or thousands of servers which comprised of software and hardware to respond the client request. A large amount of energy requires to perform the operation.. Cloud Computing is facing lot of challenges like Security of Data, Consumption of energy, Server Consolidation, etc. The research work focuses on the study of task scheduling management in a cloud environment. The main goal is to improve the performance (resource utilization and redeem the consumption of energy) in data centers. Energy-efficient scheduling of workloads helps to redeem the consumption of energy in data centers, thus helps in better USAge of resource. This is further reducing operational costs and provides benefits to the clients and also to cloud service provider. In this abstract of paper, the task scheduling in data centers have been compared. Cloudsim a toolkit for modeling and simulation of cloud computing environment has been used to implement and demonstrate the experimental results. The results aimed at analyzing the energy consumed in data centers and shows that by having reduce the consumption of energy the cloud productivity can be improved

HIL: designing an exokernel for the data center

We propose a new Exokernel-like layer to allow mutually untrusting physically deployed services to efficiently share the resources of a data center. We believe that such a layer offers not only efficiency gains, but may also enable new economic models, new applications, and new security-sensitive uses. A prototype (currently in active use) demonstrates that the proposed layer is viable, and can support a variety of existing provisioning tools and use cases.Partial support for this work was provided by the MassTech Collaborative Research Matching Grant Program, National Science Foundation awards 1347525 and 1149232 as well as the several commercial partners of the Massachusetts Open Cloud who may be found at http://www.massopencloud.or

An Energy Aware Resource Utilization Framework to Control Traffic in Cloud Network and Overloads

Energy consumption in cloud computing occur due to the unreasonable way in which tasks are scheduled. So energy aware task scheduling is a major concern in cloud computing as energy consumption results into significant waste of energy, reduce the profit margin and also high carbon emissions which is not environmentally sustainable. Hence, energy efficient task scheduling solutions are required to attain variable resource management, live migration, minimal virtual machine design, overall system efficiency, reduction in operating costs, increasing system reliability, and prompting environmental protection with minimal performance overhead. This paper provides a comprehensive overview of the energy efficient techniques and approaches and proposes the energy aware resource utilization framework to control traffic in cloud networks and overloads

Energy Efficient Virtual Machine Migration in Cloud Data Centers

Cloud computing services have been on the rise over the past few decades, which has led to an increase in the number of data centers worldwide which increasingly consume more and more amount of energy for their operation, leading to high carbon dioxide emissions and also high operation costs. Cloud computing infrastructures are designed to support the accessibility and deployment of various service oriented applications by the users. The resources are the major source of the power consumption in data centers along with air conditioning and cooling equipment. Moreover the energy consumption in the cloud is proportional to the resource utilization and data centers are almost the worlds highest consumers of electricity. It is therefore, the need of the hour to devise efficient consolidation schemes for the cloud model to minimize energy and increase Return of Investment(ROI) for the users by decreasing the operating costs. The consolidation problem is NP-complete in nature, which requires heuristic techniques to get a sub-optimal solution. The complexity of the problem increases with increase in cloud infrastructure. We have proposed a new consolidation scheme for the virtual machines(VMs) by improving the host overload detection phase of the scheme. The resulting scheme is effective in reducing the energy and the level of Service Level Agreement(SLA) violations both, to a considerable extent. For testing the performance of our implementation on cloud we need a simulation environment that can provide us an environment with system and behavioural modelling of the actual cloud computing components, and can generate results that can help us in the analysis so that we can deploy them on actual clouds. CloudSim is one such simulation toolkit that allows us to test and analyse our allocation and selection algorithms. In this thesis we have used CloudSim version 3.0.3 to test and analyse our policies and modifications in the current policies. The advantages of using CloudSim 3.0.3 is that it takes very less effort and time to implement cloud-based application and we can test the performance of application services in heterogeneous Cloud environments. The observations are validated by simulating the experiment using the CLoudSim framework and the data provided by PlanetLab