Autonomic management of virtualized resources in cloud computing

The last five years have witnessed a rapid growth of cloud computing in business, governmental and educational IT deployment. The success of cloud services depends critically on the effective management of virtualized resources. A key requirement of cloud management is the ability to dynamically match resource allocations to actual demands, To this end, we aim to design and implement a cloud resource management mechanism that manages underlying complexity, automates resource provisioning and controls client-perceived quality of service (QoS) while still achieving resource efficiency. The design of an automatic resource management centers on two questions: when to adjust resource allocations and how much to adjust. In a cloud, applications have different definitions on capacity and cloud dynamics makes it difficult to determine a static resource to performance relationship. In this dissertation, we have proposed a generic metric that measures application capacity, designed model-independent and adaptive approaches to manage resources and built a cloud management system scalable to a cluster of machines. To understand web system capacity, we propose to use a metric of productivity index (PI), which is defined as the ratio of yield to cost, to measure the system processing capability online. PI is a generic concept that can be applied to different levels to monitor system progress in order to identify if more capacity is needed. We applied the concept of PI to the problem of overload prevention in multi-tier websites. The overload predictor built on the PI metric shows more accurate and responsive overload prevention compared to conventional approaches. To address the issue of the lack of accurate server model, we propose a model-independent fuzzy control based approach for CPU allocation. For adaptive and stable control performance, we embed the controller with self-tuning output amplification and flexible rule selection. Finally, we build a QoS provisioning framework that supports multi-objective QoS control and service differentiation. Experiments on a virtual cluster with two service classes show the effectiveness of our approach in both performance and power control. To address the problems of complex interplay between resources and process delays in fine-grained multi-resource allocation, we consider capacity management as a decision-making problem and employ reinforcement learning (RL) to optimize the process. The optimization depends on the trial-and-error interactions with the cloud system. In order to improve the initial management performance, we propose a model-based RL algorithm. The neural network based environment model, which is learned from previous management history, generates simulated resource allocations for the RL agent. Experiment results on heterogeneous applications show that our approach makes efficient use of limited interactions and find near optimal resource configurations within 7 steps. Finally, we present a distributed reinforcement learning approach to the cluster-wide cloud resource management. We decompose the cluster-wide resource allocation problem into sub-problems concerning individual VM resource configurations. The cluster-wide allocation is optimized if individual VMs meet their SLA with a high resource utilization. For scalability, we develop an efficient reinforcement learning approach with continuous state space. For adaptability, we use VM low-level runtime statistics to accommodate workload dynamics. Prototyped in a iBalloon system, the distributed learning approach successfully manages 128 VMs on a 16-node close correlated cluster

Managing server energy and reducing operational cost for online service providers

The past decade has seen the energy consumption in servers and Internet Data Centers (IDCs) skyrocket. A recent survey estimated that the worldwide spending on servers and cooling have risen to above $30 billion and is likely to exceed spending on the new server hardware . The rapid rise in energy consumption has posted a serious threat to both energy resources and the environment, which makes green computing not only worthwhile but also necessary. This dissertation intends to tackle the challenges of both reducing the energy consumption of server systems and by reducing the cost for Online Service Providers (OSPs). Two distinct subsystems account for most of IDC’s power: the server system, which accounts for 56% of the total power consumption of an IDC, and the cooling and humidifcation systems, which accounts for about 30% of the total power consumption. The server system dominates the energy consumption of an IDC, and its power draw can vary drastically with data center utilization. In this dissertation, we propose three models to achieve energy effciency in web server clusters: an energy proportional model, an optimal server allocation and frequency adjustment strategy, and a constrained Markov model. The proposed models have combined Dynamic Voltage/Frequency Scaling (DV/FS) and Vary-On, Vary-off (VOVF) mechanisms that work together for more energy savings. Meanwhile, corresponding strategies are proposed to deal with the transition overheads. We further extend server energy management to the IDC’s costs management, helping the OSPs to conserve, manage their own electricity cost, and lower the carbon emissions. We have developed an optimal energy-aware load dispatching strategy that periodically maps more requests to the locations with lower electricity prices. A carbon emission limit is placed, and the volatility of the carbon offset market is also considered. Two energy effcient strategies are applied to the server system and the cooling system respectively. With the rapid development of cloud services, we also carry out research to reduce the server energy in cloud computing environments. In this work, we propose a new live virtual machine (VM) placement scheme that can effectively map VMs to Physical Machines (PMs) with substantial energy savings in a heterogeneous server cluster. A VM/PM mapping probability matrix is constructed, in which each VM request is assigned with a probability running on PMs. The VM/PM mapping probability matrix takes into account resource limitations, VM operation overheads, server reliability as well as energy effciency. The evolution of Internet Data Centers and the increasing demands of web services raise great challenges to improve the energy effciency of IDCs. We also express several potential areas for future research in each chapter

A New Efficient Cloud Model for Data Intensive Application

Cloud computing play an important role in data intensive application since it provide a consistent performance over time and it provide scalability and good fault tolerant mechanism Hadoop provide a scalable data intensive map reduce architecture Hadoop map task are executed on large cluster and consumes lot of energy and resources Executing these tasks requires lot of resource and energy which are expensive so minimizing the cost and resource is critical for a map reduce application So here in this paper we propose a new novel efficient cloud structure algorithm for data processing or computation on azure cloud Here we propose an efficient BSP based dynamic scheduling algorithm for iterative MapReduce for data intensive application on Microsoft azure cloud platform Our framework can be used on different domain application such as data analysis medical research dataminining etc Here we analyze the performance of our system by using a co-located cashing on the worker role and how it is improving the performance of data intensive application over Hadoop map reduce data intrinsic application The experimental result shows that our proposed framework properly utilizes cloud infrastructure service management overheads bandwith bottleneck and it is high scalable fault tolerant and efficien

Real-Time Virtualization and Cloud Computing

In recent years, we have observed three major trends in the development of complex real-time embedded systems. First, to reduce cost and enhance flexibility, multiple systems are sharing common computing platforms via virtualization technology, instead of being deployed separately on physically isolated hosts. Second, multi-core processors are increasingly being used in real-time systems. Third, developers are exploring the possibilities of deploying real-time applications as virtual machines in a public cloud. The integration of real-time systems as virtual machines (VMs) atop common multi-core platforms in a public cloud raises significant new research challenges in meeting the real-time latency requirements of applications. In order to address the challenges of running real-time VMs in the cloud, we first present RT-Xen, a novel real-time scheduling framework within the popular Xen hypervisor. We start with single-core scheduling in RT-Xen, and present the first work that empirically studies and compares different real-time scheduling schemes on a same platform. We then introduce RT-Xen 2.0, which focuses on multi-core scheduling and spanning multiple design spaces, including priority schemes, server schemes, and scheduling policies. Experimental results demonstrate that when combined with compositional scheduling theory, RT-Xen can deliver real-time performance to an application running in a VM, while the default credit scheduler cannot. After that, we present RT-OpenStack, a cloud management system designed to support co-hosting real-time and non-real-time VMs in a cloud. RT-OpenStack studies the problem of running real-time VMs together with non-real-time VMs in a public cloud. Leveraging the resource interface and real-time scheduling provided by RT-Xen, RT-OpenStack provides real-time performance guarantees to real-time VMs, while achieving high resource utilization by allowing non-real-time VMs to share the remaining CPU resources through a novel VM-to-host mapping scheme. Finally, we present RTCA, a real-time communication architecture for VMs sharing a same host, which maintains low latency for high priority inter-domain communication (IDC) traffic in the face of low priority IDC traffic