Live migration allows moving a continuously running VM from one physical host to another. It provides special benefit for data centers in a variety of scenarios including load balancing, maintenance and power management. However virtual machine live migration leads to performance loss and energy overhead that cannot be ignored in modern data centers, especially if critical business goals are to be met. In this paper we summarize, classify and evaluate current approaches with respect to determine costs of virtual machine live migration.

Costs of Virtual Machine Live Migration: A Survey

Flat Datacenter Storage (FDS) is a high-performance, fault-tolerant, large-scale, locality-oblivious blob store. Using a novel combination of full bisection bandwidth networks, data and metadata striping, and flow control, FDS multiplexes an application's large-scale I/O across the available throughput and latency budget of every disk in a cluster. FDS therefore makes many optimizations around data locality unnecessary. Disks also communicate with each other at their full bandwidth, making recovery from disk failures extremely fast. FDS is designed for datacenter scale, fully distributing metadata operations that might otherwise become a bottleneck. 
 
FDS applications achieve single-process read and write performance of more than 2GB/s. We measure recovery of 92GB data lost to disk failure in 6.2 s and recovery from a total machine failure with 655GB of data in 33.7 s. Application performance is also high: we describe our FDS-based sort application which set the 2012 world record for disk-to-disk sorting.

Flat Datacenter Storage.

In this work, we address problem determination in virtualized clouds. We show that high dynamism, resource sharing, frequent reconfiguration, high propensity to faults and automated management introduce significant new challenges towards fault diagnosis in clouds. Towards this, we propose CloudPD, a fault management framework for clouds. CloudPD leverages (i) a canonical representation of the operating environment to quantify the impact of sharing; (ii) an online learning process to tackle dynamism; (iii) a correlation-based performance models for higher detection accuracy; and (iv) an integrated end-to-end feedback loop to synergize with a cloud management ecosystem. Using a prototype implementation with cloud representative batch and transactional workloads like Hadoop, Olio and RUBiS, it is shown that CloudPD detects and diagnoses faults with low false positives (<; 16%) and high accuracy of 88%, 83% and 83%, respectively. In an enterprise trace-based case study, CloudPD diagnosed anomalies within 30 seconds and with an accuracy of 77%, demonstrating its effectiveness in real-life operations.

CloudPD: Problem determination and diagnosis in shared dynamic clouds

A survey of data center consolidation in cloud computing systems

Methods, apparatus, systems and articles of manufacture are disclosed to scale application deployments in cloud computing environments. An example method disclosed herein includes marking a scaling status of a virtual machine included in a first node deployed in the computing environment, the first node executing a first component of the application, the scaling status of the virtual machine indicating a change in a number of virtual machines included in the first node. The example method also includes identifying a second node including a dependency with the virtual machine, the second node deployed in the computing environment. The example method also includes updating configuration information associated with the second node based on the scaling status of the virtual machine, the configuration information including a reference to the virtual machine.

Methods and apparatus to scale application deployments in cloud computing environments

Clouds allow enterprises to increase or decrease their resource allocation on demand in response to changes in workload intensity Virtualization is one of the building blocks for cloud computing and provides the mechanisms to implement the dynamic allocation of resources These dynamic reconfiguration actions lead to performance impact during the reconfiguration duration In this paper, we model the cost of reconfiguring a cloud-based IT infrastructure in response to workload variations We show that maintaining a cloud requires frequent reconfigurations necessitating both VM resizing and VM live migration, with live migration dominating reconfiguration costs We design the CosMig model to predict the duration of live migration and its impact on application performance Our model is based on parameters that are typically monitored in enterprise data centers Further, the model faithfully captures the impact of shared resources in a virtualized environment We experimentally validate the accuracy and effectiveness of CosMig using micro benchmarks and representative applications

CosMig: Modeling the Impact of Reconfiguration in a Cloud

A virtual machine placement framework is described to enable a data center operator to develop a placement scheme to satisfy its particular constraints while simultaneously optimizing resource utilization. To generate a placement solution, the virtual machine placement problem is first characterized as a “bin packing” problem. The framework provides simple interface tools and processing modules, and a pluggable architecture for receiving placement algorithms. To generate a solution, an administrator creates an XML representation that abstracts physical entities (e.g., data center, subnet, rack, physical server, and the like) into a hierarchical tree of bins. The administrator also defines a set of “rules” that govern (direct) the placement of the virtual machines by placing constraints on the placement scheme. Using the hierarchical tree and the rules, the framework is executed to generate a placement as a solution to a bin packing problem, preferably on a layer-by-layer basis.

Virtual machine placement framework

Applications in a data center can be consolidated by identifying different combinations of software executing on hardware. The software can include a set of applications that execute upon a set of servers. The different combinations can have different arrangements of applications running on different ones of the servers. For each of the different combinations, a licensing cost, an operating cost, and a total cost can be calculated. The total cost of operation for each of the configurations can be calculated by summing the licensing costs, the operating costs adjusted for the cost savings for complementary workload patterns, and migration costs for adjusting a current configuration of the applications and servers to arrive at the configuration. The total cost results per configuration can be reported to a user for at least a set of the configurations.

Application placement among a set of consolidation servers utilizing license cost and application workload profiles as factors

Automated virtual machine placement planning using different placement solutions at different hierarchical tree levels

Methods and systems for image migration. There are received, at an image reader, files of at least one image of at least one virtual machine from a first environment. Points of variability are identified within the files, with respect to preparation for functioning in a second environment. Each point of variability is associated with corresponding metadata, and each point of variability is stored with its corresponding metadata. The at least one image is reconfigured to function in the second environment, such reconfiguring comprising adjusting the points of variability via using the metadata. The reconfigured image is deployed to the second environment.

Virtual machine image migration

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