Anja Strunk

Bio: Anja Strunk is an academic researcher. The author has contributed to research in topics: Service provider & Service system. The author has an hindex of 2, co-authored 2 publications receiving 160 citations.

Costs of Virtual Machine Live Migration: A Survey

Abstract: 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.

An Algorithm to Predict the QoS-Reliability of Service Compositions

Abstract: The idea of the future Internet of Services is to combine several services of numerous service providers to new value-added services or applications. To sell these services on so-called service marketplaces the providers have to ensure both a high quality and a high quality reliability. But how could a provider of a composed service know the quality-reliability of his service without costly tests? This article describes an approach to predict the quality-reliability of service composition based on a three-step algorithm. The algorithm calculates the expected number of service level objective (SLO) violations caused by thecomposition’s sub-services. The expected number of SLO violations can be used as metric for the QoS-reliability.

Survey: Live Migration and Disaster Recovery over Long-Distance Networks

Abstract: We study the virtual machine live migration (LM) and disaster recovery (DR) from a networking perspective, considering long-distance networks, for example, between data centers. These networks are usually constrained by limited available bandwidth, increased latency and congestion, or high cost of use when dedicated network resources are used, while their exact characteristics cannot be controlled. LM and DR present several challenges due to the large amounts of data that need to be transferred over long-distance networks, which increase with the number of migrated or protected resources. In this context, our work presents the way LM and DR are currently being performed and their operation in long-distance networking environments, discussing related issues and bottlenecks and surveying other works. We also present the way networks are evolving today and the new technologies and protocols (e.g., software-defined networking, or SDN, and flexible optical networks) that can be used to boost the efficiency of LM and DR over long distances. Traffic redirection in a long-distance environment is also an important part of the whole equation, since it directly affects the transparency of LM and DR. Related works and solutions both from academia and the industry are presented.

Allocation of Virtual Machines in Cloud Data Centers—A Survey of Problem Models and Optimization Algorithms

Abstract: Data centers in public, private, and hybrid cloud settings make it possible to provision virtual machines (VMs) with unprecedented flexibility. However, purchasing, operating, and maintaining the underlying physical resources incurs significant monetary costs and environmental impact. Therefore, cloud providers must optimize the use of physical resources by a careful allocation of VMs to hosts, continuously balancing between the conflicting requirements on performance and operational costs. In recent years, several algorithms have been proposed for this important optimization problem. Unfortunately, the proposed approaches are hardly comparable because of subtle differences in the used problem models. This article surveys the used problem formulations and optimization algorithms, highlighting their strengths and limitations, and pointing out areas that need further research.

A Survey on Virtual Machine Migration: Challenges, Techniques, and Open Issues

Abstract: When users flood in cloud data centers, how to efficiently manage hardware resources and virtual machines (VMs) in a data center to both lower economical cost and ensure a high service quality becomes an inevitable work for cloud providers. VM migration is a cornerstone technology for the majority of cloud management tasks. It frees a VM from the underlying hardware. This feature brings a plenty of benefits to cloud providers and users. Many researchers are focusing on pushing its cutting edge. In this paper, we first give an overview of VM migration and discuss both its benefits and challenges. VM migration schemes are classified from three perspectives: 1) manner; 2) distance; and 3) granularity. The studies on non-live migration are simply reviewed, and then those on live migration are comprehensively surveyed based on the three main challenges it faces: 1) memory data migration; 2) storage data migration; and 3) network connection continuity. The works on quantitative analysis of VM migration performance are also elaborated. With the development and evolution of cloud computing, user mobility becomes an important motivation for live VM migration in some scenarios (e.g., fog computing). Thus, the studies regarding linking VM migration to user mobility are summarized as well. At last, we list the open issues which are waiting for solutions or further optimizations on live VM migration.

Energy and Migration Cost-Aware Dynamic Virtual Machine Consolidation in Heterogeneous Cloud Datacenters

Abstract: Energy efficiency has become one of the major concerns for today's cloud datacenters. Dynamic virtual machine (VM) consolidation is a promising approach for improving the resource utilization and energy efficiency of datacenters. However, the live migration technology that VM consolidation relies on is costly in itself, and this migration cost is usually heterogeneous as well as the datacenter. This paper investigates the following bi-objective optimization problem: how to pay limited migration costs to save as much energy as possible via dynamic VM consolidation in a heterogeneous cloud datacenter. To capture these two conflicting objectives, a consolidation score function is designed for an overall evaluation on the basis of a migration cost estimation method and an upper bound estimation method for maximal saved power. To optimize the consolidation score, a greedy heuristic and a swap operation are introduced, and an improved grouping genetic algorithm (IGGA) based on them is proposed. Lastly, empirical studies are performed, and the evaluation results show that IGGA outperforms existing VM consolidation methods.

Does Live Migration of Virtual Machines Cost Energy

Abstract: Live migration, the process of moving a virtual machine (VM) interruption-free between physical hosts is a core concept in modern data centers. Power management strategies use live migration to consolidate services in a cluster environment and to switch off underutilized machines to save power. However, most migration models do not consider the energy cost of migration. This paper experimentally investigates the factors that affect the power consumption and the duration of virtual machine migration. We use the KVM platform for our experiment and show that a live migration entails an energy overhead and the size of this overhead varies with the size of the virtual machine and the available network bandwidth.