This paper investigates the opportunities and limitations of adaptive virtual machine (VM) migration to reduce communication costs in a virtualized environment. We introduce a new formal model for the problem of online VM migration in two scenarios: (1) VMs can be migrated arbitrarily in the substrate network; e.g., a private cloud provider may have an incentive to reduce the overall communication cost in the network. (2) VMs can only be migrated within a given tenant; e.g., a user that was assigned a set of physical machines may exchange the functionality of the VMs on these machines. We propose a simple class of Destination-Swap algorithms which are based on an aggressive collocation strategy (inspired by splay datastructures) and which maintain a minimal and local amount of per-node (amortized cost) information to decide where to migrate a VM and how; thus, the algorithms react quickly to changes in the load. The algorithms come in two main flavors, an indirect and distributed variant which keeps existing VM placements local, and a direct variant which keeps the number of affected VMs small. We show that naturally, inter-tenant optimizations yield a larger potential for optimization, but generally also a tenant itself can improve its embedding. Moreover, there exists an interesting tradeoff between direct and indirect strategies: indirect variants are preferable under skewed and sparse communication patterns due to their locality properties.
Today's networks become more and more dynamic in the sense that they are able to self-adjust to the network state, user demand, or even energy cost, and the benefits of process migration have been exploited long before the emergence of the cloud computing paradigm, e.g., for load-balancing applications @cite_27 . Generally, applications range from self-optimizing peer-to-peer topologies over green computing (e.g., due to reduced energy consumption) @cite_3 to adaptive virtual machine migrations in datacenters @cite_29 , microprocessor memory architectures @cite_6 , grids @cite_22 or elastic virtual and wide-area cloud networks @cite_30 . Other self-adjusting routing scheme were considered, e.g., in scale-free networks to overcome congestion @cite_18 .
@cite_30 @cite_18 @cite_22 @cite_29 @cite_6 @cite_3 @cite_27
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The rapid growth in demand for computational power driven by modern service applications combined with the shift to the Cloud computing model have led to the establishment of large-scale virtualized data centers. Such data centers consume enormous amounts of electrical energy resulting in high operating costs and carbon dioxide emissions. Dynamic consolidation of virtual machines (VMs) using live migration and switching idle nodes to the sleep mode allows Cloud providers to optimize resource usage and reduce energy consumption. However, the obligation of providing high quality of service to customers leads to the necessity in dealing with the energy-performance trade-off, as aggressive consolidation may lead to performance degradation. Because of the variability of workloads experienced by modern applications, the VM placement should be optimized continuously in an online manner. To understand the implications of the online nature of the problem, we conduct a competitive analysis and prove competitive ratios of optimal online deterministic algorithms for the single VM migration and dynamic VM consolidation problems. Furthermore, we propose novel adaptive heuristics for dynamic consolidation of VMs based on an analysis of historical data from the resource usage by VMs. The proposed algorithms significantly reduce energy consumption, while ensuring a high level of adherence to the service level agreement. We validate the high efficiency of the proposed algorithms by extensive simulations using real-world workload traces from more than a thousand PlanetLab VMs. Copyright © 2011 John Wiley & Sons, Ltd. Virtualization can deliver significant benefits for cloud computing by enabling VM migration to improve utilization, balance load and alleviate hotspots. While several mechanisms exist to migrate VMs, few efforts have focused on optimizing migration policies in a multi-rooted tree datacenter network. The general problem has multiple facets, two of which map to generalizations of well-studied problems: (1) Migration of VMs in a bandwidth-oversubscribed tree network generalizes the maximum multicommodity flow problem in a tree, and (2) Migrations must meet load constraints at the servers, mapping to variants of the matching problem --- generalized assignment and demand matching. While these problems have been individually studied, a new fundamental challenge is to simultaneously handle the packing constraints of server load and tree edge capacities. We give approximation algorithms for several versions of this problem, where the objective is to alleviate a maximal number of hot servers. In the full version of this work [5], we empirically demonstrate the effectiveness of these algorithms through large scale simulations on real data. Online services hosted in data centers show significant diurnal variation in load levels. Thus, there is significant potential for saving power by powering down excess servers during the troughs. However, while techniques like VM migration can consolidate computational load, storage state has always been the elephant in the room preventing this powering down. Migrating storage is not a practical way to consolidate I O load. This paper presents Sierra, a power-proportional distributed storage subsystem for data centers. Sierra allows powering down of a large fraction of servers during troughs without migrating data and without imposing extra capacity requirements. It addresses the challenges of maintaining read and write availability, no performance degradation, consistency, and fault tolerance for general I O workloads through a set of techniques including power-aware layout, a distributed virtual log, recovery and migration techniques, and predictive gear scheduling. Replaying live traces from a large, real service (Hotmail) on a cluster shows power savings of 23 . Savings of 40--50 are possible with more complex optimizations. Cloud computing is the new paradigm of operation in information technology. While cloud computing infrastructures have benefits, their energy consumption is becoming a growing concern. Data centers, which are used to provide the infrastructure and resource pool for cloud computing, consume a large amount of energy. Future energy consumption predictions of these data centers are even bigger concerns. To reduce this energy consumption, and hence the carbon footprint and greenhouse gas emission of cloud computing, and information technology in general, energy-efficient methods of operation have to be investigated and adopted. In addition, renewable energy usage in place of non-renewable can also reduce carbon emission. However, due to its intermittency and volatility, renewable energy cannot be used to its full potential. In this study, we introduce the renewable-energy- aware cloud service and virtual machine migration to relocate energy demand using dynamic and flexible cloud resource allocation techniques, and help overcome the challenges of renewable energy. Results from a U.S.-wide cloud network infrastructure show that, using simple migration techniques, up to 30 percent nonrenewable energy can be replaced by renewable energy, while consuming only a small amount of extra resources and energy to perform demand relocation.