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
2050714144 2016888862 2066741260 2055128632 148964820 2068666958
This paper attends to the problem of embedding flexibly specified virtual networks connecting cloud resources (e.g., storage or computation) on a given substrate (e.g., a data center, an ISP backbone, a router site, or a virtual provider network). We study a scenario where a substrate provider (or a potential intermediate broker or reseller) wants to optimize the embedding of these so-called by migrating them to more suitable locations. For instance, such re-embeddings can be useful if the were requested at short notice and initially placed heuristically. Subsequent optimizations can, e.g., reduce the peak resource loads in the network by spreading across the infrastructure or save energy by moving together and switching off unused components. We present the generic mathematical programming algorithm used in our prototype to compute optimal embeddings. For example, this algorithm supports different objective functions (such as load minimization or energy conservation), arbitrary resource combinations and the mapping of multiple virtual nodes of a to a single substrate node, cost-aware migrations, and it can deal with all link types that arise in practice (e.g., full-duplex or even wireless or wired broadcast links with multiple endpoints). Of course, such rigorous optimizations are time consuming, and we report on the time complexities obtained from our experiments with our network virtualization prototype architecture. It turns out that optimizing over moderate sized infrastructures is feasible, even for scenarios with high flexibility and without tuning the solver software to speed up computations further. We consider the effects of time-varying packet generation rates in the performance of communication networks. The time variations could be a result of the patterns in human activities. As a model, we study the effects of a degree-dependent packet generation rate that includes a sinusoidal term. Applying a modified traffic awareness protocol (TAP) previously proposed for static packet generation rates to the present situation leads to an altered value of the optimization parameter, when compared to that obtained in the static case. To enhance the performance and to cope with the time-varying effects better, we propose a class of self-adjusting traffic awareness protocols that makes use of instantaneous traffic information beyond that included in the modified TAP. Two special cases that make use of global and local information, respectively, are studied. Comparing results of our proposal schemes with the modified TAP, it is shown that the present self-adjusting schemes perform more effectively. The goal of data center network is to interconnect the massive number of data center servers, and provide efficient and fault-tolerant routing service to upper-layer applications. To overcome the problem of tree architecture in current practice, many new network architectures are proposed, represented by Fat-Tree, BCube, and etc. A consistent theme in these new architectures is that a large number of network devices are used to achieve 1:1 oversubscription ratio. However, at most time, data center traffic is far below the peak value. The idle network devices will waste significant amount of energy, which is now a headache for many data center owners. In this paper, we discuss how to save energy consumption in high-density data center networks in a routing perspective. We call this kind of routing energy-aware routing. The key idea is to use as few network devices to provide the routing service as possible, with no little sacrifice on the network performance. Meanwhile, the idle network devices can be shutdown or put into sleep mode for energy saving. We establish the model of energy-aware routing in data center network, and design a heuristic algorithm to achieve the idea. Our simulation in typical data center networks shows that energy-aware routing can effectively save power consumed by network devices. Driven by increasingly unbalanced technology scaling and power dissipation limits, microprocessor designers have resorted to increasing the number of cores on a single chip, and pundits expect 1000-core designs to materialize in the next few years [1]. But how will memory architectures scale and how will these next-generation multicores be programmed? One barrier to scaling current memory architectures is the offchip memory bandwidth wall [1,2]: off-chip bandwidth grows with package pin density, which scales much more slowly than on-die transistor density [3]. To reduce reliance on external memories and keep data on-chip, today’s multicores integrate very large shared last-level caches on chip [4]; interconnects used with such shared caches, however, do not scale beyond relatively few cores, and the power requirements and access latencies of large caches exclude their use in chips on a 1000-core scale. For massive-scale multicores, then, we are left with relatively small per-core caches. Per-core caches on a 1000-core scale, in turn, raise the question of memory coherence. On the one hand, a shared memory abstraction is a practical necessity for general-purpose programming, and most programmers prefer a shared memory model [5]. On the other hand, ensuring coherence among private caches is an expensive proposition: bus-based and snoopy protocols don’t scale beyond relatively few cores, and directory sizes needed in cache-coherence protocols must equal a significant portion of the combined size of the per-core caches as otherwise directory evictions will limit performance [6]. Moreover, directory-based coherence protocols are notoriously difficult to implement and verify [7]. Networks are a shared resource connecting critical IT infrastructure, and the general practice is to always leave them on. Yet, meaningful energy savings can result from improving a network's ability to scale up and down, as traffic demands ebb and flow. We present ElasticTree, a network-wide power1 manager, which dynamically adjusts the set of active network elements -- links and switches--to satisfy changing data center traffic loads. We first compare multiple strategies for finding minimum-power network subsets across a range of traffic patterns. We implement and analyze ElasticTree on a prototype testbed built with production OpenFlow switches from three network vendors. Further, we examine the trade-offs between energy efficiency, performance and robustness, with real traces from a production e-commerce website. Our results demonstrate that for data center workloads, ElasticTree can save up to 50 of network energy, while maintaining the ability to handle traffic surges. Our fast heuristic for computing network subsets enables ElasticTree to scale to data centers containing thousands of nodes. We finish by showing how a network admin might configure ElasticTree to satisfy their needs for performance and fault tolerance, while minimizing their network power bill. We consider policies for CPU load balancing in networks of workstations. We address the question of whether preemptive migration (migrating active processes) is necessary, or whether remote execution (migrating processes only at the time of birth) is sufficient for load balancing. We show that resolving this issue is strongly tied to understanding the process lifetime distribution. Our measurements indicate that the distribution of lifetimes for a UNIX process is Pareto (heavy-tailed), with a consistent functional form over a variety of workloads. We show how to apply this distribution to derive a preemptive migration policy that requires no hand-tuned parameters. We used a trace-driven simulation to show that our preemptive migration strategy is far more effective than remote execution, even when the memory transfer cost is high.