Infrastructure-as-a-Service (IaaS) providers need to offer richer services to be competitive while optimizing their resource usage to keep costs down. Richer service offerings include new resource request models involving bandwidth guarantees between virtual machines (VMs). Thus we consider the following problem: given a VM request graph (where nodes are VMs and edges represent virtual network connectivity between the VMs) and a real data center topology, find an allocation of VMs to servers that satisfies the bandwidth guarantees for every virtual network edge---which maps to a path in the physical network---and minimizes congestion of the network. Previous work has shown that for arbitrary networks and requests, finding the optimal embedding satisfying bandwidth requests is @math -hard. However, in most data center architectures, the routing protocols employed are based on a spanning tree of the physical network. In this paper, we prove that the problem remains @math -hard even when the physical network topology is restricted to be a tree, and the request graph topology is also restricted. We also present a dynamic programming algorithm for computing the optimal embedding in a tree network which runs in time @math , where @math is the number of nodes in the physical topology and @math is the size of the request graph, which is well suited for practical requests which have small @math . Such requests form a large class of web-service and enterprise workloads. Also, if we restrict the requests topology to a clique (all VMs connected to a virtual switch with uniform bandwidth requirements), we show that the dynamic programming algorithm can be modified to output the minimum congestion embedding in time @math .
Guo al @cite_8 proposed a new architectural framework, Secondnet, for embedding virtualization requests with bandwidth guarantees. This framework considers requests with bandwidth guarantees @math between every pair of VMs @math . This framework provides rigorous application performance guarantees and hence is suitable for enterprise workloads but at the same time also establishes hardness of the problem of finding such embeddings in arbitrary networks. Our results employ the SecondNet framework but restrict attention to tree networks.
@cite_8
2792336262 2038021311 2097882016 2399443652
A key challenge in network virtualization is to efficiently map a virtual network (VN) on a substrate network (SN), while accounting for possible substrate failures. This is known as the survivable VN embedding (SVNE) problem. The state-of-the-art literature has studied the SVNE problem from infrastructure providers’ (InPs’) perspective, i.e., provisioning backup resources in the SN. A rather unexplored solution spectrum is to augment the VN with sufficient spare backup capacity to survive substrate failures and embed the resulting VN accordingly. Such augmentation enables InPs to offload failure recovery decisions to the VN operator, thus providing more flexible VN management. In this paper, we study the problem of jointly optimizing spare capacity allocation in a VN and embedding the VN to guarantee full bandwidth in the presence of multiple substrate link failures. We formulate the optimal solution to this problem as a quadratic integer program that we transform into an integer linear program. We also propose a heuristic algorithm to solve larger instances of the problem. Based on analytical study and simulation, our key findings are: 1) provisioning shared backup resources in the VN can yield 33 more resource efficient embedding compared to doing the same at the SN level and 2) our heuristic allocates 21 extra resources compared to the optimal, while executing several orders of magnitude faster. Network virtualization is a promising new resource management approach that allows customized virtual networks (VNs) to be multiplexed on a shared physical infrastructure. In this paper, our focus is on the embedding of VN resources onto this infrastructure. Since this problem is known to be NP-hard, embedding proposals in literature are heuristic-based approaches that restrict the problem space in different dimensions. Limitations of these proposals are: 1) as embedding of VN links and nodes is performed in two separate stages, it may ensue in a high blocking of VN requests and a less efficient usage of substrate resources; and 2) as pricing of embedding resources is based on linear functions, it triggers no competition among VN users in order to maximize infrastructure provider profits. These drawbacks motivate us to propose a mathematical model that makes use of large-scale optimization tools and proposes a Column Generation (CG) formulation of the problem, coupled with branch-and-bound technique or rounding-off heuristic. We also propose a periodical planning of embedding process where profitable VN requests are selected through an auction mechanism. In our experiments with different substrate network topologies and many different VN request patterns, we show a clear advantage of auction-based CG models over present benchmarks . In this paper, we propose virtual data center (VDC) as the unit of resource allocation for multiple tenants in the cloud. VDCs are more desirable than physical data centers because the resources allocated to VDCs can be rapidly adjusted as tenants' needs change. To enable the VDC abstraction, we design a data center network virtualization architecture called SecondNet. SecondNet achieves scalability by distributing all the virtual-to-physical mapping, routing, and bandwidth reservation state in server hypervisors. Its port-switching based source routing (PSSR) further makes SecondNet applicable to arbitrary network topologies using commodity servers and switches. SecondNet introduces a centralized VDC allocation algorithm for bandwidth guaranteed virtual to physical mapping. Simulations demonstrate that our VDC allocation achieves high network utilization and low time complexity. Our implementation and experiments show that we can build SecondNet on top of various network topologies, and SecondNet provides bandwidth guarantee and elasticity, as designed. In cloud data centers, where hosted applications share the underlying network resources, network-bandwidth guarantees have been shown to improve predictability of application performance and cost. However, recent empirical studies have also shown that often data center devices and links are not all that reliable and that failures may cause service outages, rendering significant revenue loss for the affected tenants, as well as the cloud operator. Accordingly, cloud operators are pressed to offer both reliable and predictable performance for the hosted applications. While much work has been done on solving both problems separately, this paper seeks to develop a joint framework by which cloud operators can offer both performance and availability guarantees for the hosted tenants. In particular, this paper considers a simple model to abstract the bandwidth guarantees requirement for the tenant and presents a protection plan design which consists of backup virtual machines placement and bandwidth provisioning to optimize the internal data center traffic. We show through solid motivational examples that finding the optimal protection plan design is highly perplexing, and encompasses several constituent challenges. Owing to its complexity, we decompose it into two subproblems, and solve them separately. First, we invoke a placement subproblem of the minimum number of backup VMs and then we attempt to find the most efficient correspondence between backup and primary VMs (i.e., protection plan) which minimizes the bandwidth redundancy. Our numerical evaluation shows that our two-step method is both scalable and accurate; further, it performs much better than a baseline method where placement of backup VMs is done at random.