The variety of existing cloud services creates a challenge for service providers to enforce reasonable Software Level Agreements (SLA) stating the Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid such penalties at the same time that the infrastructure operates with minimum energy and resource wastage, constant monitoring and adaptation of the infrastructure is needed. We refer to Software-Defined Cloud Computing, or simply Software-Defined Clouds (SDC), as an approach for automating the process of optimal cloud configuration by extending virtualization concept to all resources in a data center. An SDC enables easy reconfiguration and adaptation of physical resources in a cloud infrastructure, to better accommodate the demand on QoS through a software that can describe and manage various aspects comprising the cloud environment. In this paper, we present an architecture for SDCs on data centers with emphasis on mobile cloud applications. We present an evaluation, showcasing the potential of SDC in two use cases-QoS-aware bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and discuss the research challenges and opportunities in this emerging area.
Regardless the specific approach for realization of virtual networking in a cloud data center (network hypervisors or SDNs), the problem of mapping computing and network elements to physical resources, as well as mapping virtual links into physical paths, needs to be addressed. A survey on this problem------has been proposed by Fisher at al. @cite_33 .
@cite_33
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Assigning the resources of a virtual network to the components of a physical network, called Virtual Network Mapping, plays a central role in network virtualization. Existing approaches use classical heuristics like simulated annealing or attempt a two stage solution by solving the node mapping in a first stage and doing the link mapping in a second stage. The contribution of this paper is a Virtual Network Mapping (VNM) algorithm based on subgraph isomorphism detection: it maps nodes and links during the same stage. Our experimental evaluations show that this method results in better mappings and is faster than the two stage approach, especially for large virtual networks with high resource consumption which are hard to map. While today's virtual datacenters have hypervisor based mechanisms to partition compute resources between the tenants co-located on an end host, they provide little control over how tenants shore the network. This opens cloud applications to interference from other tenants, resulting in unpredictable performance and exposure to denial of service attacks. This paper explores the design space for achieving performance isolation between tenants. We find that existing schemes for enterprise datacenters suffer from at least one of these problems: they cannot keep up with the numbers of tenants and the VM churn observed in cloud datacenters; they impose static bandwidth limits to obtain isolation at the cost of network utilization; they require switch and or NIC modifications; they cannot tolerate malicious tenants and compromised hypervisors. We propose Seawall, an edge-based solution, that achieves max-min fairness across tenant VMs by sending traffic through congestion-controlled, hypervisor-to-hypervisor tunnels. We study a basic resource allocation problem that arises in cloud computing environments. The physical network of the cloud is represented as a graph with vertices denoting servers and edges corresponding to communication links. A workload is a set of processes with processing requirements and mutual communication requirements. The workloads arrive and depart over time, and the resource allocator must map each workload upon arrival to the physical network. We consider the objective of minimizing the congestion. We show that solving a subproblem about mapping a single workload to the physical graph essentially suffices to solve the general problem. In particular, an α-approximation for this single mapping problem gives an O(α log nD)-competitive algorithm for the general problem, where n is the number of nodes in the physical network and D is the maximum to minimum workload duration ratio. We also show how to solve the single mapping problem for two natural class of workloads, namely depth-d-trees and complete-graph workloads. For depth-d tree, we give an nO(d) time O(d2 log (nd))-approximation based on a strong LP relaxation inspired by the Sherali-Adams hierarchy. 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.