Memetic Multi-Objective Particle Swarm Optimization-Based Energy-Aware Virtual Network Embedding

In cloud infrastructure, accommodating multiple virtual networks on a single physical network reduces power consumed by physical resources and minimizes cost of operating cloud data centers. However, mapping multiple virtual network resources to physical network components, called virtual network embedding (VNE), is known to be NP-hard. With considering energy efficiency, the problem becomes more complicated. In this paper, we model energy-aware virtual network embedding, devise metrics for evaluating performance of energy aware virtual network-embedding algorithms, and propose an energy aware virtual network-embedding algorithm based on multi-objective particle swarm optimization augmented with local search to speed up convergence of the proposed algorithm and improve solutions quality. Performance of the proposed algorithm is evaluated and compared with existing algorithms using extensive simulations, which show that the proposed algorithm improves virtual network embedding by increasing revenue and decreasing energy consumption.Comment: arXiv admin note: text overlap with arXiv:1504.0684

Multi-capacity bin packing with dependent items and its application to the packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP) problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem , and we evaluate its efficiency using simulations on various application workloads, and network models.This work was done while author was at Boston University. It was partially supported by NSF CISE awards #1430145, #1414119, #1239021 and #1012798. (1430145 - NSF CISE; 1414119 - NSF CISE; 1239021 - NSF CISE; 1012798 - NSF CISE

Service Function Graph Design And Embedding In Next Generation Internet

Network Function Virtualization (NFV) and Software Defined Networking (SDN) are viewed as the techniques to design, deploy and manage future Internet services. NFV provides an effective way to decouple network functions from the proprietary hardware, allowing the network providers to implement network functions as virtual machines running on standard servers. In the NFV environment, an NFV service request is provisioned in the form of a Service Function Graph (SFG). The SFG defines the exact set of actions or Virtual Network Functions (VNFs) that the data stream from the service request is subjected to. These actions or VNFs need to be embedded onto specific physical (substrate) networks to provide network services for end users. Similarly, SDN decouples the control plane from network devices such as routers and switches. The network control management is performed via an open interface and the underlying infrastructure turned into simple programmable forwarding devices. NFV and SDN are complementary to each other. Specifically, similar to running network functions on general purpose servers, SDN control plane can be implemented as pure software running on industry standard hardware. Moreover, automation and virtualization provide both NFV and SDN the tools to achieve their respective goals. In this dissertation, we motivate the importance of service function graph design, and we focus our attention on the problem of embedding network service requests. Throughout the dissertation, we highlight the unique properties of the service requests and investigate how to efficiently design and embed an SFG for a service request onto substrate network. We address variations of the embedding service requests such as dependence awareness and branch awareness in service function graph design and embedding. We propose novel algorithms to design and embed service requests with dependence and branch awareness. We also provide the intuition behind our proposed schemes and analyze our suggested approaches over multiple metrics against other embedding techniques

Network-constrained packing of brokered workloads in virtualized environments

Providing resource allocation with performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, in which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. Existing resource allocation solutions either assume that applications manage their data transfer between their virtualized resources, or that cloud providers manage their internal networking resources.With the increased prevalence of brokerage services in cloud platforms, there is a need for resource allocation solutions that provides predictability guarantees in settings, in which neither application scheduling nor cloud provider resources can be managed/controlled by the broker. This paper addresses this problem, as we define the Network-Constrained Packing (NCP)problem of finding the optimal mapping of brokered resources to applications with guaranteed performance predictability. We prove that NCP is NP-hard, and we define two special instances of the problem, for which exact solutions can be found efficiently. We develop a greedy heuristic to solve the general instance of the NCP problem, and we evaluate its efficiency using simulations on various application workloads, and network models.This work is supported by NSF CISE CNS Award #1347522, # 1239021, # 1012798