Deliverable DJRA1.2. Solutions and protocols proposal for the network control, management and monitoring in a virtualized network context

This deliverable presents several research proposals for the FEDERICA network, in different subjects, such as monitoring, routing, signalling, resource discovery, and isolation. For each topic one or more possible solutions are elaborated, explaining the background, functioning and the implications of the proposed solutions.This deliverable goes further on the research aspects within FEDERICA. First of all the architecture of the control plane for the FEDERICA infrastructure will be defined. Several possibilities could be implemented, using the basic FEDERICA infrastructure as a starting point. The focus on this document is the intra-domain aspects of the control plane and their properties. Also some inter-domain aspects are addressed. The main objective of this deliverable is to lay great stress on creating and implementing the prototype/tool for the FEDERICA slice-oriented control system using the appropriate framework. This deliverable goes deeply into the definition of the containers between entities and their syntax, preparing this tool for the future implementation of any kind of algorithm related to the control plane, for both to apply UPB policies or to configure it by hand. We opt for an open solution despite the real time limitations that we could have (for instance, opening web services connexions or applying fast recovering mechanisms). The application being developed is the central element in the control plane, and additional features must be added to this application. This control plane, from the functionality point of view, is composed by several procedures that provide a reliable application and that include some mechanisms or algorithms to be able to discover and assign resources to the user. To achieve this, several topics must be researched in order to propose new protocols for the virtual infrastructure. The topics and necessary features covered in this document include resource discovery, resource allocation, signalling, routing, isolation and monitoring. All these topics must be researched in order to find a good solution for the FEDERICA network. Some of these algorithms have started to be analyzed and will be expanded in the next deliverable. Current standardization and existing solutions have been investigated in order to find a good solution for FEDERICA. Resource discovery is an important issue within the FEDERICA network, as manual resource discovery is no option, due to scalability requirement. Furthermore, no standardization exists, so knowledge must be obtained from related work. Ideally, the proposed solutions for these topics should not only be adequate specifically for this infrastructure, but could also be applied to other virtualized networks.Postprint (published version

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

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

On distributed virtual network embedding with guarantees

To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974

On distributed virtual network embedding with guarantees

To provide wide-area network services, resources from different infrastructure providers are needed. Leveraging the consensus-based resource allocation literature, we propose a general distributed auction mechanism for the (NP-hard) virtual network (VNET) embedding problem. Under reasonable assumptions on the bidding scheme, the proposed mechanism is proven to converge, and it is shown that the solutions guarantee a worst case efficiency of (?????) relative to the optimal solution, and that this bound is optimal, that is, no better approximation exists. Using extensive simulations, we confirm superior convergence properties and resource utilization when compared with existing distributed VNET embedding solutions, and we show how byappropriate policy design, our mechanism can be instantiated to accommodate the embedding goals of different service and infrastructure providers, resulting in an attractive and flexible resource allocation solution.This work is supported in part by the National Science Foundation under grant CNS-0963974