Shuichi Okamoto

G-lambda: coordination of a grid scheduler and lambda path service over GMPLS

A vertical coordination between computing resource scheduler and network resource scheduler for Grid-based applications is described. The network resource management system virtualizes and schedules network resources to inter-work with Grid resource scheduler through Web-services interface.

Managing and controlling GMPLS network resources for grid applications

Inter-working between GMPLS network and grid computing application through Web-services interface is demonstrated for the first time. Lambda LSP-based network resource virtualization and scheduling techniques successfully achieves nation-wide grid computing environment with advance reservation operation.

Field Trial of GMPLS-Controlled All-Optical Networking Assisted with Optical Performance Monitors

A field trial of GMPLS-controlled all-optical networking was successfully demonstrated with assistance of OPMs capable of monitoring in-band OSNR. The reliable and stable network operation was achieved thanks to the GMPLS control plane and OPMs.

GMPLS-controlled all-optical mesh networking demonstration using path computation-capable NMS

A GMPLS-controlled all-optical mesh networking was successfully demonstrated using a path computation-capable network management system (NMS). The developed NMS can manage topology and physical impairments, and create GMPLS-based lightpaths according to path computation results.

JGN II Testbed Demonstration of GMPLS Inter-Carrier Network Control with Actual Operational Consideration

This paper presents successful demonstration results of inter-carrier optical path control using the JGN II network testbed. RSVP-TE signaling with hierarchical path control is verified to be beneficial to such actual operation as domain-to-domain based path establishment and restoration.

Demonstration of GMPLS-controlled inter-domain transparent optical networks

MPLS extensions such as the wavelength label supporting 3R regenerators and BGP dissemination of wavelength reachability across domain boundaries were proposed. Resilient end-to-end lighpath provisioning was successfully achieved in GMPLS-controlled inter-domain transparent optical networks.

GMPLS/OXC network testbed of JGN II

The GMPLS/OXC network testbed of JGN II is introduced from the viewpoint of GMPLS network architecture as well as actual network operation. The advanced experimental results of IPv6/GMPLS and MPLS/GMPLS interworking are described using this testbed. Ongoing research projects are introduced and the future view of GMPLS development is also discussed considering the results of the demonstration and operational experience

A dilated-CPU-consumption-based performance prediction for multi-core software routers

Network function virtualization (NFV) raises new possibilities for embedding data plane processing functions, e.g., firewalls, NAT, packet forwarding, etc., on commodity hardware. However, the advantages of flexibility, scalability and low cost of commodity hardware come at a price, such as resource over-provisioning, because the performance of softwarized network functions on shared resources is hardly predictable. This paper addresses the problem of performance prediction for multi-core software routers. Specifically, the performance prediction model is developed to predict the maximum throughput a multi-core software router can perform given assigned resources. Motivated by observations, we first mathematically analyze how many CPU cycles spent for packet forwarding in multi-core processing systems. Our analytical model based on cache contention can capture its nonlinear dilation scaled by the number of CPU cores-called dilated CPU consumption (DCC). We validate the accuracy of DCC with measured data, achieving error less than 8%. We then propose two performance prediction algorithms based on the DCC. The first algorithm relies on CPU utilization statistics (called DCC-u), while its simplified version (called sDCC) does not require CPU utilization statistics. Evaluation with measured data shows that the estimations of DCC-u has error margins less than 3% for a large packet size and 10% for a small packet size, while sDCC provides larger error than DCC-u. Remarkably, our both performance prediction algorithms yield more precise estimation than that of the benchmarking techniques.

Operational experience of JGN II GMPLS network

This paper describes the overview of the GMPLS-controlled optical network testbed of JGN II, which have been operated since 2004. Our operational experience and recently conducted evaluation results on the JGN II network are described.

Erlang-k-based packet latency prediction model for optimal configuration of software routers

Providing the optimal configuration for a software router poses a lot of technical challenges that do not present in the dedicated hardware router. One of them is how to characterize performance varying due to different configurations on commodity hardware. This paper addresses the problem of configuring a software router that provides the minimum of average packet latency. Since changing all combinations of hardware configurations of a software router for searching the optimum is cumbersome, we propose a prediction model to accurately estimate the packet latency of a software router. We first analyze the relationship of the packet latency distribution with the configured and observed parameters. Empirical measurements suggest that the Erlang-k distribution is a reasonable model for estimating the packet latency distribution. Motivated by the parameter relationship analysis, we propose a prediction model for packet latency of a software router based on the Erlang- k distribution. Our prediction model requires measurement of only two different configurations, i.e., one and two Rx queues of a network interface card, to predict the average packet latency of all combinations of configurations. We use the measured data from the testbed experiments and the data of curve fitting method to cross-verify the accuracy of our prediction model. Underlying the prediction model, we propose the optimal configuration selection (OCS) algorithm to justify which configuration yields the minimum of average packet latency. Our prediction model based OCS results in the same optimal configuration with the measured data based ones.