20050076336 Network resources comprising the switched underlay network

ABSTRACT

Resource scheduling on a switched underlay network enables coordination, scheduling, and scheduling optimization based on the availability of the data and the network resources 

A method and apparatus for resource scheduling on a switched underlay network enable coordination, scheduling, and scheduling optimization to take place, taking into account the availability of the data and the network resources comprising the switched underlay network.

Requested transfers may be fulfilled by assessing the requested transfer parameters, the availability of the network resources required to fulfill the request, the availability of the data to be transferred, the availability of sufficient storage resources to receive the data, and other potentially conflicting requested transfers. In one embodiment, the requests are under-constrained to enable transfer scheduling optimization to occur. The under-constrained nature of the requests enables requests to be scheduled, considering factors such as transfer priority, transfer duration, the amount of time it has been since the transfer request was submitted, and many other factors.

Scheduling resources on a switched underlay network

An Inventor: Dr. Tal Lavian

BACKGROUND

1. Field

• This application relates to communication networks and, more particularly, to a method and apparatus for scheduling resources on a switched underlay network.

2. Description of the Related Art

• Data communication networks may include various computers, servers, nodes, routers, switches, hubs, proxies, and other devices coupled to and configured to pass data to one another. These devices will be referred to herein as “network devices” and may provide various network resources such as communication links and bandwidths. Conventionally, data has been communicated through the data communication networks bypassing protocol data units (or cells, frames, or segments) between the network devices by utilizing one or more types of network resources. A protocol data unit may be handled by multiple network devices and cross multiple communication links as it travels between its source and destination over the network.
• Grid networks are an emerging application that builds overlay networks, i.e., computational Grids, on existing network infrastructures using Grid computing technology. In a Grid network, which forms a virtual organization, Grid nodes are distributed widely and share computational resources such as disk storage, storage servers, shared memory, computer clusters, data mining, and visualization centers, although other resources may be available as well. One example of Grids is the TeraGrid, in which Grid computing technology has been deployed to enable supercomputer clusters distributed in four distant locations in the United States to collaboratively work on computationally intense tasks, such as high-energy physics simulations and long-term global weather forecasting. Other potential uses for Grid computing include genomics, protein structure research, computational fluid dynamics, astronomy and astrophysics, Search for ExtraTerrestrial Intelligence (SETI), computational chemistry, “intelligent” drug design, electronic design automation, nuclear physics, and high-energy physics. Grid computing may be used for many other purposes as well, and this list is not intended to be inclusive of all possible uses.
• Some of these applications are or are expected to be capable of producing an incredible amount of data that must be distributed to other Grid applications for analysis. For example, high-energy physics experiments expected to begin in 2007 are expected to produce data at a rate that may exceed one petabyte of data per year (1 petabyte=1000 Terabyte=10¹⁵bytes). This data must be sent to many different sites, such as research facilities and universities around the world, for analysis and storage.
• When faced with data volumes, these large, traditional packet-switched networks, such as TCP/IP-based communication networks tend to become overloaded and incapable or inefficient at handling these large data transfers. One technology that is capable of handling these large data transfers is the use of switched optical networking. Each transfer typically uses a dedicated switched optical link, typically several hundred gigabytes to several terabytes in size. These links are typically provisioned to operate at 10 gigabits/second over each dedicated wavelength (lambda), and multiple lambdas can be multiplexed together to provide bandwidth sufficient to transfer these vast quantities of data.
• Conventional optical network reservation is made based on current availability and on-demand scheduling and reservation. This reservation scheme considers only the network aspects, such as the availability of the network resources, without considering other aspects of the data transfer, such as the availability of the data services that will be required to participate in the data transfer. Additionally, conflicts involving multiple resources and multiple requests cannot be handled by existing reservation schemes. Rather, requests are currently either satisfied or not, and no facility exists to optimize scheduling. Accordingly, a need exists to provide enhanced scheduling for large volume data transfers over switched underlay networks.

SUMMARY OF THE DISCLOSURE

• The following detailed description describes a method and apparatus for scheduling resources on a switched underlay network. One embodiment of the invention enables coordination, scheduling, and scheduling optimization to take place, considering the availability of the data and the network resources comprising the switched underlay network. 

• In this embodiment, requested transfers are fulfilled by assessing the requested transfer parameters, the network resources required to fulfill the request, the availability of the data to be transferred, the availability of sufficient storage resources to receive the data, and other potentially conflicting requested transfers. In one embodiment, the requests are under-constrained to enable transfer scheduling optimization to occur. The under-constrained nature of the requests enables requests to be scheduled, considering factors such as transfer priority, transfer duration, the amount of time it has been since the transfer request was submitted, and many other factors.