Shared resource

About: Shared resource is a research topic. Over the lifetime, 7536 publications have been published within this topic receiving 123491 citations. The topic is also known as: network share.

Using an index and count mechanism to coordinate access to a shared resource by interactive devices

Abstract: Th present invention coordinates access to a shared resource, comprised of a plurality of segments, between a first device and a second device using an index and count mechanism The present invention includes a respective descriptor, for each of the plurality of segments Entries to the respective descriptors of the segments are maintained by the first device to inform the second device of activity between the first device and the shared resource The present invention also includes a descriptor queue register, coupled to the first device and the second device The first device writes an index into the descriptor queue register for indicating a starting descriptor of a corresponding segment that is available to the second device for access The first device also writes a count into the descriptor queue register for indicating a subsequent number of descriptors, from the starting descriptor, of any corresponding segments that are available to the second device for access By thus using this index and count mechanism, the second device does not poll the descriptors to determine any available segments within the shared resource thereby substantially eliminating bandwidth, delay, and data processing overhead associated with the polling process of the prior art The present invention can be used to particular advantage when the first device is a CPU of a computer host system having a shared memory which is the shared resource and when the second device is a computer network peripheral device that couples the computer host system to a network of computers

Fabric distributed resource scheduling

Abstract: Embodiments perform centralized input/output (I/O) path selection for hosts accessing storage devices in distributed resource sharing environments. The path selection accommodates loads along the paths through the fabric and at the storage devices. Topology changes may also be identified and automatically initiated. Some embodiments contemplate the hosts executing a plurality of virtual machines (VMs) accessing logical unit numbers (LUNs) in a storage area network (SAN).

Fair bandwidth sharing among virtual networks: a capacity resizing approach

Abstract: Virtual private networks (VPN) and link sharing are cost-effective ways of realizing corporate intranets. Corporate intranets will increasingly have to provide integrated services for voice and multimedia traffic. Although packet scheduling algorithms can be used to implement integrated services in link sharing and virtual private networks, their statistical multiplexing gains are limited. We propose a new scheme called stochastic fair sharing (SFS) to carry out fair link sharing and fair sharing among virtual leased links (VLL). In the link sharing environment, capacities allocated to different classes are adjusted dynamically as sessions arrive (or depart). The SFS admission control algorithm decides which sessions to accept and which to reject depending upon the current utilizations and provisioned capacities of the classes. SFS gives protection to classes with low session arrival rate against classes with high session arrival rates by ensuring them a low blocking probability. In the case of multi-hop VLL, capacity resizing requests are sent in the service providers's network which are admission-controlled using SFS. Our simulations indicate that using SFS, the equivalent capacity of virtual links converge to their max-min fair capacity, with a fairness index of 0.97 in extreme situations. The average signaling load of the protocol was found to be reasonable. The scheme is simple to implement, efficient, and robust. The potential applications of SFS are fair and efficient resource sharing in telecommunication networks, ATM networks, virtual private networks (VPN) and integrated services or differentiated services-based IP networks.

A Framework for Implementing and Managing Platform as a Service in a Virtual Cloud Computing Lab

Abstract: With the rapid development of Internet and Cloud computing, there are more and more network resources. Sharing, management and on-demand allocation of network resources are particularly important in Cloud computing. Platform as a Service (PaaS) is one of the key services in Cloud computing. PaaS is very attractive for schools, research institutions and enterprises which need reducing IT costs, improving computing platform sharing and meeting license constraints. However, nearly all current available cloud computing platforms are either proprietary or their software infrastructure is invisible to the research community except for a few open-source platforms. For universities and research institutes, more open and testable experimental platforms are needed in a lab-level with PCs. In this paper, a framework for managing PaaS in a virtual Cloud computing lab is developed. The framework implements the user management, resource management and access management. The system has good expandability and can improve resource’s sharing and utilization.

Neural network-based autonomous allocation of resources in virtual networks

Abstract: Network virtualisation has received attention as a way to allow for sharing of physical network resources. Sharing resources involves mapping of virtual nodes and links onto physical nodes and links respectively, and thereafter managing the allocated resources to ensure efficient resource utilisation. In this paper, we apply artificial neural networks for a dynamic, decentralised and autonomous allocation of physical network resources to the virtual networks. The objective is to achieve better efficiency in the utilisation of substrate network resources while ensuring that the quality of service requirements of the virtual networks are not violated. The proposed approach is evaluated by comparison with two static resource allocation schemes and a reinforcement learning-based approach.