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.

Principles and lessons in packet communications

Abstract: After nearly a decade of experience, we reflect on the principles and lessons which have emerged in the field of packet communications. We begin by identifying the need for efficient resource sharing and review the original and recurring difficulties we had in achieving this goal in packet networks. We then discuss various lessons learned in the areas of: deadlocks; degradations; distributed control; broadcost channels; and hierarchical design. The principles which we discuss have to do with: the efficiency of Large system; the switching computer; network constraints; distributed control; flow control; stale protocols; and designers not yet experienced in packet communications. Throughout the paper, we identify various open issues which remain to be solved in packet communications.

Artificial Intelligence for Elastic Management and Orchestration of 5G Networks

Abstract: The emergence of 5G enables a broad set of diversified and heterogeneous services with complex and potentially conflicting demands. For networks to be able to satisfy those needs, a flexible, adaptable, and programmable architecture based on network slicing is being proposed. A softwarization and cloudification of the communications networks is required, where network functions (NFs) are being transformed from programs running on dedicated hardware platforms to programs running over a shared pool of computational and communication resources. This architectural framework allows the introduction of resource elasticity as a key means to make an efficient use of the computational resources of 5G systems, but adds challenges related to resource sharing and efficiency. In this article, we propose Artificial Intelligence (AI) as a built-in architectural feature that allows the exploitation of the resource elasticity of a 5G network. Building on the work of the recently formed Experiential Network Intelligence (ENI) industry specification group of the European Telecommunications Standards Institute (ETSI) to embed an AI engine in the network, we describe a novel taxonomy for learning mechanisms that target exploiting the elasticity of the network as well as three different resource elastic use cases leveraging AI. This work describes the basis of a use case recently approved at ETSI ENI.

Using a shared workspace and wireless laptops to improve collaborative project learning in an engineering design class

Abstract: Two different technologies, groupware (a shared workspace) and shared wireless laptop computers, were implemented in a project design class in a civil engineering course. The research interest was in the way these technologies supported resource sharing within and across project groups and in the forms of group collaboration that resulted. The initiative was evaluated using both qualitative (e.g. pyramid discussion) and quantitative methods (e.g. survey, logs of usage). The results showed that these technologies helped improve group sharing of resources and supported different kinds of group collaboration. The shared workspace provided a location-independent central repository of resources around which group activities were coordinated whereas the laptops provided a focal point for the face-to-face discussion of these resources. The paper discusses the importance of embedding supportive technologies and the different forms of learner collaboration mediated by each technology.

Method and system for enabling resource sharing in a communication network having a plurality of application environments

Abstract: A method for enabling resource sharing in a communication network supporting a plurality of application environments. Specifically, one embodiment of the present invention discloses a method ensuring only sufficient computational resources are used by a multi-component system as needed to meet its service level objectives. A method is disclosed wherein quality of service is monitored at each component in a multi-component application environment. A metric characterizes quality of service for each component. The method determines whether the quality of service at each component meets an associated service level objective. The method then optimizes the number of computational resources in each of components in the application environment to satisfy service level objectives.