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.

Economic models for management of resources in peer-to-peer and grid computing

Abstract: The accelerated development in Peer-to-Peer (P2P) and Grid computing has positioned them as promising next generation computing platforms. They enable the creation of Virtual Enterprises (VE) for sharing resources distributed across the world. However, resource management, application development and usage models in these environments is a complex undertaking. This is due to the geographic distribution of resources that are owned by different organizations or peers. The resource owners of each of these resources have different usage or access policies and cost models, and varying loads and availability. In order to address complex resource management issues, we have proposed a computational economy framework for resource allocation and for regulating supply and demand in Grid computing environments. The framework provides mechanisms for optimizing resource provider and consumer objective functions through trading and brokering services. In a real world market, there exist various economic models for setting the price for goods based on supply-and-demand and their value to the user. They include commodity market, posted price, tenders and auctions. In this paper, we discuss the use of these models for interaction between Grid components in deciding resource value and the necessary infrastructure to realize them. In addition to normal services offered by Grid computing systems, we need an infrastructure to support interaction protocols, allocation mechanisms, currency, secure banking, and enforcement services. Furthermore, we demonstrate the usage of some of these economic models in resource brokering through Nimrod/G deadline and cost-based scheduling for two different optimization strategies on the World Wide Grid (WWG) testbed that contains peer-to-peer resources located on five continents: Asia, Australia, Europe, North America, and South America.

Storage management integrated system and storage control method for storage management integrated system

Abstract: It is intended to achieve an arrangement wherein, when file information of shared disks which are distributed in a plurality of computers connected to a network are used, operating conditions at the time of accessing shared disks can be defined, thus enabling users to easily share files without being bothered by setting-up, or operations of computers located on the other side when they are to perform file sharing. In order to achieve the above-stated arrangement, a storage management integrated server 100 is provided in a network, and respective computers register conditions, an accessibility states and use types pertaining to shared volume to be offered to the server. Thereafter, when a certain computer is to make a file accessing to a shared volume, the computer makes an inquiry to the storage management integrated server for an adequate access destination, determines, on the basis of a response from the server, computers which are suitable to the access source, and makes direct accesses to such computers.

Wide area computing: resource sharing on a large scale

Abstract: Consider almost any computing resource today-whether hardware, software, or data-and it will invariably be networked. Computing over wide area networks has been largely ad hoc, but as needs increase, piecemeal solutions no longer make sense. The authors set out to design and build a wide-area operating system that would allow multiple organizations with diverse platforms to share and combine their resources. This system, Legion, is a network-level operating system designed from scratch to target wide-area computing demands.

Efficient Computing Resource Sharing for Mobile Edge-Cloud Computing Networks

Abstract: Both the edge and the cloud can provide computing services for mobile devices to enhance their performance. The edge can reduce the conveying delay by providing local computing services while the cloud can support enormous computing requirements. Their cooperation can improve the utilization of computing resources and ensure the QoS, and thus is critical to edge-cloud computing business models. This paper proposes an efficient framework for mobile edge-cloud computing networks, which enables the edge and the cloud to share their computing resources in the form of wholesale and buyback. To optimize the computing resource sharing process, we formulate the computing resource management problems for the edge servers to manage their wholesale and buyback scheme and the cloud to determine the wholesale price and its local computing resources. Then, we solve these problems from two perspectives: i) social welfare maximization and ii) profit maximization for the edge and the cloud. For i), we have proved the concavity of the social welfare and proposed an optimal cloud computing resource management to maximize the social welfare. For ii), since it is difficult to directly prove the convexity of the primal problem, we first proved the concavity of the wholesaled computing resources with respect to the wholesale price and designed an optimal pricing and cloud computing resource management to maximize their profits. Numerical evaluations show that the total profit can be maximized by social welfare maximization while the respective profits can be maximized by the optimal pricing and cloud computing resource management.

SIRAP: a synchronization protocol for hierarchical resource sharingin real-time open systems

Abstract: This paper presents a protocol for resource sharing in a hierarchical real-time scheduling framework Targeting real-time open systems, the protocol and the scheduling framework significantly reduce the efforts and errors associated with integrating multiple semi-independent subsystems on a single processor Thus, our proposed techniques facilitate modern software development processes, where subsystems are developed by independent teams (or subcontractors) and at a later stage integrated into a single product Using our solution, a subsystem need not know, and is not dependent on, the timing behaviour of other subsystems; even though they share mutually exclusive resources In this paper we also prove the correctness of our approach and evaluate its efficiency