307 research outputs found
P2CP: A New Cloud Storage Model to Enhance Performance of Cloud Services
This paper presents a storage model named Peer to Cloud and Peer (P2CP). Assuming that the P2CP model follows the Poisson process or Little’s law, we prove that the speed and availability of P2CP is generally better than that of the pure Peer to Peer (P2P) model, the Peer to Server, Peer (P2SP) model or the cloud model. A key feature of our P2CP is that it has three data transmission tunnels: the cloud-user data transmission tunnel, the clients’ data transmission tunnel, and the common data transmission tunnel. P2CP uses the cloud storage system as a common storage system. When data transmission occurs, the data nodes, cloud user, and the non-cloud user are all together involved to complete the transaction
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FutureGRID: A Program for long-term research into GRID systems architecture
Proceedings of the 2003 UK e-Science All Hands Meeting, 31st August - 3rd September, Nottingham UKThis is a project to carry out research into long-term GRID architecture, in the University of Cambridge
Computer Laboratory and the Cambridge eScience Center, with support from the Microsoft Research
Laboratory, Cambridge.
It is part of a larger vision for future systems architectures for public computing platforms, including
both scientitic GRID and commodity level computing such as games, peer2peer computing and storage
services and so forth, based on work in the laboratories in recent years into massively scaleable distributed systems for storage, computation, content distribution and collaboration[26]
Exploring the Impact of Serverless Computing on Peer To Peer Training Machine Learning
The increasing demand for computational power in big data and machine
learning has driven the development of distributed training methodologies.
Among these, peer-to-peer (P2P) networks provide advantages such as enhanced
scalability and fault tolerance. However, they also encounter challenges
related to resource consumption, costs, and communication overhead as the
number of participating peers grows. In this paper, we introduce a novel
architecture that combines serverless computing with P2P networks for
distributed training and present a method for efficient parallel gradient
computation under resource constraints.
Our findings show a significant enhancement in gradient computation time,
with up to a 97.34\% improvement compared to conventional P2P distributed
training methods. As for costs, our examination confirmed that the serverless
architecture could incur higher expenses, reaching up to 5.4 times more than
instance-based architectures. It is essential to consider that these higher
costs are associated with marked improvements in computation time, particularly
under resource-constrained scenarios. Despite the cost-time trade-off, the
serverless approach still holds promise due to its pay-as-you-go model.
Utilizing dynamic resource allocation, it enables faster training times and
optimized resource utilization, making it a promising candidate for a wide
range of machine learning applications
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