3,926 research outputs found
A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System
We introduce a game-theoretic framework to ex- plore revenue sharing in an
Edge-Cloud computing system, in which computing service providers at the edge
of the Internet (edge providers) and computing service providers at the cloud
(cloud providers) co-exist and collectively provide computing resources to
clients (e.g., end users or applications) at the edge. Different from
traditional cloud computing, the providers in an Edge-Cloud system are
independent and self-interested. To achieve high system-level efficiency, the
manager of the system adopts a task distribution mechanism to maximize the
total revenue received from clients and also adopts a revenue sharing mechanism
to split the received revenue among computing servers (and hence service
providers). Under those system-level mechanisms, service providers attempt to
game with the system in order to maximize their own utilities, by strategically
allocating their resources (e.g., computing servers).
Our framework models the competition among the providers in an Edge-Cloud
system as a non-cooperative game. Our simulations and experiments on an
emulation system have shown the existence of Nash equilibrium in such a game.
We find that revenue sharing mechanisms have a significant impact on the
system-level efficiency at Nash equilibria, and surprisingly the revenue
sharing mechanism based directly on actual contributions can result in
significantly worse system efficiency than Shapley value sharing mechanism and
Ortmann proportional sharing mechanism. Our framework provides an effective
economics approach to understanding and designing efficient Edge-Cloud
computing systems
Topology-aware GPU scheduling for learning workloads in cloud environments
Recent advances in hardware, such as systems with multiple GPUs and their availability in the cloud, are enabling deep learning in various domains including health care, autonomous vehicles, and Internet of Things. Multi-GPU systems exhibit complex connectivity among GPUs and between GPUs and CPUs. Workload schedulers must consider hardware topology and workload communication requirements in order to allocate CPU and GPU resources for optimal execution time and improved utilization in shared cloud environments.
This paper presents a new topology-aware workload placement strategy to schedule deep learning jobs on multi-GPU systems. The placement strategy is evaluated with a prototype on a Power8 machine with Tesla P100 cards, showing speedups of up to ≈1.30x compared to state-of-the-art strategies; the proposed algorithm achieves this result by allocating GPUs that satisfy workload requirements while preventing interference. Additionally, a large-scale simulation shows that the proposed strategy provides higher resource utilization and performance in cloud systems.This project is supported by the IBM/BSC Technology Center for Supercomputing
collaboration agreement. It has also received funding from the European Research Council (ERC) under the European Union’s Horizon
2020 research and innovation programme (grant agreement No 639595). It is
also partially supported by the Ministry of Economy of Spain under contract
TIN2015-65316-P and Generalitat de Catalunya under contract 2014SGR1051,
by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program
(SEV-2015-0493). We thank our IBM Research colleagues Alaa Youssef
and Asser Tantawi for the valuable discussions. We also thank SC17 committee
member Blair Bethwaite of Monash University for his constructive feedback on the earlier drafts of this paper.Peer ReviewedPostprint (published version
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
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