Authors:
Theo Lynn
1
;
Huanhuan Xiong
2
;
Dapeng Dong
2
;
Bilal Momani
2
;
George Gravvanis
3
;
Christos Filelis-Papadopoulos
3
;
Anne Elster
4
;
Malik Muhammad Zaki Murtaza Khan
4
;
Dimitrios Tzovaras
5
;
Konstantinos Giannoutakis
5
;
Dana Petcu
6
;
Marian Neagul
6
;
Ioan Dragan
6
;
Perumal Kuppudayar
7
;
Suryanarayanan Natarajan
7
;
Michael McGrath
7
;
Georgi Gaydadjiev
8
;
Tobias Becker
8
;
Anna Gourinovitch
1
;
David Kenny
1
and
John Morrison
2
Affiliations:
1
DCU, Ireland
;
2
UCC, Ireland
;
3
Democritus University of Thrace, Greece
;
4
Norwegian University of Science and Technology, Norway
;
5
The Centre for Research and Technology and Hellas, Greece
;
6
Institute e-Austria Timisoara and West University of Timisoara, Romania
;
7
Intel, Ireland
;
8
Maxeler, United Kingdom
Keyword(s):
Cloud Computing Models, Cloud Infrastructures, Cloud Architecture, Cloud Computing, Cloud Services Self-organisation, Self-management, Heterogeneous Resources, Resource as a Service, Cloud Orchestration, Data Flow Engine, Many-integrated Cores, MIC, GPU, FPGA, DFE.
Related
Ontology
Subjects/Areas/Topics:
Cloud Computing
;
Cloud Computing Architecture
;
Cloud Delivery Models
;
Fundamentals
Abstract:
As clouds increase in size and as machines of different types are added to the infrastructure in order to maximize performance and power efficiency, heterogeneous clouds are being created. However, exploiting different architectures poses significant challenges. To efficiently access heterogeneous resources and, at the same time, to exploit these resources to reduce application development effort, to make optimisations easier and to simplify service deployment, requires a re-evaluation of our approach to service delivery. We propose a novel cloud management and delivery architecture based on the principles of self-organisation and self-management that shifts the deployment and optimisation effort from the consumer to the software stack running on the cloud infrastructure. Our goal is to address inefficient use of resources and consequently to deliver savings to the cloud provider and consumer in terms of reduced power consumption and improved service delivery, with hyperscale systems
particularly in mind. The framework is general but also endeavours to enable cloud services for high performance computing. Infrastructure-as-a-Service provision is the primary use case, however, we posit that genomics, oil and gas exploration, and ray tracing are three downstream use cases that will benefit from the proposed architecture.
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