12,419 research outputs found
Autonomic Cloud Computing: Open Challenges and Architectural Elements
As Clouds are complex, large-scale, and heterogeneous distributed systems,
management of their resources is a challenging task. They need automated and
integrated intelligent strategies for provisioning of resources to offer
services that are secure, reliable, and cost-efficient. Hence, effective
management of services becomes fundamental in software platforms that
constitute the fabric of computing Clouds. In this direction, this paper
identifies open issues in autonomic resource provisioning and presents
innovative management techniques for supporting SaaS applications hosted on
Clouds. We present a conceptual architecture and early results evidencing the
benefits of autonomic management of Clouds.Comment: 8 pages, 6 figures, conference keynote pape
EPOBF: Energy Efficient Allocation of Virtual Machines in High Performance Computing Cloud
Cloud computing has become more popular in provision of computing resources
under virtual machine (VM) abstraction for high performance computing (HPC)
users to run their applications. A HPC cloud is such cloud computing
environment. One of challenges of energy efficient resource allocation for VMs
in HPC cloud is tradeoff between minimizing total energy consumption of
physical machines (PMs) and satisfying Quality of Service (e.g. performance).
On one hand, cloud providers want to maximize their profit by reducing the
power cost (e.g. using the smallest number of running PMs). On the other hand,
cloud customers (users) want highest performance for their applications. In
this paper, we focus on the scenario that scheduler does not know global
information about user jobs and user applications in the future. Users will
request shortterm resources at fixed start times and non interrupted durations.
We then propose a new allocation heuristic (named Energy-aware and Performance
per watt oriented Bestfit (EPOBF)) that uses metric of performance per watt to
choose which most energy-efficient PM for mapping each VM (e.g. maximum of MIPS
per Watt). Using information from Feitelson's Parallel Workload Archive to
model HPC jobs, we compare the proposed EPOBF to state of the art heuristics on
heterogeneous PMs (each PM has multicore CPU). Simulations show that the EPOBF
can reduce significant total energy consumption in comparison with state of the
art allocation heuristics.Comment: 10 pages, in Procedings of International Conference on Advanced
Computing and Applications, Journal of Science and Technology, Vietnamese
Academy of Science and Technology, ISSN 0866-708X, Vol. 51, No. 4B, 201
SAMI: Service-Based Arbitrated Multi-Tier Infrastructure for Mobile Cloud Computing
Mobile Cloud Computing (MCC) is the state-ofthe- art mobile computing
technology aims to alleviate resource poverty of mobile devices. Recently,
several approaches and techniques have been proposed to augment mobile devices
by leveraging cloud computing. However, long-WAN latency and trust are still
two major issues in MCC that hinder its vision. In this paper, we analyze MCC
and discuss its issues. We leverage Service Oriented Architecture (SOA) to
propose an arbitrated multi-tier infrastructure model named SAMI for MCC. Our
architecture consists of three major layers, namely SOA, arbitrator, and
infrastructure. The main strength of this architecture is in its multi-tier
infrastructure layer which leverages infrastructures from three main sources of
Clouds, Mobile Network Operators (MNOs), and MNOs' authorized dealers. On top
of the infrastructure layer, an arbitrator layer is designed to classify
Services and allocate them the suitable resources based on several metrics such
as resource requirement, latency and security. Utilizing SAMI facilitate
development and deployment of service-based platform-neutral mobile
applications.Comment: 6 full pages, accepted for publication in IEEE MobiCC'12 conference,
MobiCC 2012:IEEE Workshop on Mobile Cloud Computing, Beijing, Chin
LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing
LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft
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