23,866 research outputs found
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
A Self-adaptive Agent-based System for Cloud Platforms
Cloud computing is a model for enabling on-demand network access to a shared
pool of computing resources, that can be dynamically allocated and released
with minimal effort. However, this task can be complex in highly dynamic
environments with various resources to allocate for an increasing number of
different users requirements. In this work, we propose a Cloud architecture
based on a multi-agent system exhibiting a self-adaptive behavior to address
the dynamic resource allocation. This self-adaptive system follows a MAPE-K
approach to reason and act, according to QoS, Cloud service information, and
propagated run-time information, to detect QoS degradation and make better
resource allocation decisions. We validate our proposed Cloud architecture by
simulation. Results show that it can properly allocate resources to reduce
energy consumption, while satisfying the users demanded QoS
A Scalable Low-Cost-UAV Traffic Network (uNet)
This article proposes a new Unmanned Aerial Vehicle (UAV) operation paradigm
to enable a large number of relatively low-cost UAVs to fly
beyond-line-of-sight without costly sensing and communication systems or
substantial human intervention in individual UAV control. Under current
free-flight-like paradigm, wherein a UAV can travel along any route as long as
it avoids restricted airspace and altitudes. However, this requires expensive
on-board sensing and communication as well as substantial human effort in order
to ensure avoidance of obstacles and collisions. The increased cost serves as
an impediment to the emergence and development of broader UAV applications. The
main contribution of this work is to propose the use of pre-established route
network for UAV traffic management, which allows: (i) pre- mapping of obstacles
along the route network to reduce the onboard sensing requirements and the
associated costs for avoiding such obstacles; and (ii) use of well-developed
routing algorithms to select UAV schedules that avoid conflicts. Available
GPS-based navigation can be used to fly the UAV along the selected route and
time schedule with relatively low added cost, which therefore, reduces the
barrier to entry into new UAV-applications market. Finally, this article
proposes a new decoupling scheme for conflict-free transitions between edges of
the route network at each node of the route network to reduce potential
conflicts between UAVs and ensuing delays. A simulation example is used to
illustrate the proposed uNet approach.Comment: To be submitted to journal, 21 pages, 9 figure
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