13,086 research outputs found
A Taxonomy for Management and Optimization of Multiple Resources in Edge Computing
Edge computing is promoted to meet increasing performance needs of
data-driven services using computational and storage resources close to the end
devices, at the edge of the current network. To achieve higher performance in
this new paradigm one has to consider how to combine the efficiency of resource
usage at all three layers of architecture: end devices, edge devices, and the
cloud. While cloud capacity is elastically extendable, end devices and edge
devices are to various degrees resource-constrained. Hence, an efficient
resource management is essential to make edge computing a reality. In this
work, we first present terminology and architectures to characterize current
works within the field of edge computing. Then, we review a wide range of
recent articles and categorize relevant aspects in terms of 4 perspectives:
resource type, resource management objective, resource location, and resource
use. This taxonomy and the ensuing analysis is used to identify some gaps in
the existing research. Among several research gaps, we found that research is
less prevalent on data, storage, and energy as a resource, and less extensive
towards the estimation, discovery and sharing objectives. As for resource
types, the most well-studied resources are computation and communication
resources. Our analysis shows that resource management at the edge requires a
deeper understanding of how methods applied at different levels and geared
towards different resource types interact. Specifically, the impact of mobility
and collaboration schemes requiring incentives are expected to be different in
edge architectures compared to the classic cloud solutions. Finally, we find
that fewer works are dedicated to the study of non-functional properties or to
quantifying the footprint of resource management techniques, including
edge-specific means of migrating data and services.Comment: Accepted in the Special Issue Mobile Edge Computing of the Wireless
Communications and Mobile Computing journa
Disaggregating non-volatile memory for throughput-oriented genomics workloads
Massive exploitation of next-generation sequencing technologies requires dealing with both: huge amounts of data and complex bioinformatics pipelines. Computing architectures have evolved to deal with these problems, enabling approaches that were unfeasible years ago: accelerators and Non-Volatile Memories (NVM) are becoming widely used to enhance the most demanding workloads. However, bioinformatics workloads are usually part of bigger pipelines with different and dynamic needs in terms of resources. The introduction of Software Defined Infrastructures (SDI) for data centers provides roots to dramatically increase the efficiency in the management of infrastructures. SDI enables new ways to structure hardware resources through disaggregation, and provides new hardware composability and sharing mechanisms to deploy workloads in more flexible ways. In this paper we study a state-of-the-art genomics application, SMUFIN, aiming to address the challenges of future HPC facilities.This work is partially supported by the European Research Council (ERC) under the EU Horizon 2020 programme (GA 639595), the Spanish Ministry of Economy, Industry and Competitivity (TIN2015-65316-P) and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft
Software-Defined Cloud Computing: Architectural Elements and Open Challenges
The variety of existing cloud services creates a challenge for service
providers to enforce reasonable Software Level Agreements (SLA) stating the
Quality of Service (QoS) and penalties in case QoS is not achieved. To avoid
such penalties at the same time that the infrastructure operates with minimum
energy and resource wastage, constant monitoring and adaptation of the
infrastructure is needed. We refer to Software-Defined Cloud Computing, or
simply Software-Defined Clouds (SDC), as an approach for automating the process
of optimal cloud configuration by extending virtualization concept to all
resources in a data center. An SDC enables easy reconfiguration and adaptation
of physical resources in a cloud infrastructure, to better accommodate the
demand on QoS through a software that can describe and manage various aspects
comprising the cloud environment. In this paper, we present an architecture for
SDCs on data centers with emphasis on mobile cloud applications. We present an
evaluation, showcasing the potential of SDC in two use cases-QoS-aware
bandwidth allocation and bandwidth-aware, energy-efficient VM placement-and
discuss the research challenges and opportunities in this emerging area.Comment: Keynote Paper, 3rd International Conference on Advances in Computing,
Communications and Informatics (ICACCI 2014), September 24-27, 2014, Delhi,
Indi
Mobile-Based Video Caching Architecture Based on Billboard Manager
Video streaming services are very popular today. Increasingly, users can now
access multimedia applications and video playback wirelessly on their mobile
devices. However, a significant challenge remains in ensuring smooth and
uninterrupted transmission of almost any size of video file over a 3G network,
and as quickly as possible in order to optimize bandwidth consumption. In this
paper, we propose to position our Billboard Manager to provide an optimal
transmission rate to enable smooth video playback to a mobile device user
connected to a 3G network. Our work focuses on serving user requests by mobile
operators from cached resource managed by Billboard Manager, and transmitting
the video files from this pool. The aim is to reduce the load placed on
bandwidth resources of a mobile operator by routing away as much user requests
away from the internet for having to search a video and, subsequently, if
located, have it transferred back to the user.Comment: 8 pages, 1 figure, GridCom-201
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