1,453 research outputs found
Real-Time Containers: A Survey
Container-based virtualization has gained a significant importance in a deployment of software applications in cloud-based environments. The technology fully relies on operating system features and does not require a virtualization layer (hypervisor) that introduces a performance degradation. Container-based virtualization allows to co-locate multiple isolated containers on a single computation node as well as to decompose an application into multiple containers distributed among several hosts (e.g., in fog computing layer). Such a technology seems very promising in other domains as well, e.g., in industrial automation, automotive, and aviation industry where mixed criticality containerized applications from various vendors can be co-located on shared resources.
However, such industrial domains often require real-time behavior (i.e, a capability to meet predefined deadlines). These capabilities are not fully supported by the container-based virtualization yet. In this work, we provide a systematic literature survey study that summarizes the effort of the research community on bringing real-time properties in container-based virtualization. We categorize existing work into main research areas and identify possible immature points of the technology
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
Understanding the Computational Requirements of Virtualized Baseband Units using a Programmable Cloud Radio Access Network Testbed
Cloud Radio Access Network (C-RAN) is emerging as a transformative
architecture for the next generation of mobile cellular networks. In C-RAN, the
Baseband Unit (BBU) is decoupled from the Base Station (BS) and consolidated in
a centralized processing center. While the potential benefits of C-RAN have
been studied extensively from the theoretical perspective, there are only a few
works that address the system implementation issues and characterize the
computational requirements of the virtualized BBU. In this paper, a
programmable C-RAN testbed is presented where the BBU is virtualized using the
OpenAirInterface (OAI) software platform, and the eNodeB and User Equipment
(UEs) are implemented using USRP boards. Extensive experiments have been
performed in a FDD downlink LTE emulation system to characterize the
performance and computing resource consumption of the BBU under various
conditions. It is shown that the processing time and CPU utilization of the BBU
increase with the channel resources and with the Modulation and Coding Scheme
(MCS) index, and that the CPU utilization percentage can be well approximated
as a linear increasing function of the maximum downlink data rate. These
results provide real-world insights into the characteristics of the BBU in
terms of computing resource and power consumption, which may serve as inputs
for the design of efficient resource-provisioning and allocation strategies in
C-RAN systems.Comment: In Proceedings of the IEEE International Conference on Autonomic
Computing (ICAC), July 201
Edge Computing for Extreme Reliability and Scalability
The massive number of Internet of Things (IoT) devices and their continuous data collection will lead to a rapid increase in the scale of collected data. Processing all these collected data at the central cloud server is inefficient, and even is unfeasible or unnecessary. Hence, the task of processing the data is pushed to the network edges introducing the concept of Edge Computing. Processing the information closer to the source of data (e.g., on gateways and on edge micro-servers) not only reduces the huge workload of central cloud, also decreases the latency for real-time applications by avoiding the unreliable and unpredictable network latency to communicate with the central cloud
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