1,689 research outputs found
Latency-aware joint virtual machine and policy consolidation for mobile edge computing
To guarantee an efficient and high-performance environment for mobile devices to perform offloading with low end-to-end delay, it is important to ensure no network policies are violated. In this paper, we explore the simultaneous, dynamic virtual machine (VM) and policy consolidation, and formulate the Policy-VM Latency-aware Consolidation problem for Mobile Edge Computing, which is shown to be NP-Hard. We propose the PL-Edge, an efficient scheme to jointly consolidate network policies and virtual machines for mobile edge computing to reduce communication end-to-end delays among devices and virtual machines. Our simulation results demonstrate that the proposed PL-Edge can significantly reduces policy-flows end-to-end delay by nearly 45% while adhering strictly to the requirements of network policies
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
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
Offline and online power aware resource allocation algorithms with migration and delay constraints
© . This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/In order to handle advanced mobile broadband services and Internet of Things (IoT), future Internet and 5G networks are expected to leverage the use of network virtualization, be much faster, have greater capacities, provide lower latencies, and significantly be power efficient than current mobile technologies. Therefore, this paper proposes three power aware algorithms for offline, online, and migration applications, solving the resource allocation problem within the frameworks of network function virtualization (NFV) environments in fractions of a second. The proposed algorithms target minimizing the total costs and power consumptions in the physical network through sufficiently allocating the least physical resources to host the demands of the virtual network services, and put into saving mode all other not utilized physical components. Simulations and evaluations of the offline algorithm compared to the state-of-art resulted on lower total costs by 32%. In addition to that, the online algorithm was tested through four different experiments, and the results argued that the overall power consumption of the physical network was highly dependent on the demands’ lifetimes, and the strictness of the required end-to-end delay. Regarding migrations during online, the results concluded that the proposed algorithms would be most effective when applied for maintenance and emergency conditions.Peer ReviewedPreprin
Enabling Scalable and Sustainable Softwarized 5G Environments
The fifth generation of telecommunication systems (5G) is foreseen to play a fundamental
role in our socio-economic growth by supporting various and radically new vertical
applications (such as Industry 4.0, eHealth, Smart Cities/Electrical Grids, to name
a few), as a one-fits-all technology that is enabled by emerging softwarization solutions
\u2013 specifically, the Fog, Multi-access Edge Computing (MEC), Network Functions Virtualization
(NFV) and Software-Defined Networking (SDN) paradigms. Notwithstanding
the notable potential of the aforementioned technologies, a number of open issues
still need to be addressed to ensure their complete rollout. This thesis is particularly developed
towards addressing the scalability and sustainability issues in softwarized 5G
environments through contributions in three research axes: a) Infrastructure Modeling
and Analytics, b) Network Slicing and Mobility Management, and c) Network/Services Management
and Control. The main contributions include a model-based analytics approach
for real-time workload profiling and estimation of network key performance indicators
(KPIs) in NFV infrastructures (NFVIs), as well as a SDN-based multi-clustering approach
to scale geo-distributed virtual tenant networks (VTNs) and to support seamless
user/service mobility; building on these, solutions to the problems of resource consolidation,
service migration, and load balancing are also developed in the context of 5G.
All in all, this generally entails the adoption of Stochastic Models, Mathematical Programming,
Queueing Theory, Graph Theory and Team Theory principles, in the context
of Green Networking, NFV and SDN
Resource management in a containerized cloud : status and challenges
Cloud computing heavily relies on virtualization, as with cloud computing virtual resources are typically leased to the consumer, for example as virtual machines. Efficient management of these virtual resources is of great importance, as it has a direct impact on both the scalability and the operational costs of the cloud environment. Recently, containers are gaining popularity as virtualization technology, due to the minimal overhead compared to traditional virtual machines and the offered portability. Traditional resource management strategies however are typically designed for the allocation and migration of virtual machines, so the question arises how these strategies can be adapted for the management of a containerized cloud. Apart from this, the cloud is also no longer limited to the centrally hosted data center infrastructure. New deployment models have gained maturity, such as fog and mobile edge computing, bringing the cloud closer to the end user. These models could also benefit from container technology, as the newly introduced devices often have limited hardware resources. In this survey, we provide an overview of the current state of the art regarding resource management within the broad sense of cloud computing, complementary to existing surveys in literature. We investigate how research is adapting to the recent evolutions within the cloud, being the adoption of container technology and the introduction of the fog computing conceptual model. Furthermore, we identify several challenges and possible opportunities for future research
- …