12,078 research outputs found
Communication-efficient Distributed Multi-resource Allocation
In several smart city applications, multiple resources must be allocated
among competing agents that are coupled through such shared resources and are
constrained --- either through limitations of communication infrastructure or
privacy considerations. We propose a distributed algorithm to solve such
distributed multi-resource allocation problems with no direct inter-agent
communication. We do so by extending a recently introduced additive-increase
multiplicative-decrease (AIMD) algorithm, which only uses very little
communication between the system and agents. Namely, a control unit broadcasts
a one-bit signal to agents whenever one of the allocated resources exceeds
capacity. Agents then respond to this signal in a probabilistic manner. In the
proposed algorithm, each agent makes decision of its resource demand locally
and an agent is unaware of the resource allocation of other agents. In
empirical results, we observe that the average allocations converge over time
to optimal allocations.Comment: To appear in IEEE International Smart Cities Conference (ISC2 2018),
Kansas City, USA, September, 2018. arXiv admin note: substantial text overlap
with arXiv:1711.0197
Derandomized Distributed Multi-resource Allocation with Little Communication Overhead
We study a class of distributed optimization problems for multiple shared
resource allocation in Internet-connected devices. We propose a derandomized
version of an existing stochastic additive-increase and multiplicative-decrease
(AIMD) algorithm. The proposed solution uses one bit feedback signal for each
resource between the system and the Internet-connected devices and does not
require inter-device communication. Additionally, the Internet-connected
devices do not compromise their privacy and the solution does not dependent on
the number of participating devices. In the system, each Internet-connected
device has private cost functions which are strictly convex, twice continuously
differentiable and increasing. We show empirically that the long-term average
allocations of multiple shared resources converge to optimal allocations and
the system achieves minimum social cost. Furthermore, we show that the proposed
derandomized AIMD algorithm converges faster than the stochastic AIMD algorithm
and both the approaches provide approximately same solutions
A Study to Optimize Heterogeneous Resources for Open IoT
Recently, IoT technologies have been progressed, and many sensors and
actuators are connected to networks. Previously, IoT services were developed by
vertical integration style. But now Open IoT concept has attracted attentions
which achieves various IoT services by integrating horizontal separated devices
and services. For Open IoT era, we have proposed the Tacit Computing technology
to discover the devices with necessary data for users on demand and use them
dynamically. We also implemented elemental technologies of Tacit Computing. In
this paper, we propose three layers optimizations to reduce operation cost and
improve performance of Tacit computing service, in order to make as a
continuous service of discovered devices by Tacit Computing. In optimization
process, appropriate function allocation or offloading specific functions are
calculated on device, network and cloud layer before full-scale operation.Comment: 3 pages, 1 figure, 2017 Fifth International Symposium on Computing
and Networking (CANDAR2017), Nov. 201
In-Network View Synthesis for Interactive Multiview Video Systems
To enable Interactive multiview video systems with a minimum view-switching
delay, multiple camera views are sent to the users, which are used as reference
images to synthesize additional virtual views via depth-image-based rendering.
In practice, bandwidth constraints may however restrict the number of reference
views sent to clients per time unit, which may in turn limit the quality of the
synthesized viewpoints. We argue that the reference view selection should
ideally be performed close to the users, and we study the problem of in-network
reference view synthesis such that the navigation quality is maximized at the
clients. We consider a distributed cloud network architecture where data stored
in a main cloud is delivered to end users with the help of cloudlets, i.e.,
resource-rich proxies close to the users. In order to satisfy last-hop
bandwidth constraints from the cloudlet to the users, a cloudlet re-samples
viewpoints of the 3D scene into a discrete set of views (combination of
received camera views and virtual views synthesized) to be used as reference
for the synthesis of additional virtual views at the client. This in-network
synthesis leads to better viewpoint sampling given a bandwidth constraint
compared to simple selection of camera views, but it may however carry a
distortion penalty in the cloudlet-synthesized reference views. We therefore
cast a new reference view selection problem where the best subset of views is
defined as the one minimizing the distortion over a view navigation window
defined by the user under some transmission bandwidth constraints. We show that
the view selection problem is NP-hard, and propose an effective polynomial time
algorithm using dynamic programming to solve the optimization problem.
Simulation results finally confirm the performance gain offered by virtual view
synthesis in the network
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
Towards delay-aware container-based Service Function Chaining in Fog Computing
Recently, the fifth-generation mobile network (5G) is getting significant attention. Empowered by Network Function Virtualization (NFV), 5G networks aim to support diverse services coming from different business verticals (e.g. Smart Cities, Automotive, etc). To fully leverage on NFV, services must be connected in a specific order forming a Service Function Chain (SFC). SFCs allow mobile operators to benefit from the high flexibility and low operational costs introduced by network softwarization. Additionally, Cloud computing is evolving towards a distributed paradigm called Fog Computing, which aims to provide a distributed cloud infrastructure by placing computational resources close to end-users. However, most SFC research only focuses on Multi-access Edge Computing (MEC) use cases where mobile operators aim to deploy services close to end-users. Bi-directional communication between Edges and Cloud are not considered in MEC, which in contrast is highly important in a Fog environment as in distributed anomaly detection services. Therefore, in this paper, we propose an SFC controller to optimize the placement of service chains in Fog environments, specifically tailored for Smart City use cases. Our approach has been validated on the Kubernetes platform, an open-source orchestrator for the automatic deployment of micro-services. Our SFC controller has been implemented as an extension to the scheduling features available in Kubernetes, enabling the efficient provisioning of container-based SFCs while optimizing resource allocation and reducing the end-to-end (E2E) latency. Results show that the proposed approach can lower the network latency up to 18% for the studied use case while conserving bandwidth when compared to the default scheduling mechanism
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