14,548 research outputs found
Decentralized Edge-to-Cloud Load-balancing: Service Placement for the Internet of Things
Internet of Things (IoT) requires a new processing paradigm that inherits the
scalability of the cloud while minimizing network latency using resources
closer to the network edge. Building up such flexibility within the
edge-to-cloud continuum consisting of a distributed networked ecosystem of
heterogeneous computing resources is challenging. Load-balancing for fog
computing becomes a cornerstone for cost-effective system management and
operations. This paper studies two optimization objectives and formulates a
decentralized load-balancing problem for IoT service placement: (global) IoT
workload balance and (local) quality of service, in terms of minimizing the
cost of deadline violation, service deployment, and unhosted services. The
proposed solution, EPOS Fog, introduces a decentralized multiagent system for
collective learning that utilizes edge-to-cloud nodes to jointly balance the
input workload across the network and minimize the costs involved in service
execution. The agents locally generate possible assignments of requests to
resources and then cooperatively select an assignment such that their
combination maximizes edge utilization while minimizes service execution cost.
Extensive experimental evaluation with realistic Google cluster workloads on
various networks demonstrates the superior performance of EPOS Fog in terms of
workload balance and quality of service, compared to approaches such as First
Fit and exclusively Cloud-based. The findings demonstrate how distributed
computational resources on the edge can be utilized more cost-effectively by
harvesting collective intelligence.Comment: 16 pages and 15 figure
IoT Stream Processing and Analytics in The Fog
The emerging Fog paradigm has been attracting increasing interests from both
academia and industry, due to the low-latency, resilient, and cost-effective
services it can provide. Many Fog applications such as video mining and event
monitoring, rely on data stream processing and analytics, which are very
popular in the Cloud, but have not been comprehensively investigated in the
context of Fog architecture. In this article, we present the general models and
architecture of Fog data streaming, by analyzing the common properties of
several typical applications. We also analyze the design space of Fog streaming
with the consideration of four essential dimensions (system, data, human, and
optimization), where both new design challenges and the issues arise from
leveraging existing techniques are investigated, such as Cloud stream
processing, computer networks, and mobile computing
Intelligent networking with Mobile Edge Computing: Vision and Challenges for Dynamic Network Scheduling
Mobile edge computing (MEC) has been considered as a promising technique for
internet of things (IoT). By deploying edge servers at the proximity of
devices, it is expected to provide services and process data at a relatively
low delay by intelligent networking. However, the vast edge servers may face
great challenges in terms of cooperation and resource allocation. Furthermore,
intelligent networking requires online implementation in distributed mode. In
such kinds of systems, the network scheduling can not follow any previously
known rule due to complicated application environment. Then statistical
learning rises up as a promising technique for network scheduling, where edges
dynamically learn environmental elements with cooperations. It is expected such
learning based methods may relieve deficiency of model limitations, which
enhance their practical use in dynamic network scheduling. In this paper, we
investigate the vision and challenges of the intelligent IoT networking with
mobile edge computing. From the systematic viewpoint, some major research
opportunities are enumerated with respect to statistical learning
Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge Computing
With the breakthroughs in deep learning, the recent years have witnessed a
booming of artificial intelligence (AI) applications and services, spanning
from personal assistant to recommendation systems to video/audio surveillance.
More recently, with the proliferation of mobile computing and
Internet-of-Things (IoT), billions of mobile and IoT devices are connected to
the Internet, generating zillions Bytes of data at the network edge. Driving by
this trend, there is an urgent need to push the AI frontiers to the network
edge so as to fully unleash the potential of the edge big data. To meet this
demand, edge computing, an emerging paradigm that pushes computing tasks and
services from the network core to the network edge, has been widely recognized
as a promising solution. The resulted new inter-discipline, edge AI or edge
intelligence, is beginning to receive a tremendous amount of interest. However,
research on edge intelligence is still in its infancy stage, and a dedicated
venue for exchanging the recent advances of edge intelligence is highly desired
by both the computer system and artificial intelligence communities. To this
end, we conduct a comprehensive survey of the recent research efforts on edge
intelligence. Specifically, we first review the background and motivation for
artificial intelligence running at the network edge. We then provide an
overview of the overarching architectures, frameworks and emerging key
technologies for deep learning model towards training/inference at the network
edge. Finally, we discuss future research opportunities on edge intelligence.
We believe that this survey will elicit escalating attentions, stimulate
fruitful discussions and inspire further research ideas on edge intelligence.Comment: Zhi Zhou, Xu Chen, En Li, Liekang Zeng, Ke Luo, and Junshan Zhang,
"Edge Intelligence: Paving the Last Mile of Artificial Intelligence with Edge
Computing," Proceedings of the IEE
A Survey on Mobile Edge Networks: Convergence of Computing, Caching and Communications
As the explosive growth of smart devices and the advent of many new
applications, traffic volume has been growing exponentially. The traditional
centralized network architecture cannot accommodate such user demands due to
heavy burden on the backhaul links and long latency. Therefore, new
architectures which bring network functions and contents to the network edge
are proposed, i.e., mobile edge computing and caching. Mobile edge networks
provide cloud computing and caching capabilities at the edge of cellular
networks. In this survey, we make an exhaustive review on the state-of-the-art
research efforts on mobile edge networks. We first give an overview of mobile
edge networks including definition, architecture and advantages. Next, a
comprehensive survey of issues on computing, caching and communication
techniques at the network edge is presented respectively. The applications and
use cases of mobile edge networks are discussed. Subsequently, the key enablers
of mobile edge networks such as cloud technology, SDN/NFV and smart devices are
discussed. Finally, open research challenges and future directions are
presented as well
A Survey on Low Latency Towards 5G: RAN, Core Network and Caching Solutions
The fifth generation (5G) wireless network technology is to be standardized
by 2020, where main goals are to improve capacity, reliability, and energy
efficiency, while reducing latency and massively increasing connection density.
An integral part of 5G is the capability to transmit touch perception type
real-time communication empowered by applicable robotics and haptics equipment
at the network edge. In this regard, we need drastic changes in network
architecture including core and radio access network (RAN) for achieving
end-to-end latency on the order of 1 ms. In this paper, we present a detailed
survey on the emerging technologies to achieve low latency communications
considering three different solution domains: RAN, core network, and caching.
We also present a general overview of 5G cellular networks composed of software
defined network (SDN), network function virtualization (NFV), caching, and
mobile edge computing (MEC) capable of meeting latency and other 5G
requirements.Comment: Accepted in IEEE Communications Surveys and Tutorial
Portfolio-driven Resource Management for Transient Cloud Servers
Cloud providers have begun to offer their surplus capacity in the form of
low-cost transient servers, which can be revoked unilaterally at any time.
While the low cost of transient servers makes them attractive for a wide range
of applications, such as data processing and scientific computing, failures due
to server revocation can severely degrade application performance. Since
different transient server types offer different cost and availability
tradeoffs, we present the notion of server portfolios that is based on
financial portfolio modeling. Server portfolios enable construction of an
"optimal" mix of severs to meet an application's sensitivity to cost and
revocation risk. We implement model-driven portfolios in a system called
ExoSphere, and show how diverse applications can use portfolios and
application-specific policies to gracefully handle transient servers. We show
that ExoSphere enables widely-used parallel applications such as Spark, MPI,
and BOINC to be made transiency-aware with modest effort. Our experiments show
that allowing the applications to use suitable transiency-aware policies,
ExoSphere is able to achieve 80\% cost savings when compared to on-demand
servers and greatly reduces revocation risk compared to existing approaches
When Social Sensing Meets Edge Computing: Vision and Challenges
This paper overviews the state of the art, research challenges, and future
opportunities in an emerging research direction: Social Sensing based Edge
Computing (SSEC). Social sensing has emerged as a new sensing application
paradigm where measurements about the physical world are collected from humans
or from devices on their behalf. The advent of edge computing pushes the
frontier of computation, service, and data along the cloud-to-things continuum.
The merging of these two technical trends generates a set of new research
challenges that need to be addressed. In this paper, we first define the new
SSEC paradigm that is motivated by a few underlying technology trends. We then
present a few representative real-world case studies of SSEC applications and
several key research challenges that exist in those applications. Finally, we
envision a few exciting research directions in future SSEC. We hope this paper
will stimulate discussions of this emerging research direction in the
community.Comment: This manuscript has been accepted to ICCCN 201
A Survey on Geographically Distributed Big-Data Processing using MapReduce
Hadoop and Spark are widely used distributed processing frameworks for
large-scale data processing in an efficient and fault-tolerant manner on
private or public clouds. These big-data processing systems are extensively
used by many industries, e.g., Google, Facebook, and Amazon, for solving a
large class of problems, e.g., search, clustering, log analysis, different
types of join operations, matrix multiplication, pattern matching, and social
network analysis. However, all these popular systems have a major drawback in
terms of locally distributed computations, which prevent them in implementing
geographically distributed data processing. The increasing amount of
geographically distributed massive data is pushing industries and academia to
rethink the current big-data processing systems. The novel frameworks, which
will be beyond state-of-the-art architectures and technologies involved in the
current system, are expected to process geographically distributed data at
their locations without moving entire raw datasets to a single location. In
this paper, we investigate and discuss challenges and requirements in designing
geographically distributed data processing frameworks and protocols. We
classify and study batch processing (MapReduce-based systems), stream
processing (Spark-based systems), and SQL-style processing geo-distributed
frameworks, models, and algorithms with their overhead issues.Comment: IEEE Transactions on Big Data; Accepted June 2017. 20 page
Incremental Variational Inference for Latent Dirichlet Allocation
We introduce incremental variational inference and apply it to latent
Dirichlet allocation (LDA). Incremental variational inference is inspired by
incremental EM and provides an alternative to stochastic variational inference.
Incremental LDA can process massive document collections, does not require to
set a learning rate, converges faster to a local optimum of the variational
bound and enjoys the attractive property of monotonically increasing it. We
study the performance of incremental LDA on large benchmark data sets. We
further introduce a stochastic approximation of incremental variational
inference which extends to the asynchronous distributed setting. The resulting
distributed algorithm achieves comparable performance as single host
incremental variational inference, but with a significant speed-up
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