81 research outputs found
Quantifying the latency benefits of near-edge and in-network FPGA acceleration
Transmitting data to cloud datacenters in distributed IoT applications introduces significant communication latency, but is often the only feasible solution when source nodes are computationally limited. To address latency concerns, cloudlets, in-network computing, and more capable edge nodes are all being explored as a way of moving processing capability towards the edge of the network. Hardware acceleration using Field Programmable Gate Arrays (FPGAs) is also seeing increased interest due to reduced computation latency and improved efficiency. This paper evaluates the the implications of these offloading approaches using a case study neural network based image classification application, quantifying both the computation and communication latency resulting from different platform choices. We consider communication latency including the ingestion of packets for processing on the target platform, showing that this varies significantly with the choice of platform. We demonstrate that emerging in-network accelerator approaches offer much improved and predictable performance as well as better scaling to support multiple data sources
Mobility-Induced Service Migration in Mobile Micro-Clouds
Mobile micro-cloud is an emerging technology in distributed computing, which
is aimed at providing seamless computing/data access to the edge of the network
when a centralized service may suffer from poor connectivity and long latency.
Different from the traditional cloud, a mobile micro-cloud is smaller and
deployed closer to users, typically attached to a cellular basestation or
wireless network access point. Due to the relatively small coverage area of
each basestation or access point, when a user moves across areas covered by
different basestations or access points which are attached to different
micro-clouds, issues of service performance and service migration become
important. In this paper, we consider such migration issues. We model the
general problem as a Markov decision process (MDP), and show that, in the
special case where the mobile user follows a one-dimensional asymmetric random
walk mobility model, the optimal policy for service migration is a threshold
policy. We obtain the analytical solution for the cost resulting from arbitrary
thresholds, and then propose an algorithm for finding the optimal thresholds.
The proposed algorithm is more efficient than standard mechanisms for solving
MDPs.Comment: in Proc. of IEEE MILCOM 2014, Oct. 201
EYECOM: an innovative approach for computer interaction
The world is innovating rapidly, and there is a need for continuous interaction with the technology. Sadly, there do not exist promising options for paralyzed people to interact with the machines i.e., laptops, smartphones, and tabs. A few commercial solutions such as Google Glasses are costly and cannot be afforded by every paralyzed person for such interaction. Towards this end, the thesis proposes a retina-controlled device called EYECOM. The proposed device is constructed from off-the-shelf cost-effective yet robust IoT devices (i.e., Arduino microcontrollers, Xbee wireless sensors, IR diodes, and accelerometer). The device can easily be mounted on to the glasses; the paralyzed person using this device can interact with the machine using simple head movement and eye blinks. The IR detector is located in front of the eye to illuminate the eye region. As a result of illumination, the eye reflects IR light which includes electrical signals and as the eyelids close, the reflected light over eye surface is disrupted, and such change in reflected value is recorded. Further to enable cursor movement onto the computer screen for the paralyzed person a device named accelerometer is used. The accelerometer is a small device, with the size of phalanges, a human thumb bone. The device operates on the principle of axis-based motion sensing and it can be worn as a ring by a paralyzed person. A microcontroller processes the inputs from the IR sensors, accelerometer and transmits them wirelessly via Xbee wireless sensor (i.e., a radio) to another microcontroller attached to the computer. With the help of a proposed algorithm, the microcontroller attached to the computer, on receiving the signals moves cursor onto the computer screen and facilitate performing actions, as simple as opening a document to operating a word-to-speech software. EYECOM has features which can help paralyzed persons to continue their contributions towards the technological world and become an active part of the society. Resultantly, they will be able to perform number of tasks without depending upon others from as simple as reading a newspaper on the computer to activate word-to-voice software
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
JALAD: Joint Accuracy- and Latency-Aware Deep Structure Decoupling for Edge-Cloud Execution
Recent years have witnessed a rapid growth of deep-network based services and
applications. A practical and critical problem thus has emerged: how to
effectively deploy the deep neural network models such that they can be
executed efficiently. Conventional cloud-based approaches usually run the deep
models in data center servers, causing large latency because a significant
amount of data has to be transferred from the edge of network to the data
center. In this paper, we propose JALAD, a joint accuracy- and latency-aware
execution framework, which decouples a deep neural network so that a part of it
will run at edge devices and the other part inside the conventional cloud,
while only a minimum amount of data has to be transferred between them. Though
the idea seems straightforward, we are facing challenges including i) how to
find the best partition of a deep structure; ii) how to deploy the component at
an edge device that only has limited computation power; and iii) how to
minimize the overall execution latency. Our answers to these questions are a
set of strategies in JALAD, including 1) A normalization based in-layer data
compression strategy by jointly considering compression rate and model
accuracy; 2) A latency-aware deep decoupling strategy to minimize the overall
execution latency; and 3) An edge-cloud structure adaptation strategy that
dynamically changes the decoupling for different network conditions.
Experiments demonstrate that our solution can significantly reduce the
execution latency: it speeds up the overall inference execution with a
guaranteed model accuracy loss.Comment: conference, copyright transfered to IEE
A Game-theoretic Framework for Revenue Sharing in Edge-Cloud Computing System
We introduce a game-theoretic framework to ex- plore revenue sharing in an
Edge-Cloud computing system, in which computing service providers at the edge
of the Internet (edge providers) and computing service providers at the cloud
(cloud providers) co-exist and collectively provide computing resources to
clients (e.g., end users or applications) at the edge. Different from
traditional cloud computing, the providers in an Edge-Cloud system are
independent and self-interested. To achieve high system-level efficiency, the
manager of the system adopts a task distribution mechanism to maximize the
total revenue received from clients and also adopts a revenue sharing mechanism
to split the received revenue among computing servers (and hence service
providers). Under those system-level mechanisms, service providers attempt to
game with the system in order to maximize their own utilities, by strategically
allocating their resources (e.g., computing servers).
Our framework models the competition among the providers in an Edge-Cloud
system as a non-cooperative game. Our simulations and experiments on an
emulation system have shown the existence of Nash equilibrium in such a game.
We find that revenue sharing mechanisms have a significant impact on the
system-level efficiency at Nash equilibria, and surprisingly the revenue
sharing mechanism based directly on actual contributions can result in
significantly worse system efficiency than Shapley value sharing mechanism and
Ortmann proportional sharing mechanism. Our framework provides an effective
economics approach to understanding and designing efficient Edge-Cloud
computing systems
A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification
The increasing need for small and low-power Deep Neural Networks (DNNs) for edge computing applications involves the investigation of new architectures that allow good performance on low-resources/mobile devices. To this aim, many different structures have been proposed in the literature, mainly targeting the reduction in the costs introduced by the Multiply and Accumulate (MAC) primitive. In this work, a DNN layer based on the novel Sum and Max (SAM) paradigm is proposed. It does not require either the use of multiplications or the insertion of complex non-linear operations. Furthermore, it is especially prone to aggressive pruning, thus needing a very low number of parameters to work. The layer is tested on a simple classification task and its cost is compared with a classic DNN layer with equivalent accuracy based on the MAC primitive, in order to assess the reduction of resources that the use of this new structure could introduce
ExPECA: An Experimental Platform for Trustworthy Edge Computing Applications
This paper presents ExPECA, an edge computing and wireless communication
research testbed designed to tackle two pressing challenges: comprehensive
end-to-end experimentation and high levels of experimental reproducibility.
Leveraging OpenStack-based Chameleon Infrastructure (CHI) framework for its
proven flexibility and ease of operation, ExPECA is located in a unique,
isolated underground facility, providing a highly controlled setting for
wireless experiments. The testbed is engineered to facilitate integrated
studies of both communication and computation, offering a diverse array of
Software-Defined Radios (SDR) and Commercial Off-The-Shelf (COTS) wireless and
wired links, as well as containerized computational environments. We exemplify
the experimental possibilities of the testbed using OpenRTiST, a
latency-sensitive, bandwidth-intensive application, and analyze its
performance. Lastly, we highlight an array of research domains and experimental
setups that stand to gain from ExPECA's features, including closed-loop
applications and time-sensitive networking
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