46,675 research outputs found
The Network Effects of Prefetching
Prefetching has been shown to be an effective technique for reducing user perceived latency in distributed systems. In this paper we show that even when prefetching adds no extra traffic to the network, it can have serious negative performance effects. Straightforward approaches to prefetching increase the burstiness of individual sources, leading to increased average queue sizes in network switches. However, we also show that applications can avoid the undesirable queueing effects of prefetching. In fact, we show that applications employing prefetching can significantly improve network performance, to a level much better than that obtained without any prefetching at all. This is because prefetching offers increased opportunities for traffic shaping that are not available in the absence of prefetching. Using a simple transport rate control mechanism, a prefetching application can modify its behavior from a distinctly ON/OFF entity to one whose data transfer rate changes less abruptly, while still delivering all data in advance of the user's actual requests
PRETZEL: Opening the Black Box of Machine Learning Prediction Serving Systems
Machine Learning models are often composed of pipelines of transformations.
While this design allows to efficiently execute single model components at
training time, prediction serving has different requirements such as low
latency, high throughput and graceful performance degradation under heavy load.
Current prediction serving systems consider models as black boxes, whereby
prediction-time-specific optimizations are ignored in favor of ease of
deployment. In this paper, we present PRETZEL, a prediction serving system
introducing a novel white box architecture enabling both end-to-end and
multi-model optimizations. Using production-like model pipelines, our
experiments show that PRETZEL is able to introduce performance improvements
over different dimensions; compared to state-of-the-art approaches PRETZEL is
on average able to reduce 99th percentile latency by 5.5x while reducing memory
footprint by 25x, and increasing throughput by 4.7x.Comment: 16 pages, 14 figures, 13th USENIX Symposium on Operating Systems
Design and Implementation (OSDI), 201
Reputation Agent: Prompting Fair Reviews in Gig Markets
Our study presents a new tool, Reputation Agent, to promote fairer reviews
from requesters (employers or customers) on gig markets. Unfair reviews,
created when requesters consider factors outside of a worker's control, are
known to plague gig workers and can result in lost job opportunities and even
termination from the marketplace. Our tool leverages machine learning to
implement an intelligent interface that: (1) uses deep learning to
automatically detect when an individual has included unfair factors into her
review (factors outside the worker's control per the policies of the market);
and (2) prompts the individual to reconsider her review if she has incorporated
unfair factors. To study the effectiveness of Reputation Agent, we conducted a
controlled experiment over different gig markets. Our experiment illustrates
that across markets, Reputation Agent, in contrast with traditional approaches,
motivates requesters to review gig workers' performance more fairly. We discuss
how tools that bring more transparency to employers about the policies of a gig
market can help build empathy thus resulting in reasoned discussions around
potential injustices towards workers generated by these interfaces. Our vision
is that with tools that promote truth and transparency we can bring fairer
treatment to gig workers.Comment: 12 pages, 5 figures, The Web Conference 2020, ACM WWW 202
Arm Mbed – AWS IoT System Integration [Open access]
This project explores the different Internet of Things (IoT) architectures and the available platforms
to define a general IoT Architecture to connect Arm microcontrollers to Amazon Web Services. In
order to accommodate the wide range of IoT applications, the architecture was defined with different
routes that an Arm microcontroller can take to reach AWS. Once this Architecture was defined, a
performance analysis on the different routes was performed in terms of communication speed and
bandwidth. Finally, a Smart Home use case scenario is implemented to show the basic functionalities
of an IoT system such as sending data to the device and data storage in the Cloud. Furthermore, a
Cloud ML algorithm is triggered in real time by the Smart Home to receive a prediction of the current
Comfort Level in the room
Self-* overload control for distributed web systems
Unexpected increases in demand and most of all flash crowds are considered
the bane of every web application as they may cause intolerable delays or even
service unavailability. Proper quality of service policies must guarantee rapid
reactivity and responsiveness even in such critical situations. Previous
solutions fail to meet common performance requirements when the system has to
face sudden and unpredictable surges of traffic. Indeed they often rely on a
proper setting of key parameters which requires laborious manual tuning,
preventing a fast adaptation of the control policies. We contribute an original
Self-* Overload Control (SOC) policy. This allows the system to self-configure
a dynamic constraint on the rate of admitted sessions in order to respect
service level agreements and maximize the resource utilization at the same
time. Our policy does not require any prior information on the incoming traffic
or manual configuration of key parameters. We ran extensive simulations under a
wide range of operating conditions, showing that SOC rapidly adapts to time
varying traffic and self-optimizes the resource utilization. It admits as many
new sessions as possible in observance of the agreements, even under intense
workload variations. We compared our algorithm to previously proposed
approaches highlighting a more stable behavior and a better performance.Comment: The full version of this paper, titled "Self-* through self-learning:
overload control for distributed web systems", has been published on Computer
Networks, Elsevier. The simulator used for the evaluation of the proposed
algorithm is available for download at the address:
http://www.dsi.uniroma1.it/~novella/qos_web
- …