19 research outputs found
Can Orbital Servers Provide Mars-Wide Edge Computing?
Human landing, exploration and settlement on Mars will require local compute
resources at the Mars edge. Landing such resources on Mars is an expensive
endeavor. Instead, in this paper we lay out how concepts from low-Earth orbit
edge computing may be applied to Mars edge computing. This could lower
launching costs of compute resources for Mars while also providing Mars-wide
networking and compute coverage. We propose a possible Mars compute
constellation, discuss applications, analyze feasibility, and raise research
questions for future work.Comment: 1st ACM MobiCom Workshop on Satellite Networking and Computing
(SatCom '23
Edge Computing in Low-Earth Orbit -- What Could Possibly Go Wrong?
Large low-Earth orbit (LEO) satellite networks are being built to provide
low-latency broadband Internet access to a global subscriber base. In addition
to network transmissions, researchers have proposed embedding compute resources
in satellites to support LEO edge computing. To make software systems ready for
the LEO edge, they need to be adapted for its unique execution environment,
e.g., to support handovers in face of satellite mobility.
So far, research around LEO edge software systems has focused on the
predictable behavior of satellite networks, such as orbital movements.
Additionally, we must also consider failure patterns, e.g., effects of
radiation on compute hardware in space. In this paper, we present a taxonomy of
failures that may occur in LEO edge computing and how they could affect
software systems. From there, we derive considerations for LEO edge software
systems and lay out avenues for future work.Comment: 1st Workshop on Low Earth Orbit Networking and Communication (LEO-NET
'23
Towards a Benchmark for Fog Data Processing
Fog data processing systems provide key abstractions to manage data and event
processing in the geo-distributed and heterogeneous fog environment. The lack
of standardized benchmarks for such systems, however, hinders their development
and deployment, as different approaches cannot be compared quantitatively.
Existing cloud data benchmarks are inadequate for fog computing, as their focus
on workload specification ignores the tight integration of application and
infrastructure inherent in fog computing.
In this paper, we outline an approach to a fog-native data processing
benchmark that combines workload specifications with infrastructure
specifications. This holistic approach allows researchers and engineers to
quantify how a software approach performs for a given workload on given
infrastructure. Further, by basing our benchmark in a realistic IoT sensor
network scenario, we can combine paradigms such as low-latency event
processing, machine learning inference, and offline data analytics, and analyze
the performance impact of their interplay in a fog data processing system
Efficient Exchange of Metadata Information in Geo-Distributed Fog Systems
Metadata information is crucial for efficient geo-distributed fog computing
systems. Many existing solutions for metadata exchange overlook geo-awareness
or lack adequate failure tolerance, which are vital in such systems. To address
this, we propose HFCS, a novel hybrid communication system that combines
hierarchical and peer-to-peer elements, along with edge pools. HFCS utilizes a
gossip protocol for dynamic metadata exchange.
In simulation, we investigate the impact of node density and edge pool size
on HFCS performance. We observe a significant performance improvement for
clustered node distributions, aligning well with real-world scenarios.
Additionally, we compare HFCS with a hierarchical system and a peer-to-peer
broadcast approach. HFCS outperforms both in task fulfillment at the cost of an
average 16\% detected failures due to its peer-to-peer structures
Predicting Temporal Aspects of Movement for Predictive Replication in Fog Environments
To fully exploit the benefits of the fog environment, efficient management of
data locality is crucial. Blind or reactive data replication falls short in
harnessing the potential of fog computing, necessitating more advanced
techniques for predicting where and when clients will connect. While spatial
prediction has received considerable attention, temporal prediction remains
understudied.
Our paper addresses this gap by examining the advantages of incorporating
temporal prediction into existing spatial prediction models. We also provide a
comprehensive analysis of spatio-temporal prediction models, such as Deep
Neural Networks and Markov models, in the context of predictive replication. We
propose a novel model using Holt-Winter's Exponential Smoothing for temporal
prediction, leveraging sequential and periodical user movement patterns. In a
fog network simulation with real user trajectories our model achieves a 15%
reduction in excess data with a marginal 1% decrease in data availability
Identifying Nearest Fog Nodes With Network Coordinate Systems
Identifying the closest fog node is crucial for mobile clients to benefit
from fog computing. Relying on geographical location alone us insufficient for
this as it ignores real observed client access latency.
In this paper, we analyze the performance of the Meridian and Vivaldi network
coordinate systems in identifying nearest fog nodes. To that end, we simulate a
dense fog environment with mobile clients. We find that while network
coordinate systems really find fog nodes in close network proximity, a purely
latency-oriented identification approach ignores the larger problem of
balancing load across fog nodes
Eventually Consistent Configuration Management in Fog Systems with CRDTs
Current fog systems rely on centralized and strongly consistent services for
configuration management originally designed for cloud systems. In the
geo-distributed fog, such systems can exhibit high communication latency or
become unavailable in case of network partition. In this paper, we examine the
drawbacks of strong consistency for fog configuration management and propose an
alternative based on CRDTs. We prototypically implement our approach for the
FReD fog data management platform. Early results show reductions of server
response times of up to 50%
Supporting Multi-Cloud in Serverless Computing
Serverless computing is a widely adopted cloud execution model composed of
Function-as-a-Service (FaaS) and Backend-as-a-Service (BaaS) offerings. The
increased level of abstraction makes vendor lock-in inherent to serverless
computing, raising more concerns than previous cloud paradigms. Multi-cloud
serverless is a promising emerging approach against vendor lock-in, yet
multiple challenges must be overcome to tap its potential. First, we need to be
aware of both the performance and cost of each FaaS provider. Second, a
multi-cloud architecture must be proposed before deploying a multi-cloud
workflow. Domain-specific serverless offerings must then be integrated into the
multi-cloud architecture to improve performance or save costs. Moreover,
dealing with serverless offerings from multiple providers is challenging.
Finally, we require workload portability support for serverless multi-cloud.
In this paper, we present a multi-cloud library for cross-serverless
offerings. We develop the End Analysis System (EAS) to support comparison among
public FaaS providers in terms of performance and cost. Moreover, we design
proof-of-concept multi-cloud architectures with domain-specific serverless
offerings to alleviate problems such as data gravity. Finally, we deploy
workloads on these architectures to evaluate several public FaaS offerings.Comment: Accepted for the 15th IEEE/ACM International Conference on Utility
and Cloud Computing Companion (UCC'22 Companion