Edge data centers (EDCs) typically provide lower availability rates than Cloud counterparts since they lack expensive support systems such as air conditioning units and power generators. To avoid this limitation deteriorating response time which is critical for Edge applications, use of proactive optimization algorithms is essential. Such proactive algorithms, however, require an accurate method for estimating availability of a VM that runs on a certain EDC. Estimation is complicated by several dependent and independent probabilistic events (e.g. hardware, software or network failures, power outage, etc.) that affect VM availability in different levels. In this paper, we propose a Bayesian Network model of QoS related parameters to estimate availability level of a VM in Edge infrastructure. We compare our approach to other machine learning methods and to the common practice in reliability theory that is the use of prior failure probability. According to experimental results, proposed method can identify VMs that satisfy user defined availability objectives with up to 94% accuracy and decrease SLO violations by nearly 44%. In addition, we empirically investigate the trade-off between running time and accuracy of proposed approach.

1. Quality of Service Channelling for
Latency Sensitive Edge Applications
Atakan Aral, Ivona Brandić
Institute of Software Technology and Interactive Systems
Vienna University of Technology
{atakan.aral, ivona.brandic}@tuwien.ac.at
http://rucon.ec.tuwien.ac.at/

2. A Dilemma in Edge
Edge applications are;
• Latency sensitive
• Data dependent
• Hence, geographically distributed / replicated.
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NTT Press Releases, 23.01.2014

3. A Dilemma in Edge
However, Edge data centers are;
• Lacking expensive support systems
• Prone to failures (hardware, software, network, …)
• Hence, unreliable for guaranteeing QoS (e.g. response
time or throughput).
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4. Cost of Failures
Average downtime duration:
18:30 hours
Cost of downtime:
$700,000 per hour
Cascading failures
• US-EAST-1 on February 28th
• Two S3 subsystems were
shut down accidentally
• 11 hours of disruption not
only for S3 but also EC2
 Adobe services, Bitbucket,
Quora, Coursera, Docker,
Github, LonelyPlanet, …
 Connected lightbulbs,
thermostats, … 4
Operational,
45%
Natural,
35%
Human,
19%
Other, 1%
Failure causes
Sources: https://www.infrascale.com, https://aws.amazon.com

5. Challenges
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• Recovery cost is too high.
Reactive methods alone are
not sufficient
• Failures have various characteristics:
cause, effect on availability, etc.
• Failures are statistically dependent to
each other.
• Dependencies are dynamic.
• Optimization is online, failure prediction
should be done in real-time.
Proactive methods require
failure prediction

6. Proposed Solution
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7. Solution Details
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8. Experimental Setup
Server failure data collected by Los Alamos National
Laboratory (LANL)
• Collected between 1999-2005
• 19.436 failures of 122 types
• 3.577 servers from 22 systems
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9. Results
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Please, refer to the paper for additional simulation results.

10. Conclusion
Edge computing requires intelligent and efficient
mechanisms to cope with unreliability.
Key message: Exploiting the dependencies between
different failures allow us to predict them quite
accurately.
Current work:
• Link failures (e.g. network congestion, router outage, etc.)
• Dependencies between Edge nodes (e.g. geographical,
cascading, etc.)
• Node availability  Distributed service availability
• http://rucon.ec.tuwien.ac.at/
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11. Thank you for your attention!
Atakan Aral, Ivona Brandić
Institute of Software Technology and Interactive Systems
Vienna University of Technology
{atakan.aral, ivona.brandic}@tuwien.ac.at
http://rucon.ec.tuwien.ac.at/

12. Simulation Results
Number of SLO violations for each configuration using Bayesian Network Estimation (BN) or only Prior Knowledge (PK)