@conference {81,
	title = {Using Machine Learning for Black-Box Autoscaling},
	year = {2016},
	abstract = {<p>Autoscaling is the practice of automatically adding or removing resources for an application deployment to meet performance targets in response to changing workload conditions. However, existing autoscaling approaches typically require expert application and system knowledge to minimize resource costs and performance target violations, thus limiting their applicability. We present MLscale, an application-agnostic, machine learning based autoscaler that is composed of: (i) a neural network based online (black-box) performance modeler, and (ii) a regression based metrics predictor to estimate post-scaling application and system metrics. Implementation results for diverse applications across several traces highlight MLscale{\textquoteright}s application-agnostic behavior and show that MLscale (i) reduces resource costs by about 50\% compared to the optimal static policy, (ii) is within 15\% of the cost of the optimal dynamic policy, and (iii) provides similar cost-performance tradeoffs, without requiring any tuning, when compared to carefully tuned threshold-based policies.</p>
},
	author = {Muhammad Wajahat and Anshul Gandhi and Alexei Karve and Andrzej Kochut}
}
@proceedings {47,
	title = {Understanding network failures in data centers: measurement, analysis, and implications},
	year = {2011},
	month = {01/2011},
	pages = {350-361 },
	publisher = {ACM},
	abstract = {<p>We present the first large-scale analysis of failures in a data center network. Through our analysis, we seek to answer several fundamental questions: which devices/links are most unreliable, what causes failures, how do failures impact network traffic and how effective is network redundancy? We answer these questions using multiple data sources commonly collected by network operators. The key findings of our study are that (1) data center networks show high reliability, (2) commodity switches such as ToRs and AggS are highly reliable, (3) load balancers dominate in terms of failure occurrences with many short-lived software related faults,(4) failures have potential to cause loss of many small packets such as keep alive messages and ACKs, and (5) network redundancy is only 40\% effective in reducing the median impact of failure.</p>
},
	keywords = {Network Management, Network Services, Networks},
	isbn = {978-1-4503-0797-0 },
	doi = {10.1145/2018436.2018477 },
	url = {http://dl.acm.org/citation.cfm?id=2018436.2018477\&coll=DL\&dl=ACM\&CFID=448744458\&CFTOKEN=88236964},
	author = {Phillipa Gill and Navendu Jain and Nachiappan Nagappan}
}
