As the news around cloud repatriations gets real, many cloud technologists associate them with poor understanding of the applications and their usage patterns by the enterprises. Our solution, Yodea, is a tool cum methodology to analyze work-load patterns in the light of cloud suitability. We bring forward compute patterns which can benefit from cloud economics with on-demand compute scaling. Yodea further ranks workloads in terms of their cloud suitability on the basis of these metrics. After the fact analysis of storage workloads for a customer install-base, features 38% of the "already in cloud" volumes in the top 100 ranked list by Yodea.

Exascale systems of the future are predicted to have mean time between failures (MTBF) of less than one hour. At such low MTBFs, employing periodic checkpointing alone will result in low efficiency because of the high number of application failures resulting in large amount of lost work due to rollbacks. In such scenarios, it is highly necessary to have proactive fault tolerance mechanisms that can help avoid significant number of failures. In this work, we have developed a mechanism for proactive fault tolerance using partial replication of a set of application processes. Our fault tolerance framework adaptively changes the set of replicated processes periodically based on failure predictions to avoid failures. We have developed an MPI prototype implementation, PAREP-MPI that allows changing the replica set. We have shown that our strategy involving adaptive process replication significantly outperforms existing mechanisms providing up to 20 percent improvement in application efficiency even for exascale systems.

Performance problems are particularly hard to detect and diagnose in most computer systems, since there is no clear failure apart from the system being slow. In this paper, we present an empirical, data-driven methodology for detecting performance problems in data storage systems, and aiding in quick diagnosis once a problem is detected. The key feature of our solution is that it uses a combination of time-series analysis, domain knowledge and expert inputs to improve the overall efficacy. Our solution learns from a system's own history to establish the baseline of normal behavior. Hence it is not necessary to determine any static trigger-levels for metrics to raise alerts. Static triggers are ineffective since each system and its workloads are different from others. The method presented here (a) gives accurate indications of the time period when something goes wrong in a system, and (b) helps pin-point the most affected parts of the system to aid in diagnosis. Validation on more than 400 actual field support cases shows about 85% true positive rate with less than 10% false positive rate in identifying time periods of performance impact before or during the time a case was open. Results in a controlled lab environment are even better.