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A Machine Learning Approach to Live Migration Modeling

Dynamic management of resources in data centers improves data center utilization in terms of energy consumption and device utilization. Virtual machines are among techniques to achieve this goal. To enable the VM to adapt to dynamic fluctuations of workloads, Live Migrated virtual machines are introduced, which stands for moving a VM from one physical host to another while the VM keeps running. Live migration enables load balancing, fault tolerance, ease maintenance, etc. However, live migration is costly in terms of data traffic. Having an understanding of performance metrics of different live migration algorithms is important so that one can rely on an efficient algrothm to migrate a running VM is important. Therefore, this work uses a Machine Learning (ML) approach to predict key metrics of different live migration algorithms, and then find the best suitable choice for live migration. Indeed, given resource usage as well as the characteristics of VM work load, this paper predict...
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CrystalBall: Statically Analyzing Runtime Behavior via Deep Sequence Learning

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Author:  Stephen Zekany, Daniel Rings, Nathan Harada, Michael A. Laurenzano, Lingjia Tang, Jason Mars Intro: Understanding the runtime behavior of the software is critical in many aspects of program development. A conventional approach to this problem is using dynamic profiling, meaning we have to run the program multiple times with environments that representative enough for the actual working situation. Besides that, a dynamic profiling approach has other weakness such as cannot profiling a subset of functions or paths very economically and requires re-profiling each time any changes is applied to the code. This paper presents a statically profiling method, harnessing the ability of RNN to discover the hidden information of hot path in the code. Also, since the training process uses intermediate representation as input, the result will have better generalization power. Methodology: The dataset is in the form of a basic block feature; each sequence of basic blocks repr...
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A Machine Learning Approach to Live Migration Modeling

Authors:  Changyeon Jo, Youngsu Cho, Bernhard Egger Seoul National University Summary:  This paper proposed a machine learning approach to predict the system metrics for different VM migration algorithms. These system metrics are affected by migration algorithm, host machine status and user applications and feasible for machine learning approach. The prediction model takes 21 system/application input features (including which algorithm), and generates 6 metrics as the output. As a result, it can accurately estimate each metrics under different application, VM and algorithms. One of the application, the paper provide a semi-automated algorithm selection based on this machine learning model and user-provided SLA (service level agreements) constraints. Questions about Machine Learning:  Why machine learning is applied: The combination of algorithm, VM status and current running applications status, is a huge searching space. Machine learning features: which algor...
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