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Title: | Deep Reinforcement Learning for Tail-Latency Regulation in Co-located Applications through Cooperative Core and Cache Allocation |
Authors: | Κιμωνίδης, Αλέξανδος Σούντρης Δημήτριος |
Keywords: | cloud computing, resource management, deep reinforcement learn- ing, scheduling, performance monitoring counters |
Issue Date: | 25-Oct-2021 |
Abstract: | The amount of workloads ran on the Cloud is growing all the time. Data center operators and cloud providers have embraced workload co-location and multi- tenancy as first-class system design concerns to efficiently service and manage these massive computing needs. Current state-of-the-art resource managers place applications on the available pool of resources using standard metrics such as CPU or memory usage. As a result, current state-of-the-art resource managers fail to achieve adequate resource utilization. In this thesis, we design a resource manager that leverages deep reinforce- ment learning for its policy and uses performance monitoring counters which are a more complex metric that is able to determine a machine's current state. We showcase the impact of applying stress on different server resources and the need for a better scheduler that considers the correct metrics. We integrate our solution with OpenAI Gym, one of the most widely used tool-kits for devel- oping and comparing reinforcement learning algorithms, and we show that we can achieve higher resource usage compared to the default scheduler as well as other state-of-the-art schedulers. |
URI: | http://artemis.cslab.ece.ntua.gr:8080/jspui/handle/123456789/18175 |
Appears in Collections: | Διπλωματικές Εργασίες - Theses |
Files in This Item:
File | Description | Size | Format | |
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Diploma_Thesis_Kimonides__Version_1227.pdf | 5.1 MB | Adobe PDF | View/Open |
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