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|
Identifier |
000457103 |
Title |
QoS-based resource management for increasing server utilization in the cloud |
Alternative Title |
Διαχείριση πόρων νέφους για την επίτευξη υψηλής χρήσης της υποδομής και καλής απόδοσης των εφαρμογών |
Author
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Σφακιανάκης, Ιωάννης Μ.
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Thesis advisor
|
Μπίλας, Άγγελος
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Reviewer
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Κατεβαίνης, Μανώλης
Πρατικάκης, Πολύβιος
Μαγκούτης, Κωνσταντίνος
Αναστασιάδης, Στέργιος
Τσουμάκος, Δημήτριος
Ξύδης, Σωτήριος
|
Abstract |
Cloud computing is compelling because it simplifies the management of the infrastructure and allows elastic scaling of resources for applications. As a result, more and more
professionals and enterprises rely on the cloud to process and store their information.
This increasing demand for cloud resources forces providers to constantly expand their
infrastructure and maintain more servers, significantly increasing costs. At the same time,
cloud users care for application performance and tend to overprovision applications, which
results in a significant portion of the cloud resources being unused. Therefore, providers
must handle the tradeoff between application performance and resource utilization to
reduce the cost of the infrastructure and, at the same time, keep users satisfied.
In this dissertation, we propose techniques for resource management that increase
utilization while, at the same time, maintaining application performance above a userdefined level. The system estimates the resources required to achieve a certain level of
performance and then places the application appropriately in the infrastructure.
First, we design and implement a profile-based approach in a sandboxed environment
and create performance models for each application. The system uses these profiles to
accurately correlate the allocated resources to application performance and minimize the
unused resources of the infrastructure. However, profile-based approaches have limitations:
(1) the system cannot handle “unknown” applications and (2) the runs required to generate
the profiles can be overwhelming.
Next, we address these limitations using a reactive approach, generating performance
models on-the-fly during the application execution. The system gradually learns the
behavior of each application using a feedback loop controller and constantly improves the
estimations about the required resources. Therefore, the reactive approach can handle
applications without prior knowledge of their performance profiles and adapt to workload
changes. The limitation of reactive approaches is that they cannot handle well workloads
with rapid changes in their load.
Finally, we propose applying the reactive approach to serverless computing, where
we adjust the required resources based on the current changes in the workload. These
predictions help the system adjust quickly to sudden changes in the load and maintain the
tail latency of the application.
We evaluate the proposed system using synthetic workloads that resemble real data
center workloads. To achieve that, we develop a methodology that: (1) processes data
center traces from major providers, (2) extracts their most important characteristics, (3)
scales the workload to match the underlying infrastructure, and (4) executes the workload
using well-known cloud applications that match the ones used in the trace. Our results
show significant improvement in the utilization of the infrastructure without a visible drop
in application performance.
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Language |
English |
Subject |
Cloud computing |
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Cloud deployment |
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Resource allocation |
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Resource assignment |
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Task scheduling |
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Workload generation |
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Ανάθεση πόρων |
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Ανάπτυξη νέφους |
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Δημιουργία φόρτου εργασίας |
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Κατανομή πόρων |
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Προγραμματισμός εργασιών |
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Υπολογισμός νέφους |
Issue date |
2023-07-21 |
Collection
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School/Department--School of Sciences and Engineering--Department of Computer Science--Doctoral theses
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Type of Work--Doctoral theses
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Permanent Link |
https://elocus.lib.uoc.gr//dlib/e/6/9/metadata-dlib-1689066533-706047-30897.tkl
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Views |
870 |