Doctoral theses
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| Identifier |
000446521 |
| Title |
Efficient adaptation mechanisms for improving performance during internal or external changes in distributed data stores |
| Alternative Title |
Αποδοτικοί μηχανισμοί προσαρμογής για την βελτίωση της απόδοσης κατά τη διάρκεια εσωτερικών ή εξωτερικών μεταβολών σε κατανεμημένα συστήματα αποθήκευσης δεδομένων |
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Author
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Παπαϊωάννου, Αντώνιος Α
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Thesis advisor
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Μαγκούτης, Κωνσταντίνος
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Reviewer
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Πλεξουσάκης, Δημήτρης
Μαρκάτος, Ευάγγελος
Μπίλας, Άγγελος
Πρατικάκης, Πολύβιος
Parlavantzas, Nikos
Kalyvianaki, Evangelia
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| Abstract |
The evolution of distributed data management systems, especially a class of systems
developed during the last 15-20 years commonly known as NoSQL data stores, has led
to a multitude of designs optimised for different application types, data formats, and
workload characteristics. Given the complexity of the environments they operate in
as parts of multi-tier software stacks driven by Internet workloads, data stores are
facing significant challenges during their operation. An important objective for service
operators is to ensure that data store performance levels and guarantees are
maintained despite internal or external changes that they face. Such an objective can
be reached via automated adaptation mechanisms by which data stores adapt to
changes automatically and transparently while maintaining efficiency and
performance goals as the data store transitions to new configurations.
In this dissertation we explore adaptation mechanisms in distributed data stores facing
internally or externally-induced changes, with a focus on workload variations,
occasional background activities, or the evolution of an external middleware
component that interoperates with a distributed data store. We propose novel
adaptation mechanisms and improvements to existing mechanisms in three different
contexts (data store elasticity, masking background activities, and alignment with
external distributed middleware), aiming to improve the overall performance during
the aforementioned contexts in the lifecycle of scalable data stores, aiming at
challenges that had not been addressed so far.
First, this dissertation focuses on the expansion phase of a data store when the need
arises to adapt its capacity as workload demands increase and the system tries to
improve its performance by incorporating more resources. We study the performance
impact of data transfers over the network during this phase and propose a mechanism
that schedules data transfers in a fine-grain manner, reducing their performance
impact while progressively increasing the processing capacity in an incremental
fashion. The proposed method realizes early benefits from data transfers during the
elasticity action as it incorporates new resources and makes data sub-sections
available prior to completing the full data transfers.
Next, we study the performance overhead of background activities that often impact
data store performance. We propose replica-group reconfiguration as a way to mask
performance bottlenecks in replicated data stores and investigate the benefits of
changing replica-group leadership prior to resource-intensive background tasks (e.g.
internal data reorganization, garbage collection or data backup tasks). Our observation
of an occasional performance glitch during reconfiguration actions, caused by coldcache misses in the cache of a new leader that was not adequately prepared for the
transition to the new configuration, led us to propose a new mechanism to maintain
up-to-date read caches across replicas without affecting the data consistency and
availability by disseminating read-hints within the replica group.
Finally, in this dissertation we investigate the benefits of automatically aligning data
stores with distributed middleware systems that rely on those data stores to maintain
their state. We do that by appropriately co-locating data partitions of data store with
processing tasks of the distributed middleware systems. We propose a system that
continuously strives to discover such alignment opportunities across systems and
improve data locality. The alignment actions combine multiple data store mechanisms
in common use, such as data replication and migration, as well as the adaptation of
the partitioning schemes across systems, a mechanism that has not been studied
before in this context.
The evaluation of the proposed mechanisms over widely deployed systems confirms
their performance improvements, advancing the state of the art in distributed data
stores in the direction of systems that adapt more efficiently and in new ways through
internal and external changes in their lifecycle.
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| Language |
English |
| Subject |
NoSQL |
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Performance management |
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Replicated systems |
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Διαχείριση απόδοσης συστημάτων |
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Κατανεμημένα συστήματα διαχείρισης δεδομένων |
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Προσαρμογή συστημάτων |
| Issue date |
2022-03-18 |
| 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/6/4/a/metadata-dlib-1646985346-759775-19224.tkl
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| Views |
501 |
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