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State-Space Feedback Control for Elastic Distributed Storage in a Cloud Environment
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
KTH, School of Information and Communication Technology (ICT), Software and Computer systems, SCS.
2012 (English)In: ICAS 2012: The Eighth International Conference on Autonomic and Autonomous Systems, St. Maarten, Netherlands Antilles, 2012, 589-596 p.Conference paper, Published paper (Refereed)
Abstract [en]

Elasticity in Cloud computing is an ability of asystem to scale up and down (request and release resources) in response to changes in its environment and workload. Elasticity can be achieved manually or automatically. Efforts arebeing made to automate elasticity in order to improve system performance under dynamic workloads. In this paper, we reportour experience in designing an elasticity controller for a key-value storage service deployed in a Cloud environment. To design our controller, we have adopted a control theoretic approach. Automation of elasticity is achieved by providing a feedback controller that automatically increases and decreases the number of nodes in order to meet service level objectives under high load and to reduce costs under low load. Every step in the building of a controller for elastic storage, includingsystem identification and controller design, is discussed. We have evaluated our approach by using simulation. We have developed a simulation framework EStoreSim in order to simulate anelastic key-value store in a Cloud environment and be able to experiment with different controllers. We have examined the implemented controller against specific service level objectives and evaluated the controller behavior in different scenarios. Our simulation experiments have shown the feasibility of our approach to automate elasticity of storage services using state-space feedback control.

Place, publisher, year, edition, pages
St. Maarten, Netherlands Antilles, 2012. 589-596 p.
Keyword [en]
elasticity, key-value store, Cloud, state-space feedback control
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:kth:diva-101375ISBN: 978-1-61208-187-8 (print)OAI: oai:DiVA.org:kth-101375DiVA: diva2:547225
Conference
The 8th International Conference on Autonomic and Autonomous Systems (ICAS 2012)
Funder
ICT - The Next Generation
Note

QC 20130524

QC 20151216

Available from: 2012-08-27 Created: 2012-08-27 Last updated: 2015-12-16Bibliographically approved
In thesis
1. Self-Management for Large-Scale Distributed Systems
Open this publication in new window or tab >>Self-Management for Large-Scale Distributed Systems
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Autonomic computing aims at making computing systems self-managing by using autonomic managers in order to reduce obstacles caused by management complexity. This thesis presents results of research on self-management for large-scale distributed systems. This research was motivated by the increasing complexity of computing systems and their management.

In the first part, we present our platform, called Niche, for programming self-managing component-based distributed applications. In our work on Niche, we have faced and addressed the following four challenges in achieving self-management in a dynamic environment characterized by volatile resources and high churn: resource discovery, robust and efficient sensing and actuation, management bottleneck, and scale. We present results of our research on addressing the above challenges. Niche implements the autonomic computing architecture, proposed by IBM, in a fully decentralized way. Niche supports a network-transparent view of the system architecture simplifying the design of distributed self-management. Niche provides a concise and expressive API for self-management. The implementation of the platform relies on the scalability and robustness of structured overlay networks. We proceed by presenting a methodology for designing the management part of a distributed self-managing application. We define design steps that include partitioning of management functions and orchestration of multiple autonomic managers.

In the second part, we discuss robustness of management and data consistency, which are necessary in a distributed system. Dealing with the effect of churn on management increases the complexity of the management logic and thus makes its development time consuming and error prone. We propose the abstraction of Robust Management Elements, which are able to heal themselves under continuous churn. Our approach is based on replicating a management element using finite state machine replication with a reconfigurable replica set. Our algorithm automates the reconfiguration (migration) of the replica set in order to tolerate continuous churn. For data consistency, we propose a majority-based distributed key-value store supporting multiple consistency levels that is based on a peer-to-peer network. The store enables the tradeoff between high availability and data consistency. Using majority allows avoiding potential drawbacks of a master-based consistency control, namely, a single-point of failure and a potential performance bottleneck.

In the third part, we investigate self-management for Cloud-based storage systems with the focus on elasticity control using elements of control theory and machine learning. We have conducted research on a number of different designs of an elasticity controller, including a State-Space feedback controller and a controller that combines feedback and feedforward control. We describe our experience in designing an elasticity controller for a Cloud-based key-value store using state-space model that enables to trade-off performance for cost. We describe the steps in designing an elasticity controller. We continue by presenting the design and evaluation of ElastMan, an elasticity controller for Cloud-based elastic key-value stores that combines feedforward and feedback control.

Place, publisher, year, edition, pages
Stockholm: KTH Royal Institute of Technology, 2012. xix, 266 p.
Series
TRITA-ICT-ECS AVH, ISSN 1653-6363 ; 12:04
Keyword
Self-Management, Autonomic Computing, Control Theory, Distributed Systems, Grid Computing, Cloud Computing, Elastic Services, Key-Value Stores
National Category
Computer Systems
Research subject
SRA - ICT
Identifiers
urn:nbn:se:kth:diva-101661 (URN)978-91-7501-437-1 (ISBN)
Public defence
2012-09-26, Sal E, Forum IT-Universitetet, KTH, Isajordsgatan 39, Kista, 14:00 (English)
Opponent
Supervisors
Funder
ICT - The Next Generation
Note

QC 20120831

Available from: 2012-08-31 Created: 2012-08-30 Last updated: 2014-01-23Bibliographically approved

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