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- Xhagjika, Vamis, 1986-
(författare)
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Resource, Data and Application Management for Cloud Federations and Multi-Clouds
- 2017
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Distributed Real-Time Media Processing refers to classes of highly distributed, delay no-tolerant applications that account for the majority of the data traffic generated in the world today. Real-Time audio/video conferencing and live content streaming are of particular research interests as technology forecasts predict video traffic surpassing every other type of data traffic in the world in the near future. Live streaming refers to applications in which audio/video streams from a source need to be delivered to a set of geo-distributed destinations while maintaining low latency of stream delivery. Real-time conferencing platforms are application platforms that implement many-to-many audio/video real-time communications. Both of these categories exhibit high sensitivity to both network state (latency, jitter, packet loss, bit rate) as well as stream processing backend load profiles (latency and jitter introduced as Cloud processing of media packets). This thesis addresses enhancing real-time media processing both at the network level parameters as well as Cloud optimisations.We provide a novel, bandwidth management algorithm, for cloud services sharing the same network infrastructure, which provides a 2x improvement in system stability. Further examining network impact on cloud services, we provide a novel hybrid Cloud-Network distributed Cloud architecture to enable locality aware, application enhancements. This architecture led to a multi-cloud management overlay algorithm that maintains low management overhead on large scale cloud deployments. On the application level we provide a study of Media Quality parameters for a WebRTC enabled Media Cloud back-end, and provide patterns of quality metrics with respect to back-end stream load and network parameters. Additionally we empirically show that a "minimal load" algorithm for stream allocation, outperforms other Rotational, or Static Threshold based algorithms.
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| 2. |
- Al-Shishtawy, Ahmad, 1978-
(författare)
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Self-Management for Large-Scale Distributed Systems
- 2012
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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