Self-management for large-scale distributed systems

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

Public Service Performance in Small Islands: Does Management Matter?

The objective of research is to review, describe and analyze the development process in small islands of Indonesia and its implication to the availability of public service facilities. The Directorate of Small Islands Empowerment (DP2K) is the main actor behind this development through each program which involves various affected sectors. Method of research is qualitative approach, whereas type of research is descriptive. Data source is derived from observation, informants and documents. Data analysis technique is interactive model analysis suggested by Miles & Huberman (2014) which includes data collection, data reduction, data presentation, condensation and conclusion. Such approach is supported by Sugiyono (2011) and Nasir (1988). Focus of research is pursuant to Moleong (2007). Data collection technique is in conforming to Singarimbun and Effendi (1989). Research instrument is aligning with Sugiyono (2011). Result of research indicates that main activities done by the government for the interest of small islands are self-management, contractual, devolution and DAK. Result also shows that facilitation program in small islands is not effective unless there is a flexible and corporate-based managerial system. In general, the process already engages the development actors (private sector), and its implementation is underlined by the principles of efficiency, effectiveness, transparency, responsibility and accountability. This process is also supported by a performance management technique because such managerial effort is needed to investigate the achieved target of facilitating public sector in small islands that are vulnerable and backward in their development. Keywords: small islands, public service, change and performance management.

Coordinated Autonomic Managers for Energy Efficient Date Centers

The complexity of today’s data centers has led researchers to investigate ways in which autonomic methods can be used for data center management. Autonomic managers try to monitor and manage resources to ensure that the components they manage are self-configuring, self-optimizing, self-healing and self-protecting (so called “self-*” properties). In this research, we consider autonomic management systems for data centers with a particular focus on making data centers more energy-aware. In particular, we consider a policy based, multi-manager autonomic management systems for energy aware data centers. Our focus is on defining the foundations – the core concepts, entities, relationships and algorithms - for autonomic management systems capable of supporting a range of management configurations. Central to our approach is the notion of a “topology” of autonomic managers that when instantiated can support a range of different configurations of autonomic managers and communication among them. The notion of “policy” is broadened to enable some autonomic managers to have more direct control over the behavior of other managers through changes in policies. The ultimate goal is to create a management framework that would allow the data center administrator to a) define managed objects, their corresponding managers, management system topology, and policies to meet their operation needs and b) rely on the management system to maintain itself automatically. A data center simulator that computes its energy consumption (computing and cooling) at any given time is implemented to evaluate the impact of different management scenarios. The management system is evaluated with different management scenarios in our simulated data center

Live Streaming in P2P and Hybrid P2P-Cloud Environments for the Open Internet

Peer-to-Peer (P2P) live media streaming is an emerging technology that reduces the barrier to stream live events over the Internet. However, providing a high quality media stream using P2P overlay networks is challenging and gives raise to a number of issues: (i) how to guarantee quality of the service (QoS) in the presence of dynamism, (ii) how to incentivize nodes to participate in media distribution, (iii) how to avoid bottlenecks in the overlay, and (iv) how to deal with nodes that reside behind Network Address Translators gateways (NATs). In this thesis, we answer the above research questions in form of new algorithms and systems. First of all, we address problems (i) and (ii) by presenting our P2P live media streaming solutions: Sepidar, which is a multiple-tree overlay, and GLive, which is a mesh overlay. In both models, nodes with higher upload bandwidth are positioned closer to the media source. This structure reduces the playback latency and increases the playback continuity at nodes, and also incentivizes the nodes to provide more upload bandwidth. We use a reputation model to improve participating nodes in media distribution in Sepidar and GLive. In both systems, nodes audit the behaviour of their directly connected nodes by getting feedback from other nodes. Nodes who upload more of the stream get a relatively higher reputation, and proportionally higher quality streams. To construct our streaming overlay, we present a distributed market model inspired by Bertsekas auction algorithm, although our model does not rely on a central server with global knowledge. In our model, each node has only partial information about the system. Nodes acquire knowledge of the system by sampling nodes using the Gradient overlay, where it facilitates the discovery of nodes with similar upload bandwidth. We address the bottlenecks problem, problem (iii), by presenting CLive that satisfies real-time constraints on delay between the generation of the stream and its actual delivery to users. We resolve this problem by borrowing some resources (helpers) from the cloud, upon need. In our approach, helpers are added on demand to the overlay, to increase the amount of total available bandwidth, thus increasing the probability of receiving the video on time. As the use of cloud resources costs money, we model the problem as the minimization of the economical cost, provided that a set of constraints on QoS is satisfied. Finally, we solve the NAT problem, problem (iv), by presenting two NAT-aware peer sampling services (PSS): Gozar and Croupier. Traditional gossip-based PSS breaks down, where a high percentage of nodes are behind NATs. We overcome this problem in Gozar using one-hop relaying to communicate with the nodes behind NATs. Croupier similarly implements a gossip-based PSS, but without the use of relaying

Predicting Linguistic Structure with Incomplete and Cross-Lingual Supervision

Contemporary approaches to natural language processing are predominantly based on statistical machine learning from large amounts of text, which has been manually annotated with the linguistic structure of interest. However, such complete supervision is currently only available for the world's major languages, in a limited number of domains and for a limited range of tasks. As an alternative, this dissertation considers methods for linguistic structure prediction that can make use of incomplete and cross-lingual supervision, with the prospect of making linguistic processing tools more widely available at a lower cost. An overarching theme of this work is the use of structured discriminative latent variable models for learning with indirect and ambiguous supervision; as instantiated, these models admit rich model features while retaining efficient learning and inference properties. The first contribution to this end is a latent-variable model for fine-grained sentiment analysis with coarse-grained indirect supervision. The second is a model for cross-lingual word-cluster induction and the application thereof to cross-lingual model transfer. The third is a method for adapting multi-source discriminative cross-lingual transfer models to target languages, by means of typologically informed selective parameter sharing. The fourth is an ambiguity-aware self- and ensemble-training algorithm, which is applied to target language adaptation and relexicalization of delexicalized cross-lingual transfer parsers. The fifth is a set of sequence-labeling models that combine constraints at the level of tokens and types, and an instantiation of these models for part-of-speech tagging with incomplete cross-lingual and crowdsourced supervision. In addition to these contributions, comprehensive overviews are provided of structured prediction with no or incomplete supervision, as well as of learning in the multilingual and cross-lingual settings. Through careful empirical evaluation, it is established that the proposed methods can be used to create substantially more accurate tools for linguistic processing, compared to both unsupervised methods and to recently proposed cross-lingual methods. The empirical support for this claim is particularly strong in the latter case; our models for syntactic dependency parsing and part-of-speech tagging achieve the hitherto best published results for a wide number of target languages, in the setting where no annotated training data is available in the target language

RDF Data Indexing and Retrieval: A survey of Peer-to-Peer based solutions

The Semantic Web enables the possibility to model, create and query resources found on the Web. Enabling the full potential of its technologies at the Internet level requires infrastructures that can cope with scalability challenges and support various types of queries. The attractive features of the Peer-to-Peer (P2P) communication model such as decentralization, scalability, fault-tolerance seems to be a natural solution to deal with these challenges. Consequently, the combination of the Semantic Web and the P2P model can be a highly innovative attempt to harness the strengths of both technologies and come up with a scalable infrastructure for RDF data storage and retrieval. In this respect, this survey details the research works that adopt this combination and gives an insight on how to deal with the RDF data at the indexing and querying levels.Le Web Sémantique permet de modéliser, créer et faire des requêtes sur les ressources disponibles sur le Web. Afin de permettre à ses technologies d'exploiter leurs potentiels à l'échelle de l'Internet, il est nécessaire qu'elles reposent sur des infrastructures qui puissent passer à l'échelle ainsi que de répondre aux exigences d'expressivité des types de requêtes qu'elles offrent. Les bonnes propriétés qu'offrent les dernières générations de systèmes pair-à- pair en termes de décentralisation, de tolérance aux pannes ainsi que de passage à l'échelle en font d'eux des candidats prometteurs. La combinaison du modèle pair-à-pair et des technologies du Web Sémantique est une tentative innovante ayant pour but de fournir une infrastructure capable de passer à l'échelle et pouvant stocker et rechercher des données de type RDF. Dans ce contexte, ce rapport présente un état de l'art et discute en détail des travaux autour de systèmes pair-à-pair qui traitent des données de type RDF à large échelle. Nous détaillons leurs mécanismes d'indexation de données ainsi que le traitement des divers types de requêtes offerts

Self-Management for Large-Scale Distributed Systems

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.QC 20120831</p

Self management for large-scale distributed systems: An overview of the SELFMAN project

10.1007/978-3-540-92188-2_7Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)5382 LNCS153-17

Self management for large-scale distributed systems: an overview of the SELFMAN project

As Internet applications become larger and more complex, the task of managing them becomes overwhelming. "Abnormal" events such as software updates, failures, attacks, and hotspots become frequent. The SELFMAN project is tackling this problem by combining two technologies, namely structured overlay networks and advanced component models, to make the system self managing. Structured overlay networks (SONs) developed out of peer-to-peer systems and provide robustness, scalability, communication guarantees, and efficiency. Component models provide the framework to extend the self-managing properties of SONs over the whole system. SELFMAN is building a self-managing transactional storage and using it for two application demonstrators: a distributed Wiki and an on-demand media streaming service. This paper provides an introduction and motivation for the ideas underlying SELFMAN and a snapshot of its contributions midway through the project. We explain our methodology for building self-managing systems as networks of interacting feedback loops. We then summarize the work we have done to make SONs a practical basis for our architecture: using an advanced component model, handling network partitions, handling failure suspicions, and doing range queries with load balancing. Finally, we show the design of a self-managing transactional storage on a SON.Anglai

Enabling and Achieving Self-Management for Large Scale Distributed Systems : Platform and Design Methodology for Self-Management

Autonomic computing is a paradigm that aims at reducing administrative overhead by using autonomic managers to make applications self-managing. To better deal with large-scale dynamic environments; and to improve scalability, robustness, and performance; we advocate for distribution of management functions among several cooperative autonomic managers that coordinate their activities in order to achieve management objectives. Programming autonomic management in turn requires programming environment support and higher level abstractions to become feasible. In this thesis we present an introductory part and a number of papers that summaries our work in the area of autonomic computing. We focus on enabling and achieving self-management for large scale and/or dynamic distributed applications. We start by presenting our platform, called Niche, for programming self-managing component-based distributed applications. Niche supports a network-transparent view of system architecture simplifying designing application self-* code.  Niche provides a concise and expressive API for self-* code. The implementation of the framework relies on scalability and robustness of structured overlay networks. We have also developed a distributed file storage service, called YASS, to illustrate and evaluate Niche. After introducing Niche we proceed by presenting a methodology and design space for designing the management part of a distributed self-managing application in a distributed manner. We define design steps, that includes partitioning of management functions and orchestration of multiple autonomic managers. We illustrate the proposed design methodology by applying it to the design and development of an improved version of our distributed storage service YASS as a case study. We continue by presenting a generic policy-based management framework which has been integrated into Niche. Policies are sets of rules that govern the system behaviors and reflect the business goals or system management objectives. The policy based management is introduced to simplify the management and reduce the overhead, by setting up policies to govern system behaviors. A prototype of the framework is presented and two generic policy languages (policy engines and corresponding APIs), namely SPL and XACML, are evaluated using our self-managing file storage application YASS as a case study. Finally, we present a generic approach to achieve robust services that is based on finite state machine replication with dynamic reconfiguration of replica sets. We contribute a decentralized algorithm that maintains the set of resource hosting service replicas in the presence of churn. We use this approach to implement robust management elements as robust services that can operate despite of churn.  QC 2010052