A framework for the dynamic management of Peer-to-Peer overlays

Peer-to-Peer (P2P) applications have been associated with inefficient operation, interference with other network services and large operational costs for network providers. This thesis presents a framework which can help ISPs address these issues by means of intelligent management of peer behaviour. The proposed approach involves limited control of P2P overlays without interfering with the fundamental characteristics of peer autonomy and decentralised operation. At the core of the management framework lays the Active Virtual Peer (AVP). Essentially intelligent peers operated by the network providers, the AVPs interact with the overlay from within, minimising redundant or inefficient traffic, enhancing overlay stability and facilitating the efficient and balanced use of available peer and network resources. They offer an “insider‟s” view of the overlay and permit the management of P2P functions in a compatible and non-intrusive manner. AVPs can support multiple P2P protocols and coordinate to perform functions collectively. To account for the multi-faceted nature of P2P applications and allow the incorporation of modern techniques and protocols as they appear, the framework is based on a modular architecture. Core modules for overlay control and transit traffic minimisation are presented. Towards the latter, a number of suitable P2P content caching strategies are proposed. Using a purpose-built P2P network simulator and small-scale experiments, it is demonstrated that the introduction of AVPs inside the network can significantly reduce inter-AS traffic, minimise costly multi-hop flows, increase overlay stability and load-balancing and offer improved peer transfer performance

Hybrid Multicasting Using Automatic Multicast Tunnels (AMT)

Native Multicast plays an important role in distributing and managing delivery of some of the most popular Internet applications, such as IPTV and media delivery. However, due to patchy support and the existence of multiple approaches for Native Multicast, the support for Native Multicast is fragmented into isolated areas termed Multicast Islands. This renders Native Multicast unfit to be used as an Internet wide application. Instead, Application Layer Multicast, which does not have such network requirements but is more expensive in terms of bandwidth and overhead, can be used to connect the native multicast islands. This thesis proposes Opportunistic Native Multicast (ONM) which employs Application LayerMulticast (ALM), on top of a DHT-based P2P overlay network, and AutomaticMulticast Tunnelling (AMT) to connect these islands. ALM will be used for discovery and initiating the AMT tunnels. The tunnels will encapsulate the traffic going between islands' Primary Nodes (PNs). AMT was used for its added benefits such as security and being better at traffic shaping and Quality Of Service (QoS). While different approaches for connecting multicast islands exists, the system proposed in the thesis was designed with the following characteristics in mind: scalability, availability, interoperability, self-adaptation and efficiency. Importantly, by utilising AMT tunnels, this approach has unique properties that improve network security and management

Asynchronous epidemic algorithms for consistency in large-scale systems

Achieving and detecting a globally consistent state is essential to many services in the large and extreme-scale distributed systems, especially when the desired consistent state is critical for services operation. Centralised and deterministic approaches for synchronisation and distributed consistency are not scalable and not fault-tolerant. Alternatively, epidemic-based paradigms are decentralised computations based on randomised communications. They are scalable, resilient, fault-tolerant, and converge to the desired target in logarithmic time with respect to system size. Thus, many distributed services have adopted epidemic protocols to achieve the consensus and the consistent state, mainly due to scalability concerns. The convergence of epidemic protocols is stochastically guaranteed. However, the detection of the convergence is probabilistic and non-explicit. In a real-world environment, systems are unreliable, and epidemic protocols cannot converge to the desired state. Thus, achieving convergence by itself does not ensure making a system-wide consistent state under dynamic conditions. The research work presented in this thesis introduces the Phase Transition Algorithm (PTA) to achieve distributed consistent state based on the explicit detection of convergence. Each phase in PTA is a decentralised decision-making process that implements epidemic data aggregation, in which the detection of convergence implies achieving a global agreement. The phases in PTA can be cascaded to achieve higher certainty as desired. Following the PTA, two epidemic protocols, namely PTP and ECP, are proposed to acquire of consensus, i.e. for the consistency in data dissemination and data aggregation. The protocols are examined through simulations, and experimental results have validated the protocols ability to achieve and explicitly detect the consensus among system nodes. The research work has also studied the epidemic data aggregation under nodes churn and network failures, in which the analysis has identified three phases of the aggregation process. The investigations have shown a different impact of nodes churn on each phase. The phase that is critical for the aggregation process has been studied further, which led to propose new robust data aggregation protocols, REAP and REAP+. Each protocol has a different decentralised replication method, and both implements distributed failure detection and instantaneous mass restoration mechanisms. Simulations have validated the protocols, and results have shown protocols ability to converge, detect convergence, and produce competitive accuracy under various levels of nodes churn. Furthermore, distributed consistency in continuous systems is addressed in the research. The work has proposed a novel continuous epidemic protocol with the adaptive restart mechanism. The protocol restarts either upon the detection of system convergence or upon the detection of divergence. Also, the protocol introduces the seed selection method for the peak data distribution in decentralised approaches, which was a challenge that requires single-point initialisation and leader-election step. The simulations validated the performance of the algorithm under static and dynamic conditions and approved that convergence and divergence detection accuracy can be tuned as desired. Finally, the research work shows that combining and integrating of the proposed protocols enables extreme-scale distributed systems to achieve and detect global consistent states even under realistic and dynamical conditions

WebSocket vs WebRTC in the stream overlays of the Streamr Network

The Streamr Network is a decentralized publish-subscribe system. This thesis experimentally compares WebSocket and WebRTC as transport protocols in the system’s d-regular random graph type unstructured stream overlays. The thesis explores common designs for publish-subscribe and decentralized P2P systems. Underlying network protocols including NAT traversal are explored to understand how the WebSocket and WebRTC protocols function. The requirements set for the Streamr Network and how its design and implementations fulfill them are discussed. The design and implementations are validated with the use simulations, emulations and AWS deployed real-world experiments. The performance metrics measured from the real-world experiments are compared to related work. As the implementations using the two protocols are separate incompatible versions, the differences between them was taken into account during analysis of the experiments. Although the WebSocket versions overlay construction is known to be inefficient and vulnerable to churn, it is found to be unintentionally topology aware. This caused the WebSocket stream overlays to perform better in terms of latency. The WebRTC stream overlays were found to be more predictable and more optimized for small payloads as estimates for message propagation delays had a MEPA of 1.24% compared to WebSocket’s 3.98%. Moreover, the WebRTC version enables P2P connections between hosts behind NATs. As the WebRTC version’s overlay construction is more accurate, reliable, scalable, and churn tolerant, it can be used to create intentionally topology aware stream overlays to fully take over the results of the WebSocket implementation

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

A novel service discovery model for decentralised online social networks.

Online social networks (OSNs) have become the most popular Internet application that attracts billions of users to share information, disseminate opinions and interact with others in the online society. The unprecedented growing popularity of OSNs naturally makes using social network services as a pervasive phenomenon in our daily life. The majority of OSNs service providers adopts a centralised architecture because of its management simplicity and content controllability. However, the centralised architecture for large-scale OSNs applications incurs costly deployment of computing infrastructures and suffers performance bottleneck. Moreover, the centralised architecture has two major shortcomings: the single point failure problem and the lack of privacy, which challenges the uninterrupted service provision and raises serious privacy concerns. This thesis proposes a decentralised approach based on peer-to-peer (P2P) networks as an alternative to the traditional centralised architecture. Firstly, a self-organised architecture with self-sustaining social network adaptation has been designed to support decentralised topology maintenance. This self-organised architecture exhibits small-world characteristics with short average path length and large average clustering coefficient to support efficient information exchange. Based on this self-organised architecture, a novel decentralised service discovery model has been developed to achieve a semantic-aware and interest-aware query routing in the P2P social network. The proposed model encompasses a service matchmaking module to capture the hidden semantic information for query-service matching and a homophily-based query processing module to characterise user’s common social status and interests for personalised query routing. Furthermore, in order to optimise the efficiency of service discovery, a swarm intelligence inspired algorithm has been designed to reduce the query routing overhead. This algorithm employs an adaptive forwarding strategy that can adapt to various social network structures and achieves promising search performance with low redundant query overhead in dynamic environments. Finally, a configurable software simulator is implemented to simulate complex networks and to evaluate the proposed service discovery model. Extensive experiments have been conducted through simulations, and the obtained results have demonstrated the efficiency and effectiveness of the proposed model.University of Derb

Towards a Cognitive Compute Continuum: An Architecture for Ad-Hoc Self-Managed Swarms

In this paper we introduce our vision of a Cognitive Computing Continuum to address the changing IT service provisioning towards a distributed, opportunistic, self-managed collaboration between heterogeneous devices outside the traditional data center boundaries. The focal point of this continuum are cognitive devices, which have to make decisions autonomously using their on-board computation and storage capacity based on information sensed from their environment. Such devices are moving and cannot rely on fixed infrastructure elements, but instead realise on-the-fly networking and thus frequently join and leave temporal swarms. All this creates novel demands for the underlying architecture and resource management, which must bridge the gap from edge to cloud environments, while keeping the QoS parameters within required boundaries. The paper presents an initial architecture and a resource management framework for the implementation of this type of IT service provisioning.Comment: 8 pages, CCGrid 2021 Cloud2Things Worksho

Coordinated Self-Adaptation in Large-Scale Peer-to-Peer Overlays

Self-adaptive systems typically rely on a closed control loop which detects when the current behavior deviates too much from the optimal one, determines new optimal values for system parameters, and applies changes to the system configuration. In decentralized systems, implementing each of these steps is challenging, especially when nodes need to coordinate their local configurations. In this paper, we propose a decentralized method to automatically tune global system parameters in a coordinated manner. We use gossip-based protocols to continuously monitor system properties and to disseminate parameter updates. We show that this method applied to a decentralized resource selection service allows the system to quickly adapt to changes in workload types and node properties, and only incurs a negligible communication overhead

Systematizing Decentralization and Privacy: Lessons from 15 Years of Research and Deployments

Decentralized systems are a subset of distributed systems where multiple authorities control different components and no authority is fully trusted by all. This implies that any component in a decentralized system is potentially adversarial. We revise fifteen years of research on decentralization and privacy, and provide an overview of key systems, as well as key insights for designers of future systems. We show that decentralized designs can enhance privacy, integrity, and availability but also require careful trade-offs in terms of system complexity, properties provided, and degree of decentralization. These trade-offs need to be understood and navigated by designers. We argue that a combination of insights from cryptography, distributed systems, and mechanism design, aligned with the development of adequate incentives, are necessary to build scalable and successful privacy-preserving decentralized systems