Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting particularly popular as a basis for secure instant messaging. By comparison with traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for decentralized applications that are based on causal histories of publish-subscribe events: First, we prove that the MEG is a Conflict-Free Replicated Data Type for causal histories and, thus, provides Strong Eventual Consistency (SEC). With SEC being among the best known achievable trade-offs in the scope of the well-known CAP theorem, the MEG provides a powerful consistency guarantee while being available during network partition. Second, we discuss the implications of byzantine attackers on the data type’s properties. We note that the MEG, as it does not strive for consensus or strong consistency, can cope with $n>f$ environments with $n$ participants, of which $f$ are byzantine. Furthermore, we analyze scalability: Using Markov chains, we study the number of forward extremities of the MEG over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk. With the properties shown, a MEG represents a promising element in the set of data structures for decentralized applications, but with distinct trade-offs compared to traditional blockchains and distributed ledger technologies

Self-Stabilizing and Private Distributed Shared Atomic Memory in Seldomly Fair Message Passing Networks

We study the problem of privately emulating shared memory in message-passing networks. The system includes clients that store and retrieve replicated information on N servers, out of which e are data-corrupting malicious. When a client accesses a data-corrupting malicious server, the data field of that server response might be different from the value it originally stored. However, all other control variables in the server reply and protocol actions are according to the server algorithm. For the coded atomic storage algorithms by Cadambe et al., we present an enhancement that ensures no information leakage and data-corrupting malicious fault-tolerance. We also consider recovery after the occurrence of transient faults that violate the assumptions according to which the system was designed to operate. After their last occurrence, transient faults leave the system in an arbitrary state (while the program code stays intact). We present a self-stabilizing algorithm, which recovers after the occurrence of transient faults. This addition to Cadambe et al. considers asynchronous settings as long as no transient faults occur. The recovery from transient faults that bring the system counters (close) to their maximal values may include the use of a global reset procedure, which requires the system run to be controlled by a fair scheduler. After the recovery period, the safety properties are provided for asynchronous system runs that are not necessarily controlled by fair schedulers. Since the recovery period is bounded and the occurrence of transient faults is extremely rare, we call this design criteria self-stabilization in the presence of seldom fairness. Our self-stabilizing algorithm uses a bounded amount of storage during asynchronous executions (that are not necessarily controlled by fair schedulers). To the best of our knowledge, we are the first to address privacy, data-corrupting malicious behavior, and self-stabilization in the context of emulating atomic shared memory in message-passing systems

Application Agreement and Integration Services

Application agreement and integration services are required by distributed, fault-tolerant, safety critical systems to assure required performance. An analysis of distributed and hierarchical agreement strategies are developed against the backdrop of observed agreement failures in fielded systems. The documented work was performed under NASA Task Order NNL10AB32T, Validation And Verification of Safety-Critical Integrated Distributed Systems Area 2. This document is intended to satisfy the requirements for deliverable 5.2.11 under Task 4.2.2.3. This report discusses the challenges of maintaining application agreement and integration services. A literature search is presented that documents previous work in the area of replica determinism. Sources of non-deterministic behavior are identified and examples are presented where system level agreement failed to be achieved. We then explore how TTEthernet services can be extended to supply some interesting application agreement frameworks. This document assumes that the reader is familiar with the TTEthernet protocol. The reader is advised to read the TTEthernet protocol standard [1] before reading this document. This document does not re-iterate the content of the standard

Modeling and Control of Server-based Systems

When deploying networked computing-based applications, proper resource management of the server-side resources is essential for maintaining quality of service and cost efficiency. The work presented in this thesis is based on six papers, all investigating problems that relate to resource management of server-based systems. Using a queueing system approach we model the performance of a database system being subjected to write-heavy traffic. We then evaluate the model using simulations and validate that it accurately mimics the behavior of a real test bed. In collaboration with Ericsson we model and design a per-request admission control scheme for a Mobile Service Support System (MSS). The model is then validated and the control scheme is evaluated in a test bed. Also, we investigate the feasibility to estimate the state of a server in an MSS using an event-based Extended Kalman Filter. In the brownout paradigm of server resource management, the amount of work required to serve a client is adjusted to compensate for temporary resource shortages. In this thesis we investigate how to perform load balancing over self-adaptive server instances. The load balancing schemes are evaluated in both simulations and test bed experiments. Further, we investigate how to employ delay-compensated feedback control to automatically adjust the amount of resources to deploy to a cloud application in the presence of a large, stochastic delay. The delay-compensated control scheme is evaluated in simulations and the conclusion is that it can be made fast and responsive compared to an industry-standard solution

Analysis of the Matrix Event Graph Replicated Data Type

Matrix is a new kind of decentralized, topic-based publish-subscribe middleware for communication and data storage that is getting popular particularly as a basis for secure instant messaging. In comparison to traditional decentralized communication systems, Matrix replaces pure message passing with a replicated data structure. This data structure, which we extract and call the Matrix Event Graph (MEG), depicts the causal history of messages. We show that this MEG represents an interesting and important replicated data type for general decentralized applications that are based on causal histories of publish-subscribe events: we show that a MEG possesses strong properties with respect to consistency, byzantine attackers, and scalability. First, we show that the MEG provides Strong Eventual Consistency (SEC), and that it is available under partition, by proving that the MEG is a Conflict-Free Replicated Data Type for causal histories. While strong consistency is impossible here as shown by the famous CAP theorem, SEC is among the best known achievable trade-offs. Second, we discuss the implications of byzantine attackers on the data type\u27s properties. We note that the MEG, as it does not strive for consensus, can cope with $n>f$ environments with $n$ total participants of which $f$ show byzantine faults. Furthermore, we analyze scalability: Using Markov chains we study the width of the MEG, defined as the number of forward extremities, over time and observe an almost optimal evolution. We conjecture that this property is inherent to the underlying spatially inhomogeneous random walk