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151
Detecting Causal Relationships in Distributed Computations: In Search of the Holy Grail
- IN SEARCH OF THE HOLY GRAIL. DISTRIBUTED COMPUTING
, 1994
"... The paper shows that characterizing the causal relationship between significant events is an important but non-trivial aspect for understanding the behavior of distributed programs. An introduction to the notion of causality and its relation to logical time is given; some fundamental results concern ..."
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Cited by 230 (3 self)
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The paper shows that characterizing the causal relationship between significant events is an important but non-trivial aspect for understanding the behavior of distributed programs. An introduction to the notion of causality and its relation to logical time is given; some fundamental results concerning the characterization of causality are presented. Recent work on the detection of causal relationships in distributed computations is surveyed. The issue of observing distributed computations in a causally consistent way and the basic problems of detecting global predicates are discussed. To illustrate the major difficulties, some typical monitoring and debugging approaches are assessed, and it is demonstrated how their feasibility is severely limited by the fundamental problem to master the complexity of causal relationships.
Consistent global states of distributed systems: Fundamental concepts and mechanisms
- DISTRIBUTED SYSTEMS
, 1993
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Parallel and Distributed Simulation of Discrete Event Systems
, 1995
"... The achievements attained in accelerating the simulation of the dynamics of complex discrete event systems using parallel or distributed multiprocessing environments are comprehensively presented. While parallel discrete event simulation (DES) governs the evolution of the system over simulated time ..."
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Cited by 118 (17 self)
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The achievements attained in accelerating the simulation of the dynamics of complex discrete event systems using parallel or distributed multiprocessing environments are comprehensively presented. While parallel discrete event simulation (DES) governs the evolution of the system over simulated time in an iterative SIMD way, distributed DES tries to spatially decompose the event structure underlying the system, and executes event occurrences in spatial subregions by logical processes (LPs) usually assigned to different (physical) processing elements. Synchronization protocols are necessary in this approach to avoid timing inconsistencies and to guarantee the preservation of event causalities across LPs. Included in the survey are discussions on the sources and levels of parallelism, synchronous vs. asynchronous simulation and principles of LP simulation. In the context of conservative LP simulation (Chandy/Misra/Bryant) deadlock avoidance and deadlock detection/recovery strategies, Con...
A Generic Framework for Parallelization of Network Simulations
- in Proceedings of the Seventh International Symposium on Modeling, Analysis and Simulation of Computer and Telecommunication Systems
, 1999
"... Discrete event simulation is widely used within the networking community for purposes such as demonstrating the validity of network protocols and architectures. Depending on the level of detail modeled within the simulation, the running time and memory requirements can be excessive. The goal of our ..."
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Cited by 114 (20 self)
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Discrete event simulation is widely used within the networking community for purposes such as demonstrating the validity of network protocols and architectures. Depending on the level of detail modeled within the simulation, the running time and memory requirements can be excessive. The goal of our research is to develop and demonstrate a practical, scalable approach to parallel and distributed simulation that will enable widespread reuse of sequential network simulation models and software. We focus on an approach to parallelization where an existing network simulator is used to build models of subnetworks that are composed to create simulations of larger networks. Changes to the original simulator are minimized, enabling the parallel simulator to easily track enhancements to the sequential version. In this paper we describe our lessons learned in applying this approach to the publicly available ns [9] software package, and converting it to run in a parallel fashion on a network of wo...
Large-scale network simulation – how big? how fast
- In Symposium on Modeling, Analysis and Simulation of Computer Telecommunication Systems (MASCOTS
, 2003
"... Parallel and distributed simulation tools are emerging that offer the ability to perform detailed, packet-level simulations of large-scale computer networks on an unprecedented scale. The state-of-the-art in large-scale network simulation is characterized quantitatively. For this purpose, a metric b ..."
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Cited by 52 (6 self)
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Parallel and distributed simulation tools are emerging that offer the ability to perform detailed, packet-level simulations of large-scale computer networks on an unprecedented scale. The state-of-the-art in large-scale network simulation is characterized quantitatively. For this purpose, a metric based on the number of Packet Transmissions that can be processed by a simulator per Second of wallclock time (PTS) is used as a means to quantitatively assess packet-level network simulator performance. An approach to realizing scalable network simulations that leverages existing sequential simulation models and software is described. Results from a recent performance study are presented concerning large-scale network simulation on a variety of platforms ranging from workstations to cluster computers to supercomputers. These experiments include runs utilizing as many as 1536 processors yielding performance as high as 106 Million PTS. The performance of packet-level simulations of web and ftp traffic, and Denial of Service attacks on networks containing millions of network nodes are briefly described, including a run demonstrating the ability to simulate a million web traffic flows in near real-time. New opportunities and research challenges to fully exploit this capability are discussed. 1.
µsik - A Micro-Kernel for Parallel/Distributed Simulation Systems
- Workshop on Principles of Advanced and Distributed Simulation
, 2005
"... We present a novel micro-kernel approach to parallel/distributed simulation. Using the micro-kernel approach, we develop a unified architecture for incorporating multiple types of simulation processes. The processes hold potential to employ a variety of synchronization mechanisms, and could alter th ..."
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Cited by 44 (13 self)
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We present a novel micro-kernel approach to parallel/distributed simulation. Using the micro-kernel approach, we develop a unified architecture for incorporating multiple types of simulation processes. The processes hold potential to employ a variety of synchronization mechanisms, and could alter their choice of mechanism dynamically. Supported mechanisms include traditional lookahead-based conservative and state saving-based optimistic execution approaches, as well as newer mechanisms such as reverse computation-based optimistic execution and aggregation-based event processing, all within a single parsimonious application programming interface (API). We also present the internal implementation and a preliminary performance evaluation of this interface in µsik, which is an efficient parallel/distributed realization of our micro-kernel architecture in C ++. 1.
Exploiting Temporal Uncertainty in Parallel and Distributed Simulations
- in Proceedings of the 13th Workshop on Parallel and Distributed Simulation
, 1999
"... Most work to date in parallel and distributed discrete event simulation is based on assigning precise time stamps to events, and time stamp order event processing. An alternative approach is examined where modelers use time intervals rather than precise time stamps to specify uncertainty as to wh ..."
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Cited by 36 (3 self)
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Most work to date in parallel and distributed discrete event simulation is based on assigning precise time stamps to events, and time stamp order event processing. An alternative approach is examined where modelers use time intervals rather than precise time stamps to specify uncertainty as to when events occur. Partial orderings called approximate time (AT) and approximate time causal (ATC) order are proposed and synchronization algorithms developed that exploit these specifications to yield more efficient execution on parallel and distributed computers. Performance measurements of the AT-ordering mechanism on a cluster of workstations demonstrate as much as twenty-fold performance improvement compared to time stamp ordering with negligible impact on the results computed by the simulation. The context for much of this work is federated simulation systems that provided the initial motivation for this work. These results demonstrate that exploiting temporal uncertainty inhere...
A Federated Approach to Distributed Network Simulation
- ACM Transactions on Modeling and Computer Simulation
, 2004
"... We describe an approach and our experiences in applying federated simulation techniques to create large-scale parallel simulations of computer networks. Using the federated approach, the topology and the protocol stack of the simulated network is partitioned into a number of submodels, and a simulat ..."
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Cited by 31 (3 self)
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We describe an approach and our experiences in applying federated simulation techniques to create large-scale parallel simulations of computer networks. Using the federated approach, the topology and the protocol stack of the simulated network is partitioned into a number of submodels, and a simulation process is instantiated for each one. Runtime infrastructure software provides services for interprocess communication and synchronization (time management). We first describe issues that arise in homogeneous federations where a sequential simulator is federated with itself to realize a parallel implementation. We then describe additional issues that must be addressed in heterogeneous federations composed of different network simulation packages, and describe a dynamic simulation backplane mechanism that facilitates interoperability among different network simulators. Specifically, the dynamic simulation backplane provides a means of addressing key issues that arise in federating different network simulators: differing packet representations, incomplete implementations of network protocol models, and differing levels of detail among the simulation processes. We discuss two different methods for using the backplane for interactions between heterogeneous simulators: the cross-protocol stack method and the split-protocol stack method. Finally, results from an experimental study are presented for both the homogeneous and
Security and privacy for partial order time
- In PDCS
, 1994
"... Partial order time expresses issues central to many problems in asynchronous distributed systems, but suffers from inherent security and privacy risks. Secure partial order clocks provide a general method to develop application protocols that transparently protect against these risks. Our previous S ..."
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Cited by 27 (10 self)
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Partial order time expresses issues central to many problems in asynchronous distributed systems, but suffers from inherent security and privacy risks. Secure partial order clocks provide a general method to develop application protocols that transparently protect against these risks. Our previous Signed Vector Timestamp protocol provides a partial order time service with some security: no one can forge dependence on an honest process. However, that protocol still permits some forgery of dependence, permits all denial of precedence, and leaks private information. This paper uses secure coprocessors to improve the vector protocol: our new Sealed Vector Timestamp protocol detects both the presence and absence of causal paths even in the presense of malicious processes, and protects against some privacy risks as well. By solving these previously open security problems, our new protocol provides a foundation for incorporating security and privacy into distributed application protocols based on partial order time.
