A comparison of RESTART implementations

The RESTART method is a widely applicable simulation technique for the estimation of rare event probabilities. The method is based on the idea to restart the simulation in certain system states, in order to generate more occurrences of the rare event. One of the main questions for any RESTART implementation is how and when to restart the simulation, in order to achieve the most accurate results for a fixed simulation effort. We investigate and compare, both theoretically and empirically, different implementations of the RESTART method. We find that the original RESTART implementation, in which each path is split into a fixed number of copies, may not be the most efficient one. It is generally better to fix the total simulation effort for each stage of the simulation. Furthermore, given this effort, the best strategy is to restart an equal number of times from each state, rather than to restart each time from a randomly chosen stat

Personalized PageRank with Node-dependent Restart

Personalized PageRank is an algorithm to classify the improtance of web pages on a user-dependent basis. We introduce two generalizations of Personalized PageRank with node-dependent restart. The first generalization is based on the proportion of visits to nodes before the restart, whereas the second generalization is based on the probability of visited node just before the restart. In the original case of constant restart probability, the two measures coincide. We discuss interesting particular cases of restart probabilities and restart distributions. We show that the both generalizations of Personalized PageRank have an elegant expression connecting the so-called direct and reverse Personalized PageRanks that yield a symmetry property of these Personalized PageRanks

Alternative Restart Strategies for CMA-ES

This paper focuses on the restart strategy of CMA-ES on multi-modal functions. A first alternative strategy proceeds by decreasing the initial step-size of the mutation while doubling the population size at each restart. A second strategy adaptively allocates the computational budget among the restart settings in the BIPOP scheme. Both restart strategies are validated on the BBOB benchmark; their generality is also demonstrated on an independent real-world problem suite related to spacecraft trajectory optimization

Restart time correlation for core annular flow in pipeline lubrication of high-viscous oil

One of the fundamental questions that must be addressed in the effective design and operation of pipeline lubrication of heavy oil is; “how much time will be needed to restart a blocked core annular flow (CAF) line after shutdown due to fouling or pump failures”, if the pipe is to be cleaned using water only. In this work, laboratory results of shutdown and restart experiments of high-viscous oil conducted in a 5.5-m-long PVC horizontal pipe with internal diameter of 26 mm are first presented. A new correlation for the prediction of the restart time of a shutdown core annular flow line is then formulated. The predictive capabilities of the correlation are checked against measured restart time and pressure drop evolution data. Somewhat high but still reasonable predictions are obtained. The restart time correlation, together with the associated correlations formulated as well, can be of practical importance during the engineering design of high-viscous oil pipeline transportation facility for predicting restart process

A Generic Checkpoint-Restart Mechanism for Virtual Machines

It is common today to deploy complex software inside a virtual machine (VM). Snapshots provide rapid deployment, migration between hosts, dependability (fault tolerance), and security (insulating a guest VM from the host). Yet, for each virtual machine, the code for snapshots is laboriously developed on a per-VM basis. This work demonstrates a generic checkpoint-restart mechanism for virtual machines. The mechanism is based on a plugin on top of an unmodified user-space checkpoint-restart package, DMTCP. Checkpoint-restart is demonstrated for three virtual machines: Lguest, user-space QEMU, and KVM/QEMU. The plugins for Lguest and KVM/QEMU require just 200 lines of code. The Lguest kernel driver API is augmented by 40 lines of code. DMTCP checkpoints user-space QEMU without any new code. KVM/QEMU, user-space QEMU, and DMTCP need no modification. The design benefits from other DMTCP features and plugins. Experiments demonstrate checkpoint and restart in 0.2 seconds using forked checkpointing, mmap-based fast-restart, and incremental Btrfs-based snapshots

Analysis of RSVP-TE graceful restart

GMPLS is viewed as an attractive intelligent control plane for different network technologies and graceful restart is a key technique in ensuring this control plane is resilient and able to recover adequately from faults. This paper analyses the graceful restart mechanism proposed for a key GMPLS protocol, RSVP-TE. A novel analytical model, which may be readily adapted to study other protocols, is developed. This model allows the efficacy of graceful restart to be evaluated in a number of scenarios. It is found that, unsurprisingly, increasing control message loss and increasing the number of data plane connections both increased the time to complete recovery. It was also discovered that a threshold exists beyond which a relatively small change in the control message loss probability causes a disproportionately large increase in the time to complete recovery. The interesting findings in this work suggest that the performance of graceful restart is worthy of further investigation, with emphasis being placed on exploring procedures to optimise the performance of graceful restart