Process Management in Distributed Operating Systems

As part of designing and building the Amoeba distributed operating system, we have come up with a simple set of mechanisms for process management that allows downloading process migration, checkpointing, remote debugging and emulation of alien operating system interfaces.\ud The basic process management facilities are realized by the Amoeba Kernel and can be augmented by user-space services: Debug Service, Load-Balancing Service, Unix-Emulation Service, Checkpoint Service, etc.\ud The Amoeba Kernel can produce a representation of the state of a process which can be given to another Kernel where it is accepted for continued execution. This state consists of the memory contents in the form of a collection of segments, and a Process Descriptor which contains the additional state, program counters, stack pointers, system call state, etc.\ud Careful separation of mechanism and policy has resulted in a compact set of Kernel operations for process creation and management. A collection of user-space services provides process management policies and a simple interface for application programs.\ud In this paper we shall describe the mechanisms as they are being implemented in the Amoeba Distributed System at the Centre for Mathematics and Computer Science in Amsterdam. We believe that the mechanisms described here can also apply to other distributed systems

Routing Physarum with electrical flow/current

Plasmodium stage of Physarum polycephalum behaves as a distributed dynamical pattern formation mechanism who's foraging and migration is influenced by local stimuli from a wide range of attractants and repellents. Complex protoplasmic tube network structures are formed as a result, which serve as efficient `circuits' by which nutrients are distributed to all parts of the organism. We investigate whether this `bottom-up' circuit routing method may be harnessed in a controllable manner as a possible alternative to conventional template-based circuit design. We interfaced the plasmodium of Physarum polycephalum to the planar surface of the spatially represented computing device, (Mills' Extended Analog Computer, or EAC), implemented as a sheet of analog computing material whose behaviour is input and read by a regular 5x5 array of electrodes. We presented a pattern of current distribution to the array and found that we were able to select the directional migration of the plasmodium growth front by exploiting plasmodium electro-taxis towards current sinks. We utilised this directional guidance phenomenon to route the plasmodium across its habitat and were able to guide the migration around obstacles represented by repellent current sources. We replicated these findings in a collective particle model of Physarum polycephalum which suggests further methods to orient, route, confine and release the plasmodium using spatial patterns of current sources and sinks. These findings demonstrate proof of concept in the low-level dynamical routing for biologically implemented circuit design

Amoeba - A distributed Operating System for the 1990s

n the nexi decdde, computer prices will drop 50 low that IO, 20, or per-1 haps IO0 powerful microprocessors per user will be feasible. All this computing power will have to be organized in a simple, efficient, and fault-tolerant system that is easy to use. The basic problem with current networks of PCs and workstations is that they are not transparent; that is, users are aware of the other machines. The user logs into one machine and uses that machine only, until doing a remote login to another machine. Few if any programs take advantage of multiple CPUs, even when all are idle

Nonlinear Optimization of a Stochastic Function in a Cell Migration Model

The basis for many biological processes such as cell division and differentiation, immune responses, and tumor metastasis depends upon the cell\u27s ability to migrate effectively. A mathematical model for simulating cell migration can be useful in identifying the underlying contributing factors to the crawling motions observed in different types of cells. We present a cell migration model that simulates the 2D motion of amoeba, fibroblasts, keratocytes, and neurons according to a set of input parameters. In the absence of external stimuli the pattern of cell migration follows a persistent random walk which necessitates for several stochastic components in the mathematical model. Consequently, the cell metrics which provide a quantitative description of the cell motion varies between simulations. First we examine different methods for computing the error observed between the output metrics generated by our model and a set of target cell metrics. We also investigate ways of minimizing the variability of the output by varying the number of iterations within a simulation. Finally we apply finite differences, Hooke and Jeeves, and Nelder-Mead minimization methods to our nonlinear stochastic function to search for optimal input values

Evolutionary algorithm-based analysis of gravitational microlensing lightcurves

A new algorithm developed to perform autonomous fitting of gravitational microlensing lightcurves is presented. The new algorithm is conceptually simple, versatile and robust, and parallelises trivially; it combines features of extant evolutionary algorithms with some novel ones, and fares well on the problem of fitting binary-lens microlensing lightcurves, as well as on a number of other difficult optimisation problems. Success rates in excess of 90% are achieved when fitting synthetic though noisy binary-lens lightcurves, allowing no more than 20 minutes per fit on a desktop computer; this success rate is shown to compare very favourably with that of both a conventional (iterated simplex) algorithm, and a more state-of-the-art, artificial neural network-based approach. As such, this work provides proof of concept for the use of an evolutionary algorithm as the basis for real-time, autonomous modelling of microlensing events. Further work is required to investigate how the algorithm will fare when faced with more complex and realistic microlensing modelling problems; it is, however, argued here that the use of parallel computing platforms, such as inexpensive graphics processing units, should allow fitting times to be constrained to under an hour, even when dealing with complicated microlensing models. In any event, it is hoped that this work might stimulate some interest in evolutionary algorithms, and that the algorithm described here might prove useful for solving microlensing and/or more general model-fitting problems.Comment: 14 pages, 3 figures; accepted for publication in MNRA