. In this chapter, we present a history-based access-control mechanism that is suitable for mediating accesses from mobile code. The key idea behind history-based access-control is to maintain a selective history of the access requests made by individual programs and to use this history to improve the differentiation between safe and potentially dangerous requests. What a program is allowed to do depends on its own behavior and identity in addition to currently used discriminators like the location it was loaded from or the identity of its author/provider. History-based access-control has the potential to significantly expand the set of programs that can be executed without compromising security or ease of use. We describe the design and implementation of Deeds, a history-based access-control mechanism for Java. Access-control policies for Deeds are written in Java, and can be updated while the programs whose accesses are being mediated are still executing. 1 Introduction...

In this paper, we examine the utility of exploiting idle workstations for parallel computation. We attempt to answer the following questions. First, given a workstation pool, for what fraction of time can we expect to find a cluster of $k$ workstations available? This provides an estimate of the opportunity for parallel computation. Second, how stable is a cluster of free machines and how does the stability vary with the size of the cluster? This indicates how frequently a parallel computation might have to stop for adapting to changes in processor availability. Third, what is the distribution of workstation idle-times? This information is useful for selecting workstations to place computation on. Fourth, how much benefit can a user expect? To state this in concrete terms, if I have a pool of size S, how big a parallel machine should I expect to get for free by harvesting idle machines. Finally, how much benefit can be achieved on a real machine and how hard does a parallel programmer have to work to make this happen? To answer the workstation-availability questions, we have analyzed 14-day traces from three workstation pools. To determine the equivalent parallel machine, we have simulated the execution of a group of well-known parallel programs on these workstation pools. To gain an understanding of the practical problems, we have developed the system support required for adaptive parallel programs as well as an adaptive parallel CFD application. (Also cross-referenced as UMIACS-TR-96-80)

\f e conslcier the problem of efficiently couphrrg multiple cfataparakl programs at Irrntime.We propose an approach that establishes mappings between data structures m different flat a-parallel programs and implements a user-specified com wmencv model.Mappings are established at rnntlrne and can be added and deleted while the programs being coupled are in execution.Mappings, or the icleutity of the processors involved, do not ha~,r TObe kno~vn at compile-time or even link-time.Pro,gramh ran be m adc to interact tvith different granularities of interaction without requiring any re-cociing.A-priori knowledge of consistency requirements allows buffering of data as well as concurrent execution of the coupled applications.Efficient data movement is achii=w=d b~-p] e-computing an optimized schedule.~~e describe our ])lorotype mrpiernent,atiou and evaluate lts performance us-LMga wt of svnthetrc "benchmarks.\Ve examme the varlat]on of performance with varlatlon in t he conslstenc} reqrrme-[nent W+ demonstrate That the cost of' the tlexibiht}-pro-~,lded hl our coupling scheme is not prolubltl~,e whel L ronpared with a monolithic program that performs the sam~ computatlou.1