The performance improvements brought by demand paging policies with swapped working-sets depend on several factors, among which the scheduling policy, the behaviour of the programs running in the system and the secondary memory latency characteristics are the more noticeable. We present in this paper a modelling approach to quantify the effects of these factors on the performance of a system running with a swapped working-sets policy. A preliminary analysis, conducted in the virtual time of the programs, shows their influence on the paging behaviour of programs. The results of this analysis are then used within a detailed queueing network of a multiprogrammed system. Computationnaly simple expressions for the CPU time spent in user state and in supervisor state are obtained for a class of paging policies ranging from pure demand paging to demand paging with swapped working-sets. Numerical examples illustrate the analysis, and these results are compared with measurements made on a real system running with swapped working-sets policies.

In this paper, we shall use the same analytical model to investigate the behaviour of the paging drum when the degree of multiprogramming is set to its optimal value. The main conclusion, which corroborates the general feeling on performance of virtual memory systems [3, 4, 5], is that drum utilization remains in the 50% range whenever CPU utilization is maximized, if no resource is saturated. The results are validated through simulation experiments in order to relax some theoretical assumptions used in the analytical model and to take into account some detailed mechanisms such as CPU overheads. As an application, an adaptive control algorithm based on the activity of the paging device is proposed. The algorithm has been simulated and comparisons with results reported in [2] are presented.Moreover, after investigations we show also that optimal performance can only be achieved through a balanced use of the different resources of the system. This conclusion cross-checks and extends Buzen's theoretical results [6] and Wulf's practical ones [7].

The allocation of the central processing unit (CPU) of a computer system in quanta of fixed length in round-robin fashion favors jobs with shorter total CPU processing time by reducing the time they spend waiting in queue below what it would be if all the lobs were served in first-come-first-served order This effect can be accentuated by the use of short quanta. The main disadvantage of this allocation policy is the resulting time the CPU spends in overhead activities when switching from one task to the other, this too will increase with smaller quanta. Thus, it appears useful to consider adaptive CPU allocation policies to reduce the overhead during high traffic conditions when saturation of this resource is more likely while keeping a small quantum during periods of low arrival traffic. In this paper we analyse such a policy, it is assumed that each time at least r (a threshold) arrivals occur during a quantum, the job currently using the CPU is allocated an additional quantum (if It is needed). Thus, the number of job arrivals during a quantum is used as a sensor of the intensity of arrival traffic. This policy, which can be easily implemented in hardware, is analysed using a mathematical model yielding the average response time for jobs as a function of mean total CPU time, the quantum size, r, and a fixed overhead for switching tasks, with a Poisson arrival process. Numerical results to illustrate the effect of this policy are presented.

Due to the practical complexity of addressing variable length records placed in arbitrary locations of a fixed-head disk (or drum), and because of difficulty of managing secondary memory space in such cases, variable length records are often stored with their first address at a fixed location of the magnetic support. We present a queuing model of such a scheme, assuming a Poisson arrival stream and arbitrary distributed record lengths. The stationary probability distribution of the number of transfer requests in queue and the expected response time are obtained. Numerical examples illustrating the results are presented.

Queueing network models are well suited for analyzing certain resource allocation problems associated with operating system design. An example of such a problem is the selection of the level of multiprogramming in virtual memory systems. If the number of programs actively competing for main memory is allowed to reach too high a value, trashing will occur and performance will be seriously degraded. On the other hand, performance may also suffer if the level of multiprogramming drops too low since system resources can become seriously under utilized in this case. Thus it is important for virtual memory systems to maintain optimal or near optimal levels of multiprogramming at all times.