The Utility of Exploiting Idle Memory for Data-Intensive Computations
Anurag Acharya, Sanjeev K. Setia · 1998
In this paper, we examine the utility of exploiting idle memory in workstation pools. We attempt to answer the following questions. First, given a workstation pool, what fraction of the memory can be expected to be idle? This provides an estimate of the opportunity for hosting guest data. Second, what fraction of a individual host's memory can be expected to be idle? This helps determine the recruitment policy -- what is the maximum amount of memory that should be recruited on a single host? Third, what is the distribution of memory idle-times? That is, what is the probability that a chunk of memory that is currently idle will be idle for longer than time t? This information indicates how long guest data can be expected to survive; applications that access their data-sets frequently within the expected life-time of guest data are more likely to benefit from exploiting idle memory. Fourth, how much benefit can a user expect? We use two metrics for the benefit of exploiting idle memory: ...