Mean response time and availability as optimization criteria for checkpoint placement are better replaced by workable formulas that calculate the ratio between the marginal gain accrued to users who experience system failure and the (presumably slight) loss suffered on average by all users. OPTIMIZATION CRITERIA FOR CHECKPOINT PLACEMENT Checkpointing is becoming increasingly popular in real-time and database systems as a means of mitigating the consequences of failure. Checkpointing involves storing authenticated process state information that can then be used in the event of failure to restart the affected computation from the latest checkpoint, instead of from the beginning. There are a number of articles in the literature dealing with the optimal number and placement of checkpoints [1-6, 9, 10]. Our purpose here is to address an important point that has been overlooked; that is, although the analyses presented are often ingenious and elegant, their practical validity is thrown into doubt by an inappropriate choice of optimization criteria. We propose alternative criteria that we believe to be of greater practical relevance. In every published analysis of optimal checkpointing that we have seen, the only optimization criteria used are mean response time and/or availability. No explicit justification for this is ever given. A carelessness in choosing optimization criteria can result in inadequate and misleading performance analyses, for, in a real sense, optimization criteria are relative, not absolute. The simple act of choosing a particular criterion imposes a bias on the results that follow. By definition, This work has been supported in part by NASA under Grant NAG 1-296. Any opinions here expressed are those of the authors and do not necessarily represent the views of NASA. All correspondence regarding this paper should he sent to Kang G. Shin. optimization criteria specify the commodity that is of importance and therefore to be optimized. Optimization criteria have a very subtle influence on the way systems are viewed. In a sense, they are languages through which we seek to convey system performance. We know from experience with natural languages that these affect not only the way in which ideas are expressed but also the very ideas themselves. Optimization criteria are no exception. They manipulate our view of system behavior, contorting it to fit a prefabricated mold. For this reason, the choice of optimization criteria determines the practical usefulness of the results that are then derived. Optimization criteria are performance measures. To choose them correctly, it is important to determine what it is we wish to express. In this case, where we wish to measure the advantages that accrue from checkpointing, it is best to trade the benefits derived from them against the overhead they impose: in other words, to carry out a value analysis. First, we shall show why mean response time and availability do not do so effectively and are therefore inadequate performance measures for checkpoints. Mean response time has long been a favorite measure of computer performance among queuing analysts. It is much easier to compute than the higher moments of response time. That is, it is sometimes easy to obtain the mean response time even when the response time distribution is very difficult or impossible to calculate.
In REF , Krishna et al. point out that the optimization criteria for checkpoint placement is to trade the benefits derived from checkpointing with the overhead imposed.
Optimization criteria for checkpoint placement
CACM
Computer Science