#CHECKING DISK ARCHIVE HOW TO#
#CHECKING DISK ARCHIVE UPDATE#

In this paper, we present a systematic approach to provide a holistic and insightful view of disk failures. Effective disk failure management and data recovery also requires a deep understanding of the nature of disk failures.

However, there is very little understanding on the characteristics of disk failures in data centers. Disk failures as well as their impact on the performance of storage systems and operating costs are becoming an increasingly important concern for data center designers and operators. Among different server components, hard disk drives are known to contribute significantly to server failures. With the advent of cloud computing and online services, large enterprises rely heavily on their data centers to serve end users.

#CHECKING DISK ARCHIVE UPDATE#

In this paper we present a model for describing the relationship between update frequency and income derived from data, present solutions for calculating the optimal update frequency for two common classes of functions for describing decay behaviour, and validate the benefits of our framework. For businesses that are able to adapt their resources, another question is to determine the number of updates that optimizes the income derived from the data.

#CHECKING DISK ARCHIVE HOW TO#

Previous work on data freshness has investigated how to best distribute a fixed number of updates among entities, in order to maximize average freshness. Consequently, there is no uniform best update frequency for all entities. This is especially important since often entities exhibit a different speed of getting outdated, e.g., addresses of students change more frequently than addresses of retirees, or news portals change more frequently than homepages. As refreshing data usually has a cost, for instance computation time, network bandwidth or human work time, a problem is to find the right update frequency depending on the benefit gained from the information and on the speed with which the information is expected to get outdated. Therefore, if data quality shall be maintained over time, often periodical refreshing is done. Many kinds of information, e.g., addresses, crawls of webpages, or academic affiliations, are prone to becoming outdated over time. In order to show the efficacy and the accuracy of the proposed approach, the results obtained from the analytical model is validated with the results obtained from the simulations. Second and foremost, the relationship between the number of nodes and the number of repairmen is presented with an optimization analysis for large-scale systems.

The analytical model and solution presented here are capable of considering large number of nodes up to thousands and able to incorporate availability issues of the system. First, a generic approximate solution approach is adapted and developed for performability modelling which considers performance and availability issues of large number of nodes with multi-repairmen. The key contribution of this paper is twofold. In this paper, analytical modelling and optimization analysis are presented for large-scale systems. This leads to developing and designing analytical models to understand and predict of complex system behaviour in order to ensure availability of large-scale systems. However, the practical large-scale systems result in unpredictable, fault-tolerant, often detrimental outcomes. The large-scale communication systems and computer networks provide flexible, efficient, and highly available services to their users.

Checking disk archive