Abstract
Server resource allocation and traffic management is a large area of research and business concern in order to ensure proper functionality and maintenance procedures. As a result, good server reliability models that can incorporate workload and traffic stress are necessary. This paper generalizes previous dynamic server reliability models for partitioned servers with clustered-task selection by relaxing the assumption that the correlation between channels in the server remain constant. We allow the correlation to vary deterministically with time, or as a function of a random process in discrete or continuous time. The explicit form of the survival function is derived in such cases. Numerical illustrations demonstrate the dangers of erroneously assuming independence among channels, which can lead to costly and unnecessary interventions in the system. In addition, we numerically explore the effects of a variable correlation on the survival function.
Highlights
Recent years have seen an explosion in the amount of data storage devices and computing resources as well as the need for near constant accessibility, especially as the Internet of Things (IoT) grows
Customers or jobs arrive to the server via a nonhomogenous Poisson process, which allows the arrival rate to vary over time
This paper has described a generalization of the work on clustered-task server reliability with correlated channels by Korzeniowski and Traylor in which the dependency structure was relaxed from constant δ to a function of time (δ(t)) and a function of both a continuous time and discrete time finite state Markov process (δ(X(s)))
Summary
Recent years have seen an explosion in the amount of data storage devices and computing resources as well as the need for near constant accessibility, especially as the Internet of Things (IoT) grows. Much research has been done on optimal policies to handle spikes in server traffic (Iosup et al, 2011; Thomas et al, 2012; Welsh and Culler, 2003), though these are policies to handle overload, and are typically based on some sort of threshold analysis. Other attempts to model and predict server reliability include classification trees (Vishwanath, 2010) and other standard data mining and reliability theory techniques such as Weibull analysis. We propose here an extension of previous work by Cha and Lee (Cha and Lee, 2011), Korzeniowski and Traylor (Korzeniowski and Traylor, 2016), and Traylor (Traylor, 2016) that provides a more analytical and widely applicable solution as opposed to the more traditional data-driven approaches
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