Workload characterization is a well-established discipline that plays a key role in many
performance engineering studies. The large-scale social behavior inherent in the …

Evaluating the performance of large compute clusters requires benchmarks with
representative workloads. At Google, performance benchmarks are used to obtain …

The performance of a system is determined by its characteristics as well as by the
composition of the load being processed. Hence, its quantitative description is a …

As online service providers utilize cloud computing to host their services, they are
challenged by evaluating the quality of experience and designing redirection strategies in …

Past research on high performance computers for scientific applications has concentrated
on CPU speed and exploitation of parallelism, but has, until very recently, neglected 1/0 …

Abstract Service availability is an important consideration when carriers deploy new, packet-
based services. In this paper we define the service availability based on user behavior, and …

Performance is often a key factor in determining the success of a parallel software system.
Performance evaluation techniques can be classified into three categories: measurement …

Service Reliability is an important consideration for new service deployment. Traditional
system-oriented measures are no longer adequate to describe the reliability perceived by …

Workload characterization is known to be a difficult and yet a very important facet of
performance modeling. User behavior graphs have been advocated as a practical means of …

Clustering techniques are widely recommended tools for workload classification. The k-
means algorithm is widely accepted as the" standard" technique of detecting workload …