Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Modeling and predicting measured response time of cloud-based web
  services using long-memory time series   Predicting cloud performance from user's perspective is a complex task,
because of several factors involved in providing the service to the consumer.
In this work, the response time of 10 real-world services is analyzed. We have
observed long memory in terms of the measured response time of the
CPU-intensive services and statistically verified this observation using
estimators of the Hurst exponent. Then, na\"ive, mean, autoregressive
integrated moving average (ARIMA) and autoregressive fractionally integrated
moving average (ARFIMA) methods are used to forecast the future values of
quality of service (QoS) at runtime. Results of the cross-validation over the
10 datasets show that the long-memory ARFIMA model provides the mean of 37.5 %
and the maximum of 57.8 % reduction in the forecast error when compared to the
short-memory ARIMA model according to the standard error measure of mean
absolute percentage error. Our work implies that consideration of the
long-range dependence in QoS data can help to improve the selection of services
according to their possible future QoS values.

37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
23
The categories of this paper are: solv-int
35
The categories of this paper are: mtrl-th
16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
5
The categories of this paper are: gr-qc
31
The categories of this paper are: ao-sci
19
The categories of this paper are: eess
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
32
The categories of this paper are: atom-ph
12
The categories of this paper are: nucl-th
36
The categories of this paper are: cmp-lg