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Forecasting of wind speed and wind power generation is indispensable for the effective operation of a wind farm and the optimal management of revenue and risks. Hybrid forecasting of time series data is considered to be a potentially effective alternative compared with the conventional single forecasting modeling approaches such as autoregressive integrated moving average (ARIMA) and artificial neural...
Performance ticket handling is an expensive operationin highly virtualized cloud data centers where physical boxeshost multiple virtual machines (VMs). A large body of ticketsarise from the resource usage warnings, e.g., CPU and RAMusages that exceed predefined thresholds. The transient natureof CPU and RAM usage as well as their strong correlation acrosstime among co-located VMs drastically increase...
We present a web based tool to demonstrate PRACTISE, a neural network based framework for efficient and accurate prediction of server workload time series in data centers. For the evaluation, we focus on resource utilization traces of CPU, memory, disk, and network. Compared with ARIMA and baseline neural network models, PRACTISE achieves significantly smaller average prediction errors. We demonstrate...
We analyze workload traces from production data centers and focus on their VM usage patterns of CPU, memory, disk, and network bandwidth. Burstiness is a clear characteristic of many of these time series: there exist peak loads within clear periodic patterns but also within patterns that do not have clear periodicity. We present PRACTISE, a neural network based framework that can efficiently and accurately...
Motivated by the high system complexity of today's datacenters, a large body of related studies tries to understand workloads and resource utilization in datacenters. However, there is little work on exploring unsuccessful job and task executions. In this paper, we study three types of unsuccessful executions in traces of a Google datacenter, namely fail, kill, and eviction. The objective of our analysis...
Big-data applications are being increasingly used in today's large-scale data enters for a large variety of purposes, such as solving scientific problems, running enterprise services, and computing data-intensive tasks. Due to the growing scale of these systems and the complexity of running applications, jobs running in big-data systems experience unsuccessful terminations of different nature. While...
In large-scale data enters, software and hardware failures are frequent, resulting in failures of job executions that may cause significant resource waste and performance deterioration. To proactively minimize the resource inefficiency due to job failures, it is important to identify them in advance using key job attributes. However, so far, prevailing research on datacenter workload characterization...
As modern service systems are pressured to provide competitive prices via cost-effective capacity planning, especially in the paradigm of cloud computing, service level agreements (SLAs) end up becoming ever more sophisticated, i.e., fulfilling targets of different percentiles of response times. However, it is no mean feat to predict even the average response times of real systems, or even abstracted...
Mobile commerce systems create a new mobile business model and change e-commerce paradigms, having an especially significant effect on the medical and insurance industries. Furthermore, the real estate industry is increasing in the booming market, but tends to become overheated. Thus, some innovative techniques (such as mobile commerce) were adopted by estate agent to enhance their competitive advantage...
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