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Their approach attempts to satisfy workload response time by incrementally adding CPU and memory resources to the workload.
In [45], the authors propose a lightweight heuristic based on workload response time, which is defined by the customer.
The proposals in [19, 50] react to workload response time, instead of CPU/Memory thresholds, and trigger adaptation to preserve response times to the requested levels.
The Police Districting Problem (PDP) concerns the efficient and effective design of patrol sectors in terms of performance attributes such as workload, response time, etc.
Also in this work, we have considered the required clock cycles, workload, response time, etc. as performance metric to compare the effectiveness of the proposed approach with respect to other existing papers.
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The assessment is particularly difficult since both workload and response times are stochastic.
Each alternative represents a different possibility in terms of workload and response times, but assessing these by hand can be very time consuming.
Some proposals assume the ability to capture the relationships between cooperating VMs, and many proposals abstract how workload KPIs, like response time, can be captured.
In contrast, the authors in [35] used a second order ARMA model, taking into account two previous control intervals to predict workload performance, by using response time as the performance metric.
First, since each worker may have many outstanding tasks, to balance the workload, and reduce the response time, we use a threshold (eta _{ q}) to restrict the maximum number of tasks for each worker.
Further no single plan was best in both average response time and workload variation.
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