This paper addresses the challenging problem of guaranteeing quality-of-service (QoS) requirements associated with parallel running queries in distributed stream processing engines. In such platforms, the real-time processing of streaming data often requires executing a set of user-defined queries over continues data flows. However, previous studies showed that guaranteeing QoS enforcement (such as end-to-end response time) for a collection of applications is a complex problem. This paper presents an advanced resource allocation strategy to tackle such a problem by considering the traffic pattern of individual data streams. To properly allocate resource for streaming queries execution, we define a certain number of priority channels to categorize the streaming data across the system. The resource allocation is addressed as an optimization problem where a set of cost functions is defined to achieve the following goals: a) reduce the sum of QoS violation incidents across all applications; b) increase the CPU utilization level, and (c) avoid the additional costs caused by frequent reconfigurations. The proposed solution does not depend on any assumption about the incoming data rate or the query processing time. The performance of the proposed solution is benchmarked, and the experimental results reveal that the proposed scheme increases the overall resource utilization by 23% on average and reduces the QoS violations by 29% against round-robin strategy. It could also prevent QoS violation incidents at different levels by tuning the cost function.