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Devising an elastic resource allocation controller of data analytical applications in virtualized data-center has received a great attention recently, mainly due to the fact that even a slight performance improvement can translate to huge monetary savings in practical large-scale execution. Apache Flink is among modern streamed data processing run-times that can provide both low latency and high throughput computation in to execute processing pipelines over high-volume and high-velocity data-items under tight latency constraints. However, a yet to be answered challenge in a large-scale platform with tens of worker nodes is how to resolve the run-time violation in the quality of service (QoS) level in a multi-tenant data streaming platforms, particularly when the amount of workload generated by different users fluctuates. Studies showed that a static resource allocation algorithm (round-robin), which is used by default in Apache Flink, suffer from lack of responsiveness to sudden traffic surges happening unpredictably during the run-time. In this paper, we address the problem of resource management in a Flink platform for ensuring different QoS enforcement levels in a platform with shared computing resources. The proposed solution applies theoretical principals borrowed from close-loop control theory to design a CPU and memory adjustment mechanism with the primary goal to fulfill the different QoS levels requested by submitted applications while the resource interference is considered as the critical performance-limiting factor. The performance evaluation is carried out by comparing the proposed resource allocation mechanism with two static heuristics (round robin and class-based weighted fair queuing) in a 80-core cluster under multiple traffic patterns resembling sudden changes in the incoming workloads of low-priory streaming applications. The experimental results confirm the stability of the proposed controller to regulate the underlying platform resources to smo

The increasing usage of information technology in military affairs raises the need for robust high capacity radio networks. The network will be used to provide several different types of services, for example group calls and situation awareness services. All services have specific demands on packet delays and packet losses in order to be fully functional, and therefore there is a need for a Quality of Service (QoS) mechanism in the network. In this master thesis we examine the possibility to provide a QoS mechanism in Ad Hoc networks by using priority queues. The study includes two different queuing schemes, namely fixed priority queuing and weighted fair queuing. The performance of the two queuing schemes are evaluated and compared with respect to the ability to provide differentiation in network delay, i.e., provide high priority traffic with lower delays than low priority traffic. The study is mainly done by simulations, but for fixed priority queuing we also derive a analytical approximation of the network delay. Our simulations show that fixed priority queuing provides a sharp delay differentiation between service classes, while weighted fair queuing gives the ability to control the delay differentiation. One of those queuing schemes alone might not be the best solution for providing QoS, instead we suggest that a combination of them is used.

The increasing usage of information technology in military affairs raises the need for robust high capacity radio networks. The network will be used to provide several different types of services, for example group calls and situation awareness services. All services have specific demands on packet delays and packet losses in order to be fully functional, and therefore there is a need for a Quality of Service (QoS) mechanism in the network. In this master thesis we examine the possibility to provide a QoS mechanism in Ad Hoc networks by using priority queues. The study includes two different queuing schemes, namely fixed priority queuing and weighted fair queuing. The performance of the two queuing schemes are evaluated and compared with respect to the ability to provide differentiation in network delay, i.e., provide high priority traffic with lower delays than low priority traffic. The study is mainly done by simulations, but for fixed priority queuing we also derive a analytical approximation of the network delay. Our simulations show that fixed priority queuing provides a sharp delay differentiation between service classes, while weighted fair queuing gives the ability to control the delay differentiation. One of those queuing schemes alone might not be the best solution for providing QoS, instead we suggest that a combination of them is used.