The usage of Kubernetes for running microservices applications is increasing nowadays. In a particular application, all microservices do not have the same priority. Hence it is costly to allocate the same resources to both high and low-priority services. This research aims to utilize spot instances to run low-priority services with the intention of reducing the cloud cost and providing overall high availability to the application. A service called KubeEconomy has been proposed to monitor and manage Kubernetes worker nodes. Three functionalities of the KubeEconomy service have been explained and it is shown that it is possible to reduce the cloud cost while maintaining high availability for the microservices.

The performance of a distributed stream processing engine is traditionally considered in terms of fundamental measurements of latency and throughput. Recently, Apache Storm has demonstrated sub-millisecond latencies for inter-component tuple transmission, though it does so through aggressive throttling that leads to strict throughput limitations in order to keep tuple queues near empty. On the other hand, Apache Heron has excellent throughput characteristics, especially when operating near unstable conditions, but its inter-component latencies typically start around 10 milliseconds. Both of these systems require roughly 650MB of installation space. We have developed Dragon, loosely based on the same API as Storm and Heron, that is both lightweight, requiring just 7.5MB of installation space, and competitive in performance to Storm and Heron. In this paper we show experiments with all three systems using the Word Count benchmark. Dragon achieves throughput characteristics near to that of Heron and inter-component latencies less than 10ms under high load. In particular, Dragon's maximum latency is significantly less that Storm's maximum latency under high load. Finally Dragon managed to remain stable at higher effective throughput than Heron. We believe Dragon is a good "allrounder" solution and is particularly suitable for Edge computing applications, given its small installation footprint.

Data Stream Processing (DSP) is a widely used programming paradigm to process an unbounded event stream. Often, DSP frameworks are deployed on the cloud with a scalable resource model. One of the key requirements of DSP is to produce results with low latency. With the emergence of IoT, many event sources have been located outside the cloud which can result in higher end-to-end latency due to communication overhead. However, due to the abundance of resources at the IoT layer, Edge computing has emerged as a viable computational paradigm. In this paper, we devise an optimisation framework, consisting of a constraint satisfaction formulation and a system model, that aims to minimise end-to-end latency through appropriate placement of DSP operators either on cloud nodes or edge devices, i.e. deployed in an edge-cloud integrated environment. We test our optimisation framework using OMNeT++, with realistic topologies and power consumption data, and show that it is capable of achieving approx 1.65 times reduction of latency compared to edge-only and cloud-only placements, which in turn also reduces the energy consumption per event by up to approx 4% at the edge layer. To the best of our knowledge our optimisation framework is the first of its kind to integrate power, bandwidth and CPU constraints with latency minimisation.

False sharing is a major class of performance bugs in parallel applications. Detecting false sharing is difficult as it does not change the program semantics. We introduce an efficient and effective approach for detecting false sharing based on machine learning.