Streaming video applications on the Internet generally have very high bandwidth requirements and yet are often unresponsive to network congestion. In order to avoid congestion collapse and improve video quality, these applications need to respond to congestion in the network by deploying mechanisms to reduce their bandwidth requirements under conditions of heavy load. In reducing bandwidth, video with high motion will look better if all the frames are kept but the frames have low quality, while video with low motion will look better if some frames are dropped but the remaining frames have high quality. Unfortunately current video applications scale to fit the available bandwidth without regard to the video content. In this thesis, we present an adaptive content-aware scaling mechanism that reduces the bandwidth occupied by an application by either dropping frames (temporal scaling) or by reducing the quality of the frames transmitted (quality scaling). We have designed a streaming video client and server with the server capable of quantifying the amount of motion in an MPEG stream and scaling each scene either temporally or by quality as appropriate, maximizing the appearance of each video stream. We have evaluated the impact of content-aware scaling by conducting a user study wherein the subjects rated the quality of video clips that were first scaled temporally and then by quality in order to establish the optimal mechanism for scaling a particular stream. We find that content-aware scaling can improve video quality by as much as 50%. We have also evaluated the practical impact of adaptively scaling the video stream by conducting a user study for longer video clips with varying amounts of motion and available bandwidth. We find that for such clips also the improvement in perceptual quality on account of adaptive content-aware scaling is as high as 30%

Serverless functions have emerged as a prominent paradigm in software deployment, providing automated resource scaling, resulting in demand-based operational expenses. One of the most significant challenges associated with serverless functionsis the cold start delay, preventing organisations with latency-critical web applications from adopting a serverless technology. Existing research on the cold start problem primarily focuses on mitigating the delay by modifying and optimising serverless platform technologies. However, these solutions have predominantly yielded modest reductions in time delay. Consequently, the purpose of this study is to establish conditions and circumstances under which the cold start issue can be addressed through the type of approach presented in this study. Through a design science research methodology, a software artefact named AdaptiveServerless Invocation Predictor (ASIP) was developed to mitigate the cold start issue through monitoring web application user traffic in real-time. Based on the user traffic, ASIP preemptively pre-initialises serverless functions likely to be invoked, to avoid cold start occurrences. ASIP was tested against a realistic workload generated by test participants. Evaluation of ASIP was performed through analysing the reduction in time delay achieved and comparing this against existing cold start mitigation strategies. The results indicate that predicting serverless function invocations based on real-time traffic analysis is a viable approach, as a tangible reduction in response time was achieved. Conclusively, the cold start mitigation strategy assessed and presented in this study may not provide a sufficiently significant mitigation effect relative to the required implementation effort and operational expenses. However, the study has generated valuable insights regarding circumstantial factors concerning cold start mitigation. Consequently, this study provides a proof of concept for a more sophisticated version of the mitigation strategy developed in this study, with greater potential to provide a significant delay reduction without requiring substantial computational resources.