VNF performance modelling : from stand-alone to chained topologies

One of the main incentives for deploying network functions on a virtualized or cloud-based infrastructure, is the ability for on-demand orchestration and elastic resource scaling following the workload demand. This can also be combined with a multi-party service creation cycle: the service provider sources various network functions from different vendors or developers, and combines them into a modular network service. This way, multiple virtual network functions (VNFs) are connected into more complex topologies called service chains. Deployment speed is important here, and it is therefore beneficial if the service provider can limit extra validation testing of the combined service chain, and rely on the provided profiling results of the supplied single VNFs. Our research shows that it is however not always evident to accurately predict the performance of a total service chain, from the isolated benchmark or profiling tests of its discrete network functions. To mitigate this, we propose a two-step deployment workflow: First, a general trend estimation for the chain performance is derived from the stand-alone VNF profiling results, together with an initial resource allocation. This information then optimizes the second phase, where online monitored data of the service chain is used to quickly adjust the estimated performance model where needed. Our tests show that this can lead to a more efficient VNF chain deployment, needing less scaling iterations to meet the chain performance specification, while avoiding the need for a complete proactive and time-consuming VNF chain validation

Failure-awareness and dynamic adaptation in data scheduling

Over the years, scientific applications have become more complex and more data intensive. Especially large scale simulations and scientific experiments in areas such as physics, biology, astronomy and earth sciences demand highly distributed resources to satisfy excessive computational requirements. Increasing data requirements and the distributed nature of the resources made I/O the major bottleneck for end-to-end application performance. Existing systems fail to address issues such as reliability, scalability, and efficiency in dealing with wide area data access, retrieval and processing. In this study, we explore data-intensive distributed computing and study challenges in data placement in distributed environments. After analyzing different application scenarios, we develop new data scheduling methodologies and the key attributes for reliability, adaptability and performance optimization of distributed data placement tasks. Inspired by techniques used in microprocessor and operating system architectures, we extend and adapt some of the known low-level data handling and optimization techniques to distributed computing. Two major contributions of this work include (i) a failure-aware data placement paradigm for increased fault-tolerance, and (ii) adaptive scheduling of data placement tasks for improved end-to-end performance. The failure-aware data placement includes early error detection, error classification, and use of this information in scheduling decisions for the prevention of and recovery from possible future errors. The adaptive scheduling approach includes dynamically tuning data transfer parameters over wide area networks for efficient utilization of available network capacity and optimized end-to-end data transfer performance

Congestion control algorithms of TCP in emerging networks

In this dissertation we examine some of the challenges faced by the congestion control algorithms of TCP in emerging networks. We focus on three main issues. First, we propose TCP with delayed congestion response (TCP-DCR), for improving performance in the presence of non-congestion events. TCP-DCR delays the conges- tion response for a short interval of time, allowing local recovery mechanisms to handle the event, if possible. If at the end of the delay, the event persists, it is treated as congestion loss. We evaluate TCP-DCR through analysis and simulations. Results show significant performance improvements in the presence of non-congestion events with marginal impact in their absence. TCP-DCR maintains fairness with standard TCP variants that respond immediately. Second, we propose Layered TCP (LTCP), which modifies a TCP flow to behave as a collection of virtual flows (or layers), to improve eficiency in high-speed networks. The number of layers is determined by dynamic network conditions. Convergence properties and RTT-unfairness are maintained similar to that of TCP. We provide the intuition and the design for the LTCP protocol and evaluation results based on both simulations and Linux implementation. Results show that LTCP is about an order of magnitude faster than TCP in utilizing high bandwidth links while maintaining promising convergence properties. Third, we study the feasibility of employing congestion avoidance algorithms in TCP. We show that end-host based congestion prediction is more accurate than previously characterized. However, uncertainties in congestion prediction may be un- avoidable. To address these uncertainties, we propose an end-host based mechanism called Probabilistic Early Response TCP (PERT). PERT emulates the probabilistic response function of the router-based scheme RED/ECN in the congestion response function of the end-host. We show through extensive simulations that, similar to router-based RED/ECN, PERT provides fair bandwidth sharing with low queuing delays and negligible packet losses, without requiring the router support. It exhibits better characteristics than TCP-Vegas, the illustrative end-host scheme. PERT can also be used for emulating other router schemes. We illustrate this through prelim- inary results for emulating the router-based mechanism REM/ECN. Finally, we show the interactions and benefits of combining the different proposed mechanisms

A Comparison of TCP Automatic Tuning Techniques for Distributed Computing

Rather than painful, manual, static, per-connection optimization of TCP buffer sizes simply to achieve acceptable performance for distributed applications [8, 10], many researchers have proposed techniques to perform this tuning automatically [4, 7, 9, 11, 12, 14]. This paper first discusses the relative merits of the various approaches in theory, and then provides substantial experimental data concerning two competing implementations – the buffer autotuning already present in Linux 2.4.x and “Dynamic Right-Sizing. ” This paper reveals heretofore unknown aspects of the problem and current solutions, provides insight into the proper approach for different circumstances, and points toward ways to further improve performance