CoMoM: Efficient Class-Oriented Evaluation of Multiclass Performance Models

We introduce the Class-oriented Method of Moments (CoMoM), a new exact algorithm to compute performance indexes in closed multiclass queueing networks. Closed models are important for performance evaluation of multi-tier applications, but when the number of service classes is large they become too expensive to solve with exact methods such as Mean Value Analysis (MVA). CoMoM addresses this limitation by a new recursion that scales efficiently with the number of classes. Compared to the MVA algorithm, which recursively computes mean queue-lengths, CoMoM carries on in the recursion also information on higher-order moments of queue-lengths. We show that this additional information greatly reduces the number of operations needed to solve the model and makes CoMoM the best-available algorithm for networks with several classes. We conclude the paper by generalizing CoMoM to the efficient computation of marginal queue-length probabilities, which finds application in the evaluation of state-dependent attributes such as energy consumption or quality-of-service metrics

XSnap : a queueing network analysis package

Bibliography: pages 114-116.This dissertation describes the design and implementation of a sophisticated X-Windows based modelling package called XSnap, which can be used to solve product-form mixed multi-class queueing networks. A Graphical User Interface allows interactive network specification, whilst the modeller can also define complex network experiments and request customised output through the use of a language called SnapL. The solution modules used by XSnap are grouped together to form the Calculation Modules ToolBox (CMTB), which can be easily integrated into any modelling package which provides an appropriate user interface. Solution statistics are found using Reiser's Mean Value Analysis (MVA) algorithm, which has been extended to allow for the approximate solution of networks with PRIORITY servers or non-integral closed chain populations. A routing validation algorithm is used to validate the routing information for the network to be solved, and equations defining the relative throughput (or visit ratio) of each class at each centre in the network, are solved using a version of LU-Decomposition called Crout's method with partial pivoting. The dissertation also includes a study of a number of other available modelling packages. The choice of features included in the XSnap GUI has been largely influenced by this study. A number of different algorithms for solving product-form queueing networks are also discussed, and relevant points from this discussion are presented as part of the motivation for using the MVA algorithm for finding solution statistics

Predictive dynamic resource allocation for web hosting environments

E-Business applications are subject to significant variations in workload and this can cause exceptionally long response times for users, the timing out of client requests and/or the dropping of connections. One solution is to host these applications in virtualised server pools, and to dynamically reassign compute servers between pools to meet the demands on the hosted applications. Switching servers between pools is not without cost, and this must therefore be weighed against possible system gain. This work is concerned with dynamic resource allocation for multi-tiered, clusterbased web hosting environments. Dynamic resource allocation is reactive, that is, when overloading occurs in one resource pool, servers are moved from another (quieter) pool to meet this demand. Switching servers comes with some overhead, so it is important to weigh up the costs of the switch against possible system gains. In this thesis we combine the reactive behaviour of two server switching policies – the Proportional Switching Policy (PSP) and the Bottleneck Aware Switching Policy (BSP) – with the proactive properties of several workload forecasting models. We evaluate the behaviour of the two switching policies and compare them against static resource allocation under a range of reallocation intervals (the time it takes to switch a server from one resource pool to another) and observe that larger reallocation intervals have a negative impact on revenue. We also construct model- and simulation-based environments in which the combination of workload prediction and dynamic server switching can be explored. Several different (but common) predictors – Last Observation (LO), Simple Average (SA), Sample Moving Average (SMA) and Exponential Moving Average (EMA), Low Pass Filter (LPF), and an AutoRegressive Integrated Moving Average (ARIMA) – have been applied alongside the switching policies. As each of the forecasting schemes has its own bias, we also develop a number of meta-forecasting algorithms – the Active Window Model (AWM), the Voting Model (VM), the Selective Model (SM), the Dynamic Active Window Model (DAWM), and a method based on Workload Pattern Analysis (WPA). The schemes are tested with real-world workload traces from several sources to ensure consistent and improved results. We also investigate the effectiveness of these schemes on workloads containing extreme events (e.g. flash crowds). The results show that workload forecasting can be very effective when applied alongside dynamic resource allocation strategies