Resource Allocation Strategies for Multiple Job Classes

Resource management for a data center with multiple job classes is investigated in this thesis. We focus on strategies for allocating resources to an application mix such that the service level agreements (SLAs) of individual applications are met. A performance model with two interactive job classes is used to determine the smallest number of processor nodes required to meet the SLAs of both classes. For each class, the SLA is specified by the relationship: Prob(response time≤x)≥y. Two allocation strategies are considered: shared allocation (SA) and dedicated allocation (DA). For the case of FCFS scheduling, analytic results for response time distribution are used to develop a heuristic algorithm that determines the allocation strategy (SA or DA) that requires fewer processor nodes. The effectiveness of this algorithm is evaluated over a range of operating conditions. The performance of SA with non-FCFS scheduling is also investigated. Among the scheduling disciplines considered, a new discipline called probability dependent priority (PDP) is found to have the best performance in terms of requiring the smallest number of nodes. Furthermore, we extend our heuristic algorithm for FCFS to three job classes. The effectiveness of this extended algorithm is evaluated. As to priority scheduling, the performance advantage of PDP is also confirmed for the case of three job classes

Adaptive Mechanisms for Mobile Spatio-Temporal Applications

Mobile spatio-temporal applications play a key role in many mission critical fields, including Business Intelligence, Traffic Management and Disaster Management. They are characterized by high data volume, velocity and large and variable number of mobile users. The design and implementation of these applications should not only consider this variablility, but also support other quality requirements such as performance and cost. In this thesis we propose an architecture for mobile spatio-temporal applications, which enables multiple angles of adaptivity. We also introduce a two-level adaptation mechanism that ensures system performance while facilitating scalability and context-aware adaptivity. We validate the architecture and adaptation mechanisms by implementing a road quality assessment mobile application as a use case and by performing a series of experiments on cloud environment. We show that our proposed architecture can adapt at runtime and maintain service level objectives while offering cost-efficiency and robustness

Architecture-Level Software Performance Models for Online Performance Prediction

Proactive performance and resource management of modern IT infrastructures requires the ability to predict at run-time, how the performance of running services would be affected if the workload or the system changes. In this thesis, modeling and prediction facilities that enable online performance prediction during system operation are presented. Analyses about the impact of reconfigurations and workload trends can be conducted on the model level, without executing expensive performance tests