The emergence of large-scale Internet services has fueled a trend toward large-scale systems composed of geographically distributed clusters. Managing resource allocations and resource usage is an important task for such services.

Resource allocations and resource usage management mechanisms for services running across clusters play vital roles in the performance of the entire system, economical sustainability of the provider, and level of customers satisfaction provided by the system. However, when providing the utmost customer satisfaction the service provider ought to make sure not to over-commit resources beyond the agreed limit between the customer and the provider. Moreover, statistics of resources consumed by different services should be monitored and collected using an efficient mechanism with minimal overhead and interference on the system and the services. Thus, resource usage collection and allocations mechanisms should impose economical constraints to both sides, the customer and the cloud provider.

This thesis focuses on decentralized resource allocation and resource usage management for services running in multi cluster environments. Theoretical as well as experimental results indicate that our proposed approaches provide efficient management of resources for services running in a large-scale geographically distributed systems.

The emergence of large-scale Internet services coupled with the evolution of computing technologies such as distributed systems, parallel computing, utility computing, grid, and virtualization has fueled a movement toward a new resource provisioning paradigm called cloud computing. The main appeal of cloud computing lies in its ability to provide a shared pool of infinitely scalable computing resources for cloud services, which can be quickly provisioned and released on-demand with minimal effort. The rapidly growing interest in cloud computing from both the public and industry together with the rapid expansion in scale and complexity of cloud computing resources and the services hosted on them have made monitoring, controlling, and provisioning cloud computing resources at runtime into a very challenging and complex task. This thesis investigates algorithms, models and techniques for autonomously monitoring, controlling, and provisioning the various resources required to meet services’ performance requirements and account for their resource usage.

Quota management mechanisms are essential for controlling distributed shared resources so that services do not exceed their allocated or paid-for budget. Appropriate cloud-wide monitoring and controlling of quotas must be exercised to avoid over- or under-provisioning of resources. To this end, this thesis presents new distributed algorithms that efficiently manage quotas for services running across distributed nodes.

Determining the optimal amount of resources to meet services’ performance requirements is a key task in cloud computing. However, this task is extremely challenging due to multi-faceted issues such as the dynamic nature of cloud environments, the need for supporting heterogeneous services with different performance requirements, the unpredictable nature of services’ workloads, the non-triviality of mapping performance measurements into resources, and resource shortages. Models and techniques that can predict the optimal amount of resources needed to meet service performance requirements at runtime irrespective of variations in workloads are proposed. Moreover, different service differentiation schemes are proposed for managing temporary resource shortages due to, e.g., flash crowds or hardware failures.

In addition, the resources used by services must be accounted for in order to properly bill customers. Thus, monitoring data for running services should be collected and aggregated to maintain a single global state of the system that can be used to generate a single bill for each customer. However, collecting and aggregating such data across geographical distributed locations is challenging because the management task itself may consume significant computing and network resources unless done with care. A consistency and synchronization mechanism that can alleviate this task is proposed.