Cost-effective and Qos-aware resource allocation for cloud computing
Lei Wei · 2016
As the most important problem in cloud computing technology, resource allocation not only affects the cost of the cloud operators and users, but also impacts the performance of cloud jobs.Provisioning too much resource in clouds wastes energy and cost while provisioning too few resource will cause performance degradation of cloud applications.Current researches in the resource allocation field mainly focus on homogeneous resource allocation and take CPU as the most important resource in resource allocation.However, as resource demands of cloud workloads get increasingly heterogeneous on different resource types, current methods are not suitable for some other type of jobs such as memory-intensive applications.They are neither efficient in terms of offering economical and high-quality resource allocation in clouds.In this thesis, we firstly propose a resource provisioning method, namely BigMem, to consider the features of resource allocation based on memory.Memory-intensive applications have recently become popular for high-throughput and low-latency computing.Current resource provisioning methods focus more on other resources such as CPU and network bandwidth which are considered as the bottlenecks in traditional cloud applications.However, for memory-intensive jobs, main memories are always the bottleneck resource for performance.Therefore, main memory should be the first consideration in resource allocation and provisioning for VMs in clouds hosting memory-intensive applications.By considering the unique behavior of resource provisioning for memory-intensive jobs, BigMem is able to effectively reduce the resource usage for dynamic workloads in clouds.Specifically, we seek Markov Chain modeling to periodically determine the required number of PMs and further optimize the resource utilization by conducting VM migration and resource overcommit.We evaluate our design using simulation with synthetic and real world traces.Experiments results show that BigMem is able to provision