The growing demand on the performance of transparent computing systems requires good cache schemes in order to overcome the prolonged network latency. However, evaluating cache schemes, especially measuring the performance of a transparent computing system with particular cache scheme remains challenging. This is because neither method is available to evaluate the effectiveness and efficiency of the cache schemes in transparent computing, nor the simulator has been developed to measure the system performance under particular cache schemes. In this paper, we propose TranSim, a full-featured, high-performance simulation framework for transparent computing. For the first time, TranSim introduces a methodology to evaluate the performance of multi-level cache hierarchies in transparent computing under different cache configurations and cache replacement policies. TranSim can also demonstrate the behavior and performance of the entire transparent computing system rather than only the cache miss/hit rate. Using TranSim, the system designer can quickly evaluate the effectiveness of cache schemes along with the system performance. We construct several experiments to evaluate the effectiveness and efficiency of TranSim. Results show that TranSim can accurately output the performance of both the cache hierarchy and the entire transparent computing system.

Elasticity has now become the elemental feature of cloud computing as it enables the ability to dynamically add or remove virtual machine instances when workload changes. However, effective virtualized resource management is still one of the most challenging tasks. When the workload of a service increases rapidly, existing approaches cannot respond to the growing performance requirement efficiently because of either inaccuracy of adaptation decisions or the slow process of adjustments, both of which may result in insufficient resource provisioning. As a consequence, the Quality of Service (QoS) of the hosted applications may degrade and the Service Level Objective (SLO) will be thus violated. In this paper, we introduce SPRNT, a novel resource management framework, to ensure high-level QoS in the cloud computing system. SPRNT utilizes an aggressive resource provisioning strategy which encourages SPRNT to substantially increase the resource allocation in each adaptation cycle when workload increases. This strategy first provisions resources which are possibly more than actual demands, and then reduces the over-provisioned resources if needed. By applying the aggressive strategy, SPRNT can satisfy the increasing performance requirement in the first place so that the QoS can be kept at a high level. The experimental results show that SPRNT achieves up to 7.7× speedup in adaptation time, compared with existing efforts. By enabling quick adaptation, SPRNT limits the SLO violation rate up to 1.3 percent even when dealing with rapidly increasing workload.