QoS Feedback Research Articles

EnergyQARE

Power market operators have recently introduced smart grid demand response (DR), in which electricity consumers regulate their power usage following market requirements. DR helps stabilize the grid and enables integrating a larger amount of intermittent renewable power generation. Data centers provide unique opportunities for DR participation due to their flexibility in both workload servicing and power consumption. While prior studies have focused on data center participation in legacy DR programs such as dynamic energy pricing and peak shaving, this article studies data centers in emerging DR programs, i.e., demand side capacity reserves. Among different types of capacity reserves, regulation service reserves (RSRs) are especially attractive due to their relatively higher value. This article proposes EnergyQARE , the Energy and Q uality-of-Service (QoS) A ware R SR E nabler, an approach that enables data center RSR provision in real-life scenarios. EnergyQARE not only provides a bidding strategy in RSR provision, but also contains a runtime policy that adaptively modulates data center power through server power management and server provisioning based on workload QoS feedback. To reflect real-life scenarios, this runtime policy handles a heterogeneous set of jobs and considers transition time delay of servers. Simulated numerical results demonstrate that in a general data center scenario, EnergyQARE provides close to 50% of data center average power consumption as reserves to the market and saves up to 44% in data center electricity cost, while still meeting workload QoS constraints. Case studies in this article show that the percentages of savings are not sensitive to a specific type of non-interactive workload, or the size of the data center, although they depend strongly on data center utilization and parameters of server power states.

A New Resources Provisioning Method Based on QoS Differentiation and VM Resizing in IaaS

In order to improve the host energy efficiency in IaaS, we proposed an adaptive host resource provisioning method, CoST, which is based on QoS differentiation and VM resizing. The control model can adaptively adjust control parameters according to real time application performance, in order to cope with changes in load. CoST takes advantage of the fact that different types of applications have different sensitivity degrees to performance and cost. It places two different types of VMs on the same host and dynamically adjusts their sizes based on the load forecasting and QoS feedback. It not only guarantees the performance defined in SLA, but also keeps the host running in energy-efficient state. Real Google cluster trace and host power data are used to evaluate the proposed method. Experimental results show that CoST can provide performance-sensitive application with a steady QoS and simultaneously speed up the overall processing of performance-tolerant application by 20~66%. The host energy efficiency is significantly improved by 7~23%.

QoS-Aware and Federated Enhancement for UDDI

With more and more Web services appearing on the Web, Web service’s discovery mechanisms become essential. UDDI is an online registry standard to facilitate the discovery of business partners and services. Service requesters can choose good performance Web services by manual test and comparison. However, this method of choosing is inefficient and costly in terms of time and money. The ability to predict the quality of service is missing in UDDI. Furthermore, UDDI registries in federated organizations should be able to share more about the service requester’s connection information to make better predictions for services’ performances. To address these problems, we propose UX (UDDI eXtension), a system that facilitates requesters to discover services with good performance. In each enterprise domain, the requesters’ QoS feedback is received and then stored in a local UX server. By sharing these experiences from all the requesters in the local domain, the system can predict the service’s future performance. To support the discovery between different cooperation domains, we design a general federated service and then enhance the UX system’s federated discovery ability based on this federated service. The system handles the federated inquiry and provides a simple view over the whole federation. Meanwhile the UX server’s inquiry interface will still conform to the UDDI Specification.