With the development of cloud computing and virtualization technologies, more and more soft real-time applications, such as Voice over Internet Protocol (VoIP) server and cloud gaming, are running in virtualized data centers. Though previous studies optimize CPU schedulers of hypervisors to support these applications in virtualized environments, there are some important challenges in designing an efficient CPU scheduler which is suitable for real-world clouds. On one hand, hypervisors do not know whether an application in a virtual machine (VM) has real-time requirements, so manually setting the scheduling parameters is a common case for CPU schedulers, which probably increases users' burden, lacks flexibility, and causes misconfigurations. On the other hand, it has been reported that most of existing CPU schedulers designed for soft real-time applications have an obvious propensity to such applications which prevents them from being applied in practical multi-tenant cloud environments. In this paper, we design and implement an adaptive soft real-time scheduler based on Xen, named Doris, to address these challenges. It identifies the VMs running soft real-time applications (RT-VMs) and infers their scheduling parameters according to the communication behaviors of VMs adaptively. Then, it promotes the priorities of VCPUs of the RT-VMs temporarily according to I/O events and the inferred scheduling parameters of RT-VMs to support soft real-time applications adaptively while minimizing the impacts on non-real-time applications. Finally, considering the importance of privileged entities (such as Domain0 in Xen) in I/O processing, Doris sets their types and scheduling parameters dynamically, which enables the adaptive scheduling of them to guarantee the performance of soft real-time applications. Our evaluation shows Doris can support soft real-time applications adaptively and efficiently, and only introduces very slight overhead.

With the development of multi-core platforms andcloud computing, Non-Uniform Memory Access (NUMA) architecturehas been dominant in cloud data centers in recentyears. However, NUMA architecture is not well supported invirtualized environments. Because of the semantic gap introducedby the virtualization layer, hypervisors know little aboutthe characteristics of applications running in virtual machines (VMs). More importantly, in order to guarantee hypervisors' applicability, load balance strategies of virtual CPU (VCPU) schedulers do not consider the memory access characteristicsof applications running in VMs, which probably introducessignificant shared resource contention and unnecessary remotememory accesses. In this paper, we propose a NUMA-aware VCPU schedulerbased on Xen, named vProbe, to improve the performanceof memory-intensive applications while maintaining the transparencyof the virtualization layer in NUMA-based servers. It collects performance monitoring units (PMU) data for eachVCPU and analyzes their memory access characteristics. Then, according to the memory access characteristics of each VCPU, it periodically reassigns all memory-intensive VCPUs to eachNUMA node evenly while preferentially allocating them to theirlocal nodes, which aims to alleviate shared resource contentionand reduce unnecessary remote memory accesses. Moreover, when a physical CPU (PCPU) becomes idle, it preferentiallysteals a VCPU from the run queues of PCPUs in the local nodeto this PCPU, which helps to maintain balanced last-level cache (LLC) contention and reduce extra remote memory accesses. Our evaluation shows that vProbe can significantly improve theperformance of memory-intensive applications (e.g., up to 45.2% performance improvement compared with the Credit scheduler) while introducing negligible overheads.

With the prevalence of cloud computing and virtualization, more and more cloud services including parallel soft real-time applications (PSRT applications) are running in virtualized data centers. However, current hypervisors do not provide adequate support for them because of soft real-time constraints and synchronization problems, which result in frequent deadline misses and serious performance degradation. CPU schedulers in underlying hypervisors are central to these issues. In this paper, we identify and analyze CPU scheduling problems in hypervisors. Then, we design and implement a parallel soft real-time scheduler according to the analysis, namedPoris, based on Xen. It addresses both soft real-time constraints and synchronization problems simultaneously. In our proposed method,priority promotionanddynamic time slicemechanisms are introduced to determine when to schedulevirtual CPUs(VCPUs) according to the characteristics of soft real-time applications. Besides, considering that PSRT applications may run in avirtual machine(VM) or multiple VMs, we presentparallel scheduling,group schedulingandcommunication-driven group schedulingto accelerate synchronizations of these applications and make sure that tasks are finished before their deadlines under different scenarios. Our evaluation showsPoriscan significantly improve the performance of PSRT applications no matter how they run in a VM or multiple VMs. For example, compared to the Credit scheduler,Porisdecreases the response time of web search benchmark by up to 91.6 percent.

With the prevalence of multicore processors in computer systems, many soft real-time applications, such as media-based ones, use parallel programming models to utilize hardware resources better and possibly shorten response time. Meanwhile, virtualization technology is widely used in cloud data centers. More and more cloud services including such parallel soft real-time applications are running in virtualized environment. However, current hyper visors do not provide adequate support for them because of soft real-time constraints and synchronization problems, which result in frequent deadline misses and serious performance degradation. CPU schedulers in underlying hyper visors are central to these issues. In this paper, we identify and analyze CPU scheduling problems in hyper visors, and propose a novel scheduling algorithm considering both soft real-time constraints and synchronization problems. In our proposed method, real-time priority is introduced to accelerate event processing of parallel soft real-time applications, and dynamic time slice is used to schedule virtual CPUs. Besides, all runnable virtual CPUs of virtual machines running parallel soft real-time applications are scheduled simultaneously to address synchronization problems. We implement a parallel soft real-time scheduler, named Poris, based on Xen. Our evaluation shows Poris can significantly improve the performance of parallel soft real-time applications. For example, compared to the Credit scheduler, Poris improves the performance of media player by up to a factor of 1.35, and shortens the execution time of PARSEC benchmark by up to 44.12%.