Server consolidation and application consolidation through virtualization are key performance optimizations in cloud-based service delivery industry. In this paper, we argue that it is important for both cloud consumers and cloud providers to understand the various factors that may have significant impact on the performance of applications running in a virtualized cloud. This paper presents an extensive performance study of network I/O workloads in a virtualized cloud environment. We first show that current implementation of virtual machine monitor (VMM) does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing across multiple virtual machine instances (VMs) running on a single physical host machine, especially when applications running on neighboring VMs are competing for computing and communication resources. Then we study a set of representative workloads in cloud-based data centers, which compete for either CPU or network I/O resources, and present the detailed analysis on different factors that can impact the throughput performance and resource sharing effectiveness. For example, we analyze the cost and the benefit of running idle VM instances on a physical host where some applications are hosted concurrently. We also present an in-depth discussion on the performance impact of colocating applications that compete for either CPU or network I/O resources. Finally, we analyze the impact of different CPU resource scheduling strategies and different workload rates on the performance of applications running on different VMs hosted by the same physical machine.

User-perceived performance continues to be the most important QoS indicator in cloud-based data centers today. Effective allocation of virtual machines (VMs) to handle both CPU intensive and I/O intensive workloads is a crucial performance management capability in virtualized clouds. Although a fair amount of researches have dedicated to measuring and scheduling jobs among VMs, there still lacks of in-depth understanding of performance factors that impact the efficiency and effectiveness of resource multiplexing and scheduling among VMs. In this paper, we present the experimental research on performance interference in parallel processing of CPU-intensive and network-intensive workloads on Xen virtual machine monitor (VMM). Based on our study, we conclude with five key findings which are critical for effective performance management and tuning in virtualized clouds. First, colocating network-intensive workloads in isolated VMs incurs high overheads of switches and events in Dom0 and VMM. Second, colocating CPU-intensive workloads in isolated VMs incurs high CPU contention due to fast I/O processing in I/O channel. Third, running CPU-intensive and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Fourth, performance of network-intensive workload is insensitive to CPU assignment among VMs, whereas adaptive CPU assignment among VMs is critical to CPU-intensive workload. The more CPUs pinned on Dom0 the worse performance is achieved by CPU-intensive workload. Last, due to fast I/O processing in I/O channel, limitation on grant table is a potential bottleneck in Xen. We argue that identifying the factors that impact the total demand of exchanged memory pages is important to the in-depth understanding of interference costs in Dom0 and VMM.

Virtualization is a key technology for cloud based data centers to implement the vision of infrastructure as a service (IaaS) and to promote effective server consolidation and application consolidation. However, current implementation of virtual machine monitor does not provide sufficient performance isolation to guarantee the effectiveness of resource sharing, especially when the applications running on multiple virtual machines of the same physical machine are competing for computing and communication sources. In this paper, we present our performance measurement study of network I/O applications in virtualized cloud. We focus our measurement based analysis on performance impact of co-locating applications in a virtualized cloud in terms of throughput and resource sharing effectiveness, including the impact of idle instances on applications that are running concurrently on the same physical host. Our results show that by strategically co-locating network I/O applications, performance improvement for cloud consumers can be as high as 34%, and the cloud providers can achieve over 40% performance gain.

Server virtualization offers the ability to slice large, underutilized physical servers into smaller, parallel virtual machines (VMs), enabling diverse applications to run in isolated environments on a shared hardware platform. Effective management of virtualized cloud environments introduces new and unique challenges, such as efficient CPU scheduling for virtual machines, effective allocation of virtual machines to handle both CPU intensive and I/O intensive workloads. Although a fair number of research projects have dedicated to measuring, scheduling, and resource management of virtual machines, there still lacks of in-depth understanding of the performance factors that can impact the efficiency and effectiveness of resource multiplexing and resource scheduling among virtual machines. In this paper, we present our experimental study on the performance interference in parallel processing of CPU and network intensive workloads in the Xen Virtual Machine Monitors (VMMs). We conduct extensive experiments to measure the performance interference among VMs running network I/O workloads that are either CPU bound or network bound. Based on our experiments and observations, we conclude with four key findings that are critical to effective management of virtualized cloud environments for both cloud service providers and cloud consumers. First, running network-intensive workloads in isolated environments on a shared hardware platform can lead to high overheads due to extensive context switches and events in driver domain and VMM. Second, co-locating CPU-intensive workloads in isolated environments on a shared hardware platform can incur high CPU contention due to the demand for fast memory pages exchanges in I/O channel. Third, running CPU-intensive workloads and network-intensive workloads in conjunction incurs the least resource contention, delivering higher aggregate performance. Last but not the least, identifying factors that impact the total demand of the exchanged memory pages is critical to the in-depth understanding of the interference overheads in I/O channel in the driver domain and VMM.

With Cloud Computing gaining tremendous importance in the recent past, understanding low-level implications of the cloud infrastructure becomes necessary. One of the key technologies deployed in large Cloud infrastructures namely the Amazon EC2 for providing isolation and separate protection domains for multiple clients is virtualization. Therefore, identifying the performance bottlenecks in a virtualized setup and understanding the implications of workload combinations and resource configurations on the overall I/O performance helps both the cloud providers in managing their infrastructure efficiently and also their customers by means of better performance. In this paper we present the measurement results of detailed experiments conducted on a virtualized setup focusing on the storage I/O performance. We categorize our experimental evaluation into four components, each of which presenting some significant factors that affect storage I/O performance. Our experimental results can be useful for cloud application developers to tune their applications for better I/O performance and for the cloud service providers to make more effective decisions on resource provisioning and workload scheduling.

Modern multi-core processor architectures strive for the highest possible performance of various applications. This paper discusses a triple-based multi-core architecture which supports object-oriented methodology and applications in hardware level. However, the Memory Wall is still the bottleneck which should be resolved to decrease the disparity between how fast a CPU can operate on data and how fast it can get data. We present hierarchical shared memory system architecture (HSM) which is hierarchically constructed memory shared by multi-cores. Moreover, we propose a new approach mapping data among different levels of cache and memory, which is called partially-inclusive cache mapping policy that facilitates the coherence of shared memory. This paper focus on object-oriented systems combined with the HSM and partially-inclusive policy and presents a new objects management model. The analysis based on comparisons between our objects management and link structured object organization methods shows that our method is predominant in spatial and temporal aspects on memory parallel access efficiency and costs less storage space to organize objects.

Modern multi-core processors are predominant in improving performance of parallel applications. This paper discusses a triple-based multi-core architecture which provides native support for object-oriented methodology and applications in hardware level. The model explicitly represents objects and supports messaging-based communication, which maps well to the standard style of interaction in object oriented languages. However, the Memory Wall is still the bottleneck which should be resolved to decrease the disparity between how fast a CPU can operate on data and how fast it can get data. A hierarchy shared memory system (HSM) working with the partially-inclusive cache mapping policy is proposed. And a new object management model is presented, which use object table and recycle stack scheme to supports explicit dynamic object management. Our cache design presents an innovative solution to handling the costs of cache coherence by allowing applications to control the amount of sharing between cores. Experimental analysis based on comparisons between our objects management and other common link structured object organization methods shows that our method is predominant in spatial and temporal aspects on memory parallel access efficiency and costs less storage space to organize objects.