3,673 research outputs found
An Algorithm for Network and Data-aware Placement of Multi-Tier Applications in Cloud Data Centers
Today's Cloud applications are dominated by composite applications comprising
multiple computing and data components with strong communication correlations
among them. Although Cloud providers are deploying large number of computing
and storage devices to address the ever increasing demand for computing and
storage resources, network resource demands are emerging as one of the key
areas of performance bottleneck. This paper addresses network-aware placement
of virtual components (computing and data) of multi-tier applications in data
centers and formally defines the placement as an optimization problem. The
simultaneous placement of Virtual Machines and data blocks aims at reducing the
network overhead of the data center network infrastructure. A greedy heuristic
is proposed for the on-demand application components placement that localizes
network traffic in the data center interconnect. Such optimization helps
reducing communication overhead in upper layer network switches that will
eventually reduce the overall traffic volume across the data center. This, in
turn, will help reducing packet transmission delay, increasing network
performance, and minimizing the energy consumption of network components.
Experimental results demonstrate performance superiority of the proposed
algorithm over other approaches where it outperforms the state-of-the-art
network-aware application placement algorithm across all performance metrics by
reducing the average network cost up to 67% and network usage at core switches
up to 84%, as well as increasing the average number of application deployments
up to 18%.Comment: Submitted for publication consideration for the Journal of Network
and Computer Applications (JNCA). Total page: 28. Number of figures: 15
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CloudScope: diagnosing and managing performance interference in multi-tenant clouds
© 2015 IEEE.Virtual machine consolidation is attractive in cloud computing platforms for several reasons including reduced infrastructure costs, lower energy consumption and ease of management. However, the interference between co-resident workloads caused by virtualization can violate the service level objectives (SLOs) that the cloud platform guarantees. Existing solutions to minimize interference between virtual machines (VMs) are mostly based on comprehensive micro-benchmarks or online training which makes them computationally intensive. In this paper, we present CloudScope, a system for diagnosing interference for multi-tenant cloud systems in a lightweight way. CloudScope employs a discrete-time Markov Chain model for the online prediction of performance interference of co-resident VMs. It uses the results to optimally (re)assign VMs to physical machines and to optimize the hypervisor configuration, e.g. the CPU share it can use, for different workloads. We have implemented CloudScope on top of the Xen hypervisor and conducted experiments using a set of CPU, disk, and network intensive workloads and a real system (MapReduce). Our results show that CloudScope interference prediction achieves an average error of 9%. The interference-aware scheduler improves VM performance by up to 10% compared to the default scheduler. In addition, the hypervisor reconfiguration can improve network throughput by up to 30%
QoS-aware Storage Virtualization: A Framework for Multi-tier Infrastructures in Cloud Storage Systems
The emergence of the relatively modern phenomenon of cloud computing has manifested a different approach to the availability and storage of software and data on a remote online server ‘in the cloud’, which can be accessed by pre-determined users through the Internet, even allowing sharing of data in certain scenarios. Data availability, reliability, and access performance are three important factors that need to be taken into consideration by cloud providers when designing a high-performance storage system for any organization. Due to the high costs of maintaining and managing multiple local storage systems, it is now considered more applicable to design a virtualized multi-tier storage infrastructure, yet, the existing Quality of Service (QoS) must be guaranteed on the application level within the cloud without ongoing human intervention. Such interference seems necessary since the delivered QoS can vary widely both across and within storage tiers, depending on the access profile of the data. This survey paper encompasses a general framework for the optimal design of a distributed system in order to attain efficient data availability and reliability. To this extent, numerous state-of-the-art technologies and methods have been revised, especially for multi-tiered distributed cloud systems. Moreover, several critical aspects that must be taken into consideration for getting optimal performance of QoS-aware cloud systems are discussed, highlighting some solutions to handle failure situations, and the possible advantages and benefits of QoS. Finally, this papers attempts to argue the possible improvements that have been developed on QoS-aware cloud systems like Q-cloud since 2010, such as any extra attempts been carried forward to make the Q-cloud more adaptable and secure
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