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Modeling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), 2011 IEEE 19th International Symposium on

Date 25-27 July 2011

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  • [Front cover]

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  • [Title page i]

    Page(s): i
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  • [Title page iii]

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  • [Copyright notice]

    Page(s): iv
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  • Table of contents

    Page(s): v - x
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  • Message from the General Chair

    Page(s): xi
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  • Message from the Program Chairs

    Page(s): xii
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  • Organizing Committee

    Page(s): xiii
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  • Technical Program Committee

    Page(s): xiv - xv
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  • Additional Reviewers

    Page(s): xvi
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  • Keynote 1: Challenges of Virtualized System: Performance Point of View by Hai Jin

    Page(s): xvii - xviii
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    Virtualization is a rapidly evolving technology that provides a range of benefits to computing systems, such as improved resource utilization and management, application isolation and portability, and system reliability. Among these features, live migration, resources management (including vCPU scheduling and I/O management) are core functions. Live migration of virtual machine (VM) provides a significant benefit for virtual server mobility without disrupting service. It has become an extremely powerful tool for system management in a variety of key scenarios, such as VM load balancing, fault tolerance, power management and other applications. Experimentations and traces show that the performance of live migration is not good enough for different applications. Of course, based on the virtualization architecture management schemes for CPU and I/O resources also need to be reconsidered when supporting different applications with different workload. In this talk, some typical issues in virtualized system will be discussed. Firstly, to take into account the migration overhead in migration decision making, we thoroughly analyze the key parameters that affect the migration cost from theory to practice, and construct two application oblivious models for the cost prediction by using learned knowledge about the workloads at the hypervisor (also called VMM) level. We evaluate the models using five representative workloads on a Xen virtualized environment. Based on the model, we have proposed two live migration schemes for different scenarios: a novel approach that adopts checkpointing/recovery and trace/replay technology to provide fast, transparent VM migration for applications with high reliability, and a memory-compression-based VM migration system for normal applications. Secondly, the asynchronous-synchronous disk I/O model in a typical virtualized system exhibits several problems. For example, when the frontend fails abruptly, the unsaved data in the frontend's cache - ill be lost. To address the problems, we introduce a new I/O model. In this model, rather than performing the asynchronous-synchronous operations for an asynchronous I/O write request, the frontend file system uses synchronous operations to deal with the I/O request and the backend file system performs asynchronous operations to write the data to the hard disk. A prototype system called HypeGear is implemented on the Xen hypervisor. Thirdly, VMM schedulers have focused on fairly sharing the processor resources among domains, rarely consider VCPUs' behaviors. However, this can result in poor application performance to overcommitted domains if there are concurrent programs hosted in them. We review the properties of both Xen's Credit and SEDF schedulers, and show how these schedulers may seriously impact the performance of the communication-intensive and I/O-intensive concurrent applications in overcommitted domains. A novel approach, that dynamically scales the context switching-frequency by selecting variable time slices according to VCPUs behaviors, is then proposed to improve the Credit scheduler more adaptive for concurrent applications. View full abstract»

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  • Keynote 2: Massive-Scale Parallel Network Simulations, Past, Present and Future by George Riley

    Page(s): xix
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    Discrete event simulation tools for analyzing performance of computer networks have been available for decades, dating back to the early days of the venerable ns-2, continuing through GTNetS, SSFNet, ROSSNet, and most recently ns-3. At each step along the way various developers and researchers have reported on "large-scale" simulation experiments using these tools. As the available hardware platforms grow in scale, the scale of network simulation experiments have grown similarly. In this talk, we will discuss the various reported "large-scale" or "massive-scale" experiments, the approach used to achieve the larger scale and the drawbacks of the experiments. Finally, we will try to look a bit in to the future to see where this field might be in the coming years. View full abstract»

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  • CosMig: Modeling the Impact of Reconfiguration in a Cloud

    Page(s): 3 - 11
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (383 KB) |  | HTML iconHTML  

    Clouds allow enterprises to increase or decrease their resource allocation on demand in response to changes in workload intensity. Virtualization is one of the building blocks for cloud computing and provides the mechanisms to implement the dynamic allocation of resources. These dynamic reconfiguration actions lead to performance impact during the reconfiguration duration. In this paper, we model the cost of reconfiguring a cloud-based IT infrastructure in response to workload variations. We show that maintaining a cloud requires frequent reconfigurations necessitating both VM resizing and VM live migration, with live migration dominating reconfiguration costs. We design the CosMig model to predict the duration of live migration and its impact on application performance. Our model is based on parameters that are typically monitored in enterprise data centers. Further, the model faithfully captures the impact of shared resources in a virtualized environment. We experimentally validate the accuracy and effectiveness of CosMig using micro benchmarks and representative applications. View full abstract»

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  • A Model-free Learning Approach for Coordinated Configuration of Virtual Machines and Appliances

    Page(s): 12 - 21
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    Cloud computing has a key requirement for resource configuration in a real-time manner. In such virtualized environments, both virtual machines (VMs) and hosted applications need to be configured on-the-fly to adapt to system dynamics. The interplay between the layers of VMs and applications further complicates the problem of cloud configuration. Independent tuning of each aspect may not lead to optimal system wide performance. In this paper, we propose a framework, namely CoTuner, for coordinated configuration of VMs and resident applications. At the heart of the framework is a model-free hybrid reinforcement learning (RL) approach, which combines the advantages of Simplex and RL methods and is further enhanced by the use of system knowledge guided exploration policies. Experimental results on Xen-based virtualized environments with TPC-W and TPC-C benchmarks demonstrate that CoTuner is able to drive a virtual server system into an optimal or near optimal configuration state dynamically, in response to the change of workload. It improves the systems throughput by more than 30% over independent tuning strategies. In comparison with the coordinated tuning strategies based solely on Simplex or basic RL algorithm, the hybrid RL algorithm gains 30% to 40% throughput improvement. Moreover, the algorithm is able to reduce SLA violation of the applications by more than 80%. View full abstract»

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  • Activity Based Sector Synchronisation: Efficient Transfer of Disk-State for WAN Live Migration

    Page(s): 22 - 31
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    Live migration of virtual machines is now commonplace but the issue of synchronising storage remains an obstacle to wide-area migration between datacentres. We discuss the range of possible designs for synchronising disk images and argue that we have to be adaptive at the sector level to efficiently transfer disk-state to the destination. We show, using a number of production workloads, that delaying the transmission of "hot"sectors can achieve considerable bandwidth reduction over a naive eager strategy. We introduce the Activity Based Sector Synchronisation (ABSS) algorithm and show by trace-driven simulations that ABSS is able to deliver a useful trade-off with a data transmission overhead between 0.3% and 2.4% of a gigabit link whilst incurring in the majority of cases a migration latency of less than 1.1 s. View full abstract»

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  • Fuzzy Modeling Based Resource Management for Virtualized Database Systems

    Page(s): 32 - 42
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (509 KB) |  | HTML iconHTML  

    The hosting of databases on virtual machines (VMs) has great potential to improve the efficiency of resource utilization and the ease of deployment of database systems. This paper considers the problem of on-demand allocation of resources to a VM running a database serving dynamic and complex query workloads while meeting QoS (Quality of Service) requirements. An autonomic resource-management approach is proposed to address this problem. It uses adaptive fuzzy modeling to capture the behavior of a VM hosting a database with dynamically changing workloads and to predict its multi-type resource needs. A prototype of the proposed approach is implemented on Xen-based VMs and evaluated using workloads based on TPC-H and RUBiS. The results demonstrate that CPU and disk I/O bandwidth can be efficiently allocated to database VMs serving workloads with dynamically changing intensity and composition while meeting QoS targets. For TPC-H-based experiments, the resulting throughput is within 89.5-100% of what would be obtained using resource allocation based on peak loads, For RUBiS, the response time target (set based on the performance under peak-load-based allocation) is met for 97% of the time. Moreover, substantial resources are saved (about 62.6% of CPU and 76.5% of disk I/O bandwidth) in comparison to peak-load-based allocation. View full abstract»

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  • A Distributed Self-Learning Approach for Elastic Provisioning of Virtualized Cloud Resources

    Page(s): 45 - 54
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (421 KB) |  | HTML iconHTML  

    Although cloud computing has gained sufficient popularity recently, there are still some key impediments to enterprise adoption. Cloud management is one of the top challenges. The ability of on-the-fly partitioning hardware resources into virtual machine(VM) instances facilitates elastic computing environment to users. But the extra layer of resource virtualization poses challenges on effective cloud management. The factors of time-varying user demand, complicated interplay between co-hosted VMs and the arbitrary deployment of multitier applications make it difficult for administrators to plan good VM configurations. In this paper, we propose a distributed learning mechanism that facilitates self-adaptive virtual machines resource provisioning. We treat cloud resource allocation as a distributed learning task, in which each VM being a highly autonomous agent submits resource requests according to its own benefit. The mechanism evaluates the requests and replies with feedback. We develop a reinforcement learning algorithm with a highly efficient representation of experiences as the heart of the VM side learning engine. We prototype the mechanism and the distributed learning algorithm in an iBalloon system. Experiment results on an Xen-based cloud test bed demonstrate the effectiveness of iBalloon. The distributed VM agents are able to reach near-optimal configuration decisions in 7 iteration step sat no more than 5% performance cost. Most importantly, iBalloon shows good scalability on resource allocation by scaling to 128 correlated VMs. View full abstract»

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  • Estimating Application Cache Requirement for Provisioning Caches in Virtualized Systems

    Page(s): 55 - 62
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    Miss rate curves (MRCs) are a fundamental concept in determining the impact of caches on an application's performance. In our research, we use MRCs to provision caches for applications in a consolidated environment. Current techniques for building MRCs at the CPU caches level require changes to the applications and are restricted to a few processor architectures [7], [22]. In this work, we investigate two techniques to partition shared L2 and L3 caches in a server and build MRCs for the VMs. These techniques make different trade-offs across accuracy, flexibility, and intrusiveness dimensions. The first technique is based on operating system (OS) page coloring and does not require change in commodity hardware or application. We improve upon existing page-coloring based approaches by identifying and overcoming a subtle but real problem of unequal associative cache sets loading to implement accurate cache allocation. Our second technique called Cache Grabber is even less intrusive and requires no changes in hardware, OS, or application. We present a comprehensive evaluation of the relative merits of these and other techniques to estimate MRCs. Our evaluation study enables a data center administrator to select the technique most suitable to his (her) specific data center to provision caches for consolidated applications. View full abstract»

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  • Workload Characterization at the Virtualization Layer

    Page(s): 63 - 72
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    Virtualization technology has many attractive qualities including improved security, reliability, scalability, and resource sharing/management. As a result, virtualization has been deployed on an array of platforms, from mobile devices to high end enterprise servers. In this paper, we present a novel approach to working at a virtualization interface, performing workload characterization equipped with the information available at the virtual machine monitor (VMM) interface. Due to the semantic gap between the raw VMM-level data available and the true application behavior, we employ the power of regression techniques to extract meaningful information about a workload's behavior. We also demonstrate that the information available at the VMM level still retains rich workload characteristics that can be used to identify application behavior. We show that we are able to capture enough information about a workload to characterize and decompose it into a combination of CPU, memory, disk I/O, and network I/O-intensive components. Dissecting the behavior of a workload in terms of these components, we can develop significant insight into the behavior of any application. Workload characterization can be used for online performance monitoring, workload scheduling, workload trending, virtual machine (VM)health monitoring, and security analysis. We can also consider how VMM-based workload profiles can be used to detect anomalous behavior in virtualized environments by comparing a model of potentially malicious execution to that of normal execution. View full abstract»

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  • A Study of Web Services Performance Prediction: A Client's Perspective

    Page(s): 75 - 84
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (392 KB) |  | HTML iconHTML  

    The Web service (WS) paradigm is an emerging approach to building Web applications, in which software designers typically build new WSs by leveraging existing, third-party WSs. Understanding performance characteristics of third party WSs is critical to the overall system performance. Although such performance evaluation can be done through testing of third party WSs, it is quite an expensive process. This is especially the case when testing at high workloads, because performance degradations are likely to occur, which may render the WS under testing unusable during the tests' duration. Avoiding testing at high workloads by applying standard extrapolation approaches from data collected at low workloads (e.g., using regression analysis) results in a lack of accuracy. To address this challenge, in this paper, we propose a framework that utilizes the benefits of queueing models to guide the extrapolation process, while achieving accuracy in both regimes ¡V low and high workloads. Our extensive experiments show that our approach gives accurate results as compared to standard techniques (i.e., use of regression analysis alone). View full abstract»

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  • Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

    Page(s): 85 - 95
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    Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers. View full abstract»

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  • Sponsored Search Engines in Competition: Advertisers Behavior and Engines Optimal Ranking Strategies

    Page(s): 96 - 103
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    Search engines are essential actors for web browsing. We analyze here the economic competition between search engines earning money from ad word auctions. We develop a two-level game where at the largest time scale search engines decide which allocation rule to implement, between revenue-based and bid-based, and at the lowest time-scale advertisers decide how to split their advertising budget between the two search engines, depending on the benefits this will bring to them. The game at the largest time scale is solved using backward induction, the search engines anticipating the reactions of advertisers. We describe the advertisers best strategies and show how to determine, depending on parameters, an equilibrium on the ranking rule strategy for search engines, this may explain Yahoo!'s move to switch from bid-based to revenue-based ranking to follow Google's strategy. View full abstract»

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  • Software Performance Prediction with a Time Scaling Scheduling Profiler

    Page(s): 107 - 116
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    We present a new approach to software performance analysis that aims to extend conventional profiling with some of the predictive capabilities of a performance model. The idea is to execute programs in virtual time, which allows hypothetical time scaling of the constituent parts of an application to be explored in advance of any speculative redevelopment aimed at improving performance. Virtual time execution works via a virtual time scheduler that controls execution by wrapping the underlying (real time) system scheduler and enforcing a thread schedule that takes account of time scaling. The necessary instruments to achieve this are added automatically to the profiled program at load time. We present VEX, a generic low-level kernel for supporting such a `scheduling profiler' in this sense and JINE, a specific instrumentation framework for the Java programming language, that builds on VEX. An evaluation of the VEX/JINE framework with a wide range of standard benchmarks shows that the prediction error is less 10% on average and the execution time overheads less than a factor of two on average, for the benchmarks tested. We also illustrate various time scaling experiments, which yield results that are quite different to those predicted from conventional profiling measurements. View full abstract»

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  • A Theoretical Framework for Design Space Exploration of Manycore Processors

    Page(s): 117 - 125
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    With ever expanding design space and workload space in multicore era, it is a challenge to identify optimal design points quickly, desirable during the early stage of multicore processor design or programming phase. To meet this challenge, this paper proposes a theoretical framework that can capture the general performance properties for a class of multicore processors of interest over a large design space and workload space, free of scalability issues. The idea is to model multicore processors at the thread-level, overlooking instruction-level and micro architectural details. In particular, queuing network models that model multicore processors at the thread level are developed and solved based on an iterative procedure over a large design space and workload space. This framework scales to virtually unlimited numbers of cores and threads. The testing of the procedure demonstrates that the throughput performance for many-core processors with 1000 cores can be evaluated within a few seconds on an Intel Pentium 4 computer and the results are within 5% of the simulation data obtained based on a thread-level simulator. View full abstract»

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  • In-N-Out: Reproducing Out-of-Order Superscalar Processor Behavior from Reduced In-Order Traces

    Page(s): 126 - 135
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    Trace-driven simulation is a widely practiced simulation method. However, its use has been typically limited to modeling of in-order processors because of accuracy issues. In this work, we propose and explore In-N-Out, a fast approximate simulation method to reproduce the behavior of an out-of-order superscalar processor with a reduced in-order trace. During trace generation, we use a functional cache simulator to capture interesting processor events such as uncore accesses in the program order. We also collect key information about the executed program. The prepared in-order trace then drives a novel simulation algorithm that models an out-of-order processor. Our experimental results demonstrate that In-N-Out produces reasonably accurate absolute performance (7% difference on average) and fast simulation speeds (115x on average), compared with detailed execution-driven simulation. Moreover, In-N-Out was shown to preserve a processor's dynamic uncore access patterns and predict the relative performance change when the processor's core- or uncore-level parameters are changed. View full abstract»

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