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Cloud Computing (CLOUD), 2012 IEEE 5th International Conference on

Date 24-29 June 2012

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  • CLOUD 2012: 2012 IEEE 5th International Conference on Cloud Computing [Cover art]

    Publication Year: 2012 , Page(s): C4
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  • Proceedings: 2012 ieee 5th international conference on cloud computing [title page i]

    Publication Year: 2012 , Page(s): i
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  • Proceedings: 2012 IEEE 5th International Conference on Cloud Computing [Title page iii]

    Publication Year: 2012 , Page(s): iii
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  • [Copyright notice]

    Publication Year: 2012 , Page(s): iv
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  • 2012 ieee fifth international conference on cloud computing cloud 2012 - table of contents

    Publication Year: 2012 , Page(s): v - xviii
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  • Message from the CLOUD 2012 General Chairs and Program Chairs

    Publication Year: 2012 , Page(s): xix
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  • Organizing Committee

    Publication Year: 2012 , Page(s): xx - xxi
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  • Program Committee

    Publication Year: 2012 , Page(s): xxii - xxiv
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  • External Reviewers

    Publication Year: 2012 , Page(s): xxv
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  • IEEE Computer Society Technical Committee on Services Computing

    Publication Year: 2012 , Page(s): xxvi
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  • MROrchestrator: A Fine-Grained Resource Orchestration Framework for MapReduce Clusters

    Publication Year: 2012 , Page(s): 1 - 8
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (324 KB) |  | HTML iconHTML  

    Efficient resource management in data centers and clouds running large distributed data processing frameworks like MapReduce is crucial for enhancing the performance of hosted applications and increasing resource utilization. However, existing resource scheduling schemes in Hadoop MapReduce allocate resources at the granularity of fixed-size, static portions of nodes, called slots. In this work, we show that MapReduce jobs have widely varying demands for multiple resources, making the static and fixed-size slot-level resource allocation a poor choice both from the performance and resource utilization standpoints. Furthermore, lack of coordination in the management of multiple resources across nodes prevents dynamic slot reconfiguration, and leads to resource contention. Motivated by this, we propose MROrchestrator, a MapReduce resource Orchestrator framework, which can dynamically identify resource bottlenecks, and resolve them through fine-grained, coordinated, and on-demand resource allocations. We have implemented MROrchestrator on two 24-node native and virtualized Hadoop clusters. Experimental results with a suite of representative MapReduce benchmarks demonstrate up to 38% reduction in job completion times, and up to 25% increase in resource utilization. We further demonstrate the performance boost in existing resource managers like NGM and Mesos, when augmented with MROrchestrator. View full abstract»

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  • A General and Practical Datacenter Selection Framework for Cloud Services

    Publication Year: 2012 , Page(s): 9 - 16
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (515 KB) |  | HTML iconHTML  

    Many cloud services nowadays are running on top of geographically distributed infrastructures for better reliability and performance. They need an effective way to direct the user requests to a suitable data center, depending on factors including performance, cost, etc. Previous work focused on efficiency and invariably considered the simple objective of maximizing aggregated utility. These approaches favor users closer to the infrastructure. In this paper, we argue that fairness should be considered to ensure users at disadvantageous locations also enjoy reasonable performance, and performance is balanced across the entire system. We adopt a general fairness criterion based on Nash bargaining solutions, and present a general optimization framework that models the realistic environment and practical constraints that a cloud faces. We develop an efficient distributed algorithm based on dual decomposition and the sub gradient method, and evaluate its effectiveness and practicality using real-world traffic traces and electricity prices. View full abstract»

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  • A Profit-Aware Virtual Machine Deployment Optimization Framework for Cloud Platform Providers

    Publication Year: 2012 , Page(s): 17 - 24
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (483 KB) |  | HTML iconHTML  

    As a rising application paradigm, cloud computing enables the resources to be virtualized and shared among applications. In a typical cloud computing scenario, customers, Service Providers (SP), and Platform Providers (PP) are independent participants, and they have their own objectives with different revenues and costs. From PPs' viewpoints, much research work reduced the costs by optimizing VM placement and deciding when and how to perform the VM migrations. However, some work ignored the fact that the balanced use of the multi-dimensional resources can affect overall resource utilization significantly. Furthermore, some work focuses on the selection of the VMs and the target servers without considering how to perform the reconfigurations. In this paper, with a comprehensive consideration of PPs' interests, we propose a framework to improve their profits by maximizing the resource utilization and reducing the reconfiguration costs. Firstly, we use the vector arithmetic to model the objective of balancing the multi-dimensional resources use and propose a VM deployment optimization method to maximize the resource utilization. Then a two-level runtime reconfiguration strategy, including local adjustment and VM parallel migration, is presented to reduce the VM migration and shorten the total migration time. Finally, we conduct some preliminary experiments, and the results show that our framework is effective in maximizing the resource utilization and reducing the costs of the runtime reconfiguration. View full abstract»

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  • Biting Off Safely More Than You Can Chew: Predictive Analytics for Resource Over-Commit in IaaS Cloud

    Publication Year: 2012 , Page(s): 25 - 32
    Cited by:  Papers (5)  |  Patents (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (487 KB) |  | HTML iconHTML  

    Cloud service providers are constantly looking for ways to increase revenue and reduce costs either by reducing capacity requirements or by supporting more users without adding capacity. Over-commit of physical resources, without adding more capacity, is one such approach. Workloads that tend to be 'peaky' are especially attractive targets for over-commit since only occasionally such workloads use all the system resources that they are entitled to. Online identification of candidate workloads and quantification of risks are two key issues associated with over-committing resources. In this paper, to estimate the risks associated with over-commit, we describe a mechanism based on the statistical analysis of the aggregate resource usage behavior of a group of workloads. Using CPU usage data collected from an internal private Cloud, we show that our proposed approach is effective and practical. View full abstract»

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  • WIQ: Work-Intensive Query Scheduling for In-Memory Database Systems

    Publication Year: 2012 , Page(s): 33 - 40
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (426 KB) |  | HTML iconHTML  

    We propose a novel admission control policy for database queries. Our methodology uses system measurements of CPU utilization and query backlogs to determine interference between queries in execution on the same database server. Query interference may arise due to the concurrent access of hardware and software resources and can affect performance in positive and negative ways. Specifically our admission control considers the mix of jobs in service and prioritizes the query classes consuming CPU resources more efficiently. The policy ignores I/O subsystems and is therefore highly appropriate for in-memory databases. We validate our approach in trace-driven simulation and show performance increases of query slowdowns and throughputs compared to first-come first-served and shortest expected processing time first scheduling. Simulation experiments are parameterized from system traces of a SAP HANA in-memory database installation with TPC-H type workloads. View full abstract»

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  • Admission Control for Elastic Cloud Services

    Publication Year: 2012 , Page(s): 41 - 48
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (320 KB) |  | HTML iconHTML  

    This paper presents an admission control test for deciding whether or not it is worth to admit a set of services into a Cloud, and in case of acceptance, obtain the optimum allocation for each of the components that comprise the services. In the proposed model, the focus is on hosting elastic services the resource requirements of which may dynamically grow and shrink, depending on the dynamically varying number of users and patterns of requests. In finding the optimum allocation, the presented admission control test uses an optimization model, which incorporates business rules in terms of trust, eco-efficiency and cost, and also takes into account affinity rules the components that comprise the service may have. The problem is modeled on the General Algebraic Modeling System (GAMS) and solved under realistic provider's settings that demonstrate the efficiency of the proposed method. View full abstract»

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  • Center-of-Gravity Reduce Task Scheduling to Lower MapReduce Network Traffic

    Publication Year: 2012 , Page(s): 49 - 58
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1325 KB) |  | HTML iconHTML  

    MapReduce is by far one of the most successful realizations of large-scale data-intensive cloud computing platforms. MapReduce automatically parallelizes computation by running multiple map and/or reduce tasks over distributed data across multiple machines. Hadoop is an open source implementation of MapReduce. When Hadoop schedules reduce tasks, it neither exploits data locality nor addresses partitioning skew present in some MapReduce applications. This might lead to increased cluster network traffic. In this paper we investigate the problems of data locality and partitioning skew in Hadoop. We propose Center-of-Gravity Reduce Scheduler (CoGRS), a locality-aware skew-aware reduce task scheduler for saving MapReduce network traffic. In an attempt to exploit data locality, CoGRS schedules each reduce task at its center-of-gravity node, which is computed after considering partitioning skew as well. We implemented CoGRS in Hadoop-0.20.2 and tested it on a private cloud as well as on Amazon EC2. As compared to native Hadoop, our results show that CoGRS minimizes off-rack network traffic by averages of 9.6% and 38.6% on our private cloud and on an Amazon EC2 cluster, respectively. This reflects on job execution times and provides an improvement of up to 23.8%. View full abstract»

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  • Efficient Map/Reduce-Based DBSCAN Algorithm with Optimized Data Partition

    Publication Year: 2012 , Page(s): 59 - 66
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (787 KB) |  | HTML iconHTML  

    DBSCAN is a well-known algorithm for density-based clustering because it can identify the groups of arbitrary shapes and deal with noisy datasets. However, with the increasing amount of data, DBSCAN algorithm running on a single machine has to face the scalability problem. In this paper, we propose a Map/Reduce-based DBSCAN algorithm called DBSCAN-MR to solve the scalability problem. In DBSCAN-MR, the input dataset is partitioned into smaller parts and then parallel processed on the Hadoop platform. However, choosing different partition mechanisms will affect the execution efficiency and load balance of each node. Therefore, we propose a method, partition with reduce boundary points (PRBP), to select partition boundaries based on the distribution of data points. Our experimental results show that DBSCAN-MR with the design of PRBP has higher efficiency and scalability than competitors. View full abstract»

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  • Evaluating Hadoop for Data-Intensive Scientific Operations

    Publication Year: 2012 , Page(s): 67 - 74
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1561 KB) |  | HTML iconHTML  

    Emerging sensor networks, more capable instruments, and ever increasing simulation scales are generating data at a rate that exceeds our ability to effectively manage, curate, analyze, and share it. Data-intensive computing is expected to revolutionize the next-generation software stack. Hadoop, an open source implementation of the MapReduce model provides a way for large data volumes to be seamlessly processed through use of large commodity computers. The inherent parallelization, synchronization and fault-tolerance the model offers, makes it ideal for highly-parallel data-intensive applications. MapReduce and Hadoop have traditionally been used for web data processing and only recently been used for scientific applications. There is a limited understanding on the performance characteristics that scientific data intensive applications can obtain from MapReduce and Hadoop. Thus, it is important to evaluate Hadoop specifically for data-intensive scientific operations -- filter, merge and reorder-- to understand its various design considerations and performance trade-offs. In this paper, we evaluate Hadoop for these data operations in the context of High Performance Computing (HPC) environments to understand the impact of the file system, network and programming modes on performance. View full abstract»

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  • Optimal Bids for Spot VMs in a Cloud for Deadline Constrained Jobs

    Publication Year: 2012 , Page(s): 75 - 82
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (542 KB) |  | HTML iconHTML  

    Spot virtual-machine (VM) instances, such as Amazon EC2 Spot VMs, are a class of VMs that are purchased through a market mechanism of price-bids submitted by cloud users. Spot VMs can be obtained at substantially lower cost than other VM classes such as Reserved and On-demand instances, but they do not have guaranteed availability since it depends on the submitted price bids and the fluctuating spot VM price. Many applications with large computing requirements but no real-time availability constraints, such as scientific computing, financial modelling and large data analysis, can be carried out at a significantly lower cost using spot VMs. For such jobs, an important question that arises is what should the submitted price bids be so that the computation is completed within a fixed time interval while the cost is minimized. Towards this goal, we model a job as a fixed computation request with a deadline constraint and formulate the problem of designing a dynamic bidding policy that minimizes the average cost of job completion. We obtain analytical and closed-form results for the optimal strategy under a Markov spot price evolution, and then evaluate the performance of the algorithms on the actual spot price history of Amazon EC2 Spot VMs. View full abstract»

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  • Maximizing Cloud Provider Profit from Equilibrium Price Auctions

    Publication Year: 2012 , Page(s): 83 - 90
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (316 KB) |  | HTML iconHTML  

    Auctioning constitutes a market-driven scheme for the allocation of cloud-based computing capacities. It is practically applied today in the context of Infrastructure as a Service offers, specifically, virtual machines. However, the maximization of auction profits poses a challenging task for the cloud provider, because it involves the concurrent determination of equilibrium prices and distribution of virtual machine instances to the underlying physical hosts in the data center. In the work at hand, we propose an optimal approach, based on linear programming, as well as a heuristic approach to tackle this Equilibrium Price Auction Allocation Problem (EPAAP). Through an evaluation based on realistic data, we show the practical applicability and benefits of our contributions. Specifically, we find that the heuristic approach reduces the average computation time to solve an EPAAP by more than 99.9%, but still maintains a favorable average solution quality of 96.7% in terms of cloud provider profit, compared to the optimal approach. View full abstract»

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  • Towards Optimal Bidding Strategy for Amazon EC2 Cloud Spot Instance

    Publication Year: 2012 , Page(s): 91 - 98
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1804 KB) |  | HTML iconHTML  

    With the recent introduction of Spot Instances in the Amazon Elastic Compute Cloud (EC2), users can bid for resources and thus control the balance of reliability versus monetary costs. Mechanisms and tools that deal with the cost-reliability trade-offs under this schema are of great value for users seeking to lessen their costs while maintaining high reliability. In this paper, we propose a set of bidding strategies to minimize the cost and volatility of resource provisioning. Essentially, to derive an optimal bidding strategy, we formulate this problem as a Constrained Markov Decision Process (CMDP). Based on this model, we are able to obtain an optimal randomized bidding strategy through linear programming. Using real Instance Price traces and workload models, we compare several adaptive check-pointing schemes in terms of monetary costs and job completion time. We evaluate our model and demonstrate how users should bid optimally on Spot Instances to reach different objectives with desired levels of confidence. View full abstract»

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  • Attribution of Fraudulent Resource Consumption in the Cloud

    Publication Year: 2012 , Page(s): 99 - 106
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (385 KB) |  | HTML iconHTML  

    Obligated by a utility pricing model, Internet-facing web resources hosted in the public cloud are vulnerable to Fraudulent Resource Consumption (FRC) attacks. Unlike an application-layer DDoS attack that consumes resources with the goal of disrupting short-term availability, an FRC attack is a considerably more subtle attack that instead seeks to disrupt the long-term financial viability of operating in the cloud by exploiting the utility pricing model over an extended time period. By fraudulently consuming web resources in sufficient volume (i.e. data transferred out of the cloud), an attacker (e.g. botnet) is able to incur significant fraudulent charges to the victim. This paper proposes an attribution methodology to identify malicious clients participating in an FRC attack. Experimental results demonstrate that the presented methodology achieves qualified success against challenging attack scenarios. View full abstract»

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  • GARDEN: Generic Addressing and Routing for Data Center Networks

    Publication Year: 2012 , Page(s): 107 - 114
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (422 KB) |  | HTML iconHTML  

    Data centers often hold tens to hundreds of thousands of servers in order to offer cloud computing services at scale. Ethernet switching and IP routing have their own advantages and limitations in building data center networks. Recent research, such as PortLand and BCube, has proposed scalable data center network designs. A common feature of these designs is that their addressing and routing are customized to specific topologies. In this paper, we propose a generic addressing, routing and forwarding protocol for data center networks, which works on arbitrarily "layered'' network topologies. We first form the network as a multi-rooted tree. Each network node (i.e., hosts and switches) is then assigned one or more locators, and each locator encodes a downward path from the roots to this node. Data center networks often have rich path diversity, so tracking all locators of a destination node will cause switches to have very large forwarding tables. We further use a new forwarding model to reduce the forwarding states. In addition, the multiple-locator mechanism brings built-in support for multi-path routing, load balancing and fault tolerance. Evaluations based on simulations and prototype experiments demonstrate that our proposal achieves our design goals. View full abstract»

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  • Expertus: A Generator Approach to Automate Performance Testing in IaaS Clouds

    Publication Year: 2012 , Page(s): 115 - 122
    Cited by:  Papers (7)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (459 KB) |  | HTML iconHTML  

    Cloud computing is an emerging technology paradigm that revolutionizes the computing landscape by providing on-demand delivery of software, platform, and infrastructure over the Internet. Yet, architecting, deploying, and configuring enterprise applications to run well on modern clouds remains a challenge due to associated complexities and non-trivial implications. The natural and presumably unbiased approach to these questions is thorough testing before moving applications to production settings. However, thorough testing of enterprise applications on modern clouds is cumbersome and error-prone due to a large number of relevant scenarios and difficulties in testing process. We address some of these challenges through Expertus---a flexible code generation framework for automated performance testing of distributed applications in Infrastructure as a Service (IaaS) clouds. Expertus uses a multi-pass compiler approach and leverages template-driven code generation to modularly incorporate different software applications on IaaS clouds. Expertus automatically handles complex configuration dependencies of software applications and significantly reduces human errors associated with manual approaches for software configuration and testing. To date, Expertus has been used to study three distributed applications on five IaaS clouds with over 10,000 different hardware, software, and virtualization configurations. The flexibility and extensibility of Expertus and our own experience on using it shows that new clouds, applications, and software packages can easily be incorporated. View full abstract»

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