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		<title><![CDATA[ Parallel and Distributed Systems, IEEE Transactions on - new TOC ]]></title>
		<link>http://ieeexplore.ieee.org</link>
		<description>TOC Alert for Publication# 71 </description>
		<year>2013</year>
		<month>May      </month>
		<day>23</day>
		<item>
			<title><![CDATA[Guest Editors' Introduction: Special Issue on Cloud Computing]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6507530]]></link>
			<description><![CDATA[The articles in this special section focus on the topic of cloud computing, technologies, applications, and new areas of technological innovation.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6507530]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1062</startPage>
			<endPage>1065</endPage>
			<fileSize>130</fileSize>
			<authors><![CDATA[Misic, Vojislav B.;Buyya, Rajkumar;Milojicic, Dejan;Cui, Yong;]]></authors>
		</item>
		<item>
			<title><![CDATA[Anchor: A Versatile and Efficient Framework for Resource Management in the Cloud]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336751]]></link>
			<description><![CDATA[We present Anchor, a general resource management architecture that uses the stable matching framework to decouple policies from mechanisms when mapping virtual machines to physical servers. In Anchor, clients and operators are able to express a variety of distinct resource management policies as they deem fit, and these policies are captured as preferences in the stable matching framework. The highlight of Anchor is a new many-to-one stable matching theory that efficiently matches VMs with heterogeneous resource needs to servers, using both offline and online algorithms. Our theoretical analyses show the convergence and optimality of the algorithm. Our experiments with a prototype implementation on a 20-node server cluster, as well as large-scale simulations based on real-world workload traces, demonstrate that the architecture is able to realize a diverse set of policy objectives with good performance and practicality.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336751]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1066</startPage>
			<endPage>1076</endPage>
			<fileSize>1140</fileSize>
			<authors><![CDATA[Hong Xu;Baochun Li;]]></authors>
		</item>
		<item>
			<title><![CDATA[Efficient Resource Mapping Framework over Networked Clouds via Iterated Local Search-Based Request Partitioning]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226390]]></link>
			<description><![CDATA[The cloud represents a computing paradigm where shared configurable resources are provided as a service over the Internet. Adding intra- or intercloud communication resources to the resource mix leads to a networked cloud computing environment. Following the cloud infrastructure as a Service paradigm and in order to create a flexible management framework, it is of paramount importance to address efficiently the resource mapping problem within this context. To deal with the inherent complexity and scalability issue of the resource mapping problem across different administrative domains, in this paper a hierarchical framework is described. First, a novel request partitioning approach based on Iterated Local Search is introduced that facilitates the cost-efficient and online splitting of user requests among eligible cloud service providers (CPs) within a networked cloud environment. Following and capitalizing on the outcome of the request partitioning phase, the embedding phase-where the actual mapping of requested virtual to physical resources is performed can be realized through the use of a distributed intracloud resource mapping approach that allows for efficient and balanced allocation of cloud resources. Finally, a thorough evaluation of the proposed overall framework on a simulated networked cloud environment is provided and critically compared against an exact request partitioning solution as well as another common intradomain virtual resource embedding solution.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226390]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1077</startPage>
			<endPage>1086</endPage>
			<fileSize>887</fileSize>
			<authors><![CDATA[Leivadeas, A.;Papagianni, C.;Papavassiliou, S.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Optimal Multiserver Configuration for Profit Maximization in Cloud Computing]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226389]]></link>
			<description><![CDATA[As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226389]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1087</startPage>
			<endPage>1096</endPage>
			<fileSize>1361</fileSize>
			<authors><![CDATA[Junwei Cao;Kai Hwang;Keqin Li;Zomaya, A.Y.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Error-Tolerant Resource Allocation and Payment Minimization for Cloud System]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336752]]></link>
			<description><![CDATA[With virtual machine (VM) technology being increasingly mature, compute resources in cloud systems can be partitioned in fine granularity and allocated on demand. We make three contributions in this paper: 1) We formulate a deadline-driven resource allocation problem based on the cloud environment facilitated with VM resource isolation technology, and also propose a novel solution with polynomial time, which could minimize users' payment in terms of their expected deadlines. 2) By analyzing the upper bound of task execution length based on the possibly inaccurate workload prediction, we further propose an error-tolerant method to guarantee task's completion within its deadline. 3) We validate its effectiveness over a real VM-facilitated cluster environment under different levels of competition. In our experiment, by tuning algorithmic input deadline based on our derived bound, task execution length can always be limited within its deadline in the sufficient-supply situation; the mean execution length still keeps 70 percent as high as user-specified deadline under the severe competition. Under the original-deadline-based solution, about 52.5 percent of tasks are completed within 0.95-1.0 as high as their deadlines, which still conforms to the deadline-guaranteed requirement. Only 20 percent of tasks violate deadlines, yet most (17.5 percent) are still finished within 1.05 times of deadlines.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336752]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1097</startPage>
			<endPage>1106</endPage>
			<fileSize>1109</fileSize>
			<authors><![CDATA[Sheng Di;Cho-Li Wang;]]></authors>
		</item>
		<item>
			<title><![CDATA[Dynamic Resource Allocation Using Virtual Machines for Cloud Computing Environment]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6311403]]></link>
			<description><![CDATA[Cloud computing allows business customers to scale up and down their resource usage based on needs. Many of the touted gains in the cloud model come from resource multiplexing through virtualization technology. In this paper, we present a system that uses virtualization technology to allocate data center resources dynamically based on application demands and support green computing by optimizing the number of servers in use. We introduce the concept of "skewness&#x201D; to measure the unevenness in the multidimensional resource utilization of a server. By minimizing skewness, we can combine different types of workloads nicely and improve the overall utilization of server resources. We develop a set of heuristics that prevent overload in the system effectively while saving energy used. Trace driven simulation and experiment results demonstrate that our algorithm achieves good performance.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6311403]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1107</startPage>
			<endPage>1117</endPage>
			<fileSize>1672</fileSize>
			<authors><![CDATA[Zhen Xiao;Weijia Song;Qi Chen;]]></authors>
		</item>
		<item>
			<title><![CDATA[Performance Enhancement for Network I/O Virtualization with Efficient Interrupt Coalescing and Virtual Receive-Side Scaling]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6392167]]></link>
			<description><![CDATA[Virtualization is a key technology in cloud computing; it can accommodate numerous guest VMs to provide transparent services, such as live migration, high availability, and rapid checkpointing. Cloud computing using virtualization allows workloads to be deployed and scaled quickly through the rapid provisioning of virtual machines on physical machines. However, I/O virtualization, particularly for networking, suffers from significant performance degradation in the presence of high-speed networking connections. In this paper, we first analyze performance challenges in network I/O virtualization and identify two problems-conventional network I/O virtualization suffers from excessive virtual interrupts to guest VMs, and the back-end driver does not efficiently use the computing resources of underlying multicore processors. To address these challenges, we propose optimization methods for enhancing the networking performance: 1) Efficient interrupt coalescing for network I/O virtualization and 2) virtual receive-side scaling to effectively leverage multicore processors. These methods are implemented and evaluated with extensive performance tests on a Xen virtualization platform. Our experimental results confirm that the proposed optimizations can significantly improve network I/O virtualization performance and effectively solve the performance challenges.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6392167]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1118</startPage>
			<endPage>1128</endPage>
			<fileSize>1454</fileSize>
			<authors><![CDATA[HaiBing Guan;YaoZu Dong;RuHui Ma;Dongxiao Xu;Yang Zhang;Jian Li;]]></authors>
		</item>
		<item>
			<title><![CDATA[A New Disk I/O Model of Virtualized Cloud Environment]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6378366]]></link>
			<description><![CDATA[In a traditional virtualized cloud environment, using asynchronous I/O in the guest file system and synchronous I/O in the host file system to handle an asynchronous user disk write exhibits several drawbacks, such as performance disturbance among different guests and consistency maintenance across guest failures. To improve these issues, this paper introduces a novel disk I/O model for virtualized cloud system called HypeGear, where the guest file system uses synchronous operations to deal with the guest write request and the host file system performs asynchronous operations to write the data to the hard disk. A prototype system is implemented on the Xen hypervisor and our experimental results verify that this new model has many advantages over the conventional asynchronous-synchronous model. We also evaluate the overhead of asynchronous I/O at host, which is brought by our new model. The result demonstrates that it enforces little cost on host layer.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6378366]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1129</startPage>
			<endPage>1138</endPage>
			<fileSize>920</fileSize>
			<authors><![CDATA[Dingding Li;Xiaofei Liao;Hai Jin;Bingbing Zhou;Qi Zhang;]]></authors>
		</item>
		<item>
			<title><![CDATA[Improving Data Center Network Utilization Using Near-Optimal Traffic Engineering]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6392830]]></link>
			<description><![CDATA[Equal cost multiple path (ECMP) forwarding is the most prevalent multipath routing used in data center (DC) networks today. However, it fails to exploit increased path diversity that can be provided by traffic engineering techniques through the assignment of nonuniform link weights to optimize network resource usage. To this extent, constructing a routing algorithm that provides path diversity over nonuniform link weights (i.e., unequal cost links), simplicity in path discovery and optimality in minimizing maximum link utilization (MLU) is nontrivial. In this paper, we have implemented and evaluated the Penalizing Exponential Flow-spliTing (PEFT) algorithm in a cloud DC environment based on two dominant topologies, canonical and fat tree. In addition, we have proposed a new cloud DC topology which, with only a marginal modification of the current canonical tree DC architecture, can further reduce MLU and increase overall network capacity utilization through PEFT routing.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6392830]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1139</startPage>
			<endPage>1148</endPage>
			<fileSize>1160</fileSize>
			<authors><![CDATA[Fung Po Tso;Pezaros, D.P.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Electricity Cost Saving Strategy in Data Centers by Using Energy Storage]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226387]]></link>
			<description><![CDATA[Electricity expenditure comprises a significant fraction of the total operating cost in data centers. Hence, cloud service providers are required to reduce electricity cost as much as possible. In this paper, we consider utilizing existing energy storage capabilities in data centers to reduce electricity cost under wholesale electricity markets, where the electricity price exhibits both temporal and spatial variations. A stochastic program is formulated by integrating the center-level load balancing, the server-level configuration, and the battery management while at the same time guaranteeing the quality-of-service experience by end users. We use the Lyapunov optimization technique to design an online algorithm that achieves an explicit tradeoff between cost saving and energy storage capacity. We demonstrate the effectiveness of our proposed algorithm through extensive numerical evaluations based on real-world workload and electricity price data sets. As far as we know, our work is the first to explore the problem of electricity cost saving using energy storage in multiple data centers by considering both the spatial and temporal variations in wholesale electricity prices and workload arrival processes.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6226387]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1149</startPage>
			<endPage>1160</endPage>
			<fileSize>930</fileSize>
			<authors><![CDATA[Yuanxiong Guo;Yuguang Fang;]]></authors>
		</item>
		<item>
			<title><![CDATA[Simple and Effective Dynamic Provisioning for Power-Proportional Data Centers]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269026]]></link>
			<description><![CDATA[Energy consumption represents a significant cost in data center operation. A large fraction of the energy, however, is used to power idle servers when the workload is low. Dynamic provisioning techniques aim at saving this portion of the energy, by turning off unnecessary servers. In this paper, we explore how much gain knowing future workload information can bring to dynamic provisioning. In particular, we develop online dynamic provisioning solutions with and without future workload information available. We first reveal an elegant structure of the offline dynamic provisioning problem, which allows us to characterize the optimal solution in a &amp;#x201C;divide-and-conquer&amp;#x201D; manner. We then exploit this insight to design two online algorithms with competitive ratios $(2-alpha)$ and $(e/(e-1+alpha ))$, respectively, where $(0le alpha le 1)$ is the normalized size of a look-ahead window in which future workload information is available. A fundamental observation is that future workload information beyond the full-size look-ahead window (corresponding to $(alpha =1)$) will not improve dynamic provisioning performance. Our algorithms are decentralized and easy to implement. We demonstrate their effectiveness in simulations using real-world traces.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6269026]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1161</startPage>
			<endPage>1171</endPage>
			<fileSize>504</fileSize>
			<authors><![CDATA[Lu, Tan;Chen, Minghua;Andrew, Lachlan L.H.;]]></authors>
		</item>
		<item>
			<title><![CDATA[Harnessing the Cloud for Securely Outsourcing Large-Scale Systems of Linear Equations]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6231624]]></link>
			<description><![CDATA[Cloud computing economically enables customers with limited computational resources to outsource large-scale computations to the cloud. However, how to protect customers' confidential data involved in the computations then becomes a major security concern. In this paper, we present a secure outsourcing mechanism for solving large-scale systems of linear equations (LE) in cloud. Because applying traditional approaches like Gaussian elimination or LU decomposition (aka. direct method) to such large-scale LEs would be prohibitively expensive, we build the secure LE outsourcing mechanism via a completely different approach-iterative method, which is much easier to implement in practice and only demands relatively simpler matrix-vector operations. Specifically, our mechanism enables a customer to securely harness the cloud for iteratively finding successive approximations to the LE solution, while keeping both the sensitive input and output of the computation private. For robust cheating detection, we further explore the algebraic property of matrix-vector operations and propose an efficient result verification mechanism, which allows the customer to verify all answers received from previous iterative approximations in one batch with high probability. Thorough security analysis and prototype experiments on Amazon EC2 demonstrate the validity and practicality of our proposed design.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6231624]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1172</startPage>
			<endPage>1181</endPage>
			<fileSize>749</fileSize>
			<authors><![CDATA[Cong Wang;Kui Ren;Jia Wang;Qian Wang;]]></authors>
		</item>
		<item>
			<title><![CDATA[Mona: Secure Multi-Owner Data Sharing for Dynamic Groups in the Cloud]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6374615]]></link>
			<description><![CDATA[With the character of low maintenance, cloud computing provides an economical and efficient solution for sharing group resource among cloud users. Unfortunately, sharing data in a multi-owner manner while preserving data and identity privacy from an untrusted cloud is still a challenging issue, due to the frequent change of the membership. In this paper, we propose a secure multi-owner data sharing scheme, named Mona, for dynamic groups in the cloud. By leveraging group signature and dynamic broadcast encryption techniques, any cloud user can anonymously share data with others. Meanwhile, the storage overhead and encryption computation cost of our scheme are independent with the number of revoked users. In addition, we analyze the security of our scheme with rigorous proofs, and demonstrate the efficiency of our scheme in experiments.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6374615]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1182</startPage>
			<endPage>1191</endPage>
			<fileSize>690</fileSize>
			<authors><![CDATA[Xuefeng Liu;Yuqing Zhang;Boyang Wang;Jingbo Yan;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Privacy Leakage Upper Bound Constraint-Based Approach for Cost-Effective Privacy Preserving of Intermediate Data Sets in Cloud]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6263245]]></link>
			<description><![CDATA[Cloud computing provides massive computation power and storage capacity which enable users to deploy computation and data-intensive applications without infrastructure investment. Along the processing of such applications, a large volume of intermediate data sets will be generated, and often stored to save the cost of recomputing them. However, preserving the privacy of intermediate data sets becomes a challenging problem because adversaries may recover privacy-sensitive information by analyzing multiple intermediate data sets. Encrypting ALL data sets in cloud is widely adopted in existing approaches to address this challenge. But we argue that encrypting all intermediate data sets are neither efficient nor cost-effective because it is very time consuming and costly for data-intensive applications to en/decrypt data sets frequently while performing any operation on them. In this paper, we propose a novel upper bound privacy leakage constraint-based approach to identify which intermediate data sets need to be encrypted and which do not, so that privacy-preserving cost can be saved while the privacy requirements of data holders can still be satisfied. Evaluation results demonstrate that the privacy-preserving cost of intermediate data sets can be significantly reduced with our approach over existing ones where all data sets are encrypted.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6263245]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1192</startPage>
			<endPage>1202</endPage>
			<fileSize>1490</fileSize>
			<authors><![CDATA[Xuyun Zhang;Chang Liu;Nepal, S.;Pandey, S.;Jinjun Chen;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Truthful Dynamic Workflow Scheduling Mechanism for Commercial Multicloud Environments]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6295608]]></link>
			<description><![CDATA[The ultimate goal of cloud providers by providing resources is increasing their revenues. This goal leads to a selfish behavior that negatively affects the users of a commercial multicloud environment. In this paper, we introduce a pricing model and a truthful mechanism for scheduling single tasks considering two objectives: monetary cost and completion time. With respect to the social cost of the mechanism, i.e., minimizing the completion time and monetary cost, we extend the mechanism for dynamic scheduling of scientific workflows. We theoretically analyze the truthfulness and the efficiency of the mechanism and present extensive experimental results showing significant impact of the selfish behavior of the cloud providers on the efficiency of the whole system. The experiments conducted using real-world and synthetic workflow applications demonstrate that our solutions dominate in most cases the Pareto-optimal solutions estimated by two classical multiobjective evolutionary algorithms.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6295608]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1203</startPage>
			<endPage>1212</endPage>
			<fileSize>1902</fileSize>
			<authors><![CDATA[Fard, H.M.;Prodan, R.;Fahringer, T.;]]></authors>
		</item>
		<item>
			<title><![CDATA[QoS Ranking Prediction for Cloud Services]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6320550]]></link>
			<description><![CDATA[Cloud computing is becoming popular. Building high-quality cloud applications is a critical research problem. QoS rankings provide valuable information for making optimal cloud service selection from a set of functionally equivalent service candidates. To obtain QoS values, real-world invocations on the service candidates are usually required. To avoid the time-consuming and expensive real-world service invocations, this paper proposes a QoS ranking prediction framework for cloud services by taking advantage of the past service usage experiences of other consumers. Our proposed framework requires no additional invocations of cloud services when making QoS ranking prediction. Two personalized QoS ranking prediction approaches are proposed to predict the QoS rankings directly. Comprehensive experiments are conducted employing real-world QoS data, including 300 distributed users and 500 real-world web services all over the world. The experimental results show that our approaches outperform other competing approaches.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6320550]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1213</startPage>
			<endPage>1222</endPage>
			<fileSize>1171</fileSize>
			<authors><![CDATA[Zibin Zheng;Xinmiao Wu;Yilei Zhang;Lyu, M.R.;Jianmin Wang;]]></authors>
		</item>
		<item>
			<title><![CDATA[Cloudy with a Chance of Cost Savings]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336750]]></link>
			<description><![CDATA[Cloud-based hosting is claimed to possess many advantages over traditional in-house (on-premise) hosting such as better scalability, ease of management, and cost savings. It is not difficult to understand how cloud-based hosting can be used to address some of the existing limitations and extend the capabilities of many types of applications. However, one of the most important questions is whether cloud-based hosting will be economically feasible for my application if migrated into the cloud. It is not straightforward to answer this question because it is not clear how my application will benefit from the claimed advantages, and, in turn, be able to convert them into tangible cost savings. Within cloud-based hosting offerings, there is a wide range of hosting options one can choose from, each impacting the cost in a different way. Answering these questions requires an in-depth understanding of the cost implications of all the possible choices specific to my circumstances. In this study, we identify a diverse set of key factors affecting the costs of deployment choices. Using benchmarks representing two different applications (TPC-W and TPC-E) we investigate the evolution of costs for different deployment choices. We consider important application characteristics such as workload intensity, growth rate, traffic size, storage, and software license to understand their impact on the overall costs. We also discuss the impact of workload variance and cloud elasticity, and certain cost factors that are subjective in nature.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6336750]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1223</startPage>
			<endPage>1233</endPage>
			<fileSize>1628</fileSize>
			<authors><![CDATA[Byung Chul Tak;Urgaonkar, B.;Sivasubramaniam, A.;]]></authors>
		</item>
		<item>
			<title><![CDATA[A Highly Practical Approach toward Achieving Minimum Data Sets Storage Cost in the Cloud]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6410317]]></link>
			<description><![CDATA[Massive computation power and storage capacity of cloud computing systems allow scientists to deploy computation and data intensive applications without infrastructure investment, where large application data sets can be stored in the cloud. Based on the pay-as-you-go model, storage strategies and benchmarking approaches have been developed for cost-effectively storing large volume of generated application data sets in the cloud. However, they are either insufficiently cost-effective for the storage or impractical to be used at runtime. In this paper, toward achieving the minimum cost benchmark, we propose a novel highly cost-effective and practical storage strategy that can automatically decide whether a generated data set should be stored or not at runtime in the cloud. The main focus of this strategy is the local-optimization for the tradeoff between computation and storage, while secondarily also taking users' (optional) preferences on storage into consideration. Both theoretical analysis and simulations conducted on general (random) data sets as well as specific real world applications with Amazon's cost model show that the cost-effectiveness of our strategy is close to or even the same as the minimum cost benchmark, and the efficiency is very high for practical runtime utilization in the cloud.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6410317]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1234</startPage>
			<endPage>1244</endPage>
			<fileSize>1341</fileSize>
			<authors><![CDATA[Dong Yuan;Yun Yang;Xiao Liu;Wenhao Li;Lizhen Cui;Meng Xu;Jinjun Chen;]]></authors>
		</item>
		<item>
			<title><![CDATA[Toward Fine-Grained, Unsupervised, Scalable Performance Diagnosis for Production Cloud Computing Systems]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6410318]]></link>
			<description><![CDATA[Performance diagnosis is labor intensive in production cloud computing systems. Such systems typically face many real-world challenges, which the existing diagnosis techniques for such distributed systems cannot effectively solve. An efficient, unsupervised diagnosis tool for locating fine-grained performance anomalies is still lacking in production cloud computing systems. This paper proposes CloudDiag to bridge this gap. Combining a statistical technique and a fast matrix recovery algorithm, CloudDiag can efficiently pinpoint fine-grained causes of the performance problems, which does not require any domain-specific knowledge to the target system. CloudDiag has been applied in a practical production cloud computing systems to diagnose performance problems. We demonstrate the effectiveness of CloudDiag in three real-world case studies.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6410318]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1245</startPage>
			<endPage>1255</endPage>
			<fileSize>1119</fileSize>
			<authors><![CDATA[Haibo Mi;Huaimin Wang;Yangfan Zhou;Lyu, M.R.;Hua Cai;]]></authors>
		</item>
		<item>
			<title><![CDATA[C-MART: Benchmarking the Cloud]]></title>
			<link><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381404]]></link>
			<description><![CDATA[Cloud computing environments provide on-demand resource provisioning, allowing applications to elastically scale. However, application benchmarks currently being used to test cloud management systems are not designed for this purpose. This results in resource underprovisioning and quality-of-service (QoS) violations when systems tested using these benchmarks are deployed in production environments. We present C-MART, a benchmark designed to emulate a modern web application running in a cloud computing environment. It is designed using the cloud computing paradigm of elastic scalability at every application tier and utilizes modern web-based technologies such as HTML5, AJAX, jQuery, and SQLite. C-MART consists of a web application, client emulator, deployment server, and scaling API. The deployment server automatically deploys and configures the test environment in orders of magnitude less time than current benchmarks. The scaling API allows users to define and provision their own customized datacenter. The client emulator generates the web workload for the application by emulating complex and varied client behaviors, including decisions based on page content and prior history. We show that C-MART can detect problems in management systems that previous benchmarks fail to identify, such as an increase from 4.4 to 50 percent error in predicting server CPU utilization and resource underprovisioning in 22 percent of QoS measurements.]]></description>
			<pubDate><![CDATA[June  2013]]></pubDate>
			<guid><![CDATA[http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6381404]]></guid>
			<volume>24</volume>
			<issue>6</issue>
			<startPage>1256</startPage>
			<endPage>1266</endPage>
			<fileSize>1716</fileSize>
			<authors><![CDATA[Turner, A.;Fox, A.;Payne, J.;Kim, H.S.;]]></authors>
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