Affinity is common among Virtual Machines (VMs) in cloud environments. If VMs collaborating on a job are split in geographically distributed clouds, the low bandwidth and high latency inter-cloud communication via a w...
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ISBN:
(纸本)9781479940875
Affinity is common among Virtual Machines (VMs) in cloud environments. If VMs collaborating on a job are split in geographically distributed clouds, the low bandwidth and high latency inter-cloud communication via a wide area network (WAN) will dramatically degrade the system performance. A potential solution is migrating all of the VMs collaborating on a job in parallel, so as to avoid wide area communication. However, if the job is too large, it becomes impractical to migrate all of the VMs simultaneously due to limited WAN bandwidth and high block dirty rate. We propose a migration optimization mechanism called Clique Migration to partition a large group of VMs into subgroups based on the traffic affinities among VMs. Then, subgroups are migrated one at a time. Based on Clique Migration, we propose and implement two algorithms called R-Min-Cut and Kmeans-SF. Analysis of the traffic trace of 68 VMs in an IBM production cluster shows that our algorithms can reduce inter-cloud traffic by 25% to 60%, when the degree of parallel migration is from 2 to 32. Tests of MPI multi-PingPing benchmark running on simulated inter-cloud environments, show that our algorithms can significantly shorten the period during which applications undergo performance degradation. Tests of MPI Reduce scatter benchmark show that R-Min-Cut can keep the performance during migration at 26% to 75% of the non-migration scenario.
This paper provides a comparative study of several enhanced versions of the fuzzy c-means clustering algorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clu...
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ISBN:
(纸本)9781479959969
This paper provides a comparative study of several enhanced versions of the fuzzy c-means clustering algorithm in an application of histogram-based image color reduction. A common preprocessing is performed before clustering, consisting of a preliminary color quantization, histogram extraction and selection of frequently occurring colors of the image. These selected colors will be clustered by tested c-means algorithms. Clustering is followed by another common step, which creates the output image. Besides conventional hard (HCM) and fuzzy c-means (FCM) clustering, the so-called generalized improved partition FCM algorithm, and several versions of the suppressed FCM (s-FCM) in its conventional and generalized form, are included in this study. Accuracy is measured as the average color difference between pixels of the input and output image, while efficiency is mostly characterized by the total runtime of the performed color reduction. Numerical evaluation found all enhanced FCM algorithms more accurate, and four out of seven enhanced algorithms faster than FCM. All tested algorithms can create reduced color images of acceptable quality.
Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison between the observed network and a null model, which serves as a reference. To mak...
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ISBN:
(纸本)9781479958771
Modularity is a widely used measure for evaluating community structure in networks. The definition of modularity involves a comparison between the observed network and a null model, which serves as a reference. To make the comparison significant, this null model should characterize some features of the observed network. However, the previously used null models are not good representations of real-world networks. A common feature of many real-world networks is similarity attraction, i.e., nodes that are similar have a higher chance of getting connected. We propose a new null model that captures this feature. Based on our null model, we create a unified measure Dist-Modularity, which incorporates the famous Newman-Girvan modularity as a special case. We use three examples to demonstrate that Dist-Modularity is useful in detecting 1) the multi-resolution communities and 2) the geographically dispersed communities.
Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas ...
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ISBN:
(纸本)9781479953936
Deferred update replication (DUR) is an established approach to implementing highly efficient and available storage. While the throughput of read-only transactions scales linearly with the number of deployed replicas in DUR, the throughput of update transactions experiences limited improvements as replicas are added. This paper presents Parallel Deferred Update Replication (P-DUR), a variation of classical DUR that scales both read-only and update transactions with the number of cores available in a replica. In addition to introducing the new approach, we describe its full implementation and compare its performance to classical DUR and to Berkeley DB, a well-known standalone database.
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