A Hierarchy-Driven Compression Technique for Advanced OLAP Visualization of Multidimensional Data Cubes

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

Xu²,

Grasso

2015

Self Cite

Following our previous research results, in this paper we provide two authoritative application scenarios that build on top of OLAP*, a middleware for parallel processing of OLAP queries that truly realizes effective and efficiently OLAP over Big Data. We have provided two authoritative case studies, namely parallel OLAP data cube processing and virtual OLAP data cube design, for which we also propose a comprehensive performance evaluation and analysis. Derived analysis clearly confirms the benefits of our proposed framework.

Section: Discussionmentioning

confidence: 99%

Cloud-Based OLAP over Big Data: Application Scenarios and Performance Analysis

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

Xu²,

Grasso

2015

Self Cite

“…An obvious extension of the work described in this paper is to use other benchmarks in order to include other workload activities. Also, we plan to further improve the characteristics of our framework by integrating solutions for dealing with novel aspects of massive big data set processing, on top of which workloads may be still defined, such as data compression techniques (e.g., [15]), fragmentation approaches (e.g., [13]), privacypreservation approaches (e.g., [14]) that, particularly, may be extremely useful combined with malware detection issues.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Cloud-Based Machine Learning Tools for Enhanced Big Data Applications

2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

Mumolo

Corona

2015

Self Cite

We propose Cloud-based machine learning tools for enhanced Big Data applications, where the main idea is that of predicting the "next" workload occurring against the target Cloud infrastructure via an innovative ensemble-based approach that combine the effectiveness of different well-known classifiers in order to enhance the whole accuracy of the final classification, which is very relevant at now in the specific context of Big Data. So-called workload categorization problem plays a critical role towards improving the efficiency and the reliability of Cloudbased big data applications. Implementation-wise, our method proposes deploying Cloud entities that participate to the distributed classification approach on top of virtual machines, which represent classical "commodity" settings for Cloud-based big data applications. Preliminary experimental assessment and analysis clearly confirm the benefits deriving from our classification framework. 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing 978-1-4799-8006-2/15 $31.00

“…An obvious extension of the work described in this paper is to use other benchmarks in order to include other workload activities. Also, we plan to further improve the characteristics of our framework by integrating solutions for dealing with novel aspects of massive data set processing, on top of which workloads may be still defined, such as data compression techniques (e.g., [14]),jragmentation approaches (e.g., [12]), privacy-preservation approaches (e.g., [13]) that, particularly, may be extremely useful combined with malware detection issues.…”

Section: Concluding Remarks and Future Workmentioning

confidence: 99%

Coarse-grained workload categorization in virtual environments using the Dempster-Shafer fusion

2015 IEEE 19th International Conference on Computer Supported Cooperative Work in Design (CSCWD)

Mumolot

Corona

2015

Self Cite

Given a number of known reference workloads, and an unknown workload, this paper deals with the problem of finding the reference workload which is most similar to the unknown one. T he depicted scenario turns to be useful in a plethora of modern information system applications. We\ud name this problem as coarse-grained workload classification, because, instead of characterizing the unknown workload in terms of finer behaviors, such as CPU, memory, disk or network intensive patterns, we classify the whole unknown workload as one of the (possible) reference workloads. Reference workloads represent a category of workloads that are relevant in a given applicative environment. In particular, we focus our attention\ud on the classification problem described above in the special case represented by virtualized environments. Today, Virtual Machines (VMs) have become very popular because they offer important advantages to modern computing environments such as cloud computing or server farms. In virtualization frameworks, workload classification is very useful for accounting, security reasons or user profiling. Hence, our research makes more sense in such environments, and it turns to be very useful in a special context like cloud computing, which is emerging at now. In this respect, our approach consists in running several machine-learning-based classifiers of different workload models, and then deriving the best classifier produced by the Dempster-Shafer fusion, in order to magnify the accuracy of the final classification. Experimental assessment and analysis c1ealry confirm the benefits deriving from our classification framework