Adaptive Partitioning for Large-Scale Dynamic Graphs

Abstract—In the last years, large-scale graph processing has gained increasing attention, with most recent systems placing particular emphasis on latency. One possible technique to improve runtime performance in a distributed graph processing system is to reduce network communication. The most notable way to achieve this goal is to partition the graph by minimizing the num-ber of edges that connect vertices assigned to different machines, while keeping the load balanced. However, real-world graphs are highly dynamic, with vertices and edges being constantly added and removed. Carefully updating the partitioning of the graph to reflect these changes is necessary to avoid the introduction of an extensive number of cut edges, which would gradually worsen computation performance. In this paper we show that performance degradation in dynamic graph processing systems can be avoided by adapting continuously the graph partitions as the graph changes. We present a novel highly scalable adaptive partitioning strategy, and show a number of refinements that make it work under the constraints of a large-scale distributed system. The partitioning strategy is based on iterative vertex migrations, relying only on local information. We have implemented the technique in a graph processing system, and we show through three real-world scenarios how adapting graph partitioning reduces execution time by over 50 % when compared to commonly used hash-partitioning. I

Deploying Large-Scale Datasets on-Demand in the Cloud: Treats and Tricks on Data Distribution

Public clouds have democratised the access to analytics for virtually any institution in the world. Virtual Machines (VMs) can be provisioned on demand, and be used to crunch data after uploading into the VMs. While this task is trivial for a few tens of VMs, it becomes increasingly complex and time consuming when the scale grows to hundreds or thousands of VMs crunching tens or hundreds of TB. Moreover, the elapsed time comes at a price: the cost of provisioning VMs in the cloud and keeping them waiting to load the data. In this paper we present a big data provisioning service that incorporates hierarchical and peer-to-peer data distribution techniques to speed-up data loading into the VMs used for data processing. The system dynamically mutates the sources of the data for the VMs to speed-up data loading. We tested this solution with 1000 VMs and 100 TB of data, reducing time by at least 30 % over current state of the art techniques. This dynamic topology mechanism is tightly coupled with classic declarative machine configuration techniques (the system takes a single high-level declarative configuration file and configures both software and data loading). Together, these two techniques simplify the deployment of big data in the cloud for end users who may not be experts in infrastructure management. Index Terms—Large-scale data transfer, flash crowd, big data, BitTorrent, p2p overlay, provisioning, big data distribution I

Security analysis of mobile edge computing in virtualized small cell networks

Based upon the context of Mobile Edge Computing (MEC) actual research and within the innovative scope of the SESAME EU-funded research project, we propose and assess a framework for security analysis applied in virtualised Small Cell Networks, with the aim of further extending MEC in the broader 5G environment. More specifically, by applying the fundamental concepts of the SESAME original architecture that aims at providing enhanced multi-tenant MEC services through Small Cells coordination and virtualization, we focus on a realistic 5G-oriented scenario enabling the provision of large multi-tenant enterprise services by using MEC. Then we evaluate several security issues by using a formal methodology, known as the Secure Tropos

Security challenges of small cell as a service in virtualized mobile edge computing environments

Research on next-generation 5G wireless networks is currently attracting a lot of attention in both academia and industry. While 5G development and standardization activities are still at their early stage, it is widely acknowledged that 5G systems are going to extensively rely on dense small cell deployments, which would exploit infrastructure and network functions virtualization (NFV), and push the network intelligence towards network edges by embracing the concept of mobile edge computing (MEC). As security will be a fundamental enabling factor of small cell as a service (SCaaS) in 5G networks, we present the most prominent threats and vulnerabilities against a broad range of targets. As far as the related work is concerned, to the best of our knowledge, this paper is the first to investigate security challenges at the intersection of SCaaS, NFV, and MEC. It is also the first paper that proposes a set of criteria to facilitate a clear and effective taxonomy of security challenges of main elements of 5G networks. Our analysis can serve as a staring point towards the development of appropriate 5G security solutions. These will have crucial effect on legal and regulatory frameworks as well as on decisions of businesses, governments, and end-users

LDL-cholesterol lowering effect of a generic product of simvastatin compared to simvastatin (Zocor™) in Thai hypercholesterolemic subjects – a randomized crossover study, the first report from Thailand

BACKGROUND: It is commonly agreed that people with a high blood LDL-cholesterol will have a higher risk of coronary artery disease (CAD) than people with low blood LDL-cholesterol. Due to the increasingly high costs of medication in Thailand, the government has set up several measures to combat the problem. One of such strategies is to promote the utilization of locally manufactured drug products, especially those contained in the National Drug List. Simvastatin, an HMG-CoA reductase inhibitor, is listed as an essential drug for the treatment of hypercholesterolemia. Here, we reported the study on the LDL-cholesterol-lowering effect of a generic simvastatin product in comparison with the Zocor(©), in 43 healthy thai volunteers. METHOD: The generic product tested was Eucor(©), locally manufactured by Greater Pharma Ltd., Part, Thailand, and the reference product was Zocor(©) (Merck Sharp & Dohme, USA). The two products were administered as 10-mg single oral doses in a two-period crossover design. After drug administration, serial blood samples were collected every 4 weeks for 16 weeks. The major parameter monitored in this study was blood LDL-cholesterol. RESULT: After taking the drugs for the first 8 weeks, no statistically significant difference was dedected in blood LDL-cholesterol between the first (Zocor(©)-treated) and the second (Eucor(©)-treated) groups. After crossover and taking drugs for further 8 weeks, a similar result was obtained, i.e., no significant difference in blood LDL-cholesterol between the first (Eucor(©)-treated) and the second (Zocor(©)-treated) groups was observed. Upon completion of the 16-week study, there was also no statisticaly significant difference in the changes of all tested blood parameters between the two products (randomized block ANOVA, N = 37). Only minor side effects, mainly dizziness and nausea, were observed in both products. CONCLUSION: Our study demonstrated no significant differences in the therapeutic effect and safety between the generic and original simvastatin products