https://www.ime.usp.br/~kon/papers/Polato_JNCA_2014.pdf

A comprehensive view of Hadoop research—A systematic literature review

PARAMO: A PARAllel predictive MOdeling platform for healthcare analytic research using electronic health records

In this paper, we consider task-level scheduling algorithms with respect to budget and deadline constraints for a batch of MapReduce jobs on a set of provisioned heterogeneous (virtual) machines in cloud platforms. The heterogeneity is manifested in the popular “pay-as-you-go” charging model where the service machines with different performance would have different service rates. We organize the batch of jobs as a $\kappa$ -stage workflow and study two related optimization problems, depending on whether the constraints are on monetary budget or on scheduling length of the workflow. First, given a total monetary budget $B$ , by combining an in-stage local greedy algorithm (whose optimality is also proven) and dynamic programming (DP) techniques, we propose a global optimal scheduling algorithm to achieve minimum scheduling length of the workflow within $O(\kappa B^2)$ . Although the optimal algorithm is efficient when $B$ is polynomially bounded by the number of tasks in the MapReduce jobs, the quadratic time complexity is still high. To improve the efficiency, we further develop two greedy algorithms, called Global Greedy Budget (GGB) and Gradual Refinement (GR), each adopting different greedy strategies. In GGB we extend the idea of the local greedy algorithm to the efficient global distribution of the budget with minimum scheduling length as a goal whilst in GR we iteratively apply the DP algorithm to the distribution of exponentially reduced budget so that the solutions are gradually refined. Second, we consider the optimization problem of minimizing cost when the (time) deadline of the computation $D$ is fixed. We convert this problem into the standard Multiple-Choice Knapsack Problem via a parallel transformation. Our empirical studies verify the proposed optimal algorithms and show the efficiencies of the greedy algorithms in cost-effectiveness to distribute the budget for performance optimizations of the MapReduce workflows.

Budget-Driven Scheduling Algorithms for Batches of MapReduce Jobs in Heterogeneous Clouds

Protecting personal information privacy has become a controversial issue among online social network providers and users. Most social network providers have developed several techniques to decrease threats and risks to the users privacy. These risks include the misuse of personal information which may lead to illegal acts such as identity theft. This study aims to measure the awareness of users on protecting their personal information privacy, as well as the suitability of the privacy systems which they use to modify privacy settings. Survey results show high percentage of the use of smart phones for web services but the current privacy settings for online social networks need to be improved to support different type of mobile phones screens. Because most users use their mobile phones for Internet services, privacy settings that are compatible with mobile phones need to be developed. The method of selecting privacy settings should also be simplified to provide users with a clear picture of the data that will be shared with others. Results of this study can be used to develop a new privacy system which will help users control their personal information easily from different devices, including mobile Internet devices and computers.

pdf/personal-information-privacy-settings-of-online-social-2ngjq4116t.pdf

Personal Information Privacy Settings of Online Social Networks and their Suitability for Mobile Internet Devices

Congestion control for high-speed wired network

Google's MapReduce has gained significant popularity as a platform for large scale distributed data processing. Hadoop [1] is an open source implementation of MapReduce [11] framework, originally it was developed to operate over single cluster environment and could not be leveraged for distributed data processing across federated clusters. At multiple federated clusters connected with high speed networks, computing resources are provisioned from any of the clusters from the federation. Placement of map tasks close to its data split is critical for performance of Hadoop. In this work, we add network awareness in Hadoop while scheduling the map tasks over federated clusters. We observe 12% to 15 % reduction of execution time in FIFO and FAIR schedulers of Hadoop for varying workloads.

/pdf/network-aware-scheduling-of-mapreduce-framework-4kvykr00i4.pdf

Network-aware scheduling of mapreduce framework ondistributed clusters over high speed networks

A study of fairness among heterogeneous TCP variants over 10 Gbps high-speed optical networks

Queue management schemes at the routers and congestion avoidance schemes at the end points cooperate to provide good congestion solutions in computer networks While queue management schemes are still being developed, research on congestion avoidance has come a long way to serve the bandwidth requirement of the networks (eg high speed networks, data centers, etc) at the order of 10Gbps Because of considerable lack on the evaluation research work, there is no consensus on the choice of the queue management algorithms over these networks To the best of our knowledge, this is the first work that presents experimental evaluation of the effect of various queue management schemes on high speed TCP variants in realistic 10Gbps network environment Evaluation of queue management schemes such as Drop-tail, RED, CHOKe, and SFB are presented for popular high speed TCP variants such as TCP-RENO, HSTCP, and CUBIC over CRON [1], a real 10Gbps high speed network testbed Performance results are presented for several important metrics of interests such as link utilization, fairness, delay, packet drop rate, and computational complexity The presented work supports further research work on the design and deployment issues of queue management schemes for high speed networks

http://www.cct.lsu.edu/~xuelin/ICNC2012_Lin.pdf

Experimental evaluation of the effect of queue management schemes on the performance of high speed TCPs in 10Gbps network environment

Several high speed TCP variants are adopted by end users and therefore, heterogeneous congestion control has become the characteristic of newly emerging high-speed networks. In contrast to homogeneous TCP flows, fairness among heterogeneous TCP flows now depends on router parameters such as queue management scheme, buffer size, etc. To the best of our knowledge, this is the first evaluation of fairness among heterogeneous TCP variants over 10Gbps high-speed networks. Our evaluation scenarios for heterogeneous TCP flows consist of TCP variants with substantial presence in current Internet; therefore, TCP-SACK, CUBIC and HSTCP compete for bottleneck bandwidth. Experimental results for fairness are presented for different queue management schemes such as Drop-tail, RED, and CHOKe with varying degree of buffer sizes. We observed heterogeneous TCP flows induce more unfairness than homogeneous ones. RED and CHOKe improve fairness for large buffer sizes as compared to Drop-tail, whereas all three show similar fairness behavior for small buffer sizes.

An evaluation of fairness among heterogeneous TCP variants over 10Gbps high-speed networks

High speed networks have characteristics of high bandwidth, long queuing delay, and high burstiness which make it difficult to address issues such as fairness, low queuing delay and high link utilization. Current high speed networks carry heterogeneous TCP flows which makes it even more challenging to address these issues. Since sender centric approaches do not meet these challenges, there have been several proposals to address them at router level via queue management (QM) schemes. These QM schemes have been fairly successful in addressing either fairness issues or large queuing delay but not both at the same time. We propose a new QM scheme called Approximated-Fair and Controlled-Delay (AFCD) queuing for high speed networks that aims to meet following design goals: approximated fairness, controlled low queuing delay, high link utilization and simple implementation. The design of AFCD utilizes a novel synergistic approach by forming an alliance between approximated fair queuing and controlled delay queuing. It uses very small amount of state information in sending rate estimation of flows and makes drop decision based on a target delay of individual flow. Through experimental evaluation in a 10Gbps high speed networking environment, we show AFCD meets our design goals by maintaining approximated fair share of bandwidth among flows and ensuring a controlled very low queuing delay with a comparable link utilization.

AFCD: An Approximated-Fair and Controlled-Delay Queuing for High Speed Networks

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