CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes

CoCoWa: A Collaborative Contact-Based Watchdog for Detecting Selfish Nodes Mobile ad-hoc networks (MANETs) assume that mobile nodes voluntary cooperate in order to work properly. This cooperation is a cost-intensive activity and some nodes can refuse to cooperate, leading to a selfish node behaviour. Thus, the overall network performance could be seriously affected. The use of watchdogs is a well-known mechanism to detect selfish nodes. However, the detection process performed by watchdogs can fail, generating false positives and false negatives that can induce to wrong operations. Moreover, relying on local watchdogs alone can lead to…

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On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications

On Traffic-Aware Partition and Aggregation in MapReduce for Big Data Applications The MapReduce programming model simplifies large-scale data processing on commodity cluster by exploiting parallel map tasks and reduce tasks. Although many efforts have been made to improve the performance of MapReduce jobs, they ignore the network traffic generated in the shuffle phase, which plays a critical role in performance enhancement. Traditionally, a hash function is used to partition intermediate data among reduce tasks, which, however, is not traffic-efficient because network topology and data size associated with each key are…

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HConfig: Resource Adaptive Fast Bulk Loading in HBase

HConfig: Resource Adaptive Fast Bulk Loading in HBase NoSQL (Not only SQL) data stores become a vital component in many big data computing platforms due to its inherent horizontal scalability. HBase is an open-source distributed NoSQL store that is widely used by many Internet enterprises to handle their big data computing applications (e.g. Facebook handles millions of messages each day with HBase). Optimizations that can enhance the performance of HBase are of paramount interests for big data applications that use HBase or Big Table like key-value stores. In this paper…

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Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters

Self-Adjusting Slot Configurations for Homogeneous and Heterogeneous Hadoop Clusters The MapReduce framework and its open source implementation Hadoop have become the defacto platform for scalable analysis on large data sets in recent years. One of the primary concerns in Hadoop is how to minimize the completion length (i.e., makespan) of a set of MapReduce jobs. The current Hadoop only allows static slot configuration, i.e., fixed numbers of map slots and reduce slots throughout the lifetime of a cluster. However, we found that such a static configuration may lead to low…

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A Distributed Three-hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks

A Distributed Three-hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks A Distributed Three-hop Routing Protocol to Increase the Capacity of Hybrid Wireless Networks Hybrid wireless networks combining the advantages of both mobile ad-hoc networks and infrastructure wireless networks have been receiving increased attention due to their ultra-high performance. An efficient data routing protocol is important in such networks for high network capacity and scalability. However, most routing protocols for these networks simply combine the ad-hoc transmission mode with the cellular transmission mode, which inherits the drawbacks of…

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Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms

Stochastic Decision Making for Adaptive Crowdsourcing in Medical Big-Data Platforms Two novel algorithms for adaptive crowdsourcing in medical imaging big-data platforms is considered, namely, a max-weight scheduling algorithm for medical cloud platforms and a stochastic decision-making algorithm for distributed power-and-latency-aware dynamic buffer management in medical devices. In the first algorithm, medical cloud platforms perform a joint queue-backlog and rate-aware scheduling decisions for matching deployed access points (APs) and medical users where APs are eventually connected to medical clouds. In the second algorithm, each scheduled medical device computes the amounts of…

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