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Cloud infrastructures are capable of leveraging massive computational as well as data processing capabilities in virtualized environments. Emerging applications on today's clouds are data intensive and this has led to the trend of employing data-parallel frameworks, like Hadoop and its myriad descendants, for handling such massive data requirements. Scheduling of jobs in such frameworks is in essence a two-step process, where the block-data distribution follows mapping of computations among those resources. Since most Hadoop-based systems make these two decisions independently, it seems a promising prospective to map computations within cloud resources based on data blocks already distributed to them. This paper proposes data partitioning and placement aware computation scheduling scheme (DPPACS), a data and computation scheduling framework that adopts the strategy of improving computation and data co-allocation within a Hadoop cloud infrastructure based on knowledge of data blocks availability. Accordingly, this paper proposes a data-partitioning algorithm, a novel partition-cum-placement algorithm and finally proposes a computational scheduling algorithm that exploits knowledge of data availability at different clusters. The proposed DPPACS has been implemented on a test bed and its comparative performance results with respect to Hadoop's default data placement strategy have been presented. Experiments conducted herein conclusively demonstrate the efficacy of the proposed DPPACS. [ABSTRACT FROM AUTHOR]

Cellular networks are evolving to the era of 5th Generation (5G), where 5G new radio (NR) and Long-Term Evolution: Advanced (LTE-A) Pro technologies are being envisioned for enabling smart and innovative services of the Internet-of-Things (IoT). However, existing LTE-A Pro protocols such as the group paging approach is still heavily inclined towards human-to-human communications owing to the heterogeneous characteristics of a wide variety of IoT devices. Since most IoT devices are battery operated and their power consumption rate decides the battery lifetime, hence energy-efficient data transfer protocols are of paramount importance for next-generation IoT networks. Group paging is one such mechanism that has been widely accepted to improve energy efficiency of IoT networks. However, grouping approach for IoT devices is still not a much addressed topic, though a few novel group paging approaches have been studied that focus on varied IoT characteristics and mobility; though such approaches are not computationally efficient particularly for massive IoT deployments. Therefore, this paper proposes a novel multi-parameter evolutionary optimization, namely, genetic algorithm (GA) based grouping approach that also considers IoT features such as traffic patterns, delay requirements, and mobility patterns. Results obtained from simulations validate that our proposed method can significantly improve IoT devices' energy efficiency over random grouping schemes and other approaches. [ABSTRACT FROM AUTHOR]

With the electric power grid experiencing a rapid shift to the smart grid paradigm over a deregulated energy market, Internet of Things (IoT)-based solutions are gaining prominence, and innovative peer-to-peer (P2P) energy trading at a micro level is being deployed. Such advancement, however, leaves traditional security models vulnerable and paves the path for blockchain, a distributed ledger technology (DLT), with its decentralized, open, and transparency characteristics as a viable alternative. However, due to deregulation in energy trading markets, most of the prototype resilience regarding cybersecurity attack, performance and scalability of transaction broadcasting, and its direct impact on overall performances and attacks are required to be supported, which becomes a performance bottleneck with existing blockchain solutions such as Hyperledger, Ethereum, and so on. In this paper, we design a novel permissioned Corda framework for P2P energy trading peers that not only mitigates a new class of cyberattacks, i.e., delay trading (or discard), but also disseminates the transactions in a optimized propagation time, resulting in a fair transaction distribution. Sharing transactions in a permissioned R3 Corda blockchain framework is handled by the Advanced Message Queuing Protocol (AMQP) and transport layer security (TLS). The unique contribution of this paper lies in the use of an optimized CPU and JVM heap memory scenario analysis with P2P metric in addition to a far more realistic multihosted testbed for the performance analysis. The average latencies measured are 22 ms and 51 ms for sending and receiving messages. We compare the throughput by varying different types of flow such as energy request, request + pay, transfer, multiple notary, sender, receiver, and single notary. In the proposed framework, request is an energy asset that is based on payment state and contract in the P2P energy trading module, so in request flow, only one node with no notary appears on the vault of the node.Energy request + pay flow interaction deals with two nodes, such as producer and consumer, to deal with request and transfer of asset ownership with the help of a notary. Request + repeated pay flow request, on node A and repeatedly transfers a fraction of energy asset state to another node, B, through a notary.