An analytical model for on-chip interconnects in multimedia embedded systems

Using immune algorithms is generally a time-intensive process-especially for problems with a large number of variables. In this paper, we propose a distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm that is implemented using the message passing interface (MPI). The proposed algorithm is composed of three layers: objective, group and individual layers. First, for each objective in the multi-objective problem to be addressed, a subpopulation is used for optimization, and an archive population is used to optimize all the objectives. Second, the large number of variables are divided into several groups. Finally, individual evaluations are allocated across many core processing units, and calculations are performed in parallel. Consequently, the computation time is greatly reduced. The proposed algorithm integrates the idea of immune algorithms, which tend to explore sparse areas in the objective space and use simulated binary crossover for mutation. The proposed algorithm is employed to optimize the 3D terrain

Section: Parallelism Implementationmentioning

confidence: 99%

Distributed parallel cooperative coevolutionary multi-objective large-scale immune algorithm for deployment of wireless sensor networks

Cao

Zhao

Yang

et al. 2018

“…The impacts of latency sensitivity among the workloads upon energy efficiency has been studied in our earlier works [21] at both the job and task levels to uncover the energy implications of workload latency sensitivity, where it has been showed that higher level of latency sensitive workloads are more resource hungry and vulnerable for terminations. Furthermore, reducing the communication latency among multimedia workloads with nonuniform destinations has been the focus of the works of [22], for the benefits of performance scalability and flexible routing. The spatial traffic burstiness in communication networks has been captured based on their communication locality and the temporal traffic burstiness has been modelled by a markov-modulated poisson process (MMPP) [23] in multi-cluster environments.…”

Section: Related Workmentioning

confidence: 99%

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres

Panneerselvam

Liu

et al. 2018

“…We assume that the users or groups belong to the same trust domains. The proposed scheme could also be adapted to other communications networks and computing systems [33][34][35]. The relevancy and familiarity are computed through the received sensing information [36] such as services, networks and applications and so on from the mobile sensing devices.…”

Section: A Data Trustworthiness Enhanced Reputation Mechanism (Dtrm)mentioning

confidence: 99%

DTRM: A new reputation mechanism to enhance data trustworthiness for high-performance cloud computing

Lin

et al. 2018

Cloud computing and the mobile Internet have been the two most influential information technology revolutions, which intersect in mobile cloud computing (MCC). The burgeoning MCC enables the large-scale collection and processing of big data, which demand trusted, authentic, and accurate data to ensure an important but often overlooked aspect of big data --data veracity. Troublesome internal attacks launched by internal malicious users is one key problem that reduces data veracity and remains difficult to handle. To enhance data veracity and thus improve the performance of big data computing in MCC, this paper proposes a Data Trustworthiness enhanced Reputation Mechanism (DTRM) which can be used to defend against internal attacks. In the DTRM, the sensitivity-level based data category, Metagraph theory based user group division, and reputation transferring methods are integrated into the reputation query and evaluation process. The extensive simulation results based on real datasets show that the DTRM outperforms existing classic reputation mechanisms under bad mouthing attacks and mobile attacks.