In wireless sensor networks, the high density of node’s distribution will result in transmission collision and energy dissipation of redundant data. To resolve the above problems, an energy-efficient sleep scheduling mechanism with similarity measure for wireless sensor networks (ESSM) is proposed, which will schedule the sensors into the active or sleep mode to reduce energy consumption effectively. Firstly, the optimal competition radius is estimated to organize the all sensor nodes into several clusters to balance energy consumption. Secondly, according to the data collected by member nodes, a fuzzy matrix can be obtained to measure the similarity degree, and the correlation function based on fuzzy theory can be defined to divide the sensor nodes into different categories. Next, the redundant nodes will be selected to put into sleep state in the next round under the premise of ensuring the data integrity of the whole network. Simulations and results show that our method can achieve better performances both in proper distribution of clusters and improving the energy efficiency of the networks with prerequisite of guaranteeing the data accuracy.
The Cloud is a computing platform that provides on-demand access to a shared pool of configurable resources such as networks, servers and storage that can be rapidly provisioned and released with minimal management effort from clients. At its core, Cloud computing focuses on maximizing the effectiveness of the shared resources. Therefore, workflow scheduling is one of the challenges that the Cloud must tackle especially if a large number of tasks are executed on geographically distributed servers. This entails the need to adopt an effective scheduling algorithm in order to minimize task completion time (makespan). Although workflow scheduling has been the focus of many researchers, a handful efficient solutions have been proposed for Cloud computing. In this paper, we propose the LPSO, a novel algorithm for workflow scheduling problem that is based on the Particle Swarm Optimization method. Our proposed algorithm not only ensures a fast convergence but also prevents getting trapped in local extrema. We ran realistic scenarios using CloudSim and found that LPSO is superior to previously proposed algorithms and noticed that the deviation between the solution found by LPSO and the optimal solution is negligible.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">Divisible loads are those workloads that can be partitioned by a scheduler into any arbitrary chunks. The problem of scheduling divisible loads has been defined for a long time, however, a handful of solutions have been proposed. Furthermore, almost all proposed approaches attempt to perform scheduling in dedicated environments such as LANs, whereas scheduling in non-dedicated environments such as Grids remains an open problem. In Grids, the incessant variation of a worker's computing power is a chief difficulty of splitting and distributing workloads to Grid workers efficiently. In this paper, we first introduce a computation model that explains the impact of local (internal) tasks and Grid (external) tasks that arrive at a given worker. This model helps estimate the available computing power of a worker under the fluctuation of the number of local and Grid applications. Based on this model, we propose the CPU power prediction strategy. Additionally, we build a new dynamic scheduling algorithm by incorporating the prediction strategy into a static scheduling algorithm. Lastly we demonstrate that the proposed dynamic algorithm is superior to the existing dynamic and static algorithms by a comprehensive set of simulations.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>
The paper proposed a new algorithm to solve the Multiskill Resource-Constrained Project Scheduling Problem (MS-RCPSP), a combinational optimization problem proved in NP-Hard classification, so it cannot get an optimal solution in polynomial time. The NP-Hard problems can be solved using metaheuristic methods to evolve the population over many generations, thereby finding approximate solutions. However, most metaheuristics have a weakness that can be dropping into local extreme after a number of evolution generations. The new algorithm proposed in this paper will resolve that by detecting local extremes and escaping that by moving the population to new space. That is executed using the Migration technique combined with the Particle Swarm Optimization (PSO) method. The new algorithm is called M-PSO. The experiments were conducted with the iMOPSE benchmark dataset and showed that the M-PSO was more practical than the early algorithms.
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