Hybrid scheduling algorithms in cloud computing: a review

<p><span>Cloud computing is one of the emerging fields in computer science due to its several advancements like on-demand processing, resource sharing, and pay per use. There are several cloud computing issues like security, quality of service (QoS) management, data center energy consumption, and scaling. Scheduling is one of the several challenging problems in cloud computing, where several tasks need to be assigned to resources to optimize the quality of service parameters. Scheduling is a well-known NP-hard problem in cloud computing. This will require a suitable scheduling algorithm. Several heuristics and meta-heuristics algorithms were proposed for scheduling the user's task to the resources available in cloud computing in an optimal way. Hybrid scheduling algorithms have become popular in cloud computing. In this paper, we reviewed the hybrid algorithms, which are the combinations of two or more algorithms, used for scheduling in cloud computing. The basic idea behind the hybridization of the algorithm is to take useful features of the used algorithms. This article also classifies the hybrid algorithms and analyzes their objectives, quality of service (QoS) parameters, and future directions for hybrid scheduling algorithms.</span></p>

Joint selection of the MCS’s and power allocation coefficients in the two-user downlink PD-NOMA system

Power Domain Non-Orthogonal Multiple Access (PD-NOMA) is a perspective multiplexing technique for future cellular networks. Nevertheless, it is poorly studied and not applied in the existing systems due to the complexity of PD-NOMA signal processing, resource scheduling, and power allocation. The issue is that a modulation and coding scheme (MCS) selection, including power allocation, is a cooperative procedure considering the channel state information of each multiplexed user. It can be solved by enumerating all possible multiplexing combinations but at the expense of the high computational complexity. In our work, we propose a composed table with the joint MCS’s, which can be selected by the base station (BS) for the user multiplexing in a downlink PD-NOMA system based on their signal-to-noise (SNR) ratios. It allows selecting two MCS’s with two power allocation coefficients for both users and guarantees the 10% block error rate (BLER) performance in the additive white Gaussian noise (AWGN) channel. The joint MCS selection method is based on a max-rate scheduling strategy and provides system capacity maximization ignoring fairness between users. The proposed table is given in the Appendix.

Autonomous resource allocation of smart workshop for cloud machining orders

In order to realize the online allocation of collaborative processing resource of smart workshop in the context of cloud manufacturing, a multi-objective optimization model of workshop collaborative resources (MOM-WCR) was proposed. Considering the optimization objectives of processing time, processing cost, product qualification rate, and resource utilization, MOM-WCR was constructed. Based on the time sequence of workshop processing tasks, the workshop collaborative manufacturing resource was integrated in MOM-WCR. Fuzzy analytic hierarchy process (FAHP) was adopted to simplified the multi-objective problem into the single-objective problem. Then, the improved firefly algorithm which integrated the particle swarm algorithm (IFA-PSA) was used to solve MOM-WCR. Finally, a group of connecting rod processing experiments were used to verify the model proposed in this paper. The results show that the model is feasible in the application of workshop-level resource allocation in the context of cloud manufacturing, and the improved firefly algorithm shows good performance in solving the multi-objective resource allocation problem.

Secondary task type, exposure frequency, and their combined effects on second language vocabulary learning through reading

The type of processing-resource allocation (TOPRA) model predicts that the semantic processing of new second language (L2) words can impede the learning of their forms while structural processing can promote it. Using this framework, the present study examined the effects of processing type (semantic, structural, control), exposure frequency (one exposure, three exposures), and their combination on the learning of new L2 words through reading. Adult Japanese learners of English read a reading text that contained 10 target words, five of them were repeated only one time whereas the other five were repeated three times. They were asked to answer some comprehension questions as their primary task, and the participants in the semantic and structural processing groups were asked to perform the secondary vocabulary processing tasks (pleasantness rating and phonological recording, respectively) to further process target word meanings or forms. The unexpected first language (L1)-to-L2 and L2-to-L1 cued recall were administered. The positive effects of structural processing and exposure frequency were demonstrated in L1-to-L2 cued recall. The results further suggested that effects of vocabulary processing type and exposure frequency vary depending on how vocabulary gain is measured.

Sentence comprehension in patients with dementia of the Alzheimer’s type

Sentence comprehension is diminished in patients with dementia of the Alzheimer’s type (DAT). However, the underlying reason for such deficits is still not entirely clear. The Syntactic Deficit Hypothesis attributes sentence comprehension deficits in DAT patients to the impairment in syntactic ability, whereas the Processing Resource Deficit Hypothesis proposes that sentence comprehension deficits are the result of working memory deficiency. This study investigated the deficits in sentence comprehension in Chinese-speaking DAT patients with different degrees of severity using sentence-picture matching tasks. The study revealed a significant effect of syntactic complexity in patients and healthy controls, but the effect was stronger in patients than in healthy controls. When working memory demand was minimized, the effect of syntactic complexity was only significant in patients with moderate DAT, but not in healthy controls or those with mild DAT. The findings suggest that in patients with mild DAT, working memory decline was the major source of sentence comprehension difficulty and in patients with moderate DAT, working memory decline and syntactic impairment jointly contributed to the impairments in sentence comprehension. The source of sentence comprehension deficits varied with degree of dementia severity.