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>

A STUDY ON CHANNEL AND DELAY-BASED SCHEDULING ALGORITHMS FOR LIVE VIDEO STREAMING IN THE FIFTH GENERATION LONG TERM EVOLUTION-ADVANCED

This paper investigates the performance of a number of channel and delay-based scheduling algorithms for an efficient QoS (Quality of Service) provision with more live video streaming users over the Fifth Generation Long-Term Evolution-Advanced (5G LTE-A) network. These algorithms were developed for use in legacy wireless networks and minor changes were made to enable these algorithms to perform packet scheduling in the downlink 5G LTE-A. The efficacies of the EXP and M-LWDF algorithms in maximizing the number of live video streaming users at the desired transmission reliability, minimizing the average network delay and maximizing network throughput, are shown via simulations. As the M-LWDF has a simpler mathematical equation as compared to the EXP, it is more favoured for implementation in the complex downlink 5G LTE-A. ABSTRAK: Kertas ini menyiasat prestasi sebilangan saluran dan algoritma penjadualan berdasarkan kelewatan untuk penyediaan QoS (Kualiti Perkhidmatan) yang cekap dengan banyak pengguna video secara langsung melalui rangkaian Generasi Kelima Long-Term Evolution Advanced (5G LTE-A). Algoritma-algoritma yang disiasat di dalam kertas ini dicadangkan untuk digunakan dalam generasi rangkaian tanpa wayar yang lama dan sedikit perubahan dibuat untuk membolehkan algoritma ini menyokong penjadualan paket dalam downlink 5G LTE-A. Keberkesanan EXP dan M-LWDF algoritma dalam memaksimumkan jumlah pengguna pada kebolehpercayaan transmisi yang diinginkan dari streaming video secara langsung, meminimumkan kelewatan rangkaian, dan memaksimumkan truput rangkaian ditunjukkan melalui simulasi. Namun, dengan M-LWDF mempunyai formula matematik yang mudah dibandingkan dengan EXP, ia lebih sesuai untuk digunakan dalam downlink 5G LTE-A yang lebih kompleks.

Review on Mapping of Tasks to Resources in Cloud Computing

Resource allocation and scheduling algorithms are the two essential factors that determine the satisfaction of cloud users. The major cloud resources involved here are servers, storage, network, databases, software and so on based on requirements of customers. In the competitive scenario, each service provider tries to use factors like optimal configuration of resources, pricing, Quality of Service (QoS) parameters and Service Level Agreement (SLA) in order to benefit cloud users and service providers. Since, many researchers have proposed different scheduling algorithms and resource allocation strategies, it becomes a cumbersome task to conclude which ones really benefit customers and service providers. Hence, this paper analyses and presents the most relevant considerations that would help the cloud researchers in achieving their goals in terms of mapping of tasks to cloud resources.

Ranking Routes In Semiconductor Wafer Fabs

We develop a method to estimate the quality of processing routes in a wafer fabrication process. Ranking such routes can be useful for identifying the “best” and “worst” routes when making adjustments to recipes. Route categorization is also useful in developing efficient scheduling algorithms. In particular, we propose a method for ranking routes based on count-based metrics such as the number of defects on a wafer. We start with a statistical model to produce a “local” ranking of a tool and then build a “global” ranking via a heuristic procedure. Creating a fully statistical procedure for ranking routes in semiconductor fabrication plants is virtually impossible, given the number of possible routes and the limited data available. Nonetheless, our discussions with working engineers indicate that even approximate rankings are useful for making better operational decisions.