Fairness Evaluation of a DSCP Based SchedulingAlgorithm for Real-Time Traffic in Differentiated ServiceNetworks

Mukherjee, Sabyasachi

doi:10.7763/ijiee.2013.v3.349

Cited by 3 publications

(3 citation statements)

References 7 publications

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“…The basic ideas about scheduling in Cloud Computing and scheduling techniques are discussed in [1] and [2]. Paper [3] compares different types of workflow scheduling algorithms. Paper [4] describes genetic simulated annealing algorithm for optimized task scheduling by considering multiple objectives.…”

Section: Related Workmentioning

confidence: 99%

A Customized Task Scheduling in Cloud Using Genetic Algorithm

Devi¹

2015

IJRET

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Cloud computing is an emerging technology in distributed computing which provides pay per use according to user demand and requirement. The primary aim of the Cloud computing is to provide efficient access to distributed resources. Scheduling of task is a critical issue in cloud computing, because it serves many users. The An approach for categorizing the tasks as Hard Real-Time Tasks (critical tasks that need to be completed on time with high rates of confidentiality) and Soft Real-Time Tasks (tasks that can be completed with certain delay and still can be efficient in its own way) before they are scheduled is applied. From the results observed the efficient processor for a particular combination of the tasks is determined thus producing customized results for each of the tasks. Efficient task scheduling is of high criticality for obtaining high performance in heterogeneous multiprocessor systems. Since task scheduling is a NP-hard problem, The Genetic Algorithm, an Evolutionary Algorithm which make use of techniques inspired by evolutionary biology such as inheritance, mutation, selection and crossover that is capable of producing optimal solutions.

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Section: Related Workmentioning

confidence: 99%

A Customized Task Scheduling in Cloud Using Genetic Algorithm

Devi¹

2015

IJRET

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“…A two-rate three-color marker (trTCM) [24] for differentiated services (DiffServ) networks is a typical color marking algorithm. Although fair scheduling algorithms in DiffServ networks have been studied [25], [26], multicolor marking with an AQM approach [27]- [29] is capable of controlling the traffic rate more accurately using a small number of physical queues. The constraint of multicolor marking is that the color is marked on the frame header, and so the number of colors is limited.…”

Section: B Per-flow Fairnessmentioning

confidence: 99%

N Rate <inline-formula> <tex-math notation="TeX">${\rm N}+1$</tex-math></inline-formula> Color Marking: Per-Flow Fairness in Ring Aggregation Networks

Nakayama

Oota

2014

IEEE Trans. Commun.

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Our study is motivated by the need for per-flow fairness in a ring aggregation network. This paper proposes a multicolor marking and queuing delay suppression scheme called N rate N + 1 color marking (NRN + 1 CM) that is designed to achieve per-flow fairness. The key idea is to assign a color to a frame according to the flow input rate with high burst tolerance using a simple marker. The color indicates the dropping priority. When congestion occurs, frames are selectively discarded based on their color and the dropping threshold. The accumulation of queuing delay is suppressed with a dropping threshold notification process. The effect of NRN + 1 CM was confirmed by a theoretical analysis and computer simulations.Index Terms-Communication system traffic control, quality of service, metropolitan area networks, packet switching. 0090-6778

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“…Additionally, software as a service (SaaS) provides software that does not require installation on usersApostrophe; own machines but can be used directly through the cloud (Mukherjee & Khanna, ). Understanding of the different cloud services is the first steps for cloud customers.…”

Section: Introductionmentioning

confidence: 99%

Cloud data analysis using a genetic algorithm‐based job scheduling process

Vijay¹

2019

Expert Systems

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Data analysis plays a major role in different research applications that require a large volume of data. Cloud computing can provide computer processing resources and device‐to‐device data sharing based on user requirements. The main goal of cloud computing is to allow users and enterprise of varying capabilities to store and process data in an efficient way and to access and distribute resources. However, a crucial problem in cloud computing is job scheduling for numerous users. Prior to the implementation of job scheduling, jobs must be categorized according to degree of criticalness, privacy and time required. Based on the experimental results, the combination of tasks was successfully determined by the processor. In heterogeneous multiprocessor systems, customized job scheduling is highly critical for obtaining optimal job performance. In this paper, an evolutionary genetic algorithm was used for obtaining better results in job scheduling, thereby improving performance in the cloud system in this regard. The genetic algorithm‐based job scheduling process introduced minimizes the investment in time through effective allocation of user requests in order to enhance the overall efficiency of the system.

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Fairness Evaluation of a DSCP Based SchedulingAlgorithm for Real-Time Traffic in Differentiated ServiceNetworks

Cited by 3 publications

References 7 publications

A Customized Task Scheduling in Cloud Using Genetic Algorithm

A Customized Task Scheduling in Cloud Using Genetic Algorithm

N Rate <inline-formula> <tex-math notation="TeX">${\rm N}+1$</tex-math></inline-formula> Color Marking: Per-Flow Fairness in Ring Aggregation Networks

Cloud data analysis using a genetic algorithm‐based job scheduling process

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