Real-Time Cloud-Based Load Balance Algorithms and an Analysis

Gundu, Srinivasa Rao; Panem, Charan Arur; Thimmapuram, Anuradha

doi:10.1007/s42979-020-00199-8

Cited by 7 publications

(7 citation statements)

References 15 publications

(29 reference statements)

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“…Different task scheduling algorithms for cloud computing have been proposed by many researchers. Researchers in [5] introduced a SWOT analysis of the cloud computing that can be used virtually in every industry to improve the service delivery and improvement. The work in [6] empirically compares and offers an insight into the performance of some renown state-of-the-art task scheduling heuristics concerning the throughput, average resource utilization ratio and makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

A New Algorithm for Minimizing Makespan within Cloud Computing

Abdelhafiz

2022

Al-Azhar Bulletin of Science

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The environment of cloud computing has become widely used in a variety of applications and fields in recent years. Task and resource scheduling, on the other hand, is an area where there is still room for development. Task scheduling methods that allow the mapping of incoming tasks to resources are required to meet good performance data mapping in a heterogeneous computing system. Makespan is reduced and resource usage is maximized when resources and tasks are efficiently mapped. A novel scheduling approach is proposed in this work, which improves the makespan. There are two phases to the recommended method. The Tuples algorithm is used in the first phase that schedules tasks on resources. The second phase rearranges some tasks in order to improve the overall timeframe. The outcomes of the simulation show that the new approach for heterogeneous systems outperforms MASA, max-min, e-MASA, Tuples and Enhanced max-min algorithms in terms of makespan and time complexity.

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Section: Literature Reviewmentioning

confidence: 99%

A New Algorithm for Minimizing Makespan within Cloud Computing

Abdelhafiz

2022

Al-Azhar Bulletin of Science

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“…Azure [2] can be used in several different areas like, (1) application development: anybody can create any web application using this Azure platform. (2) testing: after developing an application successfully on the platform, it can be SN Computer Science tested.…”

Section: Cloud Data Centers (I) Cloud Data Centersmentioning

confidence: 99%

The Dynamic Computational Model and the New Era of Cloud Computation Using Microsoft Azure

2020

Self Cite

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The new era of computation has given new directions for advancement and sophistication. The meaning of computation has made a paradigm shift from device and location orientation to the distributed level. Storage has become cheaper. Multiple technologies and their interconnections and their wide range of services have made this planet to be more sophisticated and more flexible in computing. Service orientation based computing of cloud has achieved a huge success. Data centers has already existing even before the emergence of cloud technology, but cloud makes these data centers to communicate, between these data centers and allocate the resources properly and functioning appropriately using a network and to used these resources and maintain them properly. Most of the cloud services providers have their own Data center. 'Azure' online cloud platform provided Microsoft provides cloud services and resources to the end-user. Its pricing mechanism is economical and flexible. One can learn Azure very easily and Micro soft company provides Azure certifications for the one who qualifies with international standards.

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“…In task scheduling method, tasks were allocated to a best suitable resource for execution. In load balancing, task scheduling is a non-polynomial (NP) hard problem because number of tasks and length of tasks vary rapidly, therefore it is difficult to calculate possible mapping of tasks to resources and evaluate an optimal mapping [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm

Joshi

Devi²

2021

IJEECS

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In cloud computing, load balancing among the resources is required to schedule a task, which is a key challenge. This paper proposes a dynamic degree memory balanced allocation (D2MBA) algorithm which allocate virtual machine (VM) to a best suitable host, based on availability of random-access memory (RAM) and microprocessor without interlocked pipelined stages (MIPS) of host and allocate task to a best suitable VM by considering balanced condition of VM. The proposed D2MBA algorithm has been simulated using a simulation tool CloudSim by varying number of tasks and keeping number of VMs constant and vice versa. The D2MBA algorithm is compared with the other load balancing algorithms viz. Round Robin (RR) and dynamic degree balance with central processing unit (CPU) based (D2B_CPU based) with respect to performance parameters such as execution cost, degree of imbalance and makespan time. It is found that the D2MBA algorithm has a large reduction in the performance parameters such as execution cost, degree of imbalance and makespan time as compared with RR and D2B CPU based algorithms

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Real-Time Cloud-Based Load Balance Algorithms and an Analysis

Cited by 7 publications

References 15 publications

A New Algorithm for Minimizing Makespan within Cloud Computing

A New Algorithm for Minimizing Makespan within Cloud Computing

The Dynamic Computational Model and the New Era of Cloud Computation Using Microsoft Azure

Enhancement of cloud performance metrics using dynamic degree memory balanced allocation algorithm

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