Resource Allocation with improved Min­Min Algorithm

Smart, pervasive devices have recently experienced accelerated technological development in the fields of hardware, software, and wireless connections. The promotion of various kinds of collaborative mobile computing requires an upgrade in network connectivity with wireless technologies, as well as enhanced peer-to-peer communication. Mobile computing also requires appropriate scheduling methods to speed up the implementation and processing of various computing applications by better managing network resources. Scheduling techniques are relevant to the modern architectural models that support the IoT paradigm, particularly smart collaborative mobile computing architectures at the network periphery. In this regard, load-balancing techniques have also become necessary to exploit all the available capabilities and thus the speed of implementation. However, since the problem of scheduling and load-balancing, which we addressed in this study, is known to be NP-hard, the heuristic approach is well justified. We thus designed and validated a greedy scheduling and load-balancing algorithm to improve the utilization of resources. We conducted a comparison study with the longest cloudlet fact processing (LCFP), shortest cloudlet fact processing (SCFP), and Min-Min heuristic algorithms. The choice of those three algorithms is based on the efficiency and simplicity of their mechanisms, as reported in the literature, for allocating tasks to devices. The simulation we conducted showed the superiority of our approach over those algorithms with respect to the overall completion time criterion.

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“…Scheme 1. Categories of several task scheduling techniques in the cloud environment [37][38][39][40][41][42][43][44][45][46].…”

Section: Non-preemptive Schedulingmentioning

confidence: 99%

“…Kaur et al [38] proposed an improved Min-Min algorithm which aims to maximize the utilization of resources in a distributed environment. It adopts the traditional Min-Min algorithm as a first stage in terms of minimum completion time.…”

Section: Enhanced Min-min Algorithmmentioning

confidence: 99%

A Greedy Scheduling Approach for Peripheral Mobile Intelligent Systems

Fadlallah

Rebaïne

Mcheick

2021

IoT

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“…The restraint of this algorithm is that it fixes the least performed duties firstly along with dwell in assets which include elevated calculation influence. The superior tasks wait until the minor gets processed [9].…”

Section: B Dynamic Load Balancing Algorithmmentioning

confidence: 99%

Design and Implementation of Improved Central Load Balancing Algorithm in Cloud Environment

Kaur¹,

Sharma²

2018

ijarcs

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With the changes and advancement in technology Cloud Computing is playing a pivotal role in field of Technology with a remarkable growth. Through this paper a design and implementation of central load balancer is introduced with an effective results based upon various parameters. The proposal of the central balancer to balance the load on the cloud is introduced and weigh against existing load balancing methods .The foremost intention of these efforts is to initiate as well as appraise the projected load balancing algorithm by taking into account, the job duration of all solicited situation, the potential of every virtual machine (VM), and the correlation of numerous tasks. Rendition of the probable algorithm is deliberated besides contrasting with the existing methods.

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“…Rajwinder K et al, (5) introduced a rescheduling based task scheduling algorithm named improved MinMin Algorithm (I Min-Min). The algorithm has two stages; in the first stage the traditional Min-Min algorithm is executed and in the other stage the jobs are rescheduled to use the virtual machines that are idle.…”

Section: Optimizing Task Scheduling and Resource Allocation In Cloud mentioning

confidence: 99%

“…The traditional Min-min task scheduling algorithm select the task with minimum execution time and allocate it to the resource expected to give minimum completion time (5). The drawbacks of the min-min algorithm is that it select the smaller tasks first while the task with large execution time will wait until the smaller tasks are done as a result will increase the response time and the overall finishing time.…”

Section: Introductionmentioning

confidence: 99%