SCORE: Simulator for cloud optimization of resources and energy consumption

Fernández-Cerero, Damián; Fernández-Montes, Alejandro; Jakóbik, Agnieszka; Kołodziej, Joanna; Toro, Miguel

doi:10.1016/j.simpat.2018.01.004

Cited by 57 publications

(44 citation statements)

References 22 publications

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“…However, in the proposed algorithm, we have not considered the large‐scale data centers where an amount of workloads may exceed the capacity of a single scheduler using CloudSim simulator. Therefore, our future effort is aimed to use SCORE simulator 35, 36 as an extension to the Google Omega lightweight simulator that focused on the large‐scale data centers to analyze the efficiency of the proposed task‐scheduling model.…”

Section: Discussionmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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The massive growth of cloud computing has led to huge amounts of energy consumption and carbon emissions by a large number of servers. One of the major aspects of cloud computing is its scheduling of many task requests submitted by users. Minimizing energy consumption while ensuring the user's QoS preferences is very important to achieving profit maximization for the cloud service providers and ensuring the user's service level agreement (SLA).Therefore, in addition to implementing user's tasks, cloud data centers should meet the different criteria in applying the cloud resources by considering the multiple requirements of different users. Mapping of user requests to cloud resources for processing in a distributed environment is a well-known NPhard problem. To resolve this problem, this paper proposes an energy-efficient task-scheduling algorithm based on best-worst (BWM) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) methodology. The main objective of this paper is to determine which cloud scheduling solution is more important to select. First, a decision-making group identify the evaluation criteria. After that, a BWM process is applied to assign the importance weights for each criterion, because the selected criteria have varied importance. Then, TOPSIS uses these weighted criteria as inputs to evaluate and measure the performance of each alternative. The performance of the proposed and existing algorithms is evaluated using several benchmarks in the CloudSim toolkit and statistical testing through ANOVA, where the evaluation metrics include the makespan, energy consumption, and resource utilization. K E Y W O R D Sbest-worst method (BWM), cloud computing, energy consumption, multi-criteria decision making, TOPSIS method

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Section: Discussionmentioning

confidence: 99%

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Khorsand

Ramezanpour

2020

Int J Communication

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“…Sphere [25] is based on SCORE [26], and can create a cloudlet network based on graph; generate dynamic and parallel workloads; and specify the geographic location, resource density and deployment requirements of workload. But it does not support the mobility of nodes and lacks the migration model of a workload.…”

Section: Related Workmentioning

confidence: 99%

Support Mobile Fog Computing Test in piFogBedII

Zhang

Togookhuu

2020

Sensors

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IoT and 5G technologies are making smart devices, medical devices, cameras and various types of sensors become parts of the Internet, which provides feasibility to the realization of infrastructure and services such as smart homes, smart cities, smart medical technology and smart transportation. Fog computing (edge computing) is a new research field and can accelerate the analysis speed and decision-making for these delay-sensitive applications. It is very important to test functions and performances of various applications and services before they are deployed to the production environment, and current evaluations are more based on various simulation tools; however, the fidelity of the experimental results is a problem for most of network simulation tools. PiFogBed is a fog computing testbed built with real devices, but it does not support the testing of mobile end devices and mobile fog applications. The paper proposes the piFogBedII to support the testing of mobile fog applications by modifying some components in the piFogBed, such as extending the range of end devices, adding the mobile and migration management strategy and inserting a container agent to implement the transparent transmission between end devices and containers. The evaluation results show that it is effective and the delay resulting from the migration strategy and container agent is acceptable.

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“…The analysis of the described energy-efficiency strategies in real-life large-scale data centers is not feasible in such an immature stage. To overcome this limitation, in this work we chose a simulation tool designed to trustfully simulate energy-aware large-scale data centers called SCORE [7], which provides us with the means to reproduce realistic heterogeneous workloads and to easily implement various energy-efficiency policies.…”

Section: Experimental Analysis Of the Stackelberg Game Model A Simentioning

confidence: 99%

“…We used the SCORE simulator [7] to perform a simple experiment that simulates seven days of operation time of a data center composed of 1,000 heterogeneous machines of 4 CPU cores and 8GB RAM and one central monolithic scheduler. In this experiment, we chose an heterogeneous day-night patterned mixed workload.…”

Section: Base Simulator Tiermentioning

confidence: 99%

Stackelberg Game-Based Models In Energy-Aware Cloud Scheduling

Fernández-Cerero¹,

Fernández-Montes²,

Jakóbik³

et al. 2018

ECMS 2018 Proceedings Edited by Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen

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Energy-awareness remians the important problem in today's cloud computing (CC). Optimization of the energy consumed in cloud data centers and computing servers is usually related to the scheduling problems. It is very difficult to define an optimal scheduling policy without negoative influence into the system performance and task completion time. In this work, we define a general cloud scheduling model based on a Stackelberg game with the workload scheduler and energy-efficiency agent as the main players. In this game, the aim of the scheduler is the minimization of the makespan of the workload, which is achieved by the employ of a genetic scheduling algorithm that maps the workload tasks into the computational nodes. The energy-efficiency agent selects the energy-optimization techniques based on the idea of switchin-off of the idle machines, in response to the scheduler decisions. The efficiency of the proposed model has been tested using a SCORE cloud simmulator. Obtained results show that the proposed model performs better than static energy-optimization strategies, achieving a fair balance between low energy consumption and short queue times and makespan.

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SCORE: Simulator for cloud optimization of resources and energy consumption

Cited by 57 publications

References 22 publications

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

An energy‐efficient task‐scheduling algorithm based on a multi‐criteria decision‐making method in cloud computing

Support Mobile Fog Computing Test in piFogBedII

Stackelberg Game-Based Models In Energy-Aware Cloud Scheduling

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