CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness

Filho, Manoel C. Silva; Oliveira, Raysa; Monteiro, Claudio C.; Inácio, Pedro R. M.; Freire, Mário M.

doi:10.23919/inm.2017.7987304

Cited by 137 publications

(65 citation statements)

References 16 publications

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“…To evaluate our proposed method, simulation experiments are implemented on CloudSim [29,30] to study the effects.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Smimite¹,

Afdel²

2020

IJACSA

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Currently, data centers energy consumption in the cloud is attracting a lot of interest. One of the most approaches to optimize energy and cost in data centers is virtualization. Recently, a new type of container-based virtualization has appeared, containers are considered very light and modular virtual machines, they offer great flexibility and the possibility of migration from one environment to another, which allows optimizing applications for the cloud. Another approach to saving energy is to consolidate the workload, which is the amount of processing that the computer has to perform at any given time. In this article, we will study the container placement algorithm that takes into account the QoS requirements of different users in order to minimize energy consumption. Thus, we proposed a Hybrid approach for managing resources and workload based on ant colony optimization (ACO) and the first-fit decreasing (FFD) algorithm to avoid unnecessary power consumption. The results of the experiment indicate that using the first-fit decreasing algorithm (FFD) for container placement is better than ant colony optimization especially in a homogeneous systems. On the other hand the ant colony optimization shows very satisfying results in the case of workload management.

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“…To evaluate our proposed method, simulation experiments are implemented on CloudSim [29,30] to study the effects.…”

Section: Experimental Setup and Resultsmentioning

confidence: 99%

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Smimite¹,

Afdel²

2020

IJACSA

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“…Moreover, an experimental comparison between the vertical scaling part of our mechanism and [30] is demonstrated. The benchmarking is performed using CloudSim Plus [27], a simulation environment suitable for cloud computing and MCC experimentation, on a dual-core, macOS powered system with 8GB of available memory.…”

Section: Discussionmentioning

confidence: 99%

Adaptive Resource Allocation for Computation Offloading

Avgeris

Dechouniotis

Αθανασόπουλος

et al. 2019

ACM Trans. Internet Technol.

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Although mobile devices today have powerful hardware and networking capabilities, they fall short when it comes to executing compute-intensive applications. Computation offloading (i.e., delegating resource-consuming tasks to servers located at the edge of the network) contributes toward moving to a mobile cloud computing paradigm. In this work, a two-level resource allocation and admission control mechanism for a cluster of edge servers offers an alternative choice to mobile users for executing their tasks. At the lower level, the behavior of edge servers is modeled by a set of linear systems, and linear controllers are designed to meet the system’s constraints and quality of service metrics, whereas at the upper level, an optimizer tackles the problems of load balancing and application placement toward the maximization of the number the offloaded requests. The evaluation illustrates the effectiveness of the proposed offloading mechanism regarding the performance indicators, such as application average response time, and the optimal utilization of the computational resources of edge servers.

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“…The proposed model is implemented using CloudSim plus [25], which is an event driven tool and closely resembles distributed computing environment. Table 5 briefly describes basic terminologies used in our simulation: Following fig.…”

Section: Methodsmentioning

confidence: 99%

A Failure Detector for Crash Recovery Systems in Cloud

Sinha¹,

Singh²,

Saini³

2019

IJITCS

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Cloud computing has offered remarkable scalability and elasticity to distributed computing paradigm. It provides implicit fault tolerance through virtual machine (VM) migration. However, VM migration needs heavy replication and incurs storage overhead as well as loss of computation. In early cloud infrastructure, these factors were ignorable due to light load conditions; however, nowadays due to exploding task population, they trigger considerable performance degradation in cloud. Therefore, fault detection and recovery is gaining attention in cloud research community. The Failure Detectors (FDs) are modules employed at the nodes to perform fault detection. The paper proposes a failure detector to handle crash recoverable nodes and the system recovery is performed by a designated checkpoint in the event of failure. We use Machine Repairman model to estimate the recovery latency. The simulation experiments have been carried out using CloudSim plus.

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CloudSim Plus: A cloud computing simulation framework pursuing software engineering principles for improved modularity, extensibility and correctness

Cited by 137 publications

References 16 publications

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Hybrid Solution for Container Placement and Load Balancing based on ACO and Bin Packing

Adaptive Resource Allocation for Computation Offloading

A Failure Detector for Crash Recovery Systems in Cloud

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