SIM-Cumulus: An Academic Cloud for the Provisioning of Network-Simulation-as-a-Service (NSaaS)

Ibrahim, Muhammad; Iqbal, Muhammad Azhar; Aleem, Muhammad; Islam, Muhammad Arshad

doi:10.1109/access.2018.2833212

Cited by 19 publications

(18 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The last phase detects the remaining overloaded/under-loaded hosts and initiates the final migration and placement of VMs. For VM migration, we extended the SIM-Cumulus framework [34] by adding a Migration Management module (as shown in Figure 1). The Modified SIM-Cumulus comprises four layers: System Accessibility Layer (SAL), Cloud Instance Management Layer (CIML), Modified Virtual Platform Layer (MVPL), and Physical Infrastructure Layer (PIL).…”

Section: Efficient Adaptive Migration Algorithm (Eama)mentioning

confidence: 99%

“…At PIL layer of the architecture, the physical computing resources like machines, clusters, data center, and network storage are managed. The details pertaining to each layer are discussed in [34]. At the MVPL layer, EAMA is implemented at the Migration Management Module, which is responsible for the migration of VMs across the hosts in the CDCs.…”

Section: Efficient Adaptive Migration Algorithm (Eama)mentioning

confidence: 99%

See 1 more Smart Citation

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

et al. 2021

Self Cite

View full text Add to dashboard Cite

The rapid demand for Cloud services resulted in the establishment of large-scale Cloud Data Centers (CDCs), which ultimately consume a large amount of energy. An enormous amount of energy consumption eventually leads to high operating costs and carbon emissions. To reduce energy consumption with efficient resource utilization, various dynamic Virtual Machine (VM) consolidation approaches (i.e., Predictive Anti-Correlated Placement Algorithm (PACPA), Resource-Utilization-Aware Energy Efficient (RUAEE), Memory-bound Pre-copy Live Migration (MPLM), m Mixed migration strategy, Memory/disk operation aware Live VM Migration (MLLM), etc.) have been considered. Most of these techniques do aggressive VM consolidation that eventually results in performance degradation of CDCs in terms of resource utilization and energy consumption. In this paper, an Efficient Adaptive Migration Algorithm (EAMA) is proposed for effective migration and placement of VMs on the Physical Machines (PMs) dynamically. The proposed approach has two distinct features: first, selection of PM locations with optimum access delay where the VMs are required to be migrated, and second, reduces the number of VM migrations. Extensive simulation experiments have been conducted using the CloudSim toolkit. The results of the proposed approach are compared with the PACPA and RUAEE algorithms in terms of Service-Level Agreement (SLA) violation, resource utilization, number of hosts shut down, and energy consumption. Results show that proposed EAMA approach significantly reduces the number of migrations by 16% and 24%, SLA violation by 20% and 34%, and increases the resource utilization by 8% to 17% with increased number of hosts shut down from 10% to 13% as compared to the PACPA and RUAEE, respectively. Moreover, a 13% improvement in energy consumption has also been observed.

show abstract

Section: Efficient Adaptive Migration Algorithm (Eama)mentioning

confidence: 99%

Section: Efficient Adaptive Migration Algorithm (Eama)mentioning

confidence: 99%

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…• We formulate an explicit expression for optimal RTS threshold and optimize it adaptively in NS-3 and OM-NeT++, which guarantees the best network throughput performance and the ease to be adapted in the practical network design for IEEE 802. 11 WLANs.…”

Section: Ns-2 Ns-3 Omnet++mentioning

confidence: 99%

“…Students, developers and researchers study the behavior of real time systems by performing simulations, in order to get flexibility in terms of repeatability, scalability and stability [11]- [14]. Fig.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Study of IEEE 802.11 DCF Network Optimization From Theory to Testbed

et al. 2020

View full text Add to dashboard Cite

In IEEE 802.11 wireless local area networks (WLANs), an important technique for medium access control (MAC) is the distributed coordination function (DCF). Two access mechanisms are provided by DCF, the default basic access mechanism and the optional request-to-send/clear-to-send (RTS/CTS) mechanism. The performance of IEEE 802.11 DCF networks has been predicted recently by NS-2 simulator based on a unified analytical model presenting the delay, throughput and stability [1]. NS-3 and OMNeT++ provide an essential platform to model IEEE 802.11 physical (PHY) and MAC layers, nevertheless the accuracy of which is yet not investigated. In this paper we present two studies, first is a comparative simulation study of the unified IEEE 802.11 DCF analytical model [1], by considering distinct network conditions, various topologies, different access modes and discrete system parameters in NS-3 and OMNeT++. A Linux based testbed is setup to validate the mathematical model and the simulation results. The second is the optimization study to adaptively tune the RTS threshold, so that the network operates in an access mode which steers to the maximum network throughput performance. An explicit expression of RTS threshold, verified by the simulations in NS-3 and OMNeT++, is obtained in contrast to previous studies based on channel estimation and numerical calculations. Performance evaluation is done by comparing the simulation, testbed and theoretical results. This study not only proves the credibility of the theoretical model of IEEE 802.11 DCF, but also assures that the results obtained from NS-3 and OMNeT++ are persuasive and provides a foundation for RTS threshold analysis in IEEE 802.11 WLANs for practical network design considerations.

show abstract

“…The resources in the Cloud computing are distributed across different locations in the form of largescale data centers connected in a distributed way. In the Cloud computing environment, the resources are provisioned (to the users in an on-demand fashion) in the form of virtual machines (VMs) from a pool of configured resources (Physical Hosts (PHs)) keeping in view the requirements of the intended users [1], [2]. The Cloud service owners provide guaranteed services called Quality of Service (QoS) to the users to meet the requirements of their customers [3].…”

Section: Introductionmentioning

confidence: 99%

An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

et al. 2020

Self Cite

View full text Add to dashboard Cite

Recently, Cloud computing has emerged as one of the widely used platforms to provide compute, storage and analytics services to end-users and organizations on a pay-as-you-use basis, with high agility, availability, scalability, and resiliency. This enables individuals and organizations to have access to a large pool of high processing resources without the need for establishing a high-performance computing (HPC) platform. From the past few years, task scheduling in Cloud computing is reckoned as eminent recourse for researchers. However, task scheduling is considered an NP-hard problem. In this research work, we investigate and empirically compare some of the most prominent state-of-the-art scheduling heuristics in terms of Makespan, Average resource utilization (ARUR), Throughput, and Energy consumption. The comparison is then extended by evaluating the approaches in terms of individual VM level load imbalance. After extensive simulation, the comparative analysis has revealed that Task Aware Scheduling Algorithm (TASA) and Proactive Simulation-based Scheduling and Load Balancing (PSSLB) outperformed as compared to the rest of the approaches and seems to be optimal choice keeping in view the trade-of between the complexities involved and the performance achieved concerning Makespan, Throughput, resource utilization, and Energy consumption.

show abstract

SIM-Cumulus: An Academic Cloud for the Provisioning of Network-Simulation-as-a-Service (NSaaS)

Cited by 19 publications

References 34 publications

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

EAMA: Efficient Adaptive Migration Algorithm for Cloud Data Centers (CDCs)

A Systematic Study of IEEE 802.11 DCF Network Optimization From Theory to Testbed

An In-Depth Empirical Investigation of State-of-the-Art Scheduling Approaches for Cloud Computing

Contact Info

Product

Resources

About