QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization

Kesavaraja, D.; Shenbagavalli, A.

doi:10.1016/j.jpdc.2017.08.015

Cited by 30 publications

(12 citation statements)

References 21 publications

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“…2 illustrates both of traditional architecture and virtual architecture. Multiple users demand resources, it requires a lot of investment in physical infrastructure to react to users' demands; therefore, the cloud infrastructure providers solve and manage this situation by offering VM services based on user requests with higher quality and at low cost [5], [25]. There are different forms of virtualization such as storage, server, appliance, desktop, and network virtualization [21], [26].…”

Section: Cloud Computing Virtualizationmentioning

confidence: 99%

“…Furthermore, cloud computing provides on-demand services, where clients could reach to the services at any time from remote locations. Instead of storing and retrieving data from the local computer, cloud computing allows utilizing the services of remote computer [5]- [8]. The clients use the cloud services rather than working on their infrastructure, it means that the users don't need knowledge about the infrastructure of the network [9]- [11].…”

Section: Introductionmentioning

confidence: 99%

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Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Shukur

Zeebaree

Zebari

et al. 2020

JASTT

104

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Cloud computing is a new technology which managed by a third party “cloud provider” to provide the clients with services anywhere, at any time, and under various circumstances. In order to provide clients with cloud resources and satisfy their needs, cloud computing employs virtualization and resource provisioning techniques. The process of providing clients with shared virtualized resources (hardware, software, and platform) is a big challenge for the cloud provider because of over-provision and under-provision problems. Therefore, this paper highlighted some proposed approaches and scheduling algorithms applied for resource allocation within cloud computing through virtualization in the datacenter. The paper also aims to explore the role of virtualization in providing resources effectively based on clients’ requirements. The results of these approaches showed that each proposed approach and scheduling algorithm has an obvious role in utilizing the shared resources of the cloud data center. The paper also explored that virtualization technique has a significant impact on enhancing the network performance, save the cost by reducing the number of Physical Machines (PM) in the datacenter, balance the load, conserve the server’s energy, and allocate resources actively thus satisfying the clients’ requirements. Based on our review, the availability of Virtual Machine (VM) resource and execution time of requests are the key factors to be considered in any optimal resource allocation algorithm. As a results of our analyzing for the proposed approaches is that the requests execution time and VM availability are main issues and should in consideration in any allocating resource approach.

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Section: Cloud Computing Virtualizationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Shukur

Zeebaree

Zebari

et al. 2020

JASTT

104

View full text Add to dashboard Cite

show abstract

“…The problem of eccentrics and congestion was handled by a new proposed change and response and agreement protocol by monitoring the packet delay, latency and throughput. The experimental results of the hybrid krill herd optimization approach were compared against particle swarm optimization, ant colony optimization, genetic algorithm and simulated annealing [41].…”

Section: Related Workmentioning

confidence: 99%

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

et al. 2020

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Drastic variations in high-performance computing workloads lead to the commencement of large number of datacenters. To revolutionize themselves as green datacenters, these data centers are assured to reduce their energy consumption without compromising the performance. The energy consumption of the processor is considered as an important metric for power reduction in servers as it accounts to 60% of the total power consumption. In this research work, a power-aware algorithm (PA) and an adaptive harmony search algorithm (AHSA) are proposed for the placement of reserved virtual machines in the datacenters to reduce the power consumption of servers. Modification of the standard harmony search algorithm is inevitable to suit this specific problem with varying global search space in each allocation interval. A task distribution algorithm is also proposed to distribute and balance the workload among the servers to evade over-utilization of servers which is unique of its kind against traditional virtual machine consolidation approaches that intend to restrain the number of powered on servers to the minimum as possible. Different policies for overload host selection and virtual machine selection are discussed for load balancing. The observations endorse that the AHSA outperforms, and yields better results towards the objective than, the PA algorithm and the existing counterparts.

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“…In order to fully utilise servers' resource, the mechanism places VMs to servers according to the patterns which result in high resource utilisation. The Krill Herd (KH) algorithm for solving optimal VM allocation problem is presented in [106]. This algorithm uses a fitness function which has two main factors: utilisation rate, including primary memory utilisation and Quality of Experience (QoE).…”

Section: Vm Placementmentioning

confidence: 99%

Survey of Memory Management Techniques for HPC and Cloud Computing

Pupykina

Agosta

2019

IEEE Access

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The emergence of new classes of HPC applications and usage models, such as real-time HPC and cloud HPC, coupled with the increasingly heterogeneous nature of HPC architectures, requires a renewed investigation of memory management solutions. Traditionally, memory is shared by an operating system using segmentation and paging techniques. At the same time, new classes of applications require Quality of Service (QoS) guarantees. As such, the typical practice of reserving a subset of the supercomputer to a single application becomes less attractive, leading to the exploration of cloud technologies. In this context, a viable scenario is that of multiple applications, with different QoS levels, coexisting on the same deeply heterogeneous HPC infrastructure and sharing resources. However, for this scenario to succeed in practice, resources, including memory, need to be allocated with a vision that includes both the application requirements and the current and future state of the overall system. In this survey, challenges of memory management in HPC and Cloud Computing, different memory management systems and optimisation techniques to increase memory utilisation are discussed in detail. INDEX TERMS Clouds, high performance computing, memory management, resource management. ANNA PUPYKINA received the higher education degree in data processing and control systems engineering from Samara State Aerospace University, Russia, in 2009. She is currently pursuing the Ph.D. degree with the Politecnico di Milano, under the supervision of Prof. G. Agosta. She was with Russian State Humanitarian University (RSUH), Togliatti, and also with the Department of Applied Mathematics and Informatics, Togliatti State University (TSU). Her main research interests include runtime management algorithms and programming model extensions for deeply heterogeneous systems. GIOVANNI AGOSTA received the Laurea degree in computer engineering and the Ph.D. degree in information technology from the Politecnico di Milano, Italy, in 2000 and 2004, respectively. He is currently a tenured Researcher. He has authored more than 80 articles in international journals, conferences, and workshops. His main research interest includes compiler technology. He was a recipient of four HiPEAC Awards and three Best Paper Awards. He has been involved in eight European funded projects, since 2010, holding workpackage and task leader responsibilities. He is a Full Member of the HiPEAC Network and the Organizer of two workshops colocated with the HiPEAC Conference.

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QoE enhancement in cloud virtual machine allocation using Eagle strategy of hybrid krill herd optimization

Cited by 30 publications

References 21 publications

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Cloud Computing Virtualization of Resources Allocation for Distributed Systems

Energy-Efficient Resource Provisioning Using Adaptive Harmony Search Algorithm for Compute-Intensive Workloads with Load Balancing in Datacenters

Survey of Memory Management Techniques for HPC and Cloud Computing

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