Smart elastic scheduling algorithm for virtual machine migration in cloud computing

Nashaat, Heba; Ashry, Nesma; Rizk, Rawya

doi:10.1007/s11227-019-02748-2

Cited by 58 publications

(25 citation statements)

References 23 publications

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“…It is shown that OH_BAC has a minimum value of processing time. The time complexity of OH_BAC is calculated as follows: O(2n 2 + 17n) due to the consumed time by ABC and ACO in OH_BAC is O(n) as each of these algorithms has one inner loop to search suitable food source among whole cloud system. As well as, the consumed time by knowledge base is O(n 2 ) as knowledge base has two inner loops to sort under loaded PMs and over loaded PMs.…”

Section: B Comparison With Bio-inspired Algorithmsmentioning

confidence: 99%

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Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

et al. 2019

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Cloud computing is increasing rapidly as a successful paradigm presenting on-demand infrastructure, platform, and software services to clients. Load balancing is one of the important issues in cloud computing to distribute the dynamic workload equally among all the nodes to avoid the status that some nodes are overloaded while others are underloaded. Many algorithms have been suggested to perform this task. Recently, worldview is turning into a new paradigm for optimization search by applying the osmosis theory from chemistry science to form osmotic computing. Osmotic computing is aimed to achieve balance in highly distributed environments. The main goal of this paper is to propose a hybrid metaheuristics technique which combines the osmotic behavior with bio-inspired load balancing algorithms. The osmotic behavior enables the automatic deployment of virtual machines (VMs) that are migrated through cloud infrastructures. Since the hybrid artificial bee colony and ant colony optimization proved its efficiency in the dynamic environment in cloud computing, the paper then exploits the advantages of these bio-inspired algorithms to form an osmotic hybrid artificial bee and ant colony (OH_BAC) optimization load balancing algorithm. It overcomes the drawbacks of the existing bio-inspired algorithms in achieving load balancing between physical machines. The simulation results show that OH_BAC decreases energy consumption, the number of VMs migrations and the number of shutdown hosts compared to existing algorithms. In addition, it enhances the quality of services (QoSs) which is measured by service level agreement violation (SLAV) and performance degradation due to migrations (PDMs). INDEX TERMS Ant colony optimization, artificial bee colony, bio-inspired systems, cloud computing, load balancing, metaheuristics, osmotic computing.

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Section: B Comparison With Bio-inspired Algorithmsmentioning

confidence: 99%

“…These OSs are separated from each other and from the underlying physical infrastructure by means of a special middleware abstraction called virtual machine (VM). The software that is responsible for managing these multiple VMs on PM is called VM kernel [2].…”

Section: Introductionmentioning

confidence: 99%

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

et al. 2019

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“…In general, the cloud computing paradigm possesses numerous problems as its usage is increasing exponentially. The most significant problem is the balancing of loads between resources (1,2) . To solve this problem, LB mechanisms are used which improves the overall system efficiency, robustness, availability and other characteristics in the cloud data centers.…”

Section: Introductionmentioning

confidence: 99%

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

Gowriprakash¹,

Shankar²,

Duraisamy³

2021

IJST

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Background: The Load Balancing (LB) schemes in cloud computing consider both current and future utilization of resources to decide the most suitable Virtual Machines (VMs) to be migrated to the most appropriate Physical Machines (PMs). But, the possibility of network congestion occurrence was high when increasing the bandwidth use between VMs within the cloud data centers. Also, a less-than-optimal migration of VMs can lead to high network traffic since it causes inter-VM traffic for traversing the bottleneck network routes. Objective: To enhance the efficiency of LB and reduce the possibility of congestion occurrence during VM migration in cloud data centers. Methods: Osmotic Hybrid artificial Bee and Ant Colony with Future Utilization Prediction with Multipath Traffic Routing (OH-BAC-FUP-MTR) mechanism are presented in this article to achieve the above objective. Initially, the OH-BAC-FUP mechanism is performed to decide the most suitable VMs to be migrated to the most suitable PMs. During VM migration, if any congestion exists due to high bandwidth use or traffic flows, then the MTR algorithm is applied to partition the flows and route them through multiple link-disjoint routes. Based on this, the congestion is avoided while ensuring the bandwidth and security grade demands. Also, the highest traffic on the path is applied as a congestion factor. Moreover, the current and future network states are taken into account for MTR to select the most optimal route from multiple routes with no consideration of the past use of the paths. Findings: The simulation outcomes demonstrate the OH-BAC-FUP-MTR mechanism consumes 8.74% of overall energy, 6.8% of Service Level Agreement violation Time per Active Host (SLATAH), 27.63% of Performance Degradation due to Migration (PDM), 22.98% of SLA Violation (SLAV), 27.27% of VM migrations and 30.77% of hosts shutdown compared to the OH-BAC-FUP using Linear Regression (LR) and Optimal Piecewise LR (OPLR).

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“…In the context of the Internet of Things (IoT), the cloud provides a scalable way to manage all aspects of an IoT deployment. But particularly for time-sensitive applications, there are some downsides to the cloud that are better addressed at the edge [1][2][3][4]. Fog computing is a promising solution to deal with the demands of the ever-increasing number of Internet-connected devices.…”

Section: Introductionmentioning

confidence: 99%

IoT Application Placement Algorithm Based on Multi-Dimensional QoE Prioritization Model in Fog Computing Environment

2020

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with the increasing popularity of Internet of Things (IoT) applications in many different areas, the round-trip delay of data processing in the cloud affect the user's quality perception. But fortunately, fog computing aims to service users at the network edge similar to cloud services, which helps in supporting IoT in processing the data near to the end-user, especially for time-sensitive applications. It makes the resource allocation of application placement requests in a fog environment more necessary to satisfy the Quality of Experience (QoE) Influence Factors (IFs). In this paper, an IoT application placement algorithm based on the Multi-Dimensional QoE (MD-QoE) model is proposed in a fog computing environment. The algorithm is composed of two main phases. The first phase is to prioritize different IoT application placement requests depending on three main domains of IFs which are: Environment runtime context, application usage, and user expectations considering the Quality of Service (QoS) violation as a feedback. The second phase is to map and place the request to the appropriate fog node instance, depending on its proximity, computing capabilities, and expected response time. The proposed algorithm is evaluated by simulating a fog environment using iFogSim. Experimental results indicate that the proposed algorithm significantly improves the QoE in respect of application placement time, application delay, network usage, and power consumption. Therefore, the proposed algorithm can improve the overall system performance with a slight increasing in power consumption in fog control nodes.

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Smart elastic scheduling algorithm for virtual machine migration in cloud computing

Cited by 58 publications

References 23 publications

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Osmotic Bio-Inspired Load Balancing Algorithm in Cloud Computing

Resource utilization prediction with multipath traffic routing for congestion-aware VM migration in cloud computing

IoT Application Placement Algorithm Based on Multi-Dimensional QoE Prioritization Model in Fog Computing Environment

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