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Simulation and performance assessment of a modified throttled load balancing algorithm in cloud computing environment
<span lang="EN-US">Load balancing is crucial to ensure scalability, reliability, minimize response time, and processing time and maximize resource utilization in cloud computing. However, the load fluctuation accompanied with the distribution of a huge number of requests among a set of virtual machines (VMs) is challenging and needs effective and practical load balancers. In this work, a two listed throttled load balancer (TLT-LB) algorithm is proposed and further simulated using the CloudAnalyst simulator. The TLT-LB algorithm is based on the modification of the conventional TLB algorithm to improve the distribution of the tasks between different VMs. The performance of the TLT-LB algorithm compared to the TLB, round robin (RR), and active monitoring load balancer (AMLB) algorithms has been evaluated using two different configurations. Interestingly, the TLT-LB significantly balances the load between the VMs by reducing the loading gap between the heaviest loaded and the lightest loaded VMs to be 6.45% compared to 68.55% for the TLB and AMLB algorithms. Furthermore, the TLT-LB algorithm considerably reduces the average response time and processing time compared to the TLB, RR, and AMLB algorithms.</span>
Scalable Phylogeny Reconstruction with Disaggregated Near-memory Processing
Disaggregated computer architectures eliminate resource fragmentation in next-generation datacenters by enabling virtual machines to employ resources such as CPUs, memory, and accelerators that are physically located on different servers. While this paves the way for highly compute- and/or memory-intensive applications to potentially deploy all CPUs and/or memory resources in a datacenter, it poses a major challenge to the efficient deployment of hardware accelerators: input/output data can reside on different servers than the ones hosting accelerator resources, thereby requiring time- and energy-consuming remote data transfers that diminish the gains of hardware acceleration. Targeting a disaggregated datacenter architecture similar to the IBM dReDBox disaggregated datacenter prototype, the present work explores the potential of deploying custom acceleration units adjacently to the disaggregated-memory controller on memory bricks (in dReDBox terminology), which is implemented on FPGA technology, to reduce data movement and improve performance and energy efficiency when reconstructing large phylogenies (evolutionary relationships among organisms). A fundamental computational kernel is the Phylogenetic Likelihood Function (PLF), which dominates the total execution time (up to 95%) of widely used maximum-likelihood methods. Numerous efforts to boost PLF performance over the years focused on accelerating computation; since the PLF is a data-intensive, memory-bound operation, performance remains limited by data movement, and memory disaggregation only exacerbates the problem. We describe two near-memory processing models, one that addresses the problem of workload distribution to memory bricks, which is particularly tailored toward larger genomes (e.g., plants and mammals), and one that reduces overall memory requirements through memory-side data interpolation transparently to the application, thereby allowing the phylogeny size to scale to a larger number of organisms without requiring additional memory.
Agile Support Vector Machine for Energy-efficient Resource Allocation in IoT-oriented Cloud using PSO
Over the years cloud computing has seen significant evolution in terms of improvement in infrastructure and resource provisioning. However the continuous emergence of new applications such as the Internet of Things (IoTs) with thousands of users put a significant load on cloud infrastructure. Load balancing of resource allocation in cloud-oriented IoT is a critical factor that has a significant impact on the smooth operation of cloud services and customer satisfaction. Several load balancing strategies for cloud environment have been proposed in the past. However the existing approaches mostly consider only a few parameters and ignore many critical factors having a pivotal role in load balancing leading to less optimized resource allocation. Load balancing is a challenging problem and therefore the research community has recently focused towards employing machine learning-based metaheuristic approaches for load balancing in the cloud. In this paper we propose a metaheuristics-based scheme Data Format Classification using Support Vector Machine (DFC-SVM), to deal with the load balancing problem. The proposed scheme aims to reduce the online load balancing complexity by offline-based pre-classification of raw-data from diverse sources (such as IoT) into different formats e.g. text images media etc. SVM is utilized to classify “n” types of data formats featuring audio video text digital images and maps etc. A one-to-many classification approach has been developed so that data formats from the cloud are initially classified into their respective classes and assigned to virtual machines through the proposed modified version of Particle Swarm Optimization (PSO) which schedules the data of a particular class efficiently. The experimental results compared with the baselines have shown a significant improvement in the performance of the proposed approach. Overall an average of 94% classification accuracy is achieved along with 11.82% less energy 16% less response time and 16.08% fewer SLA violations are observed.
A Systematic Literature Review on Virtual Machine Consolidation
Virtual machine consolidation has been a widely explored topic in recent years due to Cloud Data Centers’ effect on global energy consumption. Thus, academia and companies made efforts to achieve green computing, reducing energy consumption to minimize environmental impact. By consolidating Virtual Machines into a fewer number of Physical Machines, resource provisioning mechanisms can shutdown idle Physical Machines to reduce energy consumption and improve resource utilization. However, there is a tradeoff between reducing energy consumption while assuring the Quality of Service established on the Service Level Agreement. This work introduces a Systematic Literature Review of one year of advances in virtual machine consolidation. It provides a discussion on methods used in each step of the virtual machine consolidation, a classification of papers according to their contribution, and a quantitative and qualitative analysis of datasets, scenarios, and metrics.
Cloud-based Network Virtualization in IoT with OpenStack
In Cloud computing deployments, specifically in the Infrastructure-as-a-Service (IaaS) model, networking is one of the core enabling facilities provided for the users. The IaaS approach ensures significant flexibility and manageability, since the networking resources and topologies are entirely under users’ control. In this context, considerable efforts have been devoted to promoting the Cloud paradigm as a suitable solution for managing IoT environments. Deep and genuine integration between the two ecosystems, Cloud and IoT, may only be attainable at the IaaS level. In light of extending the IoT domain capabilities’ with Cloud-based mechanisms akin to the IaaS Cloud model, network virtualization is a fundamental enabler of infrastructure-oriented IoT deployments. Indeed, an IoT deployment without networking resilience and adaptability makes it unsuitable to meet user-level demands and services’ requirements. Such a limitation makes the IoT-based services adopted in very specific and statically defined scenarios, thus leading to limited plurality and diversity of use cases. This article presents a Cloud-based approach for network virtualization in an IoT context using the de-facto standard IaaS middleware, OpenStack, and its networking subsystem, Neutron. OpenStack is being extended to enable the instantiation of virtual/overlay networks between Cloud-based instances (e.g., virtual machines, containers, and bare metal servers) and/or geographically distributed IoT nodes deployed at the network edge.
A comprehensive survey on container resource allocation approaches in cloud computing: State-of-the-art and research challenges
The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses. Containers, like virtualization technology, are gaining popularity due to their low overhead when compared to traditional virtual machines and portability. The resource allocation methodologies in the containerized cloud are intended to dynamically or statically allocate the available pool of resources such as CPU, memory, disk, and so on to users. Despite the enormous popularity of containers in cloud computing, no systematic survey of container scheduling techniques exists. In this survey, an outline of the present works on resource allocation in the containerized cloud correlative is discussed. In this work, 64 research papers are reviewed for a better understanding of resource allocation, management, and scheduling. Further, to add extra worth to this research work, the performance of the collected papers is investigated in terms of various performance measures. Along with this, the weakness of the existing resource allocation algorithms is provided, which makes the researchers to investigate with novel algorithms or techniques.
Approbation of the stochastic group virus protection model
The article discusses the implementation in Java of the stochastic collaborative virus defense model developed within the framework of the Distributed Object-Based Stochastic Hybrid Systems (DOBSHS) model and its analysis. The goal of the work is to test the model in conditions close to the real world on the way to introducing its use in the practical environment. We propose a method of translating a system specification in the SHYMaude language, intended for the specification and analysis of DOBSHS models in the rewriting logic framework, into the corresponding Java implementation. The resulting Java system is deployed on virtual machines, the virus and the group virus alert system are modeled stochastically. To analyze the system we use several metrics, such as the saturation time of the virus propagation, the proportion of infected nodes upon reaching saturation and the maximal virus propagation speed. We use Monte Carlo method with the computation of confidence intervals to obtain estimates of the selected metrics. We perform analysis on the basis of the sigmoid virus propagation graph over time in the presence of the defense system. We implemented two versions of the system using two protocols for transmitting messages between nodes, TCP/IP and UDP. We measured the influence of the protocol type and the associated costs on the defense system effectiveness. To assess the potential of cost reduction associated with the use of different message transmission protocols, we performed analysis of the original DOBSHS model modified to model message transmission delays. We measured the influence of other model parameters important for the next steps towards the practical use of the model. To address the system scalability, we propose a hierarchical approach to the system design to make possible its use with a large number of nodes.
Enhanced Virtual Machine Placement in Cloud Data Centers: Combinations of Fuzzy Logic with Reinforcement Learning and Biogeography-Based Optimization (BBO) Algorithms
Abstract The process of mapping Virtual Machines (VMs) to Physical Ma- chines (PMs), which is defined as VM placement, affects Cloud Data Centers (DCs) performance. To enhance the performance, optimal placement of VMs regarding conflicting objectives has been proposed in some research, such as Multi-Objective VM reBalance (MOVMrB) and Reinforcement Learning VM reBalance (RLVMrB) in recent years. The MOVMrB algorithm is based on the BBO meta-heuristic algorithm and the RLVMrB algorithm inspired by reinforcement learning, which in both of them the non-dominance method is used to evaluate generated solutions. Although this approach reaches accept- able results, it fails to consider other solutions which are optimal regarding all objectives, when it meets the best solution based on one of these objectives. In this paper, we propose two enhanced multi-objective algorithms, Fuzzy- RLVMrB and Fuzzy-MOVMrB, that are able to consider all objectives when evaluating candidate solutions in solution space. All four algorithms aim to balance the load between VMs in terms of processor, bandwidth, and memory as well as horizontal and vertical load balance. We simulated all algorithms using the CloudSim simulator and compared them in terms of horizontal and vertical load balance and execution time. The simulation results show that Fuzzy-RLVMrB and Fuzzy-MOVMrB algorithms outperform RLVMrB and MOVMrB algorithms in terms of vertical load balancing and horizontal load balancing. Also, the RLVMrB and Fuzzy-RLVMrB algorithms are better in execution time than the MOVMrB and Fuzzy-MOVMrB algorithms.
A frequency-aware management strategy for virtual machines in DVFS-enabled clouds
Optimization of sla aware live migration of multiple virtual machines using lagrange multiplier, export citation format, share document.
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Artificial Intelligence and Virtual Assistant—Working Model
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- First Online: 29 September 2020
- Cite this conference paper
- Shakti Arora 13 ,
- Vijay Anant Athavale 13 ,
- Himanshu Maggu 13 &
- Abhay Agarwal 13
Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 140))
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In twenty-first-century virtual assistant is playing a very crucial role in day to day activities of human. According to the survey report of Clutch in 2019, 27% of the people are using the AI-powered virtual assistant such as: Google Assistant, Amazon Alexa, Cortana, Apple Siri, etc., for performing a simple task, people are using virtual assistant designed with natural language processing. In this research paper, we have studied and analyzed the working model and the efficiency of different virtual assistants available in the market. We also designed an intelligent virtual assistant that could be integrated with Google virtual services and work with the Google virtual assistant interface. A comparative analysis of the traffic and message communication with length of conversation for approximately three days is taken as input to calculate the efficiency of the designed virtual assistant.
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Shakti Arora, Vijay Anant Athavale, Himanshu Maggu & Abhay Agarwal
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Arora, S., Athavale, V.A., Himanshu Maggu, Agarwal, A. (2021). Artificial Intelligence and Virtual Assistant—Working Model. In: Marriwala, N., Tripathi, C.C., Kumar, D., Jain, S. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 140. Springer, Singapore. https://doi.org/10.1007/978-981-15-7130-5_12
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IMAGES
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In the current technology of virtual machine system, we mainly describe the virtualization technology, the resource scheduling technology, the migration technology, the security technology and the ...
Our survey paper differs from existing works in the following aspects: 1. the current survey presents a detailed discussion of conventional and AI-based (e.g., supervised, unsupervised, reinforcement, and Q-learning) live VM migration schemes and provides a quick reference for both researchers and industry experts, 2.
tems. At least two recent research projects also use virtual machines: Disco uses virtual machines to run multiple commodity operating systems on large-scalemultiproces-sors [4]; Hypervisor uses virtual machines to replicate the execution of one computer onto a backup [3]. Our position is that the operating system and applica-
This survey is an up-to-date account of the research on virtual machine consolidation overhead. The overhead influencing factors are analyzed throughout this work. Based on these factors, we propose a categorization that classifies the most important research works on virtualization and virtual machine consolidation overhead. We have analyzed and summarized 46 selected research works from an ...
2006. TLDR. This paper describes a prototype, open source, cross-platform Virtual Machine tool (named Generic purpose Nano Virtual Machine or gNVM), that minimizes the effort of creating a virtual machine from zero code level, by providing a cross- Platform, fast, small-sized and highly extensible virtual machine.
and in use by educational institutions for research and teaching. This paper stresses on the potential advantages associated with virtualization and the use of virtual machines for scenarios, which cannot be easily implemented and/or studied in a traditional academic network environment, but need to be explored and experimented by students to ...
This survey is an up-to-date account of the research on the performance-energy trade-off in virtualized environments, specifically in virtual machine consolidation. The factors that influence the performance and energy in consolidated data centres and the performance-energy trade-off itself are analysed. Based on these factors, we propose a categorization that classifies the most important ...
virtual machine has been become the main research topic. Understanding of the current technology and future trends of virtual machine system greatly help to improve the service performance of system. Therefore, we describe the current technology and present the future trends of virtual machine system in the paper.
Survey of virtual machine research Abstract: The complete instruction-by-instruction simulation of one computer system on a different system is a well-known computing technique. It is often used for software development when a hardware base is being altered. For example, if a programmer is developing software for some new special purpose (e.g ...
Virtual Machines . State Of The Art . Research Work . Research Papers . Virtualization Technology . Comprehensive Survey . Novel Algorithms . Allocation Algorithms. The allocation of resources in the cloud environment is efficient and vital, as it directly impacts versatility and operational expenses.
Cloudvmi: Virtual machine introspection as a cloud service. In 2014 IEEE International Conference on Cloud Engineering. IEEE, 153-158. Google Scholar Digital Library; Fangzhou Yao, Read Sprabery, and Roy H Campbell. 2014. CryptVMI: A flexible and encrypted virtual machine introspection system in the cloud.
Kunshan Wang, Yi Lin, Stephen M. Blackburn, Michael Norrish, Antony L. Hosking, Thomas Ball, Rastislav Bodik, Shriram Krishnamurthi, Benjamin S. Lerner, and Greg Morrisett. 2015. Draining the Swamp: Micro Virtual Machines as Solid Foundation for Language Development. 1st Summit on Advances in Programming Languages (SNAPL 2015) 32 (2015), 321ś336.
Emerging non-volatile memory (NVM) technologies promise high density, low cost and dynamic random access memory (DRAM)-like performance, at the expense of limited write endurance and high write energy consumption. It is more practical to use NVM combining with the traditional DRAM. However, the hybrid memory management such as page migration becomes more challenging in a virtualization ...
define the machine; it is the ISA that provides the interface between the system and machine. Just as there are process and system perspectives of "machine," there are process and system virtual machines. A process VM is a virtual platform that executes an individual process. This type of VM exists solely to support the process; it is created
virtualization technologies that we study in this paper. 2.1 Hardware Virtualization Hardware virtualization involves virtualizing the hardware on a server and creating virtual machines that provide the abstraction of a physical machine. Hardware virtualization involves running a hypervisor, also referred to as a virtual machine monitor (VMM),
Their future range research and implementation has been briefly mentioned. Comparisons have been made between pre-copy and post-copy VM Migration techniques through simulation corresponding to CPU uses, memory and network as parameters.This paper reviews various virtual machine migration schemes while, through a comprehensive analysis of ...
Two research examples of such sys- Linux virtual machine for day-to-day work, and a tems are Livewire,9 a system that uses a VMM for still higher-security virtual machine comprising a advanced intrusion detection on the software in the special-purpose high-security operating system and virtual machines, and ReVirt,10 which uses the a dedicated ...
The Neural Information Processing Systems Foundation is a non-profit corporation whose purpose is to foster the exchange of research advances in Artificial Intelligence and Machine Learning, principally by hosting an annual interdisciplinary academic conference with the highest ethical standards for a diverse and inclusive community.
Layers of VA: There are four layers in a chatbot. The layers define the workflow in a better and more understandable way. These are (a) UI layer, (b) integration layer, (c) machine learning layer and (d) data layer. 1. UI Layer: This is the closest layer to the end-users as shown in the Fig. 3.