Single Virtual Machine Research Articles

Exploiting inherent elasticity of serverless in algorithms with unbalanced and irregular workloads

Function-as-a-Service execution model in serverless computing has been successful in running large-scale computations like MapReduce, linear algebra, and machine learning. However, little attention has been given to executing highly-dynamic parallel applications with unbalanced and irregular workloads. These algorithms are difficult to execute with good parallel efficiency due to the challenge of provisioning the required computing resources in time, leading to resource over- and under-provisioning in clusters of static size. We propose that the elasticity and fine-grained “pay-as-you-go model” of the FaaS model can be a key enabler for effectively running these algorithms in the cloud. We use a simple serverless executor pool abstraction, and evaluate it using three algorithms with unbalanced and irregular workloads. Results show that their serverless implementation can outperform a static Spark cluster of large virtual machines by up to 55% with the same cost, and can even outperform a single large virtual machine running locally.

GymHydro: An Innovative Modular Small-Scale Smart Agriculture System for Hydroponic Greenhouses

In response to the challenges posed by climate change, including extreme weather events, such as heavy rainfall and droughts, the agricultural sector is increasingly seeking solutions for the efficient use of resources, particularly water. Pivotal aspects of smart agriculture include the establishment of weather-independent systems and the implementation of precise monitoring and control of plant growth and environmental conditions. Hydroponic cultivation techniques have emerged as transformative solutions with the potential to reduce water consumption for cultivation and offer a sheltered environment for crops, protecting them from the unpredictable impacts of climate change. However, a significant challenge lies in the frequent need for human intervention to ensure the efficiency and effectiveness of these systems. This paper introduces a novel system with a modular architecture, offering the ability to incorporate new functionalities without necessitating a complete system redesign. The autonomous hydroponic greenhouse, designed and implemented in this study, maintains stable environmental parameters to create an ideal environment for cultivating tomato plants. Actuators, receiving commands from a cloud application situated at the network’s edge, automatically regulate environmental conditions. Decision-making within this application is facilitated by a PID control algorithm, ensuring precision in control commands transmitted through the MQTT protocol and the NGSI-LD message format. The system transitioned from a single virtual machine in the public cloud to edge computing, specifically on a Raspberry Pi 3, to address latency concerns. In this study, we analyzed various delay aspects and network latency to better understand their significance in delays. This transition resulted in a significant reduction in communication latency and a reduction in total service delay, enhancing the system’s real-time responsiveness. The utilization of LoRa communication technology connects IoT devices to a gateway, typically located at the main farm building, addressing the challenge of limited Internet connectivity in remote greenhouse locations. Monitoring data are made accessible to end-users through a smartphone app, offering real-time insights into the greenhouse environment. Furthermore, end-users have the capability to modify system parameters manually and remotely when necessary. This approach not only provides a robust solution to climate-induced challenges but also enhances the efficiency and intelligence of agricultural practices. The transition to digitization poses a significant challenge for farmers. Our proposed system not only represents a step forward toward sustainable and precise agriculture but also serves as a practical demonstrator, providing farmers with a key tool during this crucial digital transition. The demonstrator enables farmers to optimize crop growth and resource management, concretely showcasing the benefits of smart and precise agriculture.

Gamifying Cybersecurity Education: A CTF-based Approach to Engaging Students in Software Security Laboratories

CTF (Capture the Flag) competitions are increasingly being used in information technology and software engineering education to teach students about secure coding best practices. This paper presents an approach to improve engagement of software engineering students during software security laboratory assessments by using CTF-style challenges that are fun to solve and reflect modern security threats. The approach involves gamifying laboratory exercises by providing CTF-like challenges that are not only enjoyable but also reflect real-world security vulnerabilities. Additionally, students are given a single virtual machine bundled with everything needed for the entire semester, eliminating unnecessary logistic problems. Feedback from students confirms the effectiveness of this approach in reaching course objectives. Students were keen to learn more about the topic and showed greater interest in the laboratories. This approach provides a more dynamic and interactive learning experience that fosters critical thinking skills and encourages students to pursue careers in software security.

A Service Level Agreement Aware Online Algorithm for Virtual Machine Migration

The demand for cloud computing has increased manifold in the recent past. More specifically, on-demand computing has seen a rapid rise as organizations rely mostly on cloud service providers for their day-to-day computing needs. The cloud service provider fulfills different user requirements using virtualization - where a single physical machine can host multiple Virtual Machines. Each virtual machine potentially represents a different user environment such as operating system, programming environment, and applications. However, these cloud services use a large amount of electrical energy and produce greenhouse gases. To reduce the electricity cost and greenhouse gases, energy efficient algorithms must be designed. One specific area where energy efficient algorithms are required is virtual machine consolidation. With virtual machine consolidation, the objective is to utilize the minimum possible number of hosts to accommodate the required virtual machines, keeping in mind the service level agreement requirements. This research work formulates the virtual machine migration as an online problem and develops optimal offline and online algorithms for the single host virtual machine migration problem under a service level agreement constraint for an over-utilized host. The online algorithm is analyzed using a competitive analysis approach. In addition, an experimental analysis of the proposed algorithm on real-world data is conducted to showcase the improved performance of the proposed algorithm against the benchmark algorithms. Our proposed online algorithm consumed 25% less energy and performed 43% fewer migrations than the benchmark algorithms.

MixNN: A Design for Protecting Deep Learning Models.

In this paper, we propose a novel design, called MixNN, for protecting deep learning model structure and parameters since the model consists of several layers and each layer contains its own structure and parameters. The layers in a deep learning model of MixNN are fully decentralized. It hides communication address, layer parameters and operations, and forward as well as backward message flows among non-adjacent layers using the ideas from mix networks. MixNN has the following advantages: (i) an adversary cannot fully control all layers of a model, including the structure and parameters; (ii) even some layers may collude but they cannot tamper with other honest layers; (iii) model privacy is preserved in the training phase. We provide detailed descriptions for deployment. In one classification experiment, we compared a neural network deployed in a virtual machine with the same one using the MixNN design on the AWS EC2. The result shows that our MixNN retains less than 0.001 difference in terms of classification accuracy, while the whole running time of MixNN is about 7.5 times slower than the one running on a single virtual machine.

Extending CloudSim to simulate sensor networks

With the enormous growth of sensing devices tending to the use of Internet of everything, data aggregated by these devices are the biggest data streams generated in the history of IT. Thus, aggregating such data in the cloud for leveraging powerful cloud computing processing and storage is essential, and it eventually led to the emergence of Sensor-Cloud concept. This has allowed aggregation of the sensors’ data to the cloud for further processing, storage, and visualization. Furthermore, virtualization makes the sensors accessible to other end-user applications that require such data. All of these features are expected to be provided by the Sensor-Cloud invisibly, without the end-user application developer being aware of the sensor location or hardware specifications. For these reasons, a simulation platform where Sensor-Cloud infrastructure agents and components may be modeled, scheduling policies defined, and execution time assessed is essential to assure performance and quality of service. The aim of this study is to develop such a platform by enhancing CloudSim, the most well-known and powerful simulation tool for cloud computing. A user-friendly Java Script Swing-based graphical user interface (GUI) has been carefully designed and implemented for this purpose. The user can then utilize the specific interface to define the Cloudlet type as well as the scheduling on a single virtual machine. Finally, a simulation study is carried out on the platform to demonstrate its efficiency and accuracy. We were able to fully model the needed scenarios and acquire real-time results, displaying good accuracy in terms of application response time with a mean absolute percentage error (MAPE) of 3.37%, demonstrating the increased proposed platform’s proper operation.

An Efficient On-Demand Virtual Machine Migration in Cloud Using Common Deployment Model

Cloud Computing provides various services to the customer in a flexible and reliable manner. Virtual Machines (VM) are created from physical resources of the data center for handling huge number of requests as a task. These tasks are executed in the VM at the data center which needs excess hosts for satisfying the customer request. The VM migration solves this problem by migrating the VM from one host to another host and makes the resources available at any time. This process is carried out based on various algorithms which follow a predefined capacity of source VM leads to the capacity issue at the destination VM. The proposed VM migration technique performs the migration process based on the request of the requesting host machine. This technique can perform in three ways namely single VM migration, Multiple VM migration and Cluster VM migration. Common Deployment Manager (CDM) is used to support through negotiation that happens across the source host and destination host for providing the high quality service to their customer. The VM migration requests are handled with an exposure of the source host capabilities. The proposed analysis also uses the retired instructions with execution by the hypervisor to achieve high reliability. The objective of the proposed technique is to perform a VM migration process based on the prior knowledge of the resource availability in the target VM.

Virtual Disks Performance Analysis Using Flexible I/O and Powerstat

Abstract: Cloud computing has experienced significant growth in the recent years owing to the various advantages it provides such as 24/7 availability, quick provisioning of resources, easy scalability to name a few. Virtualization is the backbone of cloud computing. Virtual Machines (VMs) are created and executed by a software called Virtual Machine Monitor (VMM) or the hypervisor. It separates compute environments from the actual physical infrastructure. A disk image file representing a single virtual machine is created on the hypervisor’s file system. In this paper, we analysed the runtime performance of multiple different disk image file formats. The analysis comprises of four different parameters of performance namely- bandwidth, latency, input-output operations performed per second (IOPS) and power consumption. The impact of the hypervisor’s block and file sizes is also analysed for the different file formats. The paper aims to act as a reference for the reader in choosing the most appropriate disk file image format for their use case based on the performance comparisons made between different disk image file formats on two different hypervisors – KVM and VirtualBox. Keywords: Virtualization, Virtual disk formats, Cloud computing, fio, KVM, virt-manager, powerstat, VirtualBox.

Assessment of Network & Processor Virtualization in Cloud Computing

Cloud Computing is an emerging field in applied computer science. Cloud computing depends on virtualization, where a sole physical resource is virtualized into numerous virtual resources. Resources that are being virtualized include physical storage, processing units, volatile memories, network, and further physical resources. Processor and network virtualization offer several advantages like lessening hardware cost, energy consumption, and the human struggle for administration and management of resources. In this article, the primary concentration was on the evaluation and assessment of network virtualization between different cloud computing hypervisors and to contrast their evaluated results. This comparison is based on a few experiments performed on Citrix XenServer, Microsoft Hyper-V, and VMware vSphere hypervisors. XenServer uses Open vSwitch for its network virtualization whereas Hyper-V and vSphere use Microsoft network virtualization and vSphere switch for managing their network virtualization respectively. We have found that virtual networks are further scalable and secure than physical networks. We can assign multiple virtual interfaces or connections to a single virtual machine. In light of various experiments, in which we have compared non-virtualized with virtualized scenarios, for all these above-mentioned hypervisors, we found out that we can use virtual networks with and without VLAN Tags or VLAN Ids. Hyper-V provides further flexibility and options regarding VLAN Tag in comparison with XenServer or vSphere. We have also found that XenServer and vSphere give us extra network throughput in comparison with Hyper-V in terms of TCP and UDP whereas Round Trip Time (RTT) in the virtual network of all hypervisors are high in comparison with the non-virtual environment. Network CPU usage is also measured and found that vSphere and XenServer together consume more CPU during network activity in comparison with Hyper-V. We also found that processor virtualization did not majorly result from the average schedule time in comparison with a non-virtualized machine. We consequently conclude that in general a small reduction in the performance, in the case of virtualization is insignificant as compared with the advantages we get from virtualization when using cloud hypervisors. For the application of virtualization, this work will motivate to set up of data centers using a virtualized environment.

One Host with So Many IPs! On The Security Implications of Dynamic Virtual Private Servers

Many malicious activities require a large number of IP addresses for altering user identities. It is well known that a large number of IP addresses could be obtained through compromising numerous machines and building botnets. However, we recently found that a special type of off-the-shelf virtual private server (VPS), which we refer to as dynamic VPSs, can retrieve a huge number of IP addresses using just a single virtual machine. Although dynamic VPSs are intended for legitimate purposes, whether they are actually misused in malicious activities has never been investigated. In this article, we take the first step to study dynamic VPSs, with an emphasis on their security implications. We reveal that dynamic VPSs are surprisingly and extensively involved in malicious activities, explore their internal working mechanisms, and suggest that the linkage of dynamic VPSs to the security domain deserves adequate attention.

Small‐signal analysis of multiple virtual synchronous machines to enhance frequency stability of grid‐connected high renewables

The virtual synchronous machine technology is considered as an important technique to effectively control the shortcomings of renewable energy-based power electronics interfaces, providing backup inertia and regulating grid stability. Conventionally, the virtual synchronous machine with a large capacity is responsible for controlling the entire grid stability against renewable penetration. It is usually operated as a centralised control system. But what if virtual synchronous machines with small capacities are independently operated by their additional droop control schemes, and will they present better performance than the single virtual synchronous machine? This study proposes the multiple virtual synchronous machine system with different active power-frequency (P-f) droop characteristics to improve inertia support regarding frequency stability improvement. The comprehensive small-signal modelling of the multiple virtual synchronous machine unit is designed to include the additional P-f droop characteristics. Then, the dynamic characteristics (steady-state and transient responses) and static stability of the multiple virtual synchronous machines are compared with the single virtual synchronous machine at the same rated capacity in both eigenvalue/sensitivity-domain and time-domain analysis. The obtained results reveal that the system with the presence of several virtual synchronous machines is more stable than the system with single virtual synchronous machine, maintaining stable and secure system operation during the contingency.

Retracted] Social Governance Structure Construction and Resource Allocation Methods under the Management Mode of the Internet of Things

Aiming at the problems in the social governance structure level under the management mode of the Internet of Things, this paper designs a network agent based on the idea of service grid construction that has the functions of service resource configuration discovery, load balancing, and protocol conversion. Using it as a service governance infrastructure reduces the coupling between business services and service governance, and the network agent will intercept the service call traffic within the platform to achieve cross‐protocol and cross‐frame service communication. Through the proposed global query decomposition strategy and algorithm, a global query mapping is realized, which solves the problem of global query decomposition based on the global data model. We designed the unified management mechanism of two commonly used IoT databases based on XML. This process involves the realization of heterogeneous Internet of Things database resource configuration, data mode conversion, and global query mapping. Among them, in the process of data schema conversion, the conflict of the data in the schema mapping is resolved, the XML Schema is generated, the schema mapping metadata is established, and the XML document of the data is generated. We build a framework for optimal allocation of cloud computing resources based on load forecasting and propose an adaptive elastic control system for resource allocation based on resource status monitoring and demand forecasting. In view of the low resource utilization problem caused by the resource management mode of single virtual machine serving single user adopted by Internet of Things service providers, this paper constructs a new public cloud architecture with a five‐layer structure. On the basis of this architecture, an adaptive configuration mode of virtualized resources based on a single virtual machine serving multiple users is proposed. This mode can automatically search for optimal virtualization resources for application resource requests made by different users and run different applications on the same virtual machine without affecting the quality of service, so that IoT providers can guarantee services. At the same time, it improves the utilization efficiency of IoT resources and reduces energy consumption.

Optimal Elliptic Curve Cryptography-Based Effective Approach for Secure Data Storage in Clouds

With the rapid development of cloud advancement, a data security challenge has emerged. In this paper, a technique based on elliptical cryptography and cuckoo search algorithm is proposed. With this technique, data owners securely store their data files in the cloud server. Initially the user sends a file storage request to store a file in a cloud server provider (CSP). The input file is checked whether it is sensitive or non-sensitive by the user. If the file is sensitive, then it would be split and stored in different virtual machines (VMs), and if the file is non-sensitive, then it would be assigned in a single VM. This approach was used for the first time as per the survey. To add further security, the sensitive data retrieval needs an encryption process that is supported by the proposed algorithm. If the data owner stores the sensitive data to cloud server, the data owner's document is encrypted by the double encryption technique. Here RSA and optimal elliptic curve cryptography (OECC) algorithm is used to encrypt the document with high security. The authors have used cuckoo search algorithm to identify the optimal key in ECC. This paper has proposed a novel cryptography approach for delivering mass distributed storage by which user's original data cannot be directly reached by cloud operators. Hence, this research has proved that the proposed work will give better securable data storage solving the security issues.

Predictive Cyber Foraging for Visual Cloud Computing in Large-Scale IoT Systems

Cyber foraging has been shown to be especially effective for augmenting low-power Internet-of-Thing (IoT) devices by offloading video processing tasks to nearby edge/cloud computing servers. Factors such as dynamic network conditions, concurrent user access, and limited resource availability, cause offloading decisions that negatively impact overall processing throughput and end-user delays. Moreover, edge/cloud platforms currently offer both Virtual Machine (VM) and serverless computing pricing models, but many existing edge offloading approaches only investigate single VM-based offloading performance. In this paper, we propose a predictive (NP-complete) scheduling-based offloading framework and a heuristic-based counterpart that use machine learning to dynamically decide what combinations of functions or single VM needs to be deployed so that tasks can be efficiently scheduled. We collected over 10,000 network and device traces in a series of realistic experiments relating to a protest crowds incident management application. We then evaluated the practicality of our predictive cyber foraging approach using trace-driven simulations for up to 1000 devices. Our results indicate that predicting single VM offloading costs: (a) leads to near-optimal scheduling in 70% of the cases for service function chaining, and (b) offers a 40% gain in performance over traditional baseline estimation techniques that rely on simple statistics for estimations in the case of single VM-offloading. Considering a series of visual computing offloading scenarios, we also validate our approach benefits of using online versus offline machine learning models for predicting offloading delays.

Energy-efficient Workload Allocation and Computation Resource Configuration in Distributed Cloud/Edge Computing Systems With Stochastic Workloads

Energy efficiency is one of the most important concerns in cloud/edge computing systems. A major benefit of the Dynamic Voltage and Frequency Scaling (DVFS) technique is that a Virtual Machine (VM) can dynamically scale its computation frequency on an on-demand basis, which is helpful in reducing the energy cost of computation when dealing with stochastic workloads. In this paper, we study the joint workload allocation and computation resource configuration problem in distributed cloud/edge computing. We propose a new energy consumption model that considers the stochastic workloads for computation capacity reconfiguration-enabled VMs. We define Service Risk Probability (SRP) as the probability a VM fails to process the incoming workloads in the current time slot, and we study the energy-SRP tradeoff problem in single VM. Without specifying any distribution of the workloads, we prove that, theoretically there exists an optimal SRP that achieves minimal energy cost, and we derive the closed form of the condition to achieve this minimal energy point. We also derive the closed form for computing the optimal SRP when the workloads follow a Gaussian distribution. We then study the joint workload allocation and computation frequency configuration problem for multiple distributed VMs scenario, and we propose solutions to solve the problem for both Gaussian and unspecified distributions. Our performance evaluation results on both synthetic and real-world workload trace data demonstrate the effectiveness of the proposed model. The closeness between the simulation results and the analytical results prove that our proposed method can achieve lower energy consumption compared with fixed computation capacity configuration methods.

A Comprehensive Review of Virtual Machine Migration Techniques in Cloud Computing

As cloud users grow exponentially, virtualization of the cloud data center (CDC) is accomplished by running simultaneous virtual machines (VM) on one cloud server in parallel processing. The growing demand for resources such as communication, storage, and computing can be successfully met by using dynamic virtual machine migration. For single or multiple VM migrations, most resource management techniques are used to maximize the performance and efficiency of the cloud data center. The Qos (service quality) declines proportionally with fast increase in cloud users and associated workload. The powerful live VM migration strategy can only be used to deliver better computing facilities, manage large numbers of cloud users and reduce time and energy on cloud data center. Modern cloud computing system depends primarily on live VM migration. This paper analyzes with their advantages and disadvantages all the latest live VM migration techniques. 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 existing schemes, examining the critical aspects of virtual machine migration schemes.Existing systems are explored with key goals. In the VM migration domain, some open research issues are also mentioned.

The Effects of Soft Errors and Mitigation Strategies for Virtualization Servers

Virtualized servers compose the majority of cloud computing environments, where these nodes are used to host multiple clients over the same hardware. Many organizations run online applications by hiring elastic computing resources in order to match demand while reducing fixed costs. However, such organizations are unlikely to take advantage of these benefits for critical applications, as it would expose them to several risks. Among other threats, soft errors are a concern in large-scale reliable servers and are expected to become more frequent as a consequence of smaller transistors and lower operating voltages of integrated circuits. This article characterizes virtualized servers of cloud environments in presence of soft errors. Using fault injection, we collect experimental data to determine the failure modes of applications, operating systems, VMs, and hypervisor. The analysis exposes distinct failure modes, ranging from crash failures of a single virtual machine to silent data corruption in permanent storage. The most frequent failure mode, observed in 10–30 percent of injected errors, consists of a hang affecting multiple virtual machines. Given that such failures are a primary cause of downtime, we develop and evaluate a recovery mechanism which uses online testing and recovers a server from all hangs by rebooting its hypervisor.

Tile-in-One: an integrated system for data quality and condition assessment for the ATLAS Tile Calorimeter

The Tile Calorimeter (TileCal) is a crucial part of the ATLAS detector, which jointly with other calorimeters reconstructs hadrons, jets, tau-particles, missing transverse energy and assists in muon identification. It consists of alternating steel absorber layers and active scintillating tiles and covers the region |η| < 1.7. The TileCal is regularly monitored by several systems, which were developed mainly during the commissioning of the detector in order to meet distinct requirements. Any problem is reported and immediately investigated, which results in data quality efficiency very close to 100%, as achieved over the last few years. Although the TileCal tools are maintained, the underlying technologies are becoming gradually outdated. The Tile-in-One web platform strives to integrate all data quality and condition assessment TileCal tools into one common system. This system is implemented as a web application with the main machine being the gateway for so-called plugins. These are standalone small web application hosted on a single virtual machine. The plugins are separated into virtual machines due to the requirement of different data sources and to avoid interference in order to increase the stability of the platform. The main server is responsible for the authentication and authorization of the users, as well as the management of the plugins. Currently the platform consists of 13 plugins in various stages of development. The implementation details of the Tile-in-One web system and selected plugins are presented in this document.

QoS Guaranteed Resource Allocation for Live Virtual Machine Migration in Edge Clouds

Live Virtual Machine (VM) migration among geographically distributed edge clouds is an important strategy for providing low latency and reliable services for mobile end users. VM migration among edge clouds is more challenging than that in cloud computing, because the network bandwidth among edge clouds is more constrained than the cloud data center networks. In this paper, we study the bandwidth allocation among multiple concurrent live VM migrations in edge clouds. This problem is novel in that existing works aim to reduce the migration time for a single live VM migration among the edge clouds, and also ignores the QoS requirement for the service running on the VM in migration. However, our problem considers multiple VM migration tasks, and aims to maximize the average QoS while meeting the migration time constraint for each VM migration task. We formulate the problem as a Non-Linear Programming (NLP) problem which is also shown to be NP-Hard. We develop a new method to solve this problem. In our approach, we first transfer the problem into a Linear Programming (LP) problem by reducing the solution space. Taking the output from the LP solver as an initial solution, we then develop a heuristic to adjust it in order to find a better one to the original NLP problem. Finally, we design a set of evolutionary algorithms to select the optimal initial solution from the LP solver. Extensive simulations show that our proposed method can achieve good QoS and also has a fast convergence speed.

Load Balancing Applying Resources Based on Runtime Conditions in Cloud Computing

With the fast advancement of the Internet, numerous sellers have begun to give cloud administrations. An everincreasing number of administrations can be gotten in the cloud, and clients don't need to do activities on a nearby PC. All activities are figured on the cloud. At the point when an enormous number of clients endeavor to access cloud benefits, this frequently makes the server neglect to react. Deciding a technique by which to give clients auspicious and exact reactions is a subject deserving of cutting edge examine. A few examinations have been proposed to assess and to create calculations and load adjusting approaches for cloud-based applications. It is hard for a server to manage the progression of data created by the majority of the different endeavors endeavoring to get to it. Unreasonable stream causes server over-load with a resulting loss of data. So the primary target of this examination is to investigate the outstanding task at hand among every single virtual machine and execute upgraded load adjusting the calculation to increase the allotment of VM to cloudlet and limit the make span time.