Hypervisor Research Articles

Framework for Performance Enhancement of MPI based Application on Cloud

Cloud technology is a major revolution that has happened in the computing era that has changed the way applications and resources are used. Elasticity is the key characteristic of the cloud, wherein the required number of resources are provided as a service with a pay-as-you-go principle. This reduces the huge cost involved in buying, installing, and maintaining the resources. Cloud computing, with its highly scalable resources, can be a good platform for High-Performance Computing (HPC). HPC application performance highly depends on the quantity of the resources, which makes the cloud a suitable candidate. But the HPC community is not very happy with cloud technology, and most of the users still think cloud technology is not suitable for HPC applications. But virtualization technology, which is the foundation of the cloud, degrades the performance of applications in the urge to improve utilization. The hypervisor layer and resource sharing by the virtual machines (VMs) hosted on the same node are the main reasons for performance degradation in the cloud. The majority of the HPC applications belong to the message passing (MPI) category, and for these applications, communication cost is the major stakeholder in deciding performance. If these applications are hosted on the cloud, it leads to further performance degradation as the process on a VM communicates through the virtual network interface, which in turn shares the network interface of the host machine with other VMs. MPI-based applications hosted on MPPs work in a bandwidth shared environment as multiple processes communicate over the same network. But in the cloud, as the number of VMs increases, per node bandwidth availability per communication reduces. To address the above issues, we have built a framework to enhance the performance of MPI-based HPC applications on VMs by considering proper VM placement strategy and resource reservation policies, with knowledge of resource availability and process communication patterns. A VM placement strategy for dynamic clustering of VMs with high priority for shared memory-based communication is proposed and tested. Results show that with a medium number of processes, there is an improvement of around 70% with our placement strategy for high data communicating processes. If there are fewer processes, and the single physical node can hold all the VMs, then the performance improvement is up to 500%.

Efficient grouping approach for fault tolerant weight mapping in memristive crossbar array

The ability of resistive memory (ReRAM) to naturally conduct vector–matrix multiplication (VMM), which is the primary operation carried out during the training and inference of neural networks, has caught the interest of researchers. The memristor crossbar is one of the desirable architectures to perform VMM because it offers various benefits over other memory technologies, including in-memory computing, low power, and high density. Direct downloading and chip-on-the-loop approaches are typically used to train ReRAM-based neural networks. In these methods, all weight computations are carried out by a host machine, and the computed weights are downloaded in the crossbar. It has been seen that the network does not deliver the same precision as promised by the host system once the weights have been downloaded. This is because crossbars contain a significant number of faulty memristors and suffer from cell resistance variations because of immature manufacturing technologies. As a result, a cell may not be able to take the exact weight values that the host system generates, and may lead to incorrect inferences. Existing techniques for fault-tolerant mapping either involve network retraining or employ a graph-matching strategy that comes with hardware, power, and latency overheads. In this paper, we propose a mapping method to tolerate the effect of defective memristors. In order to lessen the impact of faulty memristors, the mapping is done in a way that allows network weights to cover up faulty memristors. Further, this work prioritizes the different faults based on the frequency of occurrence. The mapping efficiency is found to increase significantly with low power, area and latency overheads in the proposed approach. Experimental analyses show considerable improvement as compared to state-of-the-art works.

An Adaptable Approach to Fault Tolerance in Cloud Computing

Existing fault tolerance approaches in the cloud are broadly based on replication and checkpointing. Each of these approaches has its advantages and limitations. This paper presents an adaptable fault tolerance method for determining which of the two approaches will be appropriate for the successful execution of a task in the given cloud conditions. The proposed method classifies the failure risk of host machines available for task execution based on their failure history. Subsequently, fuzzy logic is used to determine the appropriate fault tolerance approach by considering a host's failure risk, user-defined task's priority, and level of resource redundancy. Setting a task's priority provides a user with control to solicit a desired fault tolerance level while the availability of resources reflects a cloud provider's capability to offer fault tolerance. Simulation experiments have verified that the proactive selection of a fault-tolerance method increases the number of tasks that complete successfully.

An Efficient ECK-Secured FCM-Based Firefly Optimization Algorithm for Dynamic Resource Sharing in Multi-Tenant SaaS Service Clouds

Multi-tenancy in cloud computing is one of the foremost approaches to share one application instance among different customers and it is generally used by Software as a service (SaaS) providers. The main objective of the proposed work is to minimize the down time of virtual machines essential for resource provisioning using cluster based secure dynamic resource sharing. The proposed secure dynamic resource sharing approach allocates the service tenants to matched Virtual Machines(VMs) and allocates the VMs into physical host machines using the elliptic curve key based firefly optimization approach. First the functional characteristics of service users are grouped into clusters using FCM (Fuzzy C_means clustering) algorithm as tenants. After clustering, the tenant users are checked for authorization with the help of elliptic curve key value. When the users in the tenants are authorized then the grouped services are scheduled dynamically using the firefly optimization algorithm. The result of the work is appraised in terms of resource utilization, execution time, speed, and speedup.

Towards Optimal Triage and Mitigation of Context-Sensitive Cyber Vulnerabilities

Cyber vulnerabilities are security deficiencies in computer and network systems of organizations, which can be exploited by an adversary to cause significant damage. The technology and security personnel resources currently available in organizations to mitigate the vulnerabilities are highly inadequate. As a result, systems routinely remain unpatched, thus making them vulnerable to security breaches from the adversaries. The potential consequences of an exploited vulnerability depend upon the context as well as the severity of the vulnerability, which may differ among networks and organizations. Furthermore, security personnel tend to have varying levels of expertise and technical proficiencies associated with different computer and network devices. There exists a critical need to develop a resource-constrained approach for effectively identifying and mitigating important context-sensitive cyber vulnerabilities. In this article, we develop an advanced analytics and optimization framework to address this need and compare our approach with rule-based methods employed in real-world cybersecurity operations centers, as well as a vulnerability prioritization method from recent literature. First, we propose a machine learning-based vulnerability priority scoring system (VPSS) to calculate the priority scores for each of the vulnerabilities found in an organization’s network and quantify organizational context-based vulnerability exposure. Next, we propose a decision-support system, which consists of a two-step sequential optimization approach. The first model selects the high priority vulnerability instances from the dense report subject to resource constraints, and the second model then optimally allocates them to the security personnel with matching skill types for mitigation. Experiment results conducted using a real-world vulnerability data set show that our approach 1) outperforms both the rule-based methods and the vulnerability prioritization method from literature in prioritizing context-sensitive vulnerabilities, which are found across highly susceptible organizationally relevant host machines, and 2) maximizes the pairs of vulnerability instance type and the respective security analyst skill type for optimal mitigation.

POWER-SUPPLaY: Leaking Sensitive Data From Air-Gapped, Audio-Gapped Systems by Turning the Power Supplies into Speakers

In this article, we introduce a new covert channel that enables attackers to leak data acoustically from highly secured, air-gapped and audio-gapped systems. The proposed malware can exploit the computer power supply unit (PSU) to play sounds and use it as an out-of-band, secondary speaker with limited capabilities. Our method enables the production of audio streams in a frequency band of 0-24 kHz and playing audio files (e.g., WAV) from a computer power supply without the need for audio hardware or speakers. Furthermore, we developed techniques that exploit the multiple CPU cores and their corresponding switching frequencies. It enables the production of multi-channel soundtracks and controlling the bit-depth and volume of sound played through the power supply. Sensitive information (documents, biometric data, encryption keys, etc.) can be covertly transmitted to a nearby receiver (e.g., smartphone). We show that our technique works with various types of systems: PC workstations and servers, as well as embedded systems and IoT devices that have no audio hardware at all. We also show that the attack can operate from an ordinary user mode process, run on virtual machines monitors (VMMs), and doesn’t need special privileges.

Data storage operations protection in mobile cloud computing

Cloud computing is revolutionizing Customer Internet computing and the IT industry by integrating with mobile environments through remote access technologies. This growth involves using smartphones and sensors as data collection nodes interacting with the cloud. However, widespread adoption faces a hurdle in data security concerns, as users are hesitant about entrusting sensitive data to public clouds operated by external providers. To tackle barriers in cloud computing, we urgently require innovative and secure management architectures. This paper introduces a comprehensive framework addressing user concerns, focusing on secure data sharing in the cloud. The framework covers aspects like transport, aggregation, usage, and destruction of sensitive data, emphasizing the semi-trusted nature of the cloud to boost user confidence and address security concerns. One key aspect of the proposed framework is the integration of the Kerberos protocol over the network. This protocol plays a pivotal role in establishing secure and authenticated communication channels, ensuring the confidentiality and integrity of the data being transferred within the cloud infrastructure. Additionally, the paper introduces a user process protection method based on a virtual machine monitor. This method contributes to enhancing overall system security by providing a layer of isolation and protection for user processes operating in the cloud environment. The combined integration of the Kerberos protocol and the virtual machine monitor-based user process protection method forms a cohesive approach towards realizing robust system functionalities. By offering a secure foundation for data management in the cloud, this framework addresses the current limitations and instills a sense of trust among users, fostering a more widespread and confident adoption of cloud computing techniques.

Vertical Pod Autoscaling in Kubernetes for Elastic Container Collaborative燜ramework

Kubernetes is an open-source container management tool which automates container deployment, container load balancing and container(de)scaling, including Horizontal Pod Autoscaler (HPA), Vertical Pod Autoscaler (VPA). HPA enables flawless operation, interactively scaling the number of resource units, or pods, without downtime. Default Resource Metrics, such as CPU and memory use of host machines and pods, are monitored by Kubernetes. Cloud Computing has emerged as a platform for individuals beside the corporate sector. It provides cost-effective infrastructure, platform and software services in a shared environment. On the other hand, the emergence of industry 4.0 brought new challenges for the adaptability and infusion of cloud computing. As the global work environment is adapting constituents of industry 4.0 in terms of robotics, artificial intelligence and IoT devices, it is becoming eminent that one emerging challenge is collaborative schematics. Provision of such autonomous mechanism that can develop, manage and operationalize digital resources like CoBots to perform tasks in a distributed and collaborative cloud environment for optimized utilization of resources, ensuring schedule completion. Collaborative schematics are also linked with Bigdata management produced by large scale industry 4.0 setups. Different use cases and simulation results showed a significant improvement in Pod CPU utilization, latency, and throughput over Kubernetes environment.

Design study on the integrated utilization system of medium temperature waste heat and LNG vaporization cold energy for 200000 DWT LNG-powered vessels

The study object for this work is a 215000 ton very large crude carrier ? liquefied natural gas ? powered vessel, intending to integrate the use of medium temperature flue gas waste heat from the exhaust turbine and cold energy from liquefied natural gas vaporization. It proposes a Rankine cycle power generating system with a two stage booster and three stage lateral nesting following the principle of ?temperature matching, stepped utilization?, taking into account real demands and circumstances of the vessel. The study shows that in this tonnage vessel, through the design and optimization of the stepped utilization scheme, the cold energy released during the vaporization of liquefied natural gas fuel from the ME-GI host machine and the medium temperature waste heat from the exhaust turbine can be fully utilized, and the system structure is tight and simple. After the non-azeotropic mixed working media was optimized and the operational parameters were optimized using genetic algorithm, the system designed in this paper can reach 54.61% exergy efficiency and 187.83 kW net output of power generation. The annual income of the final designed system can reach CNY 1, 133, 240. The capital recovery cycle is expected to be 5.06 years if the system is put into operation.

Enabling P4 Network Telemetry in Edge Micro Data Centers With Kubernetes Orchestration

Integrating computation resources with networking technologies is an hot research topic targeting the optimization of containers deployment on a set of host machines interconnected by a network infrastructure. Particularly, next generation edge nodes will offer significant advantages leveraging on integrated computation resources and networking awareness, enabling configurable, granular and monitorable quality of service to different micro-services, applications and tenants, especially in terms of bounded end-to-end latency. In this regard, SDN is a key technology enabling network telemetry and traffic switching with the granularity of the single traffic flow. However, currently available solutions are based on legacy SDN techniques, not enabling the matching of tunneled traffic, and thus require a tricky integration inside the hosts where containers are deployed. This work considers Kubernetes clusters deployed on next generation edge micro data center platforms and proposes an innovative SDN solution exploiting the P4 technology to gain visibility inside tunnelled traffic exchanged among pods. This way, the integration is achieved at the control plane level through the communication between Kubernetes and the SDN controller. The proposed solution is experimentally validated including a comprehensive framework enabling effective traffic switching and in-band telemetry at pod level. The major paper contributions consist in the design and the development of: (i) the networking applications at SDN control plane level; (ii) the P4 switch pipeline at the data plane level; (iii) the monitoring system used to collect, aggregate and elaborate the telemetry data.

Research on energy‐saving virtual machine migration algorithm for green data center

AbstractThe cloud computing center can dynamically respond to various needs, schedule computing resources, and provide users with convenient IT services. As the demand for cloud computing services continues to increase, the scale of the data center is getting larger and larger, and the problem of high energy consumption of equipment is becoming more and more prominent. Therefore, building a green data center is key to ensuring the development of the technology industry. Virtual machine online migration technology has been widely used in energy consumption management, which plays an important role in the energy‐saving management of large‐scale data centers. Considering the problem of energy consumption in a multi‐data center environment, a cross‐data center virtual machine migration strategy is proposed, EVMA. First, the target data center of the virtual machine migration is determined according to the bandwidth between data centers, and then the overload host and virtual machine selection strategy is determined according to the historical CPU load. The experimental results showed that the algorithm had a good performance in reducing the energy consumption of the data center and ensuring the quality of service.

SeL4 Microkernel for Virtualization Use-Cases: Potential Directions towards a Standard VMM

Virtualization plays an essential role in providing security to computational systems by isolating execution environments. Many software solutions, called hypervisors, have been proposed to provide virtualization capabilities. However, only a few were designed for being deployed at the edge of the network in devices with fewer computation resources when compared with servers in the Cloud. Among the few lightweight software that can play the hypervisor role, seL4 stands out by providing a small Trusted Computing Base and formally verified components, enhancing its security. Despite today being more than a decade with seL4 microkernel technology, its existing userland and tools are still scarce and not very mature. Over the last few years, the main effort has been to increase the maturity of the kernel itself, and not the tools and applications that can be hosted on top. Therefore, it currently lacks proper support for a full-featured userland Virtual Machine Monitor, and the existing one is quite fragmented. This article discusses the potential directions to a standard VMM by presenting our view of design principles and the feature set needed. This article does not intend to define a standard VMM, we intend to instigate this discussion through the seL4 community.

Secure Industrial IoT Task Containerization With Deadline Constraint: A Stackelberg Game Approach

Industrial IoT (IIoT) accomplishes digital manufacturing that incorporates various devices, simulators, and tools with multiple sensors. These sensors provide sufficient data to coordinate and monitor industrial systems. IIoT requires dedicated supporting devices for an application to execute a given task in maximum allowable response time. The requirement of dedicated devices increases system cost. Task containerization is a process of exploiting available resources of the host machines to meet out varying demands of different applications. It avoids the additional cost to buy dedicated IIoT devices while adding new applications. This article proposes an approach to securely process a given IIoT task within an allowable response time. We use the game theory approach to estimate the fractions of the task to be containerized on the machines. Next, the estimated fractions for each machine maximize the system utility. Finally, we illustrate the experimental results to validate the performance of the proposed approach.

Performance Comparison of Xen AND Hyper-V in Cloud Computing While Using Cryptosystems

Abstract Cloud computing is internet-distributed computing model transferring processes from personal computers or servers to cloud servers. Nowadays, security and performance of cloud computing is considered challenging for both users and cloud service providers. Securing data on cloud computing servers will ensures privacy, confidentiality, integrity, and availability. Using cryptographic techniques is one of the major methods to ensure the data security while storing and transmission. Hypervisor in a cloud is a software that provides abstraction and called virtual machine monitor. Hyper-V and Xen are two different types of hypervisors. In this paper, eight different types of cryptographic algorithms are deployed by using the two hypervisors with instances, to measure the hypervisors performance while encryption and decryption. CPU utilization and response time are measured while encryption and decryption are having different data types and sizes. Results show that Xen is better than Hyper-V in most results on average at 15% and 6.1% for time duration and CPU utilization respectively. Keywords: Cloud Computing, Virtualization, Hypervisors, Xen, Hyper-V, Cryptographic Algorithm

Using Docker virtual containers to launch services

Docker is a virtualization tool that allows developers and system administrators to run applications and services in containers isolated from each other and from the guest operating system. Docker containers, unlike other virtualization tools, work directly with the host machine hardware, which is good for system resource consumption. Containers run from prebuilt images. The ability to collect all dependencies and application data in an image allows you not to worry about compatibility with the systems in which the container will run. The independence of containers from each other allows you to run entire groups of related services, in which a change of version or data in one of the containers does not affect the functioning of the rest of the group.

Green Based Software Engineering Approach for Sustainable Protocol

Abstract: A sturdy green software model is the prominent necessity for various governments fields and banking areas for Energy utilization processing. The model should give all the Energy consumption aspects namely power consumption, utilization of Memory, (Co2) Carbon dioxide release which is related to development of software and make it energy efficient. For Software Development Life Cycle phases this research paper proposes a new green software model for the virtual host machine, which is the base for all the Green based Software Development process.

Virtualizing GPU direct packet I/O on commodity Ethernet to accelerate GPU-NFV

Network functions (NFs) play an important role in the rapid and dynamic deployment of various services. Network function virtualization (NFV) on commodity servers is becoming popular; and is associated with benefits in terms of cost, elasticity, and liveness, instead of dedicated hardware middleboxes. In this recent trend, graphics processing units (GPUs) have been shown to be highly suitable for NF acceleration, owing to their high parallelism and memory bandwidth. This paper proposes a new GPU-NFV architecture called Janus to accelerate NFs in virtualized environments. Janus is an extension of a previous study conducted by the present authors and involves GPU direct packet input/output (I/O) on commodity Ethernet, to a virtualization environment. Janus uses single-root I/O virtualization (SR-IOV) to enable high-performance NIC sharing between the host and guest virtual machine running GPU direct packet I/O. Internally, the received packets for GPU-NFV are transferred to the GPU memory directly through a combination of SR-IOV and peer-to-peer DMA. The evaluation results show that Janus’ GPU-NFV performance is up to 2.3x higher than that of the state-of-the-art data-plane development kit (DPDK).

Removal of Image Steganography using Generative Adversarial Network

Secret messages can be concealed in ordinary media like audio, video and images. This is called as Steganography. Steganography is used by cyber attackers to send malicious content that could harm victims. Digital steganography, or steganography in photographs, is exceedingly difficult to detect. The detection of steganography in images, has been investigated in thoroughly by a variety of parties. The use of steganographic techniques to send more malware to a compromised host in order to undertake different post-exploitation operations that affect the exploited system. Many steganalysis algorithms, on the other hand, are limited to working with a subset of all potential photos in the wild or have a high false positive rate. As a result, barring any suspected image becomes an arbitrary policy. Filtering questionable photos before they are received by the host machine is a more practical policy. In this paper, a Generative Adversarial Network based model is proposed that may be optimized to delete steganographic content while maintaining the original image's perceptual quality. For removing steganography from photos while keeping the maximum visual image quality, a model is built utilizing a combination of Generative Adversarial Network (GAN) and Image Processing. In the future, utilizing a generator to synthesize a picture will become more popular, and detection of steganography in images will become very difficult. In comparison to other models that have been addressed further, the proposed model is able to give a mean square error of 5.4204 between the generated image and the cover image, as well as better outcomes based on several metrics. As a result, a GAN-based steganography eradication method will aid in this endeavor.

Research Progress in Porcine Reproductive and Respiratory Syndrome Virus-Host Protein Interactions.

Simple SummaryPRRSV–host protein interactions are of great significance in finding new antiviral targets. We summarize the effects of all the PRRSV structural and nonstructural protein interactions with host proteins and on the protein interactions of the virus itself. These results provide a theoretical foundation for the therapeutic targeting of the intrinsic immune factors of the host protein and exploring viral replication mechanisms.Porcine reproductive and respiratory syndrome (PRRS) is a highly contagious disease caused by porcine reproductive and respiratory syndrome virus (PRRSV), which has been regarded as a persistent challenge for the pig industry in many countries. PRRSV is internalized into host cells by the interaction between PRRSV proteins and cellular receptors. When the virus invades the cells, the host antiviral immune system is quickly activated to suppress the replication of the viruses. To retain fitness and host adaptation, various viruses have evolved multiple elegant strategies to manipulate the host machine and circumvent against the host antiviral responses. Therefore, identification of virus–host interactions is critical for understanding the host defense against viral infections and the pathogenesis of the viral infectious diseases. Most viruses, including PRRSV, interact with host proteins during infection. On the one hand, such interaction promotes the virus from escaping the host immune system to complete its replication. On the other hand, the interactions regulate the host cell immune response to inhibit viral infections. As common antiviral drugs become increasingly inefficient under the pressure of viral selectivity, therapeutic agents targeting the intrinsic immune factors of the host protein are more promising because the host protein has a lower probability of mutation under drug-mediated selective pressure. This review elaborates on the virus–host interactions during PRRSV infection to summarize the pathogenic mechanisms of PRRSV, and we hope this can provide insights for designing effective vaccines or drugs to prevent and control the spread of PRRS.

An AI powered system call analysis with bag of word approaches for the detection of intrusions and malware in Australian Defence Force Academy and virtual machine monitor malware attack data set

AbstractThis study propose the use of AI enabled machine learning algorithms with the Bag‐of‐Word (BoW) methods for the detection of intrusions by analysing the system call patterns. Host based Intrusion Detection System can make use of system call patterns to differentiate between normal and anomalous program behaviours. First, the system call patterns are pre‐processed with different approaches like BoW, BoW with Boolean value, BoW with Probability value and BoW with TF‐IDF. Next machine learning algorithms are used to evaluate the performance of classifier models. We used J48 (C4.5), Random Forrest, RIPPER, KNN, SVM, and NaiveBayes ML algorithms. This process was carried out on ADFA‐LD and on our proposed virtual machine monitor (VMM) malware attack data set for analysis. The proposed work is evaluated based on detection accuracy and false alarm rate metrics. Random Forrest algorithm performs better compared with other ML algorithms in terms of intrusion detection accuracy and false alarm rate on ADFA and VMM malware data set. The proposed data set provide better results compared with ADFA‐LD analysed using ML algorithms. The classifier model trained with ADFA and VMM malware system call data sets may do predictive analytics in detecting security issues for Industry 4.0 systems.