Hypervisor Research Articles

Integrated Sensing and Computing for Wearable Human Activity Recognition with MEMS IMU and BLE Network

Abstract The miniature sensor devices and power-efficient Body Area Networks (BANs) for Human Activity Recognition (HAR) have gained increasing interest in different fields, including Daily Life Assistants (DLAs), medical treatment, sports analysis, etc. The HAR systems normally collect data with wearable sensors and implement the computational tasks with a host machine, where real-time transmission and processing of sensor data raise a challenge for both the network and the host machine. This investigation focuses on the hardware/software co-design for optimized sensing and computing of wearable HAR sensor networks. The contributions include (1) design of a miniature wearable sensor node integrating a Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS IMU) with a Bluetooth Low Energy (BLE) in-built Micro-Control Unit (MCU) for unobtrusive wearable sensing; (2) task-centric optimization of the computation by shifting data pre-processing and feature extraction to sensor nodes for in-situ computing, which reduces data transmission and relieves the load of the host machine; (3) optimization and evaluation of classification algorithms Particle Swarm Optimization-based Support Vector Machine (PSO-SVM) and Cross Validation-based K-Nearest Neighbors (CV-KNN) for HAR with the presented techniques. Finally, experimental studies were conducted with two sensor nodes worn on the wrist and elbow to verify the effectiveness of the recognition of 10 virtual handwriting activities, where 10 recruited participants each repeated an activity 5 times. The results demonstrate that the proposed system can implement HAR tasks effectively with an accuracy of 99.20 %.

Cross-VM Network Channel Attacks and Countermeasures Within Cloud Computing Environments

Cloud providers attempt to maintain the highest levels of isolation between Virtual Machines (VMs) and inter-user processes to keep co-located VMs and processes separate. This logical isolation creates an internal virtual network to separate VMs co-residing within a shared physical network. However, as co-residing VMs share their underlying VMM (Virtual Machine Monitor), virtual network, and hardware are susceptible to cross VM attacks. It is possible for a malicious VM to potentially access or control other VMs through network connections, shared memory, other shared resources, or by gaining the privilege level of its non-root machine. This research presents a two novel zero-day cross-VM network channel attacks. In the first attack, a malicious VM can redirect the network traffic of target VMs to a specific destination by impersonating the Virtual Network Interface Controller (VNIC). The malicious VM can extract the decrypted information from target VMs by using open source decryption tools such as Aircrack. The second contribution of this research is a privilege escalation attack in a cross VM cloud environment with Xen hypervisor. An adversary having limited privileges rights may execute Return-Oriented Programming (ROP), establish a connection with the root domain by exploiting the network channel, and acquiring the tool stack (root domain) which it is not authorized to access directly. Countermeasures against this attacks are also presented

PYTHON FOR WEB DEVELOPMENT

With evolution of web, several competitive languages such as Java, PHP, Python, Ruby are catching the attention of the developers. Recently Python has emerged as a popular and the preferred web programming language, because of its simplicity to code and ease of learning. Being a flexible language, it offers fast development of web-based applications. It offers development using CGI and WSGI. Web development in Python is aided by the powerful frameworks such as Django, web2py, Pyramid, and Flask that Python supports. Thus, Python promises to emerge as one of the preferred choice language for web applications. Web is a rapidly growing repository of resources. Internet is used as a medium for accessing these resources. Web architecture mainly comprises of two entities, namely client and server. Web client is an application (browser) on host machine that urges these resources, and web server is a machine on web that is responsible for fulfilling the request issued by client. Hypertext Transfer Protocol (HTTP) is the most popular protocol used by client and server for web communication. In a static web, browser issues HTTP request to the HTTP server, which searches for the requisite resource in its database and returns it as an HTTP response. To avoid any compatibility issues, every request issued by browser is in form of a URL (Uniform Resource Locator). The URL protocol defines the rules for communication between client and server. It comprise of host name (IP address) which helps in identifying the server system on the web, port number which determines the service (for example, FTP, email service) on the server that should respond to request, and the access path of the resource (web page) on server. The web where responses are already stored in server database in form of static web pages is termed static web. However, response returned by server to the client may be generated on the fly depending upon the request of the client. Web applications offer several benefits over traditional applications that are required to be installed at each host computer that wishes to use them. Web applications do not incur publishing and distribution costs as opposed to traditional applications where the software (applications) were published using CD’s and distributed. They need not be installed at each client host; rather they are placed at a central server and accessed by large number of clients. Since a web application is managed centrally, application updates and data backups can be performed easily. The web applications are easily accessible irrespective of the boundaries of space and time. Since, they are accessed through browser, the platform accessing them is not an issue, and thus they provide cross-platform compatibility. Inspite of above- mentioned advantages, web applications have a few limitations. Internet connectivity and server availability is required for accessing web application through browser. However, accessing them through Internet my take more time as compared to applications installed on host systems. Also, web applications require compatible web browsers. Since they are deployed on web, they are vulnerable to several Internet attacks. Web programming using CGI and WSGI requires building web applications from the scratch by using Python standard libraries. Python provides with web frameworks in the form of packages/ modules that simplify the task of writing application programs. These frameworks lighten tedious job of developers. They support server and client side programming by providing support for several activities such as request interpretation (getting form parameters, handling cookies and sessions), response generation (generating data in HTML or other format such as pdf, excel), and storing data. The web frameworks are further categorized as full- stack and non-full-stack frameworks. Full-stack frameworks provide components for every phase of programming in contrast to non-full-stack frameworks. All the frameworks include templates and data persistence as key ingredients for constructing web. Templates are used to avoid complex code that results when HTML and Python code is mixed in a single file. Templates are HTML files with placeholder for the data depending upon user input. Data persistence deals with storing and retrieving data and maintaining consistency. The data can be stored and maintained using plain text files, relational database engines such as MYSQL, Oracle, or some object-oriented databases. The web framework providing support for WSGI should be preferred. This makes deploying an application easier.

Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines

Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines

Optimisation of energy consumption in cloud video surveillance centre based on monitoring and placement of virtual machines

Cloud computing is one of the most popular computational models, which requires plenty of physical devices where services are provided based on user demand. A majority of data centres need plenty of energy consumption which has become a challenge in recent years. Regarding cloud video surveillance - as a contemporary research field of cloud computing and big data, the service, due to the high demand for monitoring remote places, continually consumes surplus energy to process the high-volume data. This study considers the importance of energy consumption in cloud video surveillance, and it has been tried to increase the efficiency of servers concerning energy usage. The proposed method employs virtual machine placement in two steps, including monitoring and placement, to reduce energy consumption and increase the efficiency of servers. Implementation results in Cloudsim showed that it reduces energy consumption and increases resource efficiency.

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.

Efficient Resources Allocation and Energy Reduction with Virtual Machines for Cloud Computing

Cloud computing is an internet provisioned method for sharing the resources on demand by network management, storage, services, applications and the serves that necessitate management optimal effort. VMM (virtual machine migration) plays a major role in enhancing the resource utilization, application isolation, processing nodes, fault tolerance in VMs for enhancing nodes portability and for maximizing the efficiency of physical server. For balancing the clouds with resources for the enhanced performance, varied users are served with application deployment in the cloud environment is considered as the major task. The user can rent or request the resources when it becomes significant. The emphasis of this paper is on different energy VM energy efficient module as per machine learning methods. While allocating the VMs to the host machines, MBFD (Modified Best Fit Decreasing) is considered and the classification of host machine capability such as overloaded, normal loaded and underloaded is executed according to SVM (Support vector machine). SVM is utilized as a classifier for analyzing the MBFD algorithm and for the classification of the host as per the job properties. In this procedure, the numbers of jobs that are not allocated are examined via simulation which is computed by means of time consumption, energy consumption and a total number of migrations.

Comparison of classical machine learning algorithms in the task of handwritten digits classification

The purpose of this paper is to compare classical machine learning algorithms for handwritten number classification. The following algorithms were chosen for comparison: Logistic Regression, SVM, Decision Tree, Random Forest and k-NN. MNIST handwritten digit database is used in the task of training and testing the above algorithms. The dataset consists of 70,000 images of numbers from 0 to 9. The algorithms are compared considering such criteria as the learning speed, prediction construction speed, host machine load, and classification accuracy. Each algorithm went through the training and testing phases 100 times, with the desired KPIs retained at each iteration. The results were averaged to reach reliable outcomes.

Smart Architecture Energy Management through Dynamic Bin-Packing Algorithms on Cloud

Smart Home Architecture is suitable for progressive and symmetric urbanization. Data being generated in smart home appliances using internet of things should be stored in cloud where computing resources can analyze the data and generate the decisive pattern within no time. This additional requirement of storage, majorly, comprising of unfiltered data escalates requirement of host machines which carries with itself extra overhead of energy consumption; thus, extra cost has to be beard by service providers. Various static algorithms are already proposed to improve energy management of cloud data centers by reducing number of active bins. These algorithms are not able to cater to the needs of present heterogeneous requests generated in cloud machines by people of diversified work environment with adhering to the requirements of quality parameters. Therefore, the paper has proposed and implemented dynamic bin-packing approaches for smart architecture that can significantly reduce energy consumption without compromising upon makespan, resource utilization and Quality of Service (QoS) parameters. The novelty of the proposed dynamic approaches in comparison to the existing static approaches is that the proposed approach dynamically creates and dissolves virtual machines as per incoming and completed requests which is a dire need of present computing paradigms via attachment of time-frame with each virtual machine. The simulations have been performed on JAVA platform and dynamic energy utilized-best fit decreasing bin packing technique has produced better results in maximum runs.

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.

Hardening the Security of Multi-Access Edge Computing through Bio-Inspired VM Introspection

The extreme bandwidth and performance of 5G mobile networks changes the way we develop and utilize digital services. Within a few years, 5G will not only touch technology and applications, but dramatically change the economy, our society and individual life. One of the emerging technologies that enables the evolution to 5G by bringing cloud capabilities near to the end users is Edge Computing or also known as Multi-Access Edge Computing (MEC) that will become pertinent towards the evolution of 5G. This evolution also entails growth in the threat landscape and increase privacy in concerns at different application areas, hence security and privacy plays a central role in the evolution towards 5G. Since MEC application instantiated in the virtualized infrastructure, in this paper we present a distributed application that aims to constantly introspect multiple virtual machines (VMs) in order to detect malicious activities based on their anomalous behavior. Once suspicious processes detected, our IDS in real-time notifies system administrator about the potential threat. Developed software is able to detect keyloggers, rootkits, trojans, process hiding and other intrusion artifacts via agent-less operation, by operating remotely or directly from the host machine. Remote memory introspection means no software to install, no notice to malware to evacuate or destroy data. Experimental results of remote VMI on more than 50 different malicious code demonstrate average anomaly detection rate close to 97%. We have established wide testbed environment connecting networks of two universities Kyushu Institute of Technology and The City College of New York through secure GRE tunnel. Conducted experiments on this testbed deliver high response time of the proposed system.

Virtualization-Based Digitization of a Retail Store: An Enhanced Implementation of Digital Transformation

The rise of neoteric technologies like Machine Learning, the Internet of Things, Cloud Services, etc has affected the life of a common man at various levels. Irrespective of the size or domain, almost all companies are now incorporating digitization to various degrees and thus progressing towards a new business model with little or no significance to geographical and physical barriers. This shift from traditional store models to automated entities is referred to as Digital Transformation. It is the simplified way of outlining how digital technologies are transforming and automating business operations across all organizations irrespective of their domain. This digital revolution relies on a whole range of machinery, networks, services, and operations to expand their power of communication, thus ensuring seamless integration with the technologies. At this juncture, there would be many challenges in both technical and non-technical aspects. To facilitate successful automation by resolving those issues, using the concept of virtualization can be very helpful. Virtualization is the process of creating a virtual instance of hardware resources like virtual applications, servers, or storage by logically separating them from the hardware. It enables multiple applications or operations to gain access to the hardware resources/ software resources of the host machine. In a sense, virtualization is always at the center of all this revolution providing a rock-solid foundation. For example, when digitizing an organization, machine learning algorithms are applied to the IoT data in addition to the organizational data. Given the huge size of data, companies adapting to this automation rely on cloud services for data management because of the reliability it provides. This issue is solved using the Edge Computing concept which is an advanced implementation of Virtualization. In this paper, we try to discuss such challenges and try to understand how virtualization can be useful in solving them. This can be exemplified using a hypothetical digitalization in a retail store scenario.

Optimal resource allocation with deep reinforcement learning and greedy adaptive firefly algorithm in cloud computing

AbstractThe allocation of resources is a foremost demanding task in cloud computing. Scholars are yet finding it difficult to allocate appropriate resources to the set of user tasks. Our objective is to provide a platform that optimizes a dynamic resource allocation scheme. Multi‐agent deep reinforcement learning‐based greedy adaptive firefly algorithm (MAD‐GAF) has been proposed herein includes both the resource management and allocation techniques. This chooses the best Quality of Service (QoS) measured host for a group of tasks efficiently and subsequently minimize the task execution time. The proposed cloud brokering architecture comprises a multi‐agent system, the cloud provider and the user. Initially, deep reinforcement learning has been built to recreate the request of cloud customers by forecasting the value of unused resources. Then the recreated customer request is forwarded to the global broker agent, which maps the virtual machine (VM) to the most appropriate cluster of physical machine (PM). The virtual machine monitor (VMM) selects VMs by managing and accessing the physical resources. The global utility agent allocates VMs using the GAF optimization algorithm, which specifies the best QoS measured host to decrease the whole tasks' average response time, thus optimizing resource allocation compared to the current approaches.

Dynamic Resource Allocation Strategy in Mobile Edge Cloud Computing Environment

With the development of the mobile Internet, smart mobile terminals have become an indispensable tool for people's lives and mobile applications are becoming more and more powerful. This research mainly discusses the dynamic resource allocation strategy of the mobile edge cloud computing environment. The physical resource layer in the network model is responsible for providing specific resources that are actually available, such as hardware resources, computing resources, storage resources, mainly including base stations, mobile edge computing servers, spectrum, power, and other communications of different infrastructure vendor basic components of the system. The functions of the virtual machine monitor include resource virtualization and resource management. As an important component of wireless network virtualization, virtual machine monitors are usually deployed in physical base stations to provide physical resources and to consider the connection between the virtual machine stations. The business of the business cache model is an application that is requested by users running on the mobile edge computing server or cloud at the base station. The computing task scheduling in the mobile edge environment can be classified as a wireless interaction model. This model captures the user throughput in cellular network interaction. The physical layer channel access strategy (CDMA) allows all mobile users to efficiently share the same spectrum resources at the same time. When the preference coefficient for task energy consumption varies between 0.35–0.55 and 0.65–1, the superior range of maximum system efficiency achieved by RAOM accounts for 55% of the entire range. This research contributes to the reasonable allocation of resources, and the mobile edge computing model improves the fairness of users with a lower transmission cost.

A Delay-Based Machine Learning Model for DMA Attack Mitigation

Direct Memory Access (DMA) is a state-of-the-art technique to optimize the speed of memory access and to efficiently use processing power during data transfers between the main system and a peripheral device. However, this advanced feature opens security vulnerabilities of access compromise and to manipulate the main memory of the victim host machine. The paper outlines a lightweight process that creates resilience against DMA attacks minimal modification to the configuration of the DMA protocol. The proposed scheme performs device identification of the trusted PCIe devices that have DMA capabilities and constructs a database of profiling time to authenticate the trusted devices before they can access the system. The results show that the proposed scheme generates a unique identifier for trusted devices and authenticates the devices. Furthermore, a machine learning–based real-time authentication scheme is proposed that enables runtime authentication and share the results of the time required for training and respective accuracy.

Performance Analysis of Virtual Machine in Cloud Architecture

Cloud computing is a technique for storing and processing data that makes use of a network of remote servers. Cloud computing is gaining popularity due to its vast storage capacity, ease of access, and diverse variety of services. When cloud computing advanced and technologies such as virtual machines appeared, virtualization entered the scene. When customers’ computing demands for storage and servers increased, however, virtual machines were unable to match those expectations due to scalability and resource allocation limits. As a consequence, containerization became a reality. Containerization is the process of packaging software code along with all of its essential components, including frameworks, libraries, and other dependencies, such that they may be separated or separated in their own container. The program operating in containers may execute reliably in any environment or infrastructure. Containers provide OS-level virtualization, which reduces the computational load on the host machine and enables programs to run much faster and more reliably. Performance analysis is very important in comparing the throughput of both VM-based and Container-based designs. To analyze it same web application is running in both the designs. CPU usage and RAM usage in both designs were compared. Results obtained are tabulated and a Proper conclusion has been given.

A REVIEW ON REMOTE VIRTUAL MACHINE DETECTION AND MASKING FROM THE BROWSER ECOSYSTEM

With the development of computer science, the detection of virtual machines and the masking of the virtual machine monitor is an important topic in academia. However, limited research has been conducted in the field of remote detection of virtual machines. Remote virtual machine detection is used in lots of fields such as cheating detection, sandboxing, and in phishing websites. In this paper, we review the existing methods of detection of virtual machines remotely from with the access limited to the browser ecosystem of the client side. We review 7 different attacks and verify their usability in different setups comprising combinations of multiple host OS and browser ecosystems. We also provide methods to hide the presence of virtual machines in these particular attack strategies. The goal of this paper is to encourage interest in this field.

Secure VM placement analysis against co-location based attack in cloud

Cloud computing is most popular platform for IT industries provides numerous benefits to cloud computing users. Day by day increasing the number of users on cloud so security is a most important issue. Virtual machine co-residency and resource sharing rise chance to leak sensitive information, lack of restrictions for placement of virtual machine on same host machine, increase the chance for an attacker to conducting co-location based attack on neighbor virtual machine that running on same host machine. Without security scheme placement of VM in cloud environment may increase chance to thieve confidential information by the attackers. In this paper, we analyze the existing VM placement scheme against cross VM side channel attack. We categorized side channel attack (SCA) according to access of hardware and way to extract the confidential information. We have also done comparative study of VM placement scheme on the basis of previous history of user, number of VM placed and average running time of VM on cloud.

VM placement and cache based side channel attack at computational level in cloud computing : A Survey

There are different issues that need to focus in respect of security and privacy. One of the most security issue at computational level implementation of secure virtualization is cloud computing. In this, we explain virtualization security and their existing defense system at computational level. In computational level need to increase security of cloud and categorized the virtual machine level security challenges that are faced by cloud users at computational level in cloud computing. Day by day cloud attribute are introducing new challenges in cloud environment. In this paper, we focus on cloud security at computational level. In the day scenario to secure a cloud data on shared environment is the important task for IT industries. In virtual environment need to focus on secure VM placement on the host machine that is running on cloud. This paper demonstrates lack of VM placement and side channel attack (SCA). Analysis of different type of scheme is done and comparative study of game theory based, graph theory based and bin packing VM placement scheme. Also we discussed virtualization in cloud computing and their security aspects and comparative study of different type side channel attack (SCA).

SPEChecker: Checking the feasibility of Slow-port-exhaustion attack on various hypervisors

Nowadays, with the emergence of IoT technologies, the number of devices connected to the Internet is rapidly increasing. As a result, Network Address Translation (NAT) mechanism is widely used to let multiple devices within a private network make use of a fewer number of public IP addresses. In this research, we focus on Port Address Translation (PAT), an extension of the NAT mechanism that can translate both the IP address and the port number of a TCP/IP packet. An apparent downside of PAT is the fact that when all of the ports are used, no more new outbound connections could be made from the local addresses. In 2018, we introduced Slow-port-exhaustion DoS Attack on a virtual network, a novel type of DoS Attack that exploits some flaws of the TCP protocol and the limitation of the PAT mechanism. In this attack, a compromised internal virtual machine with a low amount of attack bandwidth can occupy the host machine’s ports for a long time and therefore makes other machines on the same virtual network could not connect to the external network. In this paper, we present SPEChecker, a testing tool that can be used to check the feasibility of the Slow-port-exhaustion attack on virtual networks. In the analysis, we also explore a case of a TCP Host that could support this attack. Our testing results also show the potential of SPEChecker in evaluating virtual network security.