Conventional Virtual Machine Research Articles

Multi‐objective based container placement strategy in CaaS

AbstractIn contrast to a conventional virtual machine (VM), a container is a lightweight virtualization technology. Containers are becoming a prominent technology for cloud services because of their portable, scalable, and flexible deployments, especially in the Internet of Things (IoT), smart devices, and fog and edge computing. It is a type of operating system‐level virtualization in which the kernel allows multiple isolated containers to run independently. Container placement (CP) is a nontrivial problem in Container‐as‐a‐Service (CaaS). CP is mapping to a container over virtual machines (VMs) to execute an application. Designing an efficient CP strategy is complex due to several intertwined challenges. These challenges arise from a diverse spectrum of computing resources, like on‐demand and unpredictable fluctuations of IT resources by multiple tenants. In this article, we propose a modified sum‐based container placement algorithm called a multi‐objective optimization‐based container placement algorithm (MSBCPA). In the proposed algorithm, we have considered two metrics: makespan and monetary costs for optimizing available IT resources. We have conducted comprehensive simulation experiments to validate the effectiveness of the proposed algorithm over the CloudSim 4.0 simulator. The proposed optimization algorithm (MSBCPA) aims to minimize the makespan and the execution monetary costs simultaneously. In the simulation, we found that the execution cost and energy consumption cost reduce by 20% to 30% and achieve the best possible cost‐makespan trade‐offs compared to competing algorithms.

An in-Depth Study of Virtual Machines, Docker Technology, and Performance

Inventors and directors now have access to a robust platform for organizing, disseminating, and carrying out operations thanks to Docker, which has emerged as a leading containerization technology. This paper gives an extensive examination of Docker's innovation, variables, execution, and relative benefits over conventional virtual machines. We delve into Docker's architecture, features, security considerations, and future plans through a comprehensive literature review. The discoveries propose that while Docker offers huge advantages with regards to adequacy and adaptability, cautious thought of safety, comity, and execution enhancement is fundamental for its fruitful execution in various environmental factors.

Live virtual machine migration: A survey, research challenges, and future directions

In recent years, cloud computing has emerged as a promising paradigm providing various resources in the Cloud Data Center (CDC), including computations, storage, and even platform as a service. The virtualization technology plays a pivotal role in CDC resources management and provides an enchanting feature of virtual machine (VM) migration which provides several benefits in terms of VM scheduling, fault tolerance, load balancing, energy efficiency, power management, and security. To achieve efficient VM migration, a plethora of VM migration schemes have been proposed in the literature aiming to serve the quality of service-driven user requirements. This paper surveys the most recent and state-of-the-art Artificial Intelligence (AI) based and conventional load balancing, energy-aware, SLA aware, and network-aware live VM migration schemes. Through an extensive literature review, this paper investigates the most critical aspects of conventional and AI-driven live VM migration schemes. Finally, a few open research challenges that require further consideration from the research community are highlighted.

Secure Computing Enclaves Using FPGAs

A new scheme for securing users' data and applications in public clouds and data centers using Field Programmable Gate Arrays (FPGAs) has been developed. This scheme incorporates all necessary protocols, hardware, and software components to provide protection against many known potential attacks including internal attacks. It achieves perfect forward secrecy, provides FPGA authentication and integrity checks, and securely establishes a symmetric session key between the user and the FPGA. A complete prototype has been implemented to show the feasibility of the proposed scheme with current FPGAs. Experimental results showed that an FPGA-based compute node can be set up in a cloud in 3.36s; 12.6 times faster than booting a medium-size conventional Virtual Machine (VM) on the same cloud. Based on the average global Internet speed, the time it takes to set up the FPGA-based machine from anywhere in the world was estimated to be 15s. Also, running an experimental secure image processing application on the FPGA took 50 percent less time than running the same application on a conventional state-of-the art processor (without a secure container).

KEIDS: Kubernetes-Based Energy and Interference Driven Scheduler for Industrial IoT in Edge-Cloud Ecosystem

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

Cloud-Based FPGA Custom Computing Machines for Streaming Applications

A novel platform for launching and using field-programmable gate arrays (FPFA) custom computing machines (CCMs) in clouds and data centers is proposed. Based on a developed FPGA virtualization scheme, it allows users to create independent computing services on network-attached standalone FPGAs. The interface of the virtual FPGA (vFPGA)-based CCM is automatically generated by a virtualization layer and based on the user’s specifications. An FPGA hypervisor has been developed that can be easily integrated with any cloud management tool. It allows the users to launch/use/tear down vFPGA-based CCMs in a similar manner to conventional virtual machines (VMs). A complete prototype of the proposed platform has been realized and tested with a streamed image processing application. Its performance was 3–4 $\times$ and ~1.4–2.4 $\times$ times better than an SW implementation on a VM and a powerful server, respectively. Compared with other platforms for FPGA attachment to a cloud or datacenter, the proposed platform has relatively low overhead in terms of FPGA resources while providing the highest level of abstraction and virtualization.

SnapMig: Accelerating VM Live Storage Migration by Leveraging the Existing VM Snapshots in the Cloud

Virtual Machine (VM) live storage migration is becoming increasingly important and indispensable in the current cloud data centers, for the purposes of load balance, hardware maintenance and system upgrade. Nevertheless, conventional VM migration approaches induce significant extra storage and network traffic to the source server that is already heavily loaded or scheduled for upgrade or repair. As a result, both the VM performance perceived by the application/user and the migration performance are degraded significantly. In this paper, we aim to address this problem by proposing a novel scheme, called SnapMig , to improve the VM live storage migration efficiency and eliminate its performance impact on user applications at the source server by effectively leveraging the existing VM snapshots in backup servers. By outsourcing the task of transferring VM base image and snapshots to the destination server to backup servers, the source server only needs to migrate the latest state changes to the destination server, leading to simultaneous improvement on VM performance, migration time and multiple-VM migration efficiency. Our lightweight prototype implementation of the SnapMig scheme demonstrates that, compared with the state-of-the-art approaches, SnagMig can significantly reduce the migration time and improve the source-server VM performance at the same time. Moreover, the performance improvement provided by SnapMig becomes much more pronounced with multiple concurrent VM migrations.

MemFlex: A Shared Memory Swapper for High Performance VM Execution

Ballooning is a popular solution for dynamic memory balancing. However, existing solutions may perform poorly in the presence of heavy guest swapping. Furthermore, when the host has sufficient free memory, guest virtual machines (VMs) under memory pressure is not be able to use it in a timely fashion. Even after the guest VM has been recharged with sufficient memory via ballooning, the applications running on the VM are unable to utilize the free memory in guest VM to quickly recover from the severe performance degradation. To address these problems, we present MemFlex , a shared memory swapper for improving guest swapping performance in virtualized environment with three novel features: (1) MemFlex effectively utilizes host idle memory by redirecting the VM swapping traffic to the host-guest shared memory area. (2) MemFlex provides a hybrid memory swapping model, which treats a fast but small shared memory swap partition as the primary swap area whenever it is possible, and smoothly transits to the conventional disk-based VM swapping on demand. (3) Upon ballooned with sufficient VM memory, MemFlex provides a fast swap-in optimization, which enables the VM to proactively swap in the pages from the shared memory using an efficient batch implementation. Instead of relying on costly page faults, this optimization offers just-in-time performance recovery by enabling the memory intensive applications to quickly regain their runtime momentum. Performance evaluation results are presented to demonstrate the effectiveness of MemFlex when compared with existing swapping approaches.

Joint routing and mobile VM selection algorithm in multihop C‐RAN networks

SummaryBy combining the cloud radio access network (C‐RAN) and mobile cloud computing, the communication and computation cooperation is convenient to implement, and the mobile operators are able to provide better service for mobile users as well as decrease their construction funds. However, owing to the deficient bandwidths and far distances, some mobile devices are not able to access mobile cloud through C‐RAN directly. In this paper, we propose a multihop C‐RAN network to access mobile cloud that organizes the user equipment (UE) in the C‐RAN into self‐organized network, regarding UEs as relay routers to access the mobile cloud. Under the proposed architecture, the UEs far from remote radio heads are able to access C‐RAN by ad hoc network while the UEs near the remote radio heads will access C‐RAN directly. Besides, considering the benefit of both operators and mobile users, we propose a joint routing and mobile virtual machine (VM) selection algorithm based on the proposed multihop C‐RAN networks where the route is selected for task data transmission, and the VM in the mobile cloud is selected for task execution, which minimizes the total energy cost under the premise of satisfying the task response time required by user. The simulation result shows that the proposed algorithm outperforms conventional separate routing and VM selection on energy saving.

A review of virtual machine (VM) resource scheduling algorithms in cloud computing environment

Cloud Computing (CC) is the latest cost-effective computing model with the purpose of distributes on-demand access to services on pay-per-usage basis. The CC technology offers many benefits to together cloud customers and service providers. The major objective of this work is to offer a new cloud services with the purpose of maximizes the computational cost profit via the use of its datacenter resources during Virtualization Machine (VM) technology and effectual scheduling algorithms with the limits of Service-Level Agreement (SLA) to cloud users and restricted power usage. Among its increasing application and popularization, IT companies are quickly organizing distributed data centers globally, introducing new problems in terms of scheduling of resources under different VM. This perspective brings out certain vital factors used for effectual scheduling of resources given that an extensive type of characteristics, range in background level of service agreements and that too among user-dependent flexibility. This survey work presents a detailed survey of the conventional resource scheduling methods focusing on the proficient features and issues of CC technology. Review studies of several VM resource scheduling algorithms are focusing on key performance of mounting the speed and capability of the resource allocation. This survey paper also studies the major issues to the conventional VM resource scheduling algorithms and Swarm Intelligence (SI) based VM resource scheduling algorithms in CC environment. Based on the literature review, the issues faced by the CC in terms of high energy consumption of distributed data centers, high computation cost, resources consumption, customer satisfaction, service provider success etc. are recognized in CC environment.

Container-as-a-Service at the Edge: Trade-off between Energy Efficiency and Service Availability at Fog Nano Data Centers

In the last few years, we have witnessed the huge popularity of one of the most promising technologies of the modern era: the Internet of Things. In IoT, various smart objects (smart sensors, embedded devices, PDAs, and smartphones) share their data with one another irrespective of their geographical locations using the Internet. The amount of data generated by these connected smart objects will be on the order of zettabytes in the coming years. This huge amount of data creates challenges with respect to storage and analytics given the resource constraints of these smart devices. Additionally, to process the large volume of information generated, the traditional cloud-based infrastructure may lead to long response time and higher bandwidth consumption. To cope up with these challenges, a new powerful technology, edge computing, promises to support data processing and service availability to end users at the edge of the network. However, the integration of IoT and edge computing is still in its infancy. Task scheduling will play a pivotal role in this integrated architecture. To handle all the above mentioned issues, we present a novel architecture for task selection and scheduling at the edge of the network using container-as-a-service (CoaaS). We solve the problem of task selection and scheduling by using cooperative game theory. For this purpose, we developed a multi-objective function in order to reduce the energy consumption and makespan by considering different constraints such as memory, CPU, and the user's budget. We also present a real-time internal and external container migration technique for minimizing the energy consumption. For task selection and scheduling, we have used lightweight containers instead of the conventional virtual machines to reduce the overhead and response time as well as the overall energy consumption of fog devices, that is, nano data centers (nDCs). Our empirical results demonstrate that the proposed scheme reduces the energy consumption and the average number of SLA violations by 21.75 and 11.82 percent, respectively.

MBSA: a lightweight and flexible storage architecture for virtual machines

SummaryWith the advantages of extremely high access speed, low energy consumption, nonvolatility, and byte addressability, nonvolatile memory (NVM) device has already been setting off a revolution in storage field. Conventional storage architecture needs to be optimized or even redesigned from scratch to fully explore the performance potential of NVM device. However, most previous NVM‐related works only explore its low access latency and low energy consumption. Few works have been done to explore the appropriate way to use NVM device for improving virtual machine's storage performance. In this paper, we comprehensively evaluate and analyze conventional virtual machine's storage architecture. We find that, even with cutting‐edge optimization technologies, virtual machine can only achieve 30% of NVM device's original performance. Based on this observation, we propose a memory bus–based storage architecture, which we named MBSA. Memory bus–based storage architecture can greatly shorten the length of virtual machine's storage input/output stack and improve NVM device's use flexibility. In addition, an efficient wear‐leveling algorithm is proposed to prolong NVM device's lifespan. To evaluate the new architecture, we implement it as well as the wear‐leveling algorithm on real hardware and software platform. Experimental results show that MBSA can provide a big performance improvement, about 2.55X, and the wear‐leveling algorithm can efficiently balance write operations on NVM device with a negligible performance overhead (no more than 3%).

Enhancing Performance of Applications in Cloud using Hybrid Scaling Technique

Infrastructure as a Service (IaaS) model of cloud computing paradigm, users acquire computing resources such as CPU, memory, storage and network bandwidth from an IaaS provider and these resources are used to deploy and run their applications. Cloud service providers share computing resources of a physical machine by running isolated Virtual Machines (VM) for web applications. As the load on web application increases, the associated VM must be able to scale up resources to support the increasing load. At the same time, VM should also be able to scale-down resources at light load. In this paper the novel architecture is proposed that provides the hybrid solution of vertical followed by horizontal scaling techniques of resource management in cloud data center. As per the dynamic load on web applications, the proposed algorithm takes the appropriate scaling decision to allocate resources from available pool of resources. Generally dynamic scaling is achieved by the conventional live VM migration technique to create additional VM instances, but VM migration spends CPU time and consumes large amount of IO and network traffic. The proposed technique postpones live VM migration as long as possible with the help of vertical scaling technique to improve the performance of applications. KeywordsComputing, Infrastructure as a Service, Virtualization, Cloud Resource Management, Virtual Machine Migration, Horizontal Scaling, Vertical Scaling

Superblock compilation and other optimization techniques for a Java-based DBT machine emulator

Superblock compilation techniques such as control flow graph (CFG) or trace compilation have become a widely adopted approach to increase the performance of dynamically compiling virtual machines even further. While this was shown to be successful for many conventional virtual machines, it did not result in a higher performance for Java-based DBT machine emulators so far. These emulators dynamically translate application binaries of a target machine into Java bytecode, which is then eventually compiled into the native code of the emulating host by the Java Virtual Machine (JVM). Successful superblock compilation techniques for this class of emulators must consider the special requirements that result from the two-stage translation as well as the characteristics of the JVM, such as the inability of most Java JIT compilers to handle large bytecode methods efficiently. In this paper, we present a superblock compilation approach for a Java-based DBT machine emulator that generates a performance increase of up to 90 percent and of 32 percent on average. The key idea of our design is to provide a large scope over the control flow of target applications across basic block boundaries for the JVM, while still keeping small bytecode methods for the execution units. In addition, we also present two further optimizations -- interpreter context elimination and program counter elimination -- which increase the emulation performance by 16 percent again. In total, the optimization techniques discussed in this paper provide an average performance gain of 48 percent for the surveyed emulator.

Accuracy-Enhanced Power Metering Technique in Virtualized Environments

In virtualized environments, accurate metering the power consumption of individual virtual machine (VM) is a challenging issue. Conventional VM power metering techniques rely on the assumption that the power consumption is linear to the utilization of hardware utilization. However, such a utilization-based technique can only provide coarse-grained power measurement with unbounded error. In this paper, we firstly formulize the relationship between the resource utilization and the accuracy of power metering. Then, we proposed a novel VM scheduling algorithm, which uses the information of performance monitoring counters (PMC) to compensate the recursive power consumption. Theoretical analysis indicates that the proposed algorithm can provide bounded error when metering per-VM power consumption. Massive experiments are conducted by using various benchmarks on different platforms, and the results shown the error of per-VM power metering can be limited below 5.2%.

Honeyguide: A VM Migration-Aware Network Topology for Saving Energy Consumption in Data Center Networks

Current network elements consume 10-20% of the total power in data centers. Today's network elements are not energy-proportional and consume a constant amount of energy* regardless of the amount of traffic. Thus, turning off unused network switches is the most efficient way of reducing the energy consumption of data center networks. This paper presents Honeyguide, an energy optimizer for data center networks that not only turns off inactive switches but also increases the number of inactive switches for better energy-efficiency. To this end, Honeyguide combines two techniques: 1) virtual machine (VM) and traffic consolidation, and 2) a slight extension to the existing tree-based topologies. Honeyguide has the following advantages. The VM consolidation, which is gracefully combined with traffic consolidation, can handle severe requirements on fault tolerance. It can be introduced into existing data centers without replacing the already-deployed tree-based topologies. Our simulation results demonstrate that Honeyguide can reduce the energy consumption of network elements better than the conventional VM migration schemes, and the savings are up to 7.8% in a fat tree with k = 12.

Selective hardware/software memory virtualization

As virtualization becomes a key technique for supporting cloud computing, much effort has been made to reduce virtualization overhead, so a virtualized system can match its native performance. One major overhead is due to memory or page table virtualization. Conventional virtual machines rely on a shadow mechanism to manage page tables, where a shadow page table maintained by the VMM (Virtual Machine Monitor) maps virtual addresses to machine addresses while a guest maintains its own virtual to physical page table. This shadow mechanism will result in expensive VM exits whenever there is a page fault that requires synchronization between the two page tables. To avoid this cost, both Intel and AMD provide hardware assists, EPT (extended page table) and NPT (nested page table), to facilitate address translation. With the hardware assists, the MMU (Memory Management Unit) maintains an ordinary guest page table that translates virtual addresses to guest physical addresses. In addition, the extended page table as provided by EPT translates from guest physical addresses to host physical or machine addresses. NPT works in a similar style. With EPT or NPT, a guest page fault can be handled by the guest itself without triggering VM exits. However, the hardware assists do have their disadvantage compared to the conventional shadow mechanism -- the page walk yields more memory accesses and thus longer latency. Our experimental results show that neither hardware-assisted paging (HAP) nor shadow paging (SP) can be a definite winner. Despite the fact that in over half of the cases, there is no noticeable gap between the two mechanisms, an up to 34% performance gap exists for a few benchmarks. We propose a dynamic switching mechanism that monitors TLB misses and guest page faults on the fly, and dynam-ically switches between the two paging modes. Our experiments show that this new mechanism can match and, sometimes, even beat the better performance of HAP and SP.

Scale and performance in the Denali isolation kernel

This paper describes the Denali isolation kernel, an operating system architecture that safely multiplexes a large number of untrusted Internet services on shared hardware. Denali's goal is to allow new Internet services to be "pushed" into third party infrastructure, relieving Internet service authors from the burden of acquiring and maintaining physical infrastructure. Our isolation kernel exposes a virtual machine abstraction, but unlike conventional virtual machine monitors, Denali does not attempt to emulate the underlying physical architecture precisely, and instead modifies the virtual architecture to gain scale, performance, and simplicity of implementation. In this paper, we first discuss design principles of isolation kernels, and then we describe the design and implementation of Denali. Following this, we present a detailed evaluation of Denali, demonstrating that the overhead of virtualization is small, that our architectural choices are warranted, and that we can successfully scale to more than 10,000 virtual machines on commodity hardware.

Sharing data and services in a virtual machine system

Experimental additions have been made to a conventional virtual machine system (VM/370) in order to support a centralized program library management service for a group of interdependent users. These additions enable users to share read/write access to a data base as well as processing services. Although the primary motivation was the enhancement of performance, considerable attention was given to retaining the inherent advantages of the virtual machine system. Extended applications of the basic technique are described and the implications of such extensions on operating system design are considered.