Virtual Machine Scheduling Research Articles

VMSAGE: A virtual machine scheduling algorithm based on the gravitational effect for green Cloud computing

The area of sustainable green smart computing highlights key challenges towards reducing cost and carbon dioxide emissions due to the high-energy consumption of Cloud data centres. Here, we focus on the Cloud virtual machine (VM) scheduling that is usually based on simple algorithms, e.g. VM placement on nodes with low memory usage. This approach fails to consider the actual configuration of nodes inside the server rack resulting in local overheating of Cloud data centres. To solve this, we propose a VM scheduling algorithm based on the gravitational effect, called VMSAGE, to optimize energy efficiency of Cloud computing systems. Inspired by the physical gravitation model, we define the thermal repulsion and logical gravitation factors between physical nodes and VMs. To achieve optimized VM scheduling, we propose a gravitation function that refers to the calculation of the logical quality of each VM, host and rack through the algorithm, so as to draw the attractiveness between them. Based on the concept of dimension reduction, VMSAGE conducts the two-dimensional plane target selection twice to reduce the computational cost. Additionally, VMSAGE evaluates attributes of the computer room to carry out the VM deployment. To demonstrate the effectiveness of our solution, we compare it with the Best Fit Heuristic (BFH) and the dynamic voltage and frequency scaling (DVFS) algorithms. The results indicate that our algorithm achieves 10% and 20% optimized energy consumption respectively. The experimental results highlight our contribution, in where VMSAGE can significantly reduce energy consumption rates and VM migration times.

Energy-Efficient Cloudlet Management for Privacy Preservation in Wireless Metropolitan Area Networks

Currently, cloudlet based wireless metropolitan area network (WMAN) is emerging as an effective paradigm to extend the performance of mobile devices, which enables the execution of computational intensive mobile applications. But the normal operation of cloudlets consumes a large amount of energy, which brings about carbon dioxide emissions, aggravation of the greenhouse effect, etc. Meanwhile the data transmission of various mobile users among cloudlets would cause leakage of personal privacy. In view of this challenge, we propose an energy-efficient cloudlet management method, named ECM, for privacy preservation in WMAN. Technically, an optimization model is designed to formalize our problem. Then, based on live virtual machine (VM) migration technique, a corresponding privacy-aware VM scheduling method for energy saving is designed to determine which VMs should be migrated and where they should be migrated. Finally, experimental data demonstrate that the proposed method is both efficient and effective.

Green energy efficient scheduling management

The analysis of the energy efficiency in Cloud Computing infrastructures has become an important research domain as the utilization rate of the various on-demand services is daily higher and higher and its management is now considered as a main objective. Today, to tackle this challenging issue, Cloud providers integrate renewable energy sources to feed their infrastructure. Energy saving is part often an integral many companies goal. Unlike the classic supply of grid energy, the production of green energy is unstable and depends on nature of the weather or wind. It introduces new challenges as pervasive jobs to reduce a server consumption. In this article, studies based on the use and the storage of photovoltaic energy are exposed. We detail our design of a scheduler which uses solar energy production to make an off-line decision. This enables us to schedule virtual machines into a datacenter via different algorithms which consumes the least amount of brown energy as possible. We based our analysis through an existing workload from Google. We describe and study this workload to create one corresponding to our need. We also proposed to evaluate the storage size of a smartgrid related to the solar panel size. It is an analysis of the reliance between both storage (battery) and renewable energy production (solar panel) components sizing.

A loosely coupled scalable cloud infrastructure

Large-scale virtual computing system requires a loosely coupled virtual resource management solution. The solution should be able to flexibly increase or decrease virtual resources and to smooth upgrade the cloud platform. OpenStack provides large-scale virtualization solution such as Cells v2.0 and Tricircle/Trio2o. But it’s so complex and difficult to be deployed and maintain for small cloud computing teams. We discuss a loosely coupled cloud infrastructure. It is based on a central database and plugin to achieve information collection, virtual machine scheduling and controlling and uses a simplified method to achieve unified management of network and images. It can flexibly expand or reduce cluster members, deploy different versions of OpenStack and even a heterogeneous cloud platform. The test bed works well at IHEP. We will show and analyse the infrastructure.

Chemical reaction optimization for virtual machine placement in cloud computing

With the development of virtualization technologies, cloud data centers are faced with more and more virtual machines (VMs) requests. How to realize an efficient virtual machine placement (VMP) becomes a hot research topic. The optimal resource consumption and the utilization rate of a physical machine in the whole cloud data center can be realized by optimizing the process from the virtual machine to the physical machine. VMP is a combinatorial optimization problem which was demonstrated to be NP-hard. In this paper, a new formulation of VMP problem is presented by taking into consideration the optimization of the two following objectives: (i) minimize the energy consumption, and (ii) maximize the resource utilization. In order to achieve these targets, we propose two algorithms based on chemical reaction optimization (CRO) algorithm, namely CVP and CVV, with two types of solution representation. The proposed algorithms are compared with other optimal placement strategies, namely Cuckoo Search Optimization (CSO), Reordered Grouping Genetic Algorithm (RGGA), First Fit Decreasing (FFD) and Best Fit Decreasing (BFD). Experimental results show that the proposed CVP and CVV give better performance comparing with the other compared algorithms in terms of resource consumption and resource utilization. In term of scalability, the proposed CVV algorithm benefits from the high computational speed and performs well when there are a large number of virtual machine scheduling requests in the cloud data center.

On Non-Preemptive VM Scheduling in the Cloud

We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service cannot be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio r , our scheduling algorithm can provide rβ fraction of the throughput region for β < r. In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to β<1. The parameters β and r can be tuned to provide a tradeoff between achievable throughput, delay, and computational complexity of the scheduling algorithm. Finally extensive simulation results using both synthetic and real traffic traces are presented to verify the performance of our algorithm.

Holistic Virtual Machine Scheduling in Cloud Datacenters towards Minimizing Total Energy

Energy consumed by Cloud datacenters has dramatically increased, driven by rapid uptake of applications and services globally provisioned through virtualization. By applying energy-aware virtual machine scheduling, Cloud providers are able to achieve enhanced energy efficiency and reduced operation cost. Energy consumption of datacenters consists of computing energy and cooling energy. However, due to the complexity of energy and thermal modeling of realistic Cloud datacenter operation, traditional approaches are unable to provide a comprehensive in-depth solution for virtual machine scheduling which encompasses both computing and cooling energy. This paper addresses this challenge by presenting an elaborate thermal model that analyzes the temperature distribution of airflow and server CPU. We propose GRANITE - a holistic virtual machine scheduling algorithm capable of minimizing total datacenter energy consumption. The algorithm is evaluated against other existing workload scheduling algorithms MaxUtil, TASA, IQR and Random using real Cloud workload characteristics extracted from Google datacenter tracelog. Results demonstrate that GRANITE consumes 4.3-43.6 percent less total energy in comparison to the state-of-the-art, and reduces the probability of critical temperature violation by 99.2 with 0.17 percent SLA violation rate as the performance penalty.

Mitigation technique for performance degradation of virtual machine owing to GPU pass-through in fog computing

As the size of data increases and computation becomes complicated in fog computing environments, the need for highperformance computation is increasing. One of the most popular ways to improve the performance of a virtual machine (VM) is to allocate a graphic processing unit (GPU) to the VM for supporting general purpose computing on graphic processing unit (GPGPU) operations. The direct pass-through, often used for GPUs in VMs, is popular in the cloud because VMs can use the full functionality of the GPU and experience virtually no performance degradation owing to virtualization. Direct pass-through is very useful for improving the performance of VMs. However, since the GPU usage time is not considered in the VM scheduler that operates based on the central processing unit (CPU) usage time of the VM, the VM performing the GPGPU operation degrades the performance of other VMs. In this paper, we analyze the effect of the VM performing the GPGPU operation (GPGPU-intensive VM) on other VMs through experiments. Then, we propose a method to mitigate the performance degradation of other VMs by dynamically allocating the resource usage time of the VM and preventing the priority preemption of the GPGPU-intensive VM.

Building an IaaS cloud with droplets: a collaborative experience with OpenStack

The goal of a collaborative cloud is to promote resource sharing among different institutions using existing computational infrastructure. To accomplish that, we propose the PID (Platform for IaaS Distribution) cloud, where users from research institutions can benefit from IaaS services with minimum management and maintenance effort and with no subscription fees. The only requirement is to share at least one server capable of running virtual machines. To build PID, we rely on the OpenStack orchestrator as the basis of our implementation and, consequently, modifications are needed to accommodate the geo-distributed architecture. We present such modifications, which are concentrated on a different virtual machine scheduling, new hierarchical roles for users, and adapted web interfaces for management and control. Based on these modifications, we deploy a prototype in three universities in Rio de Janeiro. We experimentally evaluate PID regarding the latency between sites and the scalability according to the number of servers and virtual machines. Based on these experiments, we can estimate that an OpenStack cloud with a few hundred servers with tens of virtual machines each would add, on average, a few Mb/s of control traffic toward the Controller. We then conclude that the amount of control traffic generated by OpenStack services is not enough to surpass network capacities of our academic sites. Our results also show that the additional latency expected in a geo-distributed cloud has little or no impact on the OpenStack network component, called Neutron.

On minimizing total energy consumption in the scheduling of virtual machine reservations

This paper considers the energy-efficient scheduling of virtual machine (VM) reservations in a Cloud Data center. Concentrating on CPU-intensive applications, the objective is to schedule all reservations non-preemptively, subjecting to constraints of physical machine (PM) capacities and running time interval spans, such that the total energy consumption of all PMs is minimized (called MinTEC for abbreviation). The MinTEC problem is NP-complete in general. The best known results for this problem is a 5-approximation algorithm for special instances using First-Fit-Decreasing algorithm and 3-approximation algorithm for general offline parallel machine scheduling with unit demand. By combining the features of optimality and workload in interval spans, we propose a method to find the optimal solution with the minimum number of job migrations, and a 2-approximation algorithm called LLIF for general cases. We then show how our algorithms are applied to minimize the total energy consumption in a Cloud Data center. Our theoretical results are validated by intensive simulation using trace-driven and synthetically generated data.

Incentive-aware virtual machine scheduling in cloud computing

As cloud computing is a market-oriented utility, optimal virtual machine (VM) scheduling in cloud computing should take into account the incentives for both cloud users and the cloud provider. However, most of existing studies on VM scheduling only consider the incentive for one party, i.e., either the cloud users or the cloud provider. Very few related studies consider the incentives for both parties, in which the cost, one of the most attractive incentives for cloud users, is not well addressed. In this paper, we investigate the problem of VM scheduling in cloud computing by optimizing the incentives for both parties. The problem is formulated as a multi-objective optimization model, i.e., maximizing the successful execution rate of VM requests and minimizing the combined cost (incentives for cloud users), and minimizing the fairness deviation of profits (incentive for the cloud provider). The proposed multi-objective optimization model can offer sufficient incentives for the two parties to stay and play in the cloud and keep the cloud system sustainable. A heuristic-based scheduling algorithm, called cost-greedy dynamic price scheduling, is then developed to optimize the incentives for both parties. Experimental results show that, compared with some popular algorithms, the developed algorithm can achieve higher successful execution rate, lower execution cost, smaller fairness deviation and most important, higher degree of user satisfaction in most cases.

Energy efficient workflow scheduling with virtual machine consolidation for green cloud computing

Cloud computing offers utility-based IT services on-demand to the users on a pay-per-use-basis. The cloud centers consist of physical machines (PMs) with virtual machines (VMs). These data centers consume a large amount of energy due to the improper resource utilization and lack of efficient schedu ling algorithms to perform the task-resource mapping. These issues lead to huge energy consumption along with high maintenance costs and carbon emissions. In this paper, a Power Efficient Scheduling and VM Consolidation (PESVMC) algorithm is proposed to address these issues and the associated challenges. The numerous existing research works concentrated on the application of energy management techniques to hardware level support for the reduction of energy consumption. The proposed algorithm emphasizes on the software level by taking the flexibility of the virtualization technology and it consists of two phases, VM Scheduling phase, and VM Consolidation phase. In the Scheduling phase, the tasks with maximum runtime are allocated to VM, which is expected to consume minimal energy. In the VM Consolidation phase, overloaded and underloaded hosts are determined based on the double-threshold scheme. Further, Live Migration technique is applied for migrating the VMs from over-utilized or underutilized hosts to other hosts with the normal utilization. A power efficient utilization factor is introduced to determine the underloaded hosts. This utilization factor is proven to reduce the number of migrations, which can cause additional energy consumption. Energy efficient Scheduling combined with VM Consolidation is successful in maximizing the resource utilization and minimizing the energy consumption. The experimental evaluation is performed using WorkflowSim and the proposed algorithm achieves significant energy conservation and resource utilization.

Optimal Scheduling of VMs in Queueing Cloud Computing Systems With a Heterogeneous Workload

This paper studies the delay-optimal virtual machine (VM) scheduling problem in cloud computing systems, which have a constant amount of infrastructure resources, such as CPU, memory, and storage in the resource pool. The cloud computing system provides VMs as services to users. Cloud users request various types of VMs randomly over time, and the requested VM-hosting durations vary vastly. We first adopt a queuing model for the heterogeneous and dynamic workloads. Then, we formulate the VM scheduling in such a queuing cloud computing system as a decision-making process, where the decision variable is the vector of VM configurations and the optimization objective is the delay performance in terms of average job completion time. A low-complexity online scheme that combines the shortest-job-first (SJF) buffering and min–min best fit (MMBF) scheduling algorithms, i.e., SJF-MMBF, is proposed to determine the solutions. Another scheme that combines the SJF buffering and reinforcement learning (RL)-based scheduling algorithms, i.e., SJF-RL, is further proposed to avoid the potential of job starvation in SJF-MMBF. The simulation results show that SJF-RL achieves its goal of delay-optimal scheduling of VMs by provisioning a low delay at various job arrival rates for various shapes of job length distributions. The simulation results also illustrate that although SJF-MMBF is sub-delay-optimal in a heavy-loaded and highly dynamic environment, it is efficient in throughput performance in terms of the average job hosting rate provisioning.

Energy-Aware Virtual Machine Scheduling on Data Centers with Heterogeneous Bandwidths

Ethernet is the main choice of connectivity in data centers operating in clouds. The Ethernet standards body (IEEE 802.3) has generated Ethernet specifications with increasing transmission rates since its inception. As a result, it is observed that data centers with groups of physical hosts having heterogeneous bandwidths. In this paper, we present an energy-aware virtual machine’s scheduling method, taking into account this heterogeneity while aiming at energy efficiency. This method consists of two algorithms: one to determine to which physical hosts virtual machines should be allocated, and the other for the provision of bandwidth on physical hosts to virtual machines. Our study shows that, regarding energy savings for the studied scenarios, the presented method is comparable to other energy-aware methods in data centers with groups of machines with homogeneous settings, surpassing them in groups of machines with heterogeneous configurations, bringing improvements especially to data centers with heterogeneous bandwidths’ networks or to where restrictive SLAs are used for the provision of bandwidth to virtual machines.

A novel virtual machine scheduling policy based on performance prediction model

In cloud platforms, virtual machine scheduling policy plays an important role for providing desirable service quality for users. In many existing scheduling policies, the task execution time is often assumed to be constant or defined by users. However, either unpredictable workload or resource unreliability may significantly affect task execution time, which in turn results in inefficient scheduling decisions. In this paper, we first present a task execution time model by applying queue theory; then we use this model to predict the performance of application at runtime and propose a novel virtual machine scheduling policy. By conducting extensive experiments, we investigate the effectiveness and efficiency of the proposed scheduling policy. The experimental results indicate that it can significantly reduce the response time of cloud application comparing with other existing scheduling policies.

A novel virtual machine scheduling policy based on performance prediction model

In cloud platforms, virtual machine scheduling policy plays an important role for providing desirable service quality for users. In many existing scheduling policies, the task execution time is often assumed to be constant or defined by users. However, either unpredictable workload or resource unreliability may significantly affect task execution time, which in turn results in inefficient scheduling decisions. In this paper, we first present a task execution time model by applying queue theory; then we use this model to predict the performance of application at runtime and propose a novel virtual machine scheduling policy. By conducting extensive experiments, we investigate the effectiveness and efficiency of the proposed scheduling policy. The experimental results indicate that it can significantly reduce the response time of cloud application comparing with other existing scheduling policies.

LACE: A Locust-Inspired Scheduling Algorithm to Reduce Energy Consumption in Cloud Datacenters

The growth of cloud computing has resulted in uneconomic energy consumption, which has negatively impacted the environment through the generation of carbon emissions. This paper proposes a distributed Locust-inspired scheduling algorithm to reduce cloud computing consumed energy (LACE). LACE schedules and optimizes the allocation of virtual machines (VMs) based on behavior derived from locusts. LACE distributes scheduling among servers; each server is responsible for allocating and migrating its VMs. Hence, the scheduling load is distributed between servers rather than being centralized in one component. LACE was thoroughly evaluated by comparing it with long-standing VM scheduling algorithms: dynamic voltage–frequency scaling (DVFS), energy-aware scheduling using the workload-aware consolidation technique, and the static threshold with minimum utilization policy. The experimental results show that LACE considerably outperforms the other algorithms in almost every area. Most importantly, LACE exhibited significant levels of fault tolerance under heavy workloads that benchmarks were unable to sustain.

On Non-Preemptive VM Scheduling in the Cloud

We study the problem of scheduling VMs (Virtual Machines) in a distributed server platform, motivated by cloud computing applications. The VMs arrive dynamically over time to the system, and require a certain amount of resources (e.g. memory, CPU, etc) for the duration of their service. To avoid costly preemptions, we consider non-preemptive scheduling: Each VM has to be assigned to a server which has enough residual capacity to accommodate it, and once a VM is assigned to a server, its service cannot be disrupted (preempted). Prior approaches to this problem either have high complexity, require synchronization among the servers, or yield queue sizes/delays which are excessively large. We propose a non-preemptive scheduling algorithm that resolves these issues. In general, given an approximation algorithm to Knapsack with approximation ratio r , our scheduling algorithm can provide rβ fraction of the throughput region for β < r . In the special case of a greedy approximation algorithm to Knapsack, we further show that this condition can be relaxed to β<1 . The parameters β and r can be tuned to provide a trade-off between achievable throughput, delay, and computational complexity of the scheduling algorithm. Finally extensive simulation results using both synthetic and real traffic traces are presented to verify the performance of our algorithm.

Traffic-Aware Virtual Machine Migration in Topology-Adaptive DCN

Virtual machine (VM) migration is a key technique for network resource optimization in modern data center networks. Previous work generally focuses on how to place the VMs efficiently in a static network topology by migrating the VMs with large traffic demands to close servers. As the flow demands between VMs change, however, a great cost will be paid for the VM migration. In this paper, we propose a new paradigm for VM migration by dynamically constructing adaptive topologies based on the VM demands to lower the cost of both VM migration and communication. We formulate the traffic-aware VM migration problem in an adaptive topology and show its NP-hardness. For periodic traffic, we develop a novel progressive-decompose-rounding algorithm to schedule VM migration in polynomial time with a proved approximation ratio. For highly dynamic flows, we design an online decision-maker (ODM) algorithm with proved performance bound. Extensive trace-based simulations show that PDR and ODM can achieve about four times flow throughput among VMs with less than a quarter of the migration cost compared to other state-of-art VM migration solutions. We finally implement an OpenvSwitch-based testbed and demonstrate the efficiency of our solutions.

Software Performance Analysis Through Resource Provisioning in Cloud Computing

Cloud computing provides dynamic services to the users by means of virtualized resources over the internet. Resource scheduling of multiple virtual machines is the core of platform as a service (PaaS). The proposed system signifies the resource scheduling as a platform of service. This serves as a consumer-provider model. Cloud provider provides resources to the consumer as per their demands as per SLA (Service Level Agreement) guarantee. Datacenters are increasingly used for virtualization to add level of automation Virtual Machine scheduling policies improve the performance of applications Resource allocation and scheduling algorithms play an important role in allocation and managing resources. This paper proposes an efficient mechanism for allocating virtual machines for consumer resources. Round robin algorithm is used to implement the scheduling for best significance on resource allocation. CloudSim is a framework for modelling and simulation of cloud computing infrastructure and services. It is originally built for cloud computing and Distributed Systems Laboratory.