Virtual Machine Consolidation Problem Research Articles

A cut-and-solve algorithm for virtual machine consolidation problem

The virtual machine consolidation problem attempts to determine which servers to be activated, how to allocate virtual machines to the activated servers, and how to migrate virtual machines among servers such that the summation of activated, allocation, and migration costs is minimized subject to the resource constraints of the servers and other practical constraints. In this paper, we first propose a new mixed integer linear programming formulation for the virtual machine consolidation problem. We show that compared with existing formulations, the proposed formulation is much more compact in terms of smaller numbers of variables or constraints, which makes it suitable for solving large-scale problems. We then develop a cut-and-solve algorithm, a tree search algorithm to efficiently solve the virtual machine consolidation problem to optimality. The proposed cut-and-solve algorithm is based on a novel relaxation of the virtual machine consolidation problem that provides a stronger lower bound than the natural continuous relaxation of the virtual machine consolidation problem, making a smaller search tree. By extensive computational experiments, we show that (i) the proposed formulation significantly outperforms existing formulations in terms of solution efficiency; (ii) compared with standard mixed integer linear programming solvers, the proposed cut-and-solve algorithm is much more efficient; and (iii) compared with an existing heuristic algorithm, the proposed cut-and-solve algorithm is better able to balance workload distribution across servers and achieve higher resource utilization.

A kernel search algorithm for virtual machine consolidation problem in cloud computing

Virtual machine consolidation refers to the process of reallocating virtual machines across a set of target servers. It can be formulated as a mixed integer linear programming problem, which is known to be NP-hard. In this paper, we propose a kernel search (KS) heuristic algorithm based on hard variable fixing to quickly obtain high-quality solutions for large-scale virtual machine consolidation problems (VMCPs). Since existing variable fixing strategies in KS algorithms may result in some VMCP instances being infeasible, our proposed KS algorithm employs a more efficient strategy to choose a set of fixed variables based on their corresponding reduced costs. Our numerical results on VMCP instances demonstrate that our proposed KS algorithm significantly outperforms mixed integer linear programming solvers in terms of CPU time. Moreover, our proposed strategy of variable fixing improves the efficiency of the KS algorithm significantly, with negligible degradation in solution quality.

The importance of nature-inspired meta-heuristic algorithms for solving virtual machine consolidation problem in cloud environments

Nowadays, cloud computing is known as an internet-based modern area among emerging technologies that brings up an environment, in which computing resources such as hardware, software, storage, etc. can be rented by cloud users based on a pay per use model. Since the size of cloud computing is widely expanding and the number of cloud users is also increasing day by day, high energy consumption becomes a serious concern in the operation of complex cloud data centers. In this regards, Virtual Machine (VM) consolidation plays a vital role in utilizing cloud resources in an efficient manner. It migrates the running VMs from overloaded Physical Machines (PMs) to other PMs considering multiple factors, such as migration overhead, energy consumption, resource utilization, and migration time. Since the VM consolidation issue is known as an NP-hard problem, various nature‐inspired meta-heuristic algorithms aiming to solve this problem have been utilized in recent years. However, a lack of systematic and detailed survey study in this field is obvious. Therefore, this gap motivated us to provide the current paper aiming to highlight the role of nature-inspired meta-heuristic algorithms in the VM consolidation problem, review the existing approaches, offer a detailed comparison of approaches based on important factors, and finally, outline the future directions.

Applying Reinforcement Learning towards automating energy efficient virtual machine consolidation in cloud data centers

Energy awareness presents an immense challenge for cloud computing infrastructure and the development of next generation data centers. Virtual Machine (VM) consolidation is one technique that can be harnessed to reduce energy related costs and environmental sustainability issues of data centers. In recent times intelligent learning approaches have proven to be effective for managing resources in cloud data centers. In this paper we explore the application of Reinforcement Learning (RL) algorithms for the VM consolidation problem demonstrating their capacity to optimize the distribution of virtual machines across the data center for improved resource management. Determining efficient policies in dynamic environments can be a difficult task, however, the proposed RL approach learns optimal behavior in the absence of complete knowledge due to its innate ability to reason under uncertainty. Using real workload data we provide a comparative analysis of popular RL algorithms including SARSA and Q-learning. Our empirical results demonstrate how our approach improves energy efficiency by 25% while also reducing service violations by 63% over the popular Power-Aware heuristic algorithm.

A Virtual Machine Consolidation Algorithm Based on Ant Colony System and Extreme Learning Machine for Cloud Data Center

The energy consumption issue of large-scale data centers is attracting more and more attention. Virtual machine consolidation can significantly reduce energy consumption by migrating virtual machines from one physical machine to another. However, excessive virtual machine consolidation can lead to dangerous Service Level Agreement (SLA) violations. Therefore, how to balance between effective energy consumption and SLA violations avoidance effectively is a paradox to be mediated. The virtual machine consolidation problem is NP-hard. The traditional heuristic algorithm is easy to fall into the local optimal and some meta-heuristic algorithms can help to avoid it. However, the existing meta-heuristic algorithms are with high complexity. Therefore, we propose a lower complexity multi-population ant colony system algorithm with the Extreme Learning Machine (ELM) prediction (ELM_MPACS). The algorithm firstly predicts the host state employing ELM and then the virtual machine on the overloaded host will be migrated to the normal host, while the virtual machine on the underloaded host will be consolidated to another underloaded host with higher utilization. Multiple populations concurrently construct migration plans and local search further optimizes the results obtained by each population to reduce SLA violations. We compare ELM_MPACS with the benchmark, heuristic and meta-heuristic algorithms. The experimental results have shown that compared with these algorithms, our algorithm reduces energy consumption, migration times and SLA violations effectively.

Hybrid Algorithm Naive Bayesian Classifier and Random Key Cuckoo Search for Virtual Machine Consolidation Problem

The trade-off between Energy consumption and SLA violation presents a serious challenge in cloud computing environments. A non-aggressive virtual machine consolidation algorithm is a good approach to reduce the consumed energy as well as SLA violation. A well-known strategy to deal with the virtual machine consolidation problem consists of four steps: host overloading detection, host under-loading detection, virtual machine selection and virtual machine placement. In this paper, the previous strategy is modified by merging the last two steps virtual machine selection and virtual machine placement, to avoid any poor solutions caused by solving both steps separately. In the host overloading/under-loading detection steps, we classified host status into five classes: Over-Utilized, Nearly Over-Utilized, Normal Utilized, Under-Utilized and Switched Off, then an algorithm, based on the Naive Bayesian Classifier, was introduced in order to detect the future host state for minimizing the number of virtual machine migrations; as a result, the energy consumption and performance degradation due to migrations will be minimized. In the virtual machine selection and placement steps, we introduced an algorithm based on the Random Key Cuckoo Search to reduce the energy consumption and enhance the SLA violation. To assess the algorithm, real data traces for 10 days, were used to verify the proposed algorithms. The experimental results proved that the proposed algorithms can significantly reduce the consumed energy as well as the SLA violation in data centers.

Adaptive Markov‐based approach for dynamic virtual machine consolidation in cloud data centers with quality‐of‐service constraints

SummaryDynamic virtual machine (VM) consolidation is one of the emerging technologies that has been considered for low‐cost computing in cloud data centers. Quality‐of‐service (QoS) assurance is one of the challenging issues in the VM consolidation problem since it is directly affected by the increase of resource utilization due to the consolidations. In this paper, we take advantage of Markov chain models to propose a novel approach for VM consolidation that can be used to explicitly set a desired level of QoS constraint in a data center to ensure the QoS goals while improving system utilization. For this purpose, an energy‐efficient and QoS‐aware best fit decreasing algorithm for VM placement is proposed, which considers QoS objective when determining the location of a migrating VM. This algorithm employs an online transition matrix estimator method to deal with the nonstationary nature of real workload data. We also propose new policies for detecting overloaded and underloaded hosts. The performance of our proposed algorithms is evaluated through simulations. The results show that the proposed VM consolidation algorithms in this paper outperforms the benchmark algorithms in terms of energy consumption, service‐level agreement violations, and other cost factors.

Migration Cost and Energy-Aware Virtual Machine Consolidation Under Cloud Environments Considering Remaining Runtime

By live migration technology, multiple virtual machines (VMs) can be consolidated into a fewer physical servers and the idle ones can be shut down or switched to low-power mode, thus reducing the energy consumption of cloud data centers. However, live migration can result in performance degradation of migrated VMs, or even interrupting their services. At the same time, live migration can also aggravate the overheads of data transmissions and produce additional energy consumption in cloud data centers. All these negative influences belong to migration cost (MC) caused by VM migration, which becomes an important cost factor that can’t be ignored. Otherwise, another important concern, remaining runtime of the migrated VM, also has influence on the efficiency of VM consolidation, which is not well addressed as well. This paper investigates MC-aware VM consolidation problem and formulates the problem as a multi-constraint optimization model by considering migration cost and remaining runtime of VMs. Based on the proposed model, a heuristic algorithm, called MC-aware VM consolidation (MVC) algorithm, is developed. Finally, based on a real-world cloud trace, we conduct extensive experimental studies to verify the validity of the proposed model and algorithm. Experimental results show that, compared with some popular algorithms, MVC algorithm can effectively decrease the migration cost and, at the same time guarantee the energy consumption within a certain low level.

Multi-Objective VM Consolidation Based on Thresholds and Ant Colony System in Cloud Computing

With the large-scale deployment of cloud datacenters, high energy consumption and serious service level agreement (SLA) violations in datacenters have become an increasingly urgent problem to be addressed. Implementing an effective virtual machine (VM) consolidation methods is of great significance to reduce energy consumption and SLA violations. The VM consolidation problem is a well-known NP-hard problem. Meanwhile, efficient VM consolidation should consider multiple factors synthetically, including quality of service, energy consumption, and migration overhead, which is a multi-objective optimization problem. To solve the problem above, we propose a new multi-objective VM consolidation approach based on double thresholds and ant colony system (ACS). The proposed approach leverages double thresholds of CPU utilization to identify the host load status, VM consolidation is triggered when the host is overloaded or underloaded. During consolidation, the approach selects migration VMs and destination hosts simultaneously based on ACS, utilizing diverse selection policies according to the host load status. The extensive experiment is conducted to compare our proposed approach with the state-of-art VM consolidation approaches. The experimental results demonstrate that the proposed approach remarkably reduces energy consumption and optimizes SLA violation rates thus achieving better comprehensive performance.

Hierarchical, Portfolio Theory-Based Virtual Machine Consolidation in a Compute Cloud

Improving the energy efficiency of cloud computing systems has become an important issue because the electric energy bill for 24/7 operation of these systems can be quite large. The focus of this paper is on the virtual machine consolidation in a cloud computing system as a way of lowering daily energy consumption of the system. In contrast to the existing works that assume resource demands of virtual machines are given as scalar variables, this paper treats these demands as random variables with known means and standard deviations because the demands are not deterministic in many situations. These random variables may be correlated with one another, and there are several types of resources which can be performance bottlenecks. Therefore, both correlations and resource type heterogeneity must be considered. The virtual machine consolidation problem is thus formulated as a multi-capacity stochastic bin packing problem. This problem is NP-hard, so we present a heuristic method to efficiently solve the problem. Simulation results show that, in spite of its simplicity and scalability, the proposed method produces high quality solutions.

Optimising for energy or robustness? Trade-offs for VM consolidation in virtualized datacenters under uncertainty

Reducing the energy consumption of virtualized datacenters and the Cloud is very important in order to lower CO $$ _2 $$ footprint and operational cost of a Cloud operator. However, there is a trade-off between energy consumption and perceived application performance. In order to save energy, Cloud operators want to consolidate as many Virtual Machines (VM) on the fewest possible physical servers, possibly involving overbooking of resources. However, that may involve SLA violations when many VMs run on peak load. Such consolidation is typically done using VM migration techniques, which stress the network. As a consequence, it is important to find the right balance between the energy consumption and the number of migrations to perform. Unfortunately, the resources that a VM requires are not precisely known in advance, which makes it very difficult to optimise the VM migration schedule. In this paper, we therefore propose a novel approach based on the theory of robust optimisation. We model the VM consolidation problem as a robust Mixed Integer Linear Program and allow to specify bounds for e.g. resource requirements of the VMs. We show that, by using our model, Cloud operators can effectively trade-off uncertainty of resource requirements with total energy consumption. Also, our model allows us to quantify the price of the robustness in terms of energy saving against resource requirement violations.

Decomposed multi-objective bin-packing for virtual machine consolidation

In this paper, we describe a novel solution to the problem of virtual machine (VM) consolidation, otherwise known as VM-Packing, as applicable to Infrastructure-as-a-Service cloud data centers. Our solution relies on the observation that virtual machines are not infinitely variable in resource consumption. Generally, cloud compute providers offer them in fixed resource allocations. Effectively this makes all VMs of that allocation type (or instance type) generally interchangeable for the purposes of consolidation from a cloud compute provider viewpoint. The main contribution of this work is to demonstrate the advantages to our approach of deconstructing the VM consolidation problem into a two-step process of multidimensional bin packing. The first step is to determine the optimal, but abstract, solution composed of finite groups of equivalent VMs that should reside on each host. The second step selects concrete VMs from the managed compute pool to satisfy the optimal abstract solution while enforcing anti-colocation and preferential colocation of the virtual machines through VM contracts. We demonstrate our high-performance, deterministic packing solution generation, with over 7,500 VMs packed in under 2 min. We demonstrating comparable runtimes to other VM management solutions published in the literature allowing for favorable extrapolations of the prior work in the field in order to deal with larger VM management problem sizes our solution scales to.

An Approach for Energy Efficient Dynamic Virtual Machine Consolidation in Cloud Environment

Nowadays, as the use of cloud computing service becomes more extensive and the customers welcome this service, an increasing trend in energy consumption and operational costs of these centers may be seen. To reduce operational costs, the providers should decrease energy consumption to an extent that Service Level Agreement (SLA) maintains at a desirable level. This paper adopts the virtual machine consolidation problem in cloud computing data centers as a solution to achieve this goal, putting forward solutions to make the decision regarding the necessity of migration from hosts and finding appropriate hosts as destinations of migration. Using time-series forecasting method and Double Exponential Smoothing (DES) technique, the proposed algorithm predicts CPU utilization in near future. It also proposes an optimal equation for the dynamic lower threshold. Comparing current and predicted CPU utilization with dynamic upper and lower thresholds, this algorithm identifies and categorizes underloaded and overloaded hosts. According to this categorization, migration then occurs from the hosts that meet the necessary conditions for migration. This paper identifies a certain type of hosts as “troublemaker hosts”. Most probably, the process of prediction and decision making regarding the necessity of migration will be disrupted in the case of these hosts. Upon encountering this type of hosts, the algorithm adopts policies to modify them or switch them to sleep mode, thereby preventing the adverse effects caused by their existence. The researchers excluded all overloaded, prone-to-be-overloaded, underloaded, and prone-to-be-underloaded hosts from the list of suitable hosts to find suitable hosts as destinations of migration. An average improvement of 86.2%, 28.4%, and 87.2% respectively for the number of migrations of virtual machines, energy consumption, and SLA violation is among the simulation achievements of this algorithm using Clouds tool.

Improving Energy Efficiency for Mobile Media Cloud via Virtual Machine Consolidation

Mobile media applications are on the rise due to the explosive popularity of mobile devices. Advances in mobile media cloud (MMC) make it a promising solution to serve those huge multimedia applications. In MMC, virtualization is adopted to allocate resources elastically from a shared resource pool. Therefore, effective virtual machine (VM) consolidation is of paramount importance to maximize energy efficiency. In this paper, we consider a scenario that mobile media cloud performs video streaming and transcoding for viewers with different mobile devices. We formulate the VM consolidation problem as a mixed integer linear programming. Under this framework, the minimum energy consumption and the number of physical machines (PMs) in operation are derived. Based on these analytical results, for homogeneous media cloud, we develop an approximation algorithm for VM consolidation and placement which jointly considers CPU and bandwidth constraints. For heterogeneous media cloud, we derive an upper and a lower bound of the number of PMs and their energy consumption. Trace-driven simulations demonstrate that our proposed algorithm significantly reduces energy consumption and the number of PMs used.

Rigorous results on the effectiveness of some heuristics for the consolidation of virtual machines in a cloud data center

Dynamic consolidation of virtual machines (VMs) in a cloud data center can be used to minimize power consumption. Beloglazov et al. have proposed the MM (Minimization of Migrations) heuristic for selecting the VMs to migrate from under- or over-utilized hosts, as well as the MBFD (Modified Best Fit Decreasing) heuristic for deciding the placement of the migrated VMs. According to their simulation results, these heuristics work very well in practice. In this paper, we investigate what performance guarantees can be rigorously proven for the heuristics. In particular, we establish that MM is optimal with respect to the number of selected VMs of an over-utilized host and it is a 1.5-approximation with respect to the decrease in utilization. On the other hand, we show that the result of MBFD can be arbitrarily far from the optimum. Moreover, we show that even if both MM and MBFD deliver optimal results, their combination does not necessarily result in optimal VM consolidation, but approximation results can be proven under suitable technical conditions. To the best of our knowledge, these are the first rigorously proven results on the effectiveness of also practically useful heuristic algorithms for the VM consolidation problem.

Using Ant Colony System to Consolidate VMs for Green Cloud Computing

High energy consumption of cloud data centers is a matter of great concern. Dynamic consolidation of Virtual Machines (VMs) presents a significant opportunity to save energy in data centers. A VM consolidation approach uses live migration of VMs so that some of the under-loaded Physical Machines (PMs) can be switched-off or put into a low-power mode. On the other hand, achieving the desired level of Quality of Service (QoS) between cloud providers and their users is critical. Therefore, the main challenge is to reduce energy consumption of data centers while satisfying QoS requirements. In this paper, we present a distributed system architecture to perform dynamic VM consolidation to reduce energy consumption of cloud data centers while maintaining the desired QoS. Since the VM consolidation problem is strictly NP-hard, we use an online optimization metaheuristic algorithm called Ant Colony System (ACS). The proposed ACS-based VM Consolidation (ACS-VMC) approach finds a near-optimal solution based on a specified objective function. Experimental results on real workload traces show that ACS-VMC reduces energy consumption while maintaining the required performance levels in a cloud data center. It outperforms existing VM consolidation approaches in terms of energy consumption, number of VM migrations, and QoS requirements concerning performance.

VM consolidation: A real case based on OpenStack Cloud

In recent years, Cloud computing has been emerging as the next big revolution in both computer networks and Web provisioning. Because of raised expectations, several vendors, such as Amazon and IBM, started designing, developing, and deploying Cloud solutions to optimize the usage of their own data centers, and some open-source solutions are also underway, such as Eucalyptus and OpenStack. Cloud architectures exploit virtualization techniques to provision multiple Virtual Machines (VMs) on the same physical host, so as to efficiently use available resources, for instance, to consolidate VMs in the minimal number of physical servers to reduce the runtime power consumption. VM consolidation has to carefully consider the aggregated resource consumption of co-located VMs, in order to avoid performance reductions and Service Level Agreement (SLA) violations. While various works have already treated the VM consolidation problem from a theoretical perspective, this paper focuses on it from a more practical viewpoint, with specific attention on the consolidation aspects related to power, CPU, and networking resource sharing. Moreover, the paper proposes a Cloud management platform to optimize VM consolidation along three main dimensions, namely power consumption, host resources, and networking. Reported experimental results point out that interferences between co-located VMs have to be carefully considered to avoid placement solutions that, although being feasible from a more theoretical viewpoint, cannot ensure VM provisioning with SLA guarantees.