Server Consolidation Research Articles

An Overview of the State-of-the-art Virtual Machine Placement Algorithms for Green Cloud Data Centres

Increased energy consumption in Cloud Data Centres (CDCs) increases the carbon footprint. Efficiency of the data centres thus needs to be improved through server consolidation using effective virtual machine (VM) placement and migration techniques and minimizing the number of active physical machines (PMs). One of the problems is how to operationally allocate the VMs to PMs. These allocations have both operational costs and energy consumption issues. To achieve the aim of ‘Green Computing’ a number of state-of-the-art machine learning algorithms have been proposed for the VM placement. The authors of this paper have provided a detailed discussion and comparison of some of the current research works on energy efficiency. and cons of each of these techniques have been discussed. Some future research prospects in this field have also been mentioned at the end.

Addressing IT Capacity Management Concerns Using Machine Learning Techniques

The digitization of various industries is emerging from and being supported by the Information Technology (IT) industry. However, bringing about the practical implementation of the fourth industrial revolution will require different fields of IT to undergo their own transformations. One of the research fields that draws a lot of attention nowadays is machine learning and its application in different areas. Therefore, in this paper, we present an analysis on the applicability of various machine learning techniques to address different problems in the field of capacity management for Commercial-off-the-shelf enterprise applications. Our investigation of the selected machine learning techniques is based on real monitoring data from over 18,000 SAP applications and database instances that are hosted on more than 16,000 different physical servers. These data are used to train various performance models, such as support vector machines with different kernels, random forests, and AdaBoost, for standard business functions. Boosted trees achieve sufficient accuracy to predict mean response times for ten frequently used transactions. To evaluate the suggested models, we applied them successfully to address different concerns in the context of capacity management. The evaluation includes multiple scenarios in the fields of server sizing, load testing, and server consolidation, with the objective to identify cost-effective designs. Based on the same monitoring data, we also present an anomaly detection scenario. In this scenario, we aim to demonstrate the use of machine learning techniques on historical data to detect possible performance anomalies for a suggested design or even predict possible anomalies in future scenarios. Results strongly emphasize the need to integrate monitoring data from standardized business applications to allow for novel and cost-effective capacity management service offerings.

Online Service Function Chain Deployment for Live-Streaming in Virtualized Content Delivery Networks: A Deep Reinforcement Learning Approach

Video delivery is exploiting 5G networks to enable higher server consolidation and deployment flexibility. Performance optimization is also a key target in such network systems. We present a multi-objective optimization framework for service function chain deployment in the particular context of Live-Streaming in virtualized content delivery networks using deep reinforcement learning. We use an Enhanced Exploration, Dense-reward mechanism over a Dueling Double Deep Q Network (E2-D4QN). Our model assumes to use network function virtualization at the container level. We carefully model processing times as a function of current resource utilization in data ingestion and streaming processes. We assess the performance of our algorithm under bounded network resource conditions to build a safe exploration strategy that enables the market entry of new bounded-budget vCDN players. Trace-driven simulations with real-world data reveal that our approach is the only one to adapt to the complexity of the particular context of Live-Video delivery concerning the state-of-art algorithms designed for general-case service function chain deployment. In particular, our simulation test revealed a substantial QoS/QoE performance improvement in terms of session acceptance ratio against the compared algorithms while keeping operational costs within proper bounds.

Next-Generation Blockchain-Enabled Virtualized Cloud Security Solutions: Review and Open Challenges

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective IT solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from the grid and parallel computing models. It uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better IT solutions for large-scale computational data centres. It encompasses different services for supporting data storage, networking, and computing for facilities and amenities for businesses and multinational corporations. The enormous elastic on-demand cloud provisioning resources and services and datasets are processed and stored in tier-level virtualized cloud data centres operated by third-party service providers called cloud owners. The primary issue with these cloud service providers is to provide and maintain data security, privacy, and confidentiality and service availability and data support for end-users. This paper reviews, highlights, and discusses some of the common cloud computing vulnerabilities primarily related to virtualization platforms and their implementations while outsourcing services and resources to different end-users and business enterprises. We then provided blockchain-enabled solutions for virtualized cloud platforms involving both the end-users and cloud service providers (CSP) to address and solve various security and privacy-related vulnerabilities. These solutions will help the data centre industry to improve its virtualized cloud services and resource provisioning facilities. Finally, we discussed different blockchain-related implementation challenges in cloud infrastructures.

Direct-Virtio: A New Direct Virtualized I/O Framework for NVMe SSDs

Virtualization is a core technology for cloud computing, server consolidation and multi-platform support. However, there is a concern regarding performance degradation due to the duplicated I/O stacks virtualization environments. In this paper, we propose a new I/O framework, we refer to it as Direct-Virtio, that manipulates storage directly, which makes it feasible to avoid the duplicated overhead. In addition, we devise two novel mechanisms, called vectored I/O and adaptive polling, to process multiple I/O requests collectively and to check I/O completion efficiently. Real implementation-based evaluation shows that our proposal can enhance performance for both micro and macro benchmarks.

An introduction to stochastic bin packing-based server consolidation with conflicts

The energy consumption of large-scale data centers or server clusters is expected to grow significantly in the next couple of years contributing to up to 13% of the worldwide energy demand in 2030. As the involved processing units require a disproportional amount of energy when they are idle, underutilized, or overloaded, balancing the supply of and the demand for computing resources is a key issue to obtain energy-efficient server consolidations. Whereas traditional concepts mostly consider deterministic predictions of the future workloads or only aim at finding approximate solutions, in this article, we propose an exact approach to tackle the problem of assigning jobs with (not necessarily independent) stochastic characteristics to a minimal amount of servers subject to further practically relevant constraints. As a main contribution, the problem under consideration is reformulated as a stochastic bin packing problem with conflicts and modeled by an integer linear program. Finally, this new approach is tested on real-world instances obtained from a Google data center.

Threshold Compare and Load Balancing Algorithm to for Resource Optimization in a Green Cloud

The reduction in the consumption of energy by the cloud data centers is called as green cloud. Green cloud also helps in restraining the waste disposed to the environment. With the increasing demand in cloud computing, there has been an increase in the energy consumption. Green cloud can be achieved by server consolidation and proper load balancing techniques using virtual machine (VM) migration. It is a feature provided by virtualization. The virtual machines are transferred from one host to another. The overhead associated with migration is performance degradation. This can be overcome by proper load balancing techniques. This helps to curtail the number of VM migrations as well as the energy consumption. In this paper, a technique called as threshold compare and load balance algorithm (TCLBA) is proposed for optimization of the resources at the cloud provider. Two types of threshold are defined for load balancing, namely the lower and upper threshold. This algorithm works on the principle of shifting the load from a server if the load is above the upper threshold or shifting to the server if its load is below the lower threshold. The load is balanced by migrating the VMs. The workload is consolidated to smaller number of hosts such that the remaining hosts are shut down. This method solves the purpose of effective utilization of available resources with lesser energy consumption.

Challenges Healthcare Management Business: ISP Innovation SMEs with Technology Virtualization and Server Consolidation


 The adaptation and innovation of the healthcare business strategy during the pandemic and new normal is needed now, for Small and Medium Enterprises (SMEs) which are engaged in Internet Service Providers (ISP) especially in running their business in pandemic also face the future challenging. This study aims to prove that virtualization technology planning, virtualization technology adaptability, and server consolidation services have a positive effect on healthcare management and business, such as operational costs with an impact on more efficient of healthcare small and medium enterprises (SMEs) Internet Service Providers (ISPs) in Indonesia. The data were taken from distributing questionnaires to 77 company representative respondents with the positions of IT Manager, IT Supervisor, and CIO. Data were analyzed by SEM using Smart PLS. The results of the research obtained prove that the operational cost effectiveness of Data Center management is influenced by planning, adaptability, and application of virtualization technology for 5.0. SMES ISPs in Indonesia healthcare sector need to pay attention to new result of planning, adaptability, and implementation of server consolidation services as a determinant of operational and cost effectiveness related to business objectives and management in managing a Data Center in the pandemic, new normal and future challenges.

Mathematical models and approximate solution approaches for the stochastic bin packing problem

We consider the (single-stage) stochastic bin packing problem (SBPP) which is based on a given list of items the sizes of which are represented by stochastically independent random variables. The SBPP requires to determine the minimum number of unit capacity bins needed to pack all the items, such that for each bin the probability of exceeding the capacity is bounded by a given constant. Such calculations are particularly relevant in the field of server consolidation, where (not precisely known) jobs have to be assigned to as few processing units as possible to keep the energy consumption and operational costs low. Although not being limited to this assumption, our theoretical investigations are only exemplified for normally distributed item sizes, especially since real-world characteristics can often be reasonably approximated by that type of distribution. From a mathematical point of view, such item characteristics are particularly challenging as the corresponding SBPP turns out to be a nonlinear integer program. For this scenario, we first derive several (improved) lower and upper bounds and study their worst-case performance metrics. Moreover, we describe various linearization techniques to overcome the drawback of nonlinearity. Finally, all approaches are tested based on extensive computational experiments, involving both randomly generated and real-world instances.

Multi-Dependency and Time Based Resource Scheduling Algorithm for Scientific Applications in Cloud Computing

Workflow scheduling is one of the significant issues for scientific applications among virtual machine migration, database management, security, performance, fault tolerance, server consolidation, etc. In this paper, existing time-based scheduling algorithms, such as first come first serve (FCFS), min–min, max–min, and minimum completion time (MCT), along with dependency-based scheduling algorithm MaxChild have been considered. These time-based scheduling algorithms only compare the burst time of tasks. Based on the burst time, these schedulers, schedule the sub-tasks of the application on suitable virtual machines according to the scheduling criteria. During this process, not much attention was given to the proper utilization of the resources. A novel dependency and time-based scheduling algorithm is proposed that considers the parent to child (P2C) node dependencies, child to parent node dependencies, and the time of different tasks in the workflows. The proposed P2C algorithm emphasizes proper utilization of the resources and overcomes the limitations of these time-based schedulers. The scientific applications, such as CyberShake, Montage, Epigenomics, Inspiral, and SIPHT, are represented in terms of the workflow. The tasks can be represented as the nodes, and relationships between the tasks can be represented as the dependencies in the workflows. All the results have been validated by using the simulation-based environment created with the help of the WorkflowSim simulator for the cloud environment. It has been observed that the proposed approach outperforms the mentioned time and dependency-based scheduling algorithms in terms of the total execution time by efficiently utilizing the resources.

Comprehensive survey on energy-aware server consolidation techniques in cloud computing

The objective of cloud computing is to provide seamless services using virtualization technology over the Internet to serve the Quality of Service (QoS)-driven end users requirements. In order to provide various services like Software as a Service, Platform as a Service, Infrastructure as a Service, the cloud server’s datacenters are kept active, which have large electrical consumption. Due to improper utilization of resources, optimizing the servers energy consumption becomes a significant challenge for service vendors from environmental and economic perspectives as well. The challenge to provide services with a low energy consumption profile opens up a new dimension of optimized server use for intelligent management of resources (such as CPU/disk/memory), with reduced power consumption through server consolidation. This enables fewer active physical servers to provide the required services without compromising the QoS. This article presents a narrative recent advancement comprehensive as well as systematic survey of existing energy-efficient techniques along with their limitations and the challenges associated in implementing them.

Server consolidation: A technique to enhance cloud data center power efficiency and overall cost of ownership

Cloud computing is a well-known technology that provides flexible, efficient, and cost-effective information technology solutions for multinationals to offer improved and enhanced quality of business services to end-users. The cloud computing paradigm is instigated from grid and parallel computing models as it uses virtualization, server consolidation, utility computing, and other computing technologies and models for providing better information technology solutions for large-scale computational data centers. The recent intensifying computational demands from multinationals enterprises have motivated the magnification for large complicated cloud data centers to handle business, monetary, Internet, and commercial applications of different enterprises. A cloud data center encompasses thousands of millions of physical server machines arranged in racks along with network, storage, and other equipment that entails an extensive amount of power to process different processes and amenities required by business firms to run their business applications. This data center infrastructure leads to different challenges like enormous power consumption, underutilization of installed equipment especially physical server machines, CO2 emission causing global warming, and so on. In this article, we highlight the data center issues in the context of Pakistan where the data center industry is facing huge power deficits and shortcomings to fulfill the power demands to provide data and operational services to business enterprises. The research investigates these challenges and provides solutions to reduce the number of installed physical server machines and their related device equipment. In this article, we proposed server consolidation technique to increase the utilization of already existing server machines and their workloads by migrating them to virtual server machines to implement green energy-efficient cloud data centers. To achieve this objective, we also introduced a novel Virtualized Task Scheduling Algorithm to manage and properly distribute the physical server machine workloads onto virtual server machines. The results are generated from a case study performed in Pakistan where the proposed server consolidation technique and virtualized task scheduling algorithm are applied on a tier-level data center. The results obtained from the case study demonstrate that there are annual power savings of 23,600 W and overall cost savings of US$78,362. The results also highlight that the utilization ratio of already existing physical server machines has increased to 30% compared to 10%, whereas the number of server machines has reduced to 50% contributing enormously toward huge power savings.

GreenCloudNet++: Simulation framework for energy efficient and secure, green job scheduling in geographically distributed data centers

AbstractGeographically distributed data centers are used as backbone infrastructure to meet rapidly increasing service demands of computations and data storage in cloud computing. This increase results in high energy consumption, increased operational expenditures, and high carbon footprint which are becoming points of great concern for service providers. In this research work, we present a simulation framework named GreenCloudNet++ for simulation and evaluation of energy‐efficient, green‐aware, and secure job scheduling mechanisms for geographically distributed data centers. GreenCloudNet++ has been developed as an extension to CloudNetSim++, which is designed to simulate distributed data center architectures connected with high speed networks. The functionality of CloudNetSim++ is extended by adding hierarchical job scheduling mechanism, hierarchical statistics collection mechanism and integration mechanism for green energy. Proposed model considers availability of green energy at each data center and maximizes its utilization. It considers the amount of underutilized computational resources at individual data centers while assigning jobs to the data centers, which helps to achieve better server consolidation resulting in better energy efficiency. Proposed model also relies on network load inside each data center which helps avoiding hotspots.

Combined particle swarm optimization and Ant Colony System for energy efficient cloud data centers

AbstractOne of the foremost issues deliberated by the cloud infrastructure providers is to minimize the costs by reducing energy consumption. Though many heuristic algorithms such as Genetic Algorithm (GA), Simulated Annealing (SA), and so on, have been existed for task scheduling or server consolidation to reduce energy consumption, more energy savings in data centers is required. In this paper, a combination of two distinctive heuristic algorithms, namely Particle Swarm Optimization (PSO) technique for proficient task scheduling and Ant Colony System (ACS) for effective server consolidation are proposed. In PSO, each particle in the swarm finds a task scheduling solution and updates the best solution. It is faster than many meta‐heuristic algorithms. In ACS, each ant in the ant colony tries to find the best destination PMs for migrating VMs from overloaded or underloaded PMs. The proposed PSO‐ACS algorithm is experimented with PlanetLab real workload traces available in the CloudSim toolkit. The algorithm provides reduction in energy consumption, number of VM migrations, and Energy SLA Violation (ESV) metrics while maintaining QoS. In the best‐case scenario, the proposed method reduces the ESV metric by 28%, 27%, and 18% in comparison with other algorithms like LR, ACS, and PSO‐LR.

Low overhead performance monitoring for shared infrastructures

Modern systems make use of virtualization to build scalable and elastic applications. A major example is cloud computing, which has emerged in the last decades as a cost-effective paradigm for hosting and delivering services over the Internet. Cloud providers adopt server consolidation strategies to manage resources, reducing underutilized hosts. However, collocated applications compete for physical resources. So, their particularities, as well as workload fluctuations, may negatively affect cloud applications’ performance. Performance monitors are useful tools for tracking performance behavior. The observation of events occurring in the system provides a detailed view of how applications affect resource usage. This way, performance counters can assist developers and enable the detection of bottlenecks, allowing administrators to verify whether service level agreements are met. However, the monitoring itself can be a contention source, especially in large scale systems, where hundreds of performance counters are collected, and the acquired data are stored at a high frequency. In this work, the influence of monitoring overhead in virtualized environments is analyzed. As a mean to reduce shared resource contention, it is proposed an approach to alleviate monitoring costs, bringing as benefits: an automatic method for performance counters selection; and overhead reduction by discarding unnecessary performance counters. Through experimental evaluation, this work characterizes the monitoring overhead in virtualized environments. A performance comparison using both the complete and the reduced set of performance counters highlights the benefits of adopting the proposed approach.

A multi-objective Monarch Butterfly Algorithm for virtual machine placement in cloud computing

The growing demand for cloud computing adoption presents more challenges for researchers to make cloud computing more efficient and affordable for infrastructure providers and end users. The management of cloud computing involves investing in IT infrastructure in the first phase and investing in energy, maintenance and space costs in the second phase. However, energy costs account for a large portion of cloud management costs, and saving energy consumption can significantly reduce overall cloud management costs. Server consolidation is a strategy to improve data center energy efficiency and resource utilization. Virtual machine (VM) placement is considered one of the main problems with VM consolidation. The VM placement problem aims to reduce the number of active physical machines in data center to reduce data center power consumption and maintenance costs. However, the waste of data center resources has a significant impact on the energy efficiency of the data center, so it should be considered in the VM placement strategy. This paper proposes a new method based on the Monarch Butterfly Optimization algorithm (MBO) called MBO-VM for new virtual machine placement, designed to maximize packaging efficiency and reduce the number of active physical servers. CloudSim toolkit is used to test the efficiency of the proposed MBO-VM approach under real cloud workloads as well as synthetic workloads. Simulation results show that MBO-VM produces significantly better results compared with known VM placement techniques. The proposed MBO-VM can reduce the number of active servers more effectively and maximize the packaging efficiency.

Energy Reduction Through Memory Aware Real-Time Scheduling on Virtual Machine in Multi-Cores Server

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.

A discrete chaotic multi-objective SCA-ALO optimization algorithm for an optimal virtual machine placement in cloud data center

Cloud computing, with its immense potentials in low cost and on-demand services, is a promising computing platform for both commercial and non-commercial computation applications. It focuses on the sharing of information and computation in a large network that are quite likely to be owned by geographically disbursed different venders. Power efficiency in cloud data centers (DCs) has become an important topic in recent years as more and larger DCs have been established and the electricity cost has become a major expense for operating them. Server consolidation using virtualization technology has become an important technology to improve the energy efficiency of DCs. Virtual machine (VM) assignment is the key in server consolidation. In the past few years, many methods to VM assignment have been proposed, but existing VM assignment approaches to the VM assignment problem consider the energy consumption by physical machines (PM). In current paper a new approach is proposed that using a combination of the sine cosine algorithm (SCA) and ant lion optimizer (ALO) as discrete multi-objective and chaotic functions for optimal VM assignment. First objective of our proposed model is minimizing the power consumption in cloud DCs by balancing the number of active PMs. Second objective is reducing the resources wastage by using optimal VM assignment on PMs in cloud DCs. Reducing SLA levels was another purpose of this research. By using the method, the number of increase of migration of VMs to PMs is prevented. In this paper, several performance metrics such as resource wastage, power consumption, overall memory utilization, overall CPU utilization, overall storage space, and overall bandwidth, a number of active PMs, a number of shutdowns, a number of migrations, and SLA are used. Ultimately, the results obtained from the proposed algorithm were compared with those of the algorithms used in this regard, including First Fit (FF), VMPACS and MGGA.

Intra- and Inter-Server Smart Task Scheduling for Profit and Energy Optimization of HPC Data Centers

Servers in a data center are underutilized due to over-provisioning, which contributes heavily toward the high-power consumption of the data centers. Recent research in optimizing the energy consumption of High Performance Computing (HPC) data centers mostly focuses on consolidation of Virtual Machines (VMs) and using dynamic voltage and frequency scaling (DVFS). These approaches are inherently hardware-based, are frequently unique to individual systems, and often use simulation due to lack of access to HPC data centers. Other approaches require profiling information on the jobs in the HPC system to be available before run-time. In this paper, we propose a reinforcement learning based approach, which jointly optimizes profit and energy in the allocation of jobs to available resources, without the need for such prior information. The approach is implemented in a software scheduler used to allocate real applications from the Princeton Application Repository for Shared-Memory Computers (PARSEC) benchmark suite to a number of hardware nodes realized with Odroid-XU3 boards. Experiments show that the proposed approach increases the profit earned by 40% while simultaneously reducing energy consumption by 20% when compared to a heuristic-based approach. We also present a network-aware server consolidation algorithm called Bandwidth-Constrained Consolidation (BCC), for HPC data centers which can address the under-utilization problem of the servers. Our experiments show that the BCC consolidation technique can reduce the power consumption of a data center by up-to 37%.

NASCon: Network-Aware Server Consolidation for server-centric wireless datacenters

Servers in datacenter are underutilized due to over-provisioning, which contributes heavily toward high-power consumption of the datacenters. Server consolidation can drastically reduce power consumption of datacenters by reducing the number of active servers. However, server consolidation alone can adversely affect network performance. Recently, wireless datacenters using millimeter-wave communications have been proposed and shown to drastically reduce power consumption of networking equipment. Therefore, server-consolidation strategies should leverage novel datacenter network-architectures to minimize power consumption with minimal impact on performance. We present network-aware bandwidth-constrained server consolidation algorithm called Network-Aware Server Consolidation (NASCon), for wireless datacenters that can reduce the power consumption up to 37%. In wireless datacenter, NASCon server consolidation consumes comparable power compared to consolidation optimized with exhaustive search, while being much less computationally expensive and also improving the network performance. However, due to arrival of new-tasks and completion of existing-tasks, consolidated utilization profile of servers change, which may have adverse effect on overall power consumption over time. To overcome this, NASCon algorithm needs to be executed periodically. We propose a mechanism to find optimal inter-consolidation time and derived a mathematical model for the same. The model reduces the computing overheads for determining the optimal inter-consolidation time enabling real-time implementation.