Server Consolidation Research Articles

Experimental assessment of containers running on top of virtual machines

AbstractOver the past two decades, the cloud computing paradigm has gradually attracted more popularity due to its efficient resource usage and simple service access model. Virtualisation technology is the fundamental element of cloud computing that brings several benefits to cloud users and providers, such as workload isolation, energy efficiency, server consolidation, and cost reduction. This paper examines the combination of operating system‐level virtualisation (containers) and hardware‐level virtualisation (virtual machines). To this end, the performance of containers running on top of virtual machines is experimentally compared with standalone virtual machines and containers based on different hardware resources, including the processor, main memory, disk, and network in a real testbed by running the most commonly used benchmarks. Paravirtualisation and full virtualisation as well as type 1 and type 2 hypervisors are covered in this study. In addition, three prevalent containerisation platforms are examined.

The Goodness of Nesting Containers in Virtual Machines for Server Consolidation

Virtualization and server consolidation are the technologies that govern today’s data centers, allowing both efficient management at the functionality level as well as at the energy and performance levels. There are two main ways to virtualize either using virtual machines or containers. Both have a series of characteristics and applications, sometimes being not compatible with each other. Not to lose the advantages of each of them, there is a trend to load data centers by nesting containers in virtual machines. Although there are good experiences at a functional level, the performance and energy consumption trade-off of these solutions is not completely clear. Therefore, it is necessary to study how this new trend affects both energy consumption and performance. In this work, we present an experimental study aimed to investigate the behavior of nesting containers in virtual machines while executing CPU-intensive workloads. Our objective is to understand what performance and energy nesting configurations are equivalent or not. In this way, administrators will be able to manage their data centers more efficiently.

The Role of Virtualization Technology to Increase Operational Cost Efficiency of Indonesian SMEs: Case Study of Internet Service Providers

This research investigates the role of virtualization technology in enhancing operational cost efficiency for Indonesian Small and Medium-sized Enterprises (SMEs), specifically Internet Service Providers (ISPs). A quantitative approach, utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) version 2.0, was employed to analyze data collected from 68 respondents representing 216 Indonesian SME ISPs. The study focused on the relationship between virtualization technology planning, adaptation capability, server consolidation services, and operational cost effectiveness. The findings demonstrate that adaptability to virtualization technology is the most significant factor influencing operational cost efficiency. Effective implementation requires careful planning and a proactive approach to adapting to the technology's demands. Server consolidation services play a crucial role in optimizing resource utilization and reducing costs while virtualization technology offers substantial benefits, SMEs should carefully assess their specific needs and resources before implementation. Factors such as infrastructure, skills, and business objectives should be evaluated to ensure successful adoption and maximize cost savings. This research provides a foundation for further exploration into the impact of virtualization technology on SMEs. Future studies could investigate the applicability of these findings in different contexts, such as other industry sectors or regions. Additionally, examining the long-term effects of virtualization technology adoption and the potential for scalability could offer valuable insights. Further research could also focus on the challenges and opportunities associated with virtualization technology adoption in SMEs with varying sizes and resources.

On the scalability of the speedup considering the overhead of consolidating virtual machines in servers for data centers

Virtualization technologies are extensively utilized in data centers, particularly cloud computing. This facilitates data center management and diminishes the number of physical machines (servers) and, subsequently, their cooling requirements, leading to cost, space, and power consumption reductions. When applications in data centers are executing independent parallel transactions, but with similar performance requirements, the appropriate level of virtual machine consolidation on a server poses a fundamental challenge for capacity planning. This article introduces a method to evaluate the performance speedup achieved through virtualization on any server and the effects of virtualization and consolidation overheads on physical or virtual machine scalability. This research formalizes the speedup and overheads, using classical computer architecture statements. but at the same time proposes a new method to analyze these overhead amounts and types, showing the scalability and efficiency of different consolidations in the same server and its comparison against no consolidation. This work also proposes a new way to determine the optimal number of physical servers and the optimal number of consolidated virtual machines for a given transaction workload. The real experimentation was performed with different workload sizes, types of virtualizations and different servers. The method presented also facilitates the representation of linear scalability against the real degree of parallelism of either physical machines or consolidated virtual machines for a given transaction workload, as well as striking the right balance between speedup and energy in virtual server consolidation.

Green Computing with Deep Learning for Data Centers

Due to development in cloud services, lots of data is transferred between users and servers of the cloud. This transmission of data consumes huge amounts of energy. This energy consumption occurs during the operation of network infrastructure, the conversion of electrical to optical signals to travel long distances, and signal amplification. As Green computing is the use of computing devices in an environmentally friendly way, i.e., using electrical energy efficiently as Data centers require a significant amount of electricity to operate and cool the servers, leading to carbon emissions from the burning of fossil fuels. Green computing in cloud services is about optimizing energy consumption and by incorporating deep learning algorithms, we can enhance the energy efficiency of cloud infrastructure. These algorithms can analyze real-time data from sensors, optimize resource allocation, and dynamically adjust power usage. Through intelligent workload scheduling, server consolidation, and power management, deep learning enables the reduction of energy waste and carbon emissions. The integration of deep learning in cloud services not only improves energy efficiency but also enhances performance and cost-effectiveness. Here we are using a deep learning model which can be used for workload prediction and resource provisioning. By analyzing historical workload patterns and user behavior, deep learning algorithmscan predict future resource demands and allocate resources accordingly, leading to more efficient resource utilization and energy savings

Agent-based cloud simulation model for resource management

Driven by the successful service model and growing demand, cloud computing has evolved from a moderate-sized data center consisting of homogeneous resources to a heterogeneous hyper-scale computing ecosystem. This evolution has made the modern cloud environment increasingly complex. Large-scale empirical studies of essential concepts such as resource allocation, virtual machine migration, and operational cost reduction have typically been conducted using simulations. This paper presents an agent-based cloud simulation model for resource management. The focus is on how service placement strategies, service migration, and server consolidation affect the overall performance of homogeneous and heterogeneous clouds, in terms of energy consumption, resource utilization, and violation of service-level agreements. The main cloud elements are modeled as autonomous agents whose properties are encapsulated. The complex relationships between components are realized through asynchronous agent-to-agent interactions. Operating states and statistics are displayed in real time. In the evaluation, the efficiency of the simulator is studied empirically. The performance of various resource management algorithms is assessed using statistical methods, and the accuracy of server energy consumption models is examined. The results show that agent-based models can accurately reflect cloud status at a fine-grained level.

High performance computing with inter-virtual-machine communication

ABSTRACT Most applications use Transport Layer Security (TLS) as a default to encrypt network traffic to increase privacy and security. The adversary, however, also used LS encryption to conceal attacks or command-and-control communications. Encryption is essential for deciphering and assessing risks associated with communications. This issue should be resolved in a way that provides effective and covert decryption without compromising overall security. As a result of server consolidation, virtualization has emerged as a key technique for increasing data center energy efficiency. Consolidation of servers relies on the location of virtual machines. The placement of virtual machines has been approached from many different perspectives in recent years. Virtual machine placement approaches, however, only take into account the energy consumption of the physical machines within a data center, not the energy consumption of the communication network. Through the application domain, CVM offers a model-driven, user-centered approach to design, synthesis, and communication solutions by utilizing a fundamentally new method for handling these issues. This proposal discusses the concept, architecture, model languages, design prototypes, and implementation of CVM, arguing that it is a significantly more effective approach to technical communication solutions.

Server virtualization in higher educational institutions: a case study

Virtualization is a concept in which multiple guest operating systems share a single piece of hardware. Server virtualization is the widely used type of virtualization in which each operating system believes that it has sole control of the underlying hardware. Server virtualization has already got its place in companies. Higher education institutes have also started to migrate to virtualized servers. The motivation for higher education institutes to adopt server virtualization is to reduce the maintenance of the complex information technology (IT) infrastructure. Data security is also one of the parameters considered by higher education institutes to move to virtualization. Virtualization enables organizations to reduce expenditure by avoiding building out more data center space. Server consolidation benefits the educational institutes by reducing energy costs, easing maintenance, optimizing the use of hardware, provisioning the resources for research. As the hybrid mode of learning is gaining momentum, the online mode of teaching and working from home options can be enabled with a strengthened infrastructure. The paper presents activities conducted during server virtualization implementation at RV College of Engineering, Bengaluru, one of the reputed engineering institutes in India. The activities carried out include study of the current scenario, evaluation of new proposals and post-implementation review.

An efficient and optimized Markov chain-based prediction for server consolidation in cloud environment

Cloud service providers are aggressively expanding their data centers to meet end-user demand, resulting in increased power and energy consumption. As a result, it becomes extremely important to implement server consolidation solutions in the context of cloud computing. One of the difficult problems in cloud data centers is efficient server consolidation, which is related to both environmental and financial aspects. Using resources efficiently and reducing energy consumption are the main goals of consolidating servers for cloud computing. Regrettably, the majority of methods now in use have been geared towards either lowering system performance or reducing energy consumption. Using the Markov chain principle for each server transition, we suggested a strategy in this study to optimize the server consolidation process. An early review of the study’s findings is encouraging and comes to the conclusion that a successful server consolidation strategy may be implemented with the least amount of resources and the best possible energy use. The suggested strategy takes into account the following three elements: consolidating the resources, cutting down energy consumption, and trading off of both (composite case).

Load Balancing and Server Consolidation for Energy Management in Cloud Data Center

Users worldwide can access utility-oriented computing services through cloud computing. It enables the pay-as-you-go model to host well-known consumer, educational, and business applications. However, cloud data centers encounter significant issues in effectively managing resources, ensuring optimal performance, and reducing energy usage as the demand for cloud computing services is increasing rapidly. A possible strategy to reduce energy consumption and operating expenses is the energy-efficient management of virtual machines (VMs) and server consolidation. In this paper, a VM migration and server consolidation algorithm is proposed, which is energy efficient and SLA aware. The proposed approach is evaluated using the CloudSim simulation kit and PlanetLab workload. The experiment result shows that the proposed approach has reduced energy consumption and SLA violation.

CLR: Cloud Linear Regression Environment as a More Effective Resource-Task Scheduling Environment (State-of-the-Art)

The cloud paradigm has swiftly developed, and it is now well known as one of the emerging technologies that will have a significant influence on technology and society in the next few years. Cloud computing also has several benefits, including lower operating costs, server consolidation, flexible system setup, and elastic resource supply. However, there are still technological hurdles to overcome, particularly with real-time applications by providing resources. Resources allocation management most charming part of cloud computing; therefore, several authors have worked in the area of resource usage. This study introduces an innovative cloud machine learning framework-based linear regression approach called cloud linear regression (CLR), which entails both cloud technology and machine learning concept. CLR using machine learning yielded good prediction results for resource allocation management, as appeared with many researching, and still seek, research to raise optimal solutions to the resources' allocation problem as the aim of this study. This study discusses the relation between cloud resource allocation management and machine learning techniques by illustrating the role of linear regression methods, resource distribution, and task scheduling. The analytical analysis shows that the CLR promises to present an effective solution for resources (scheduling, provisioning, allocation, and availability).

An Effective Secured Dynamic Network-Aware Multi-Objective Cuckoo Search Optimization for Live VM Migration in Sustainable Data Centers

With the increasing use of cloud computing by organizations, cloud data centers are proliferating to meet customers’ demands and host various applications using virtual machines installed in physical servers. Through Live Virtual Machine Migration (LVMM) methods, cloud service providers can provide improved computing capabilities for server consolidation maintenance of systems and potential power savings through a reduction in the distribution process to customers. However, Live Virtual Machine Migration has its challenges when choosing the best network path for maximizing the efficiency of resources, reducing consumption, and providing security. Most research has focused on the load balancing of resources and the reduction in energy consumption; however, they could not provide secure and optimal resource utilization. A framework has been created for sustainable data centers that pick the most secure and optimal dynamic network path using an intelligent metaheuristic algorithm, namely, the Network-aware Dynamic multi-objective Cuckoo Search algorithm (NDCS). The developed hybrid movement strategy enhances the search capability by expanding the search space and adopting a combined risk score estimation of each physical machine (PM) as a fitness criterion for ensuring security with rapid convergence compared to the existing strategies. The proposed method was assessed using the Google cluster dataset to ascertain its worthiness. The experimental results show the supremacy of the proposed method over existing methods by ensuring services with a lower total migration time, lower energy consumption, less makespan time, and secure optimum resource utilization.

More than Meets One Core: An Energy-Aware Cost Optimization in Dynamic Multi-Core Processor Server Consolidation for Cloud Data Center

The massive number of users has brought severe challenges in managing cloud data centers (CDCs) composed of multi-core processor that host cloud service providers. Guaranteeing the quality of service (QoS) of multiple users as well as reducing the operating costs of CDCs are major problems that need to be solved. To solve these problems, this paper establishes a cost model based on multi-core hosts in CDCs, which comprehensively consider the hosts’ energy costs, virtual machine (VM) migration costs, and service level agreement violation (SLAV) penalty costs. To optimize the goal, we design the following solution. We employ a DAE-based filter to preprocess the VM historical workload and use an SRU-based method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migrations before the hosts move into the overloaded state to reduce the occurrence of SLAV. A multi-core-aware heuristic algorithm is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing baseline methods, our proposed method reduces the total operating cost by 20.9~34.4%.

Server Consolidation Algorithms for Cloud Computing

In recent years, companies and researchers have hosted and rented computer resources over ‎the ‎‎internet due to cloud computing, which led to an increase in the energy consumed by ‎data centers. This ‎‎consumption is considered one of the world's highest, ‎which pushed many ‎researchers to propose ‎several techniques such as server ‎consolidation (SC) to solve the‎‏ ‏trade‏-‏off‏ ‏‏‎between energy saving and ‎quality of service ‎‎(QoS). SC requires maintaining service level ‎agreements (SLA) violations and ‎minimizing ‎the number of active physical machines (PMs). ‎Furthermore, to achieve this balance and ‎‎avoid ‎increasing hardware costs, the SC challenge targets ‎placing new virtual machines ‎‎(VMs) in ‎suitable PMs. This work explored the existing SC algorithms ‎that include ‎CloudSim as a simulator ‎environment and PlanetLab as a dataset. The authors compared ‎the well-known optimization methods ‎and extracted the weaknesses of the main three deployed ‎‎approaches involved in the consolidation ‎process: bin-packing model, metaheuristics, ‎and machine ‎learning-based solutions.‎

Weight-Aware Cache for Application-Level Proportional I/O Sharing

Virtualization technology has enabled server consolidation where multiple servers are co-located on a single physical machine to improve resource utilization. In such systems, proportional I/O sharing is critical to meet the SLO (Service-Level Objectives) of the applications running in each virtual instance. However, previous studies focus on block-level I/O proportionality without considering the upper-layer I/O caches, which handle I/O requests on behalf of the underlying storage devices, thereby failing to achieve application-level proportional I/O sharing. To overcome this limitation, we propose a new cache management scheme, Weight-aware Cache (WaC), which reflects the I/O weights on cache allocation and reclamation. Specifically, WaC prioritizes higher-weighted applications in the lock acquisition process of cache allocation by re-ordering the lock waiting queue based on I/O weight. Additionally, WaC keeps the number of cache entries of each application proportional to its I/O weight, through weight-aware cache reclamation. To verify the efficacy of our scheme, we implement and evaluate WaC on both the page cache and bcache. The experimental results demonstrate that our scheme improves I/O proportionality with negligible overhead in various cases.

Complementary in Time and Space: Optimization on Cost and Performance with Multiple Resources Usage by Server Consolidation in Cloud Data Center

The recent COVID-19 pandemic has accelerated the use of cloud computing. The surge in the number of users presents cloud service providers with severe challenges in managing computing resources. Guaranteeing the QoS of multiple users while reducing the operating cost of the cloud data center (CDC) is a major problem that needs to be solved urgently. To solve this problem, this paper establishes a cost model based on multiple computing resources in CDC, which comprehensively considers the hosts’ energy cost, virtual machine (VM) migration cost, and SLAV penalty cost. To minimize this cost, we design the following solution. We employ a convolutional autoencoder-based filter to preprocess the VM historical workload and use an attention-based RNN method to predict the computing resource usage of the VMs in future periods. Based on the predicted results, we trigger VM migration before the host enters an overloaded state to reduce the occurrence of SLAV. A heuristic algorithm based on the complementary use of multiple resources in space and time is proposed to solve the placement problem. Simulations driven by the VM real workload dataset validate the effectiveness of our proposed method. Compared with the existing methods, our proposed method reduces the energy consumption of the hosts and SLAV and reduces the total cost by 26.1~39.3%.

Learning-Based Virtual Machine Selection in Cloud Server Consolidation

In cloud data center (CDC), reducing energy consumption while maintaining performance has always been a hot issue. In server consolidation, the traditional solution is to divide the problem into multiple small problems such as host overloading detection, virtual machine (VM) selection, and VM placement and solve them step by step. However, the design of host overloading detection strategies and VM selection strategies cannot be directly linked to the ultimate goal of reducing energy consumption and ensuring performance. This paper proposes a learning-based VM selection strategy that selects appropriate VMs for migration without direct host overloading detection, thereby reducing the generation of SLAV, ensuring the performance, and reducing the energy consumption of CDC. Simulations driven by real VM workload traces show that our method outperforms the existing methods in reducing SLAV generation and CDC energy consumption.

Reliability-Ensured Efficient Scheduling With Replication in Cloud Environment

Server consolidation in a virtualized cloud environment improves resource utilization by hosting many virtual machines (VMs) on a single machine. The host server failure induces a serious risk of the interruption of services provided through VMs hosted on the failed server. Ensuring high reliability and availability from the unpredicted host server failures is a challenging task, and this assurance guarantee is the quality of service to the users. Executing latency-sensitive workloads within a time frame, multireplica strategy-based reliability assurance is used in general. In this article, we propose two reliability-ensured scheduling approaches by strategically allocating the jobs to machines to satisfy their reliability and deadline requirements. The proposed approaches “Replication with equal failure rate” and “Efficient reliability replication method” are used to schedule the jobs to machines with equal and different failure rates, respectively, while satisfying both the deadline and reliability requirements of the jobs. Both the scheduling approaches dynamically select the machines for the jobs to minimize the number of replicas based on job characteristics and failure rate of machines. Also, we adopt an overlapping mechanism to further minimize the resource utilization. The extensive simulation results on randomly generated and real-world data at different scales show that the proposed approaches perform better than other state-of-the-art approaches.

Power and Energy-efficient VM scheduling in OpenStack Cloud Through Migration and Consolidation using Wake-on-LAN

On-demand provisioning of computing resources using the network over the Cloud consumes a tremendous amount of energy. The energy consumed does not scale out efficiently with the computational use of the resources. A large portion accounts for the power consumption to drive the hardware and cooling facilities for the hardware resources. Nowadays, energy and power consumption have become the most critical concerns in data centers. This work highlights a novel technique to achieve energy efficiency through server consolidation to address the abovementioned situations by effectively scheduling Virtual Machines (VMs) onto the servers and rescheduling active VMs upon deletion of existing VMs through cold migration. An advanced technology, i.e. Wake on LAN (WoL), is employed through power wake for dynamic and wake up of the servers on private Cloud. Compared to existing models, the proposed approach's maximum estimated energy and power saved are experimentally verified to be 54% and 50%, respectively.

A general method for evaluating the overhead when consolidating servers: performance degradation in virtual machines and containers

Server consolidation is one of the most commonly used techniques for reducing energy consumption in datacenters; however, this results in inherent performance degradation due to the coallocation of virtual servers, i.e., virtual machines (VMs) and containers, in physical ones. Given the widespread use of containers and their combination with VMs, it is necessary to quantify the performance degradation in these new consolidation scenarios, as this information will help system administrators make decisions based on server performance management. In this paper, a general method for quantifying performance degradation, that is, server overhead, is proposed for arbitrary consolidation scenarios. To demonstrate the applicability of the method, we develop a set of experiments with varying combinations of VMs, containers, and workload demands. From the results, we can obtain a suitable method for quantifying performance degradation that can be implemented as a recursive algorithm. From the set of experiments addressing the hypothetical consolidation scenarios, we show that the overhead depends not only on the type of hypervisor and the workload distribution but also on the combination of VMs and containers and their nesting, if feasible.