Decentralized cloud datacenter reconsolidation through emergent and topology-aware behavior

By employing the virtual machines (VMs) consolidation technique at a virtualized data center, optimal mapping of VMs to physical machines (PMs) can be performed. The type of optimization approach and the policy of detecting the appropriate time to implement the consolidation process are influential in the performance of the consolidation technique. In a majority of researches, the consolidation approach merely focuses on the management of underloaded or overloaded PMs, while a number of VMs could also be in an underload or overload state. Managing an abnormal state of VM results in the postponement of PM getting into an abnormal state as well and affects the implementation time of the consolidation process. For the aim of optimal VM consolidation in this research, a self-adaptive architecture is presented to detect and manage underloaded and overloaded VMs /PMs in reaction to workload changes in the data center. The goal of consolidation process is employing the minimum number of active VMs and PMs, while guaranteeing the quality of service (QoS). Assessment criteria of QoS are two parameters including average number of requests in the PM buffer and average waiting time in the VM. To evaluate these two parameters, a probabilistic model of the data center is proposed by applying the queuing theory. The assessment results of the probabilistic model form a basis for decision-making in the modules of the proposed architecture. Numerical results obtained from the assessment of the probabilistic model via discrete-event simulator under various parameter settings confirm the efficiency of the proposed architecture in achieving the aims of the consolidation process.

Cloud computing is a new computation technology that provides services to consumers and businesses. The main idea of Cloud computing is to present software and hardware services through the Internet to the users and organizations at all levels. In Cloud computing, the users pay for the services, which means a usage-based payment system is used in this technology. Using virtualization technology in computation resources enables the appropriate utilization of resources in cloud computing. One of the most significant challenging issues in virtualization technology is the placement of optimal virtual machines on physical machines in cloud data centers. The placement of virtual machines comprises a process wherein virtual machines are mapped onto physical machines in cloud data centers. Optimal deployment leads to the reduction in power consumption, optimal use of resources, traffic reduction in data centers, costs reduction, and efficiency enhancement of the data center in the cloud. The present article proposed a new approach using a combination of the Sine–Cosine Algorithm and Salp Swarm Algorithm as discrete multi-objective and chaotic functions for optimal virtual machine placement. The first goal of the proposed algorithm was to reduce the power consumption in cloud data centers by condensing the number of active physical machines. The second goal was to reduce the waste of resources and manage it by optimally virtual machine placement on physical machines in cloud data centers. The third objective was to minimize and reduce Service Level Agreement among the active physical machines in cloud data centers. The proposed method prevent the increase in the migration of virtual machines onto physical machines. Ultimately, the results obtained from the proposed algorithm were compared with those of previous akin algorithms in the literature, including First Fit, Virtual Machine Placement Ant Colony System, and Modified Best Fit Decreasing. The proposed scheme is tested using Amazon EC2 Instances and the result indicated that the proposed algorithm performs better than the existing algorithms for various performance metrics.

Nowadays, cloud computing is an essential commodity for the computational world which offers a large number of server capabilities through virtualized services. The data centers which promise their clients to deliver services contain a large number of computational nodes which are responsible for consuming a large amount of energy. Virtual machine (VM) in somehow solves this problem. Through VM consolidation, the number of physical machines (PM) in the data centers is minimized and so the energy consumption. Optimized VM placement is the medium for VM consolidation. In this paper, we proposed a mathematical model to reduce the power consumption as well as active VMs and then used meta-heuristic algorithm which uses GA for generating an optimized solution. The proposed model is compared with first fit, first fit decreasing and permutation pack algorithms and found our meta-heuristic algorithm is performing well compared to others.

In Cloud data centers, Virtual Machines (VMs) as resources for Infrastructure as a service (IaaS) can be dynamically allocated to different customers to meet their application goals. In this paper, from the providers' point of view of reducing power consumption, we investigate the power-aware scheduling of real-time VMs by considering fixed processing intervals. In the case of all VMs sharing random portions of the total capacity of a Physical Machine (PM), finding the optimal solution of minimizing the total number of PMs is NP complete as proved in many open literature. Hence, we model the problem as a modified interval partitioning problem to provide approximate solutions and propose scheduling schemes to reduce the power consumption. Simulation results show our proposed scheduling schemes incur 8%-40% less power consumptions than existing algorithms.

Cloud computing providers have to deal with the energy-performance trade-off: minimizing energy consumption, while meeting service level agreement (SLA) requirements. This paper proposes a new heuristic approach for the dynamic consolidation of virtual machines (VMs) in cloud data centers. The fast best-fit decreasing (FBFD) algorithm for intelligent VMs allocating into hosts and dynamic utilization rate (DUR) algorithm for utilization space and VM migration are successfully proposed. We performed simulations using PlanetLab and GWDG data center workloads to compare our approach against the existing models. It has been observed that the FBFD heuristic algorithm produces better results compared to modified BFD algorithm in terms of energy consumption and SLA violation. Additionally, the time complexity of FBFD algorithm is significantly improved from the order of O(\(m\,*\,n\)) to O(\(m\,*\,\log _2{n}\)). Furthermore, leaving some rates of capacity in the physical machines by the proposed DUR algorithm for VMs to be extended reduces the number of migrations which in turn improves the energy consumption and SLA violation. Our heuristic approach is evaluated using CloudSim and the results show that it performs better than the current state-of-the-art approaches.

In modern data centres (DCs), energy consumption and resource management are challenging tasks. So in large virtualized DCs, performance of applications is highly dependent on energy-aware DC architecture and smooth network communication between virtual machines (VMs), while minimizing the communication burden to avoid congestion, latency etc. The communication cost of a network can be reduced by minimizing the VMs migration between physical machines (PMs). Therefore customer as well as service providers need cloud computing solutions that not only minimize operational costs, but also total network load. Minimizing the use of energy/network communication overhead with maximum resource utilization in large DCs is a challenging task as computing applications and data are growing so quickly for which increasing larger servers and disks are required to process them fast enough within the required time period. In order to minimize network load and maximize resource utilization in cloud DCs, we simulated our proposed network load-aware scheduling algorithm that ensures minimum VMs migration while delivering the negotiated Quality-of-Service (QoS). Our simulation also shows results with different bit rates.

Live migration of virtual machine (VM) is a promising technology that helps physical machines (PMs) adapt to load changes and guarantees Quality of Service (QoS) in cloud data center. Many individual-based VM migration studies ignore the association between VMs, resulting in high communication cost. Some research on multiple VMs migration migrates the VM group as a whole, which is likely to result in ineffective migration and increase the network burden. In this paper, a VM migration algorithm based on group selection (VMMAGS) is proposed, which takes into account the migration cost, communication cost, and VM heat to optimize migration performance. The appropriate VM groups are selected as migration options, and the optimal migration scheme is obtained according to the integration cost of partitions of selected VM groups. Extensive experiments show that our algorithm can effectively reduce the migration cost and communication cost, improve the system reliability compared with other related algorithms.

The high demand for big data applications, such as the Internet of Things (IoT), healthcare, business, and academia, as well as government, fosters the creation of large-scale cloud data centers. Cloud data centers contain thousands of physical machines (PMs), so resource management is necessary for allocating the tremendous amount of data to them. Knowing the workload demand in advance enables control of those resources, saving energy, reducing CPU and memory usage, and improving service. Workload prediction can be used to determine how many resources need to be allocated in the future. In this paper, we propose machine learning–based techniques to predict the daily operational workload. The proposed approach can predict the amount of power consumption (PC) and the number of PMs required to fulfill the demands of the cloud data center. Workload prediction accuracy varies based on the prediction methods used and the type of workload. In this work, we investigate three different methods: polynomial regression, support vector regression, and random forest regression (RFR). Considering both accuracy and computation time, results show that RFR provides the best performance, in our case, with a minimum root-mean-square error of 11.68 for PMs and 4869.08 for PC prediction. The computation time solidifies our selection with 2 seconds training time in all instances.

Over-committing computing resources is a widely adopted strategy for increased cluster utilization in Infrastructure as a Service (IaaS) cloud data centers. A potential consequence of over-committing computing resources is memory overload of physical machines (PMs). Memory overload occurs if memory usage exceeds a defined alarm threshold, exposing running computation tasks at a risk of being terminated by the operating system. A prevailing measure to handle memory overload of a PM is live migration of virtual machines (VMs). However, this not only consumes network bandwidth, CPU, and other resources, but also compels a temporary unavailability of the VMs being migrated. To handle memory overload, we present a memory sharing system in this paper for PMs in cloud data centers. With memory sharing, a PM automatically borrows memory from a remote PM when necessary, and releases the borrowed memory when memory overload disappears. This is implemented through swapping inactive memory pages to remote memory resource. Experimental studies conducted on InfiniBand-networked PMs show that the memory sharing system is fully functional. The measured throughput and latency are around 929 Mbps and 1.3 mus, respectively, on average for remote memory access. They are similar to those from accessing a local-volatile memory express solid-state drive, and thus are promising in real applications.

A multi-objective approach for energy-efficient and reliable dynamic VM consolidation in cloud data centers

Nowadays cloud computing provides an effective way of implementing infrastructure as a service (IaaS). However virtualized data centers still face many challenges, such as low resource utilization of physical machines (PMs) and imbalanced server loads. Virtual machine (VM) consolidation based on live migration allows administrator to dynamically redeploy VMs into PMs for better resource utilization. Common VM consolidation methods usually focus on one challenge, and pay little attention to others or just ignore them, while effective VM redeployment should make tradeoffs between these challenges, and more importantly, should not let other challenges become worse. On the other hand, since VM live migration leads to performance degradation of applications, consolidation work should control migration cost. In this paper, we provide a manner to comprehensively consider power consumption, load balancing, communication delay and migration cost during VM redeployment. And we formalize VM consolidation as a multiobjective optimization problem, then solve this problem with an improved genetic algorithm. Simulation experiments based on real world workload trace show that compared with single objective optimization approaches our method effectively make tradeoffs between optimized objectives and has better overall performance, which is more practical in real data centers.

The rapid revolution of cloud computing model is accompanied by huge amounts of energy consumed by the cloud data centers. So, enhancing the energy efficiency of those data centers has become a major challenge. This paper tackles the problem of enhancing the energy consumption of cloud data centers by proposing a novel virtual machine placement strategy. The proposed strategy suits both static and dynamic placement process. It aims to better utilize the involved physical machines which host the virtual machines. As different types of jobs do not intensively use the compute and/or non-compute resources in the hosted physical machine, virtual machines allocated to the jobs of different types are placed on the same physical machine where possible. The paper presents a mathematical formulation of the virtual machine placement process based on the Multiple Choice Knapsack Problem which is a generalization of the classical Knapsack Problem. The performance evaluation of the proposed strategy shows that it can enhance the energy efficiency of the cloud data centers by trying to minimize the number of the involved physical machines which host the virtual machines, and by optimally utilizing the involved physical machines.

Containers are a popular way of deploying software in cloud data centers. Containers are allocated to Virtual machines (VMs) which are allocated to Physical machines (PMs) within the data center. Since the resources required by containers often do not match those of VMs, where to allocate them must be decided. A poor solution can result in high energy costs. Many existing methods to solve this problem use heuristics which do not consider containers leaving the data center after being allocated. Some do consider migrating containers between VMs but few do for energy efficiency reasons. These overlooked aspects may lead to increased energy usage, particularly since studies have demonstrated that many containers run for only a brief duration. In this paper, we develop a model of the container-based cloud resource allocation problem that considers the energy impact of leaving and migrating containers. We then design a new Genetic Programming Hyper-Heuristic (GPHH) algorithm to jointly evolve three heuristics for container placement, VM placement and container migration control. We utilize newly designed terminals to ensure the effectiveness of our GPHH algorithm. Experiments have been conducted with results indicating that the heuristics evolved by our GPHH algorithm can achieve better performance compared to several state-of-the-art techniques.

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.

Cloud computing is an internet based computing technology that provide on demand computing for end users. Normally, data centers allocation for application on statically based. But today so many data centers have a problem how to reduce energy consumption? Due to increase use of cloud services and infrastructure by various cloud providers, uses of energy day by day increase that's why energy consumption increase lots. Large numbers of data centers that consume lots of energy which increase the level of co2. Hence there is need for green computing and for energy efficient management of cloud data center resources besides meeting the QoS constraints. Within a data center, maximum energy is consumed by cooling systems and ICT Infrastructure, particularly, the servers. So it is of utmost importance to optimize energy utilization in datac enter servers. In order to achieve this, we can leverage the power of virtualization which is intrinsic in Cloud Computing. Virtualization opens the doors for VM consolidation by allowing dynamic migration of virtual machines across physical machines. In this study, the problem of VM consolidation has been formulated as a mathematical optimization problem.