A Technique to Identify Data Exchange Between Cloud Virtual Machines

The swift advancement of cloud computing and virtualization technologies has reshaped the IT infrastructure domain, rendering virtual machine (VM) allocation a pivotal fac- tor in achieving both optimal performance and cost-effectiveness. This paper presents an in-depth examination of current VM allocation methodologies, classifying them into three primary categories: static allocation, dynamic allocation, and heuristic- based approaches. We investigate the foundational principles of each strategy, analyzing their operational frameworks, benefits, and drawbacks across varying workload scenarios. Static allocation, although simple, frequently results in re- source under utilization due to its lack of adaptability to changing demands. In contrast, dynamic allocation techniques, which mod- ify resources based on real-time consumption, provide greater flexibility but introduce challenges related to overhead and latency. Heuristicbased approaches, which utilize optimization algorithms, can effectively manage resource distribution but may necessitate considerable computational resources and time for execution.To assess the effectiveness of these strategies, we adopt a simulation-based approach that replicates actual cloud environments. Our experiments concentrate on critical performance metrics, such as response time, throughput, resource utilization, and energy consumption. The results highlight notable performance variations among the different strategies, emphasizing the necessity for a more adaptive and intelligent method for VM allocation. In light of these challenges, we introduce an innovative VM allocation policy that integrates machine learning techniques to forecast workload patterns based on historical data. By utilizing predictive analytics, our policy dynamically allocates resources, ensuring that VMs receive the optimal amount of resources in real-time, thereby reducing latency and enhancing throughput. Our findings indicate that this advanced policy significantly sur- passes traditional allocation methods, achieving superior resource utilization, lower operational costs, and enhanced overall system efficiency. The implications of this research extend beyond theoretical exploration, offering practical insights for cloud service providers and organizations aiming to refine their VM allocation strategies. By adopting adaptive allocation techniques, stakeholders can more effectively respond to the demands of contemporary cloud workloads, ultimately fostering improved performance and user satisfaction.

DataABC: A fast ABC based energy-efficient live VM consolidation policy with data-intensive energy evaluation model

Virtual Machine (VM) allocation for multiple tenants is an important and challenging problem to provide efficient infrastructure services in cloud data centers. Tenants run applications on their allocated VMs, and the network distance between a tenant's VMs may considerably impact the tenant's Quality of Service (QoS). In this study, we define and formulate the multi-tenant VM allocation problem in cloud data centers, considering the VM requirements of different tenants, and introducing the allocation goal of minimizing the sum of the VMs' network diameters of all tenants. Then, we propose a Layered Progressive resource allocation algorithm for multi-tenant cloud data centers based on the Multiple Knapsack Problem (LP-MKP). The LP-MKP algorithm uses a multi-stage layered progressive method for multi-tenant VM allocation and efficiently handles unprocessed tenants at each stage. This reduces resource fragmentation in cloud data centers, decreases the differences in the QoS among tenants, and improves tenants' overall QoS in cloud data centers. We perform experiments to evaluate the LP-MKP algorithm and demonstrate that it can provide significant gains over other allocation algorithms.

Cloud computing (CC) could be a massive distributed computing driven by business, during which the services and resources are area unit delivered on request to external consumer via the Web. The distributed computing environment comprises of physical servers, virtual machines, data centers, and load balancers which are appended in an efficient way. With the increasing size of a number of physical servers and utilization of cloud services in data centers (DC), the power consumption is a critical and challenging research problem. Minimizing the operational cost and power in a DC becomes essential for cloud service provider (CSP). To resolve this problem, we introduced a novel approach that leads to nominal operational cost and power consumption in DCs. We propose a multi-objective modified differential evolution algorithm for first placement of virtual machine (VM) in the physical hosts and optimize the power consumption during resource allocation using live migration. The experimental results reveal that our proposed method is significantly better against state-of-the-art techniques in terms of limited power consumption and SLA for any given workload.

This paper addresses the problem of Data Centers (DCs) energy efficiency from a thermal perspective by extending the CloudSim framework to allow simulation and testing of thermal aware resource allocation policies aiming to minimize the cooling system energy consumption. The proposed framework, CoolCloudSim, can be used to develop and test new thermal aware Virtual Machine (VM) allocation strategies aiming to optimize the energy consumption of both cooling system and IT resources while meeting Service Level Agreements (SLAs). The default CloudSim architecture is extended by adding classes which contain mathematical models of the thermal processes within the server room. Furthermore, four new VM allocation policies that consider the cooling system energy consumption are developed based on the thermal and cooling system models. Finally, experiments are run to evaluate various metrics on a set of default CloudSim allocation algorithms and the proposed allocation algorithms. The results show that the proposed algorithms outperform the default CloudSim allocation strategy, Power Aware Best-Fit Decreasing (PABFD), in terms of overall energy consumption and the number of VM migrations, and have on average better results than other existing allocation strategies.

The need for centralized processing and storage of data is growing every year. Data centers of still greater capacity and computing power are put into operation for this purpose. To reduce their expenses the owners of data centers pay special attention to optimizing the energy consumed by the data center infrastructure. An approach for improving the data center’s overall energy efficiency through redistributing its load with subsequently shutting down the idle servers is considered. A mechanism governing the migration of virtual machines for optimizing power consumption by the servers is outlined. The existing methods used to redistribute virtual machines either perform a full search which is impossible in the case of real dimensions or disregard the power consumption criterion. A procedure is proposed that excludes the need to perform a full search and places focus on improving the efficiency according to the criterion of total energy consumption by the data center servers due to redistribution of the operating virtual machines among the data center servers. The proposed method is based on a heuristic procedure of selecting the destination servers and consists of a static part, which collects information about the available resources, and a dynamic part in which the virtual machines are distributed among the servers depending on the initial data and the preset limiting parameters. A model example demonstrating the operability of the method is given which outlines the steps in applying the method and shows how the destination servers are selected for migrations.

Energy consumption in data center is currently the main focus of many large-scale enterprises and cloud service providers. Dynamic virtual machine (VM) consolidation technologies are widely used to improve resource utilization of data centers and reduce energy. They try to identify the poorly utilized physical hosts and make the most of the resources. Then idle hosts can be switched to sleep or active mode, meanwhile considering the real-time fluctuation of service workload. In this paper, we propose a Reinforcement Learning (RL) based Virtual Machine Consolidation (RL-VMC) framework with an application to the cloud data center operation. The RL-VMC method uses an agent corresponding to a VM planner to interact with the environment, which encapsulates the data center running state, and learns the optimal policy to determine the migration mapping from VMs to physical hosts. Specifically, we adopt the on-policy method State-Action-Reward-State-Action (SARSA) in RL-VMC, which learns from experience to get the optimal VMs migration strategy and manage the host power mode as well. Additionally, we use the PBRS technology to speed up the convergence of the RL-VMC. Experimental results show that the RL-VMC method can adapt to the dynamic workload maintaining an improved balance between energy consumption and Service Level Agreements (SLA).

Improving the energy efficiency while guaranteeing quality of services (QoS) is one of the main challenges of efficient resource management of large-scale data centers. Dynamic virtual machine (VM) consolidation is a promising approach that aims to reduce the energy consumption by reallocating VMs to hosts dynamically. Previous works mostly have considered only the current utilization of resources in the dynamic VM consolidation procedure, which imposes unnecessary migrations and host power mode transitions. Moreover, they select the destinations of VM migrations with conservative approaches to keep the service-level agreements , which is not in line with packing VMs on fewer physical hosts. In this paper, we propose a regression-based approach that predicts the resource utilization of the VMs and hosts based on their historical data and uses the predictions in different problems of the whole process. Predicting future utilization provides the opportunity of selecting the host with higher utilization for the destination of a VM migration, which leads to a better VMs placement from the viewpoint of VM consolidation. Results show that our proposed approach reduces the energy consumption of the modeled data center by up to 38% compared to other works in the area, guaranteeing the same QoS. Moreover, the results show a better scalability than all other approaches. Our proposed approach improves the energy efficiency even for the largest simulated benchmarks and takes less than 5% time overhead to execute for a data center with 7600 physical hosts.

The last decade witnesses a dramatic advance of cloud computing research and techniques. One of the key faced challenges in this field is how to reduce the massive amount of energy consumption in cloud computing data centers. To address this issue, many power-aware virtual machine (VM) allocation and consolidation approaches are proposed to reduce energy consumption efficiently. However, most of those existing efficient cloud solutions save energy cost at a price of the significant performance degradation. In this paper, we present a novel VM allocation algorithm called "PPRGear", which leverages the Performance-to-Power ratios for various host types. By achieving the optimal balance between host utilization and energy consumption, PPRGear is able to guarantee that host computers run at the most power-efficient levels (i.e., the levels with highest Performance-to-Power ratios) so that the energy consumption can be tremendously reduced with little sacrifice of performance. Our extensive experiments with real world traces show that compared with three baseline energy-efficient VM allocation and selection algorithms, PPRGear is able to reduce the energy consumption up to 69.31% for various host computer types with fewer migration and shutdown times and little performance degradation for cloud computing data centers.

A metaheuristic method for joint task scheduling and virtual machine placement in cloud data centers

With the consolidation of computer services in large cloud-based data centers, almost all applications and even application development execute in virtualized systems (VS's), sometimes nested. Whether it is inside a container, a virtual machine (VM) running on a physical host, or in a nested virtual machine, every process eventually runs on a physical CPU. Consequently, multiple virtualized systems might unknowingly compete with each other for physical resources. In this paper we study the interactions between all the VS's running on a physical machine. We introduce an analysis based on kernel tracing that erases the bounds between VS's and their host, to display a multilayer system as a single layer. As a result, it becomes possible to know exactly which process is currently running on a physical CPU, even if it is launched inside multiple layers of containers, themselves enclosed into two layers of VMs. To use this analysis, we developed in Trace Compass a view that displays a time line for each host CPU, showing across time which process is running. Moreover, the full hierarchy of the VS's is retrieved from the analysis and is displayed in the view. By using a system of dynamic and permanent filters, we added the possibility to highlight in this view either traced VMs, virtual CPUs, specific processes and containers. This last feature, combined with our view, allows to thoroughly apprehend the execution flow on the physical host, although it may involve multiple nested virtualized systems.

Infrastructure-as-a-Service (IaaS) clouds have become more popular enabling users to run applications under virtual machines. This paper investigates the energy-aware virtual machine (VM) allocation problems in IaaS clouds along characteristics: multiple resources, and fixed interval times and non-preemption of virtual machines. Many previous works proposed to use a minimum number of physical machines, however, this is not necessarily a good solution to minimize total energy consumption in the VM placement with multiple resources, fixed interval times and non-preemption. We observed that minimizing total energy consumption of physical machines is equivalent to minimize the sum of total completion time of all physical machines. Based on the observation, we propose EMinRET algorithm. The EMinRET algorithm swaps an allocating VM with a suitable overlapped VM, which is of the same VM type and is allocated on the same physical machine, to minimize total completion time of all physical machines. The EMinRET uses resource utilization during executing time period of a physical machine as the evaluation metric, and will then choose a host that minimizes the metric to allocate a new VM. In addition, this work studies some heuristics for sorting the list of virtual machines (e.g., sorting by the earliest starting time, or the longest duration time first, etc.) to allocate VM. Using the realistic log-trace in the Feitelson's Parallel Workloads Archive, our simulation results show that the EMinRET algorithm could reduce from 25% to 45% energy consumption compared with power-aware best-fit decreasing (PABFD) [1]) and vector binpacking norm-based greedy algorithms (VBP-Norm-L1/L2 [2]). Moreover, the EMinRET heuristic has also less total energy consumption than our previous heuristics (e.g. MinDFT and EPOBF) in the simulations (using same virtual machines sorting method).

Cloud providers generally run one or more Virtual Machine (VM) instances on the same physical machine. Though it increases data center utilization, it exposes VM to a co-location attack. VM placement and migration are the two strategies adopted for mitigating co-locations. Current methods for VM placement or VM migration consider only security as decision criteria and do not consider other factors like Quality-of-Service degradation, data center utilization, etc. This work proposes a placement and migration strategy for mitigation of co-location attacks with joint consideration of multi objectives like QoS, data center utilization, energy consumption, and security risks. A security-driven multi-criteria optimization -based VM placement policy is proposed. A joint consideration of multiobjective performance optimization along with co-location security risk minimization is done to design a novel VM placement policy based on user categorization. The policy can reduce the likelihood of co-location target VM with attacker VM without much degradation to the performance of VM and data center utilization. The solution mitigates co-location risks without much compromise to the performance of VM and data center resource utilization. The co-residence risk is mitigated by the categorization of users into three levels i.e. unlabeled, risky, and safe, and physical machines into two groups as safe and unsafe. The PMs available in data center is grouped into three different VM placement policies, they are undecided pool, safe pool and unsafe pool.

Cloud computing provides online services to users from anywhere in a pay‐as‐you‐go basis. This leads to the construction of different forms of data centers around the world, which usually consumes huge amount of energy and produce pollution (heat and carbon dioxide footprint). Many efforts have focused on managing cloud resources to decrease their energy consumption. Since servers are the highest energy consumers in data centers, most researchers have focused on optimizing their energy consumption while other resources such as network devices and cooling systems have received less attention. In this paper, we propose a network‐aware virtual machine (VM) relocation algorithm, which optimizes the distribution of the VMs running on the data center in order to conserve energy in both servers and network devices (switches). The proposed technique targets the communicated VMs and places them closer to each other. This would reduce the traffic overhead between any communicating VMs so that fewer network devices are involved in the communication process. This also increases the VMs consolidation to some servers while leaving other servers idle. Switching off the idle servers and network devices would significantly reduce the total energy consumed by the data center. The proposed algorithm is evaluated using simulation and the performance of the proposed algorithm is compared against different previously proposed algorithms in the literature. Performance metrics used are the energy consumed by the host and by the network switches, the service level agreement violations, and the number of VM migrations. The impact of different packet sizes, the efficiency of switching the network elements to sleep mode, and the impact of resource homogeneity and heterogeneity are also studied and discussed. Simulation results show that the proposed algorithm outperforms the existing algorithms in terms of all evaluation metrics.

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.