Server Consolidation Algorithm Research Articles

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.

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.

Server Consolidation Based on Culture Multiple-Ant-Colony Algorithm in Cloud Computing.

High-energy consumption in data centers has become a critical issue. The dynamic server consolidation has significant effects on saving energy of a data center. An effective way to consolidate virtual machines is to migrate virtual machines in real time so that some light load physical machines can be turned off or switched to low-power mode. The present challenge is to reduce the energy consumption of cloud data centers. In this paper, for the first time, a server consolidation algorithm based on the culture multiple-ant-colony algorithm was proposed for dynamic execution of virtual machine migration, thus reducing the energy consumption of cloud data centers. The server consolidation algorithm based on the culture multiple-ant-colony algorithm (CMACA) finds an approximate optimal solution through a specific target function. The simulation results show that the proposed algorithm not only reduces the energy consumption but also reduces the number of virtual machine migration.

Server Consolidation Energy-Saving Algorithm Based on Resource Reservation and Resource Allocation Strategy

Energy consumption has overtaken equipment costs given the growth of computing power, thereby becoming the dominant cost to data centers. Constrained by ensuring that peak resource requirements of VMs are met, the effective use of resource fragments is an effective means of energy conservation. We apply resource reservation to the resource-utilization-aware energy efficient server consolidation algorithm (RUAEE), and propose a server consolidation energy-saving algorithm (SCES). We adjust the allocations of VMs by computing the resource reservation and resource allocation ratio dynamically. This technique maintains the host resource utilization to within a reasonable range, reduces resource fragmentation and reserves resources to satisfy the peak resource requests simultaneously. Compared with the RUAEE algorithm, our algorithm can reduce the risk of system overload and improve the stability of the system. The experimental results based on the actual workload of the Google cluster show that the algorithm is effective at reducing resource waste, improving system stability, and achieving energy savings.

Resource-utilization-aware energy efficient server consolidation algorithm for green computing in IIOT

Cloud computing has become an indispensable infrastructure that provides multi-granularity services to support large applications in the Industrial Internet of Things (IIOT), Cloud data centers have been built or extensively enlarged to cope with the growing computation and storage requirements of IIOT. The energy consumption of cloud data centers is dramatically increasing, which has created a lot of problems with greenhouse gas emissions and service costs. Server consolidation is a popular approach to reduce cloud data centers’ energy consumption by minimizing the number of active physical machines. Most of the extant research has focused on server reduction in the consolidation process, but unbalanced resource utilization among different physical machines can cause the waste of physical resources. This paper proposes a resource-utilization-aware energy efficient server consolidation algorithm (RUAEE) that can be used to improve resource utilization while reducing the number of virtual machine live migrations. Experimental results show that RUAEE can reduce the energy consumption and service-level agreement (SLA) violation in cloud data center.

Burstiness-Aware Resource Reservation for Server Consolidation in Computing Clouds

In computing clouds, burstiness of a virtual machine (VM) workload widely exists in real applications, where spikes usually occur aperiodically with low frequency and short duration. This could be effectively handled through dynamically scaling up/down in a virtualization-based computing cloud; however, to minimize energy consumption, VMs are often highly consolidated with the minimum number of physical machines (PMs) used. In this case, to meet the dynamic runtime resource demands of VMs in a PM, some VMs have to be migrated to some other PMs, which may cause potential performance degradation. In this paper, we investigate the burstiness-aware server consolidation problem from the perspective of resource reservation, i.e., reserving a certain amount of extra resources on each PM to avoid live migrations, and propose a novel server consolidation algorithm, $\sf {QUEUE}$ . We first model the resource requirement pattern of each VM as a two-state Markov chain to capture burstiness, then we design a resource reservation strategy for each PM based on the stationary distribution of a Markov chain. Finally, we present $\sf {QUEUE}$ , a complete server consolidation algorithm with a reasonable time complexity. We also show how to cope with heterogenous spikes and provide remarks on several extensions. Simulation and testbed results show that, $\sf {QUEUE}$ improves the consolidation ratio by up to 45 percent with large spike size and around 30 percent with normal spike size compared with the strategy that provisions for peak workload, and achieves a better balance between performance and energy consumption in comparison with other commonly-used consolidation algorithms.

Multi-core Processors in Cloud Computing using Cloudsim

Multi-core processor is viewed as the future production of microprocessor design. It is not only a solution for CPU speeds but also it decreases the power consumption, because many cores in a lower frequency collectively produce less heat dissipation than one core with their total frequency. From this point of view, Cloud computing will be mostly built on top of multi-core technologies. However, to fully take advantage of the computational capability and the other advantages of multi-cores, a lot of new techniques must be proposed and considered. In this research paper, a resourcescheduling algorithm along with a server consolidation algorithm is applied to multi-core processors. It is be shown by experimental results that adding cores to the processors in data centers increases the system performance, decreases the power consumption, along with other benefits.

Power‐conservative server consolidation based resource management in cloud

SUMMARYThe cloud computing environment is a real‐time communication network that involves a large number of systems connected in a distributed fashion, for which resources are available on demand. In recent years, due to the enormous growth of data and information, data maintenance tasks involve a major effort in information technology (IT) industries. So, IT industries are concentrating on the cloud computing environment in order to maintain their data and manage their resources. Owing to the increase in the number of data centres, which have an impact on electrical energy cost, peak power dissipation, cooling and carbon emission, power‐conservation‐based resource management is essential. A best‐fit heuristic job placement algorithm is proposed in this paper in order to increase the job allocation percentage, a worst‐fit heuristic virtual machine (VM) placement algorithm is also proposed in order to place the VMs over the physical machines (PMs) thereby reducing the number of the latter allotted, and a server consolidation algorithm is proposed in order to improve power conservation. Copyright © 2014 John Wiley & Sons, Ltd.

Performance-controlled server consolidation for virtualized data centers with multi-tier applications

Modern data centers must provide performance assurance for complex system software such as multi-tier web applications. In addition, the power consumption of data centers needs to be minimized to reduce operating costs and avoid system overheating. Various power-efficient performance management strategies have been proposed based on dynamic voltage and frequency scaling (DVFS). Virtualization technologies have also made it possible to consolidate multiple virtual machines (VMs) onto a smaller number of active physical servers for even greater power savings, but at the cost of a higher overhead. This paper proposes a performance-controlled power optimization solution for virtualized server clusters with multi-tier applications. While most existing work relies on either DVFS or server consolidation in a separate manner, our solution utilizes both strategies for maximized power savings by integrating feedback control with optimization strategies. At the application level, a novel multi-input–multi-output controller is designed to achieve the desired performance for applications spanning multiple VMs, on a short time scale, by reallocating the CPU resources and conducting DVFS. At the cluster level, a power optimizer is proposed to incrementally consolidate VMs onto the most power-efficient servers on a longer time scale. Empirical results on a hardware testbed demonstrate that our solution outperforms pMapper, a state-of-the-art server consolidation algorithm, by having greater power savings and smaller consolidation overheads while achieving the required application performance. Extensive simulation results, based on a trace file of 5415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

Sercon: Server Consolidation Algorithm using Live Migration of Virtual Machines for Green Computing

Virtualization technologies changed the way data centers of enterprises utilize their server resources. Instead of using dedicated servers for each type of application, virtualization allows viewing resources as a pool of unified resources, thereby reducing complexity and easing manageability. Server consolidation technique, which deals with reducing the number of servers used by consolidating applications, is one of the main applications of virtualization in data centers. The latter technique helps to use computing resources more effectively and has many benefits, such as reducing costs of power, cooling and, hence, contributes to the Green IT initiative. In a dynamic data center environment, where applications encapsulated as virtual machines are mapped to and released from the nodes frequently, reducing the number of server nodes used can be achieved by migrating applications without stopping their services, the technology known as live migration. However, live migration is a costly operation; hence, how to perform periodic server consolidation operation in a migration-aware way is a challenging task. We propose server consolidation algorithm - Sercon, which not only minimizes the overall number of used servers, but also minimizes the number of migrations. We verify the feasibility of our algorithm along with showing its scalability by conducting experiments with eight different test cases.