Utility analysis for Internet-oriented server consolidation in VM-based data centers

Renewed focus on virtualization technologies and increased awareness about management and power costs of running under-utilized servers has spurred interest in consolidating existing applications on fewer number of servers in the data center. The ability to migrate virtual machines dynamically between physical servers in real-time has also added a dynamic aspect to consolidation. However, there is a lack of planning tools that can analyze historical data collected from an existing environment and compute the potential benefits of server consolidation especially in the dynamic setting. In this paper we describe such a consolidation recommendation tool, called ReCon. Recon takes static and dynamic costs of given servers, the costs of VM migration, the historical resource consumption data from the existing environment and provides an optimal dynamic plan of VM to physical server mapping over time. We also present the results of applying the tool on historical data obtained from a large production environment.

Today's data centers offer IT services mostly hosted on dedicated physical servers. Server virtualization provides a technical means for server consolidation. Thus, multiple virtual servers can be hosted on a single server. Server consolidation describes the process of combining the workloads of several different servers on a set of target servers. We focus on server consolidation with dozens or hundreds of servers, which can be regularly found in enterprise data centers. Cost saving is among the key drivers for such projects. This paper presents decision models to optimally allocate source servers to physical target servers while considering real-world constraints. Our central model is proven to be an NP-hard problem. Therefore, besides an exact solution method, a heuristic is presented to address large-scale server consolidation projects. In addition, a preprocessing method for server load data is introduced allowing for the consideration of quality-of-service levels. Extensive experiments were conducted based on a large set of server load data from a data center provider focusing on managerial concerns over what types of problems can be solved. Results show that, on average, server savings of 31 percent can be achieved only by taking cycles in the server workload into account.

Cloud computing is very promising technology to deliver computing resources like infrastructure (IaaS), platform (PaaS), software (SaaS) etc. in form of services over the Internet. Server consolidation is the mechanism to minimize active (running) physical servers in the data center of cloud service provider. Consolidation helps the service provider to make efficient usage data center resources and in turn to minimize running cost of data center. Users have to pay as per service usage. SLA is like contract between cloud service provider and users. Service Level Agreement (SLA) is the mechanism of providing guaranteed services to cloud users. SLA Violation may cause high penalties to service provider. In this paper it is shown that if consolidation is maximized, it may cause more number of SLA violation. A balanced approach of server consolidation with minimal SLA violations is suggested in this paper.

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 data centers with multi-tier applications. While 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 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 DVFS. At the data center 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 can effectively achieve performance-assured power savings. Extensive simulation results, based on a trace file of 5,415 real servers, demonstrate the efficacy of our solution in large-scale data centers.

Power demand of data centers is a topic of great interest and numerous research papers. Most handle power saving by using virtualization technologies and server consolidation. In this position paper, we propose the idea of approaching a specific power consumption of an entire data center by using virtual machine migration strategies. To achieve this goal, several models of data center components are needed. We illustrate the models we developed as well as the measurement set-up and results we have. These are especially models for server power estimation and for a virtual machine power breakdown. At the end of the paper, we present our further ideas and future research intentions.

Application service providers face enormous challenges and rising costs in managing and operating a growing number of heterogeneous system and computing landscapes. Limitations of traditional computing environments force IT decision-makers to reorganize computing resources within the data center, as continuous growth leads to an inefficient utilization of the underlying hardware infrastructure. This paper discusses a way for infrastructure providers to improve data center operations based on the findings of a case study on resource utilization of very large business applications and presents an outlook beyond server consolidation endeavors, transforming corporate data centers into compute clouds.

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

A hybrid energy–Aware virtual machine placement algorithm for cloud environments

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.

Nowadays, large-scale applications are moved to the Data centers due to reduce power consumption, latency and attain QOS. Data centers are served as a backbone of cloud computing which is built through various techniques including virtualization. It provides access to computing resources to customers based on "pay-per-usage" basis. The energy cost has become a top operational cost of a large-scale data centers and it reduces the Total Cost of Acquisition (TCA). There are many techniques for reduce the power utilization of a data center. Amongst, Virtualization is widely used to minimize the power consumption through server consolidation. Most of the existing techniques have used dynamic consolidation to improve both resource utilization and energy efficiency. However, they are failed to consider QoS of an applications that runs on virtual machine during consolidation. This paper presents a new approach to reduce power consumption by considering QoS and resource utilization while relocating the virtual machines. This paper describes the idea behind various power aware techniques that are used for creating green data centers.

Cross-ISA system-mode emulation has many important applications. For example, Cross-ISA system-mode emulation helps computer architects and OS developers trace and debug kernel execution-flow efficiently by emulating a slower platform (such as ARM) on a more powerful plat-form (such as an x86 machine). Cross-ISA system-mode emulation also enables workload consolidation in data centers with platforms of different instruction-set architectures (ISAs). However, system-mode emulation is much slower. One major overhead in system-mode emulation is the multi-level memory address translation that maps guest virtual address to host physical address. Shadow page tables (SPT) have been used to reduce such overheads, but primarily for same-ISA virtualization. In this paper we propose a novel approach called embedded shadow page tables (ESPT). EPST embeds a shadow page table into the address space of a cross-ISA dynamic binary translation (DBT) and uses hardware memory management unit in the CPU to translate memory addresses, instead of software translation in a current DBT emulator like QEMU. We also use the larger address space on modern 64-bit CPUs to accommodate our DBT emulator so that it will not interfere with the guest operating system. We incorporate our new scheme into QEMU, a popular, retargetable cross-ISA system emulator. SPEC CINT2006 benchmark results indicate that our technique achieves an average speedup of 1.51 times in system mode when emulating ARM on x86, and a 1.59 times speedup for emulating IA32 on x86_64.

This paper focuses on power minimization in a data center accounting for both the information technology equipment and the air conditioning power usage. In particular we address the server consolidation (on/off state assignment) concurrently with the task assignment. We formulate the resulting optimization problem as an Integer Linear Programming problem and present a heuristic algorithm that solves it in polynomial time. Experimental results show an average of 13% power saving for different data center utilization rates compared to a baseline task assignment technique, which does not perform server consolidation.

Q*: Energy and delay-efficient dynamic queue management in TCP/IP virtualized data centers

Virtual machine technology and the ease with which VMs can be migrated within the LAN, has changed the scope of resource management from allocating resources on a single server to manipulating pools of resources within a data center. We expect WAN migration of virtual machines to likewise transform the scope of provisioning compute resources from a single data center to multiple data centers spread across the country or around the world. In this paper we present the CloudNet architecure as a cloud framework consisting of cloud computing platforms linked with a VPN based network infrastructure to provide seamless and secure connectivity between enterprise and cloud data center sites. To realize our vision of efficiently pooling geographically distributed data center resources, CloudNet provides optimized support for live WAN migration of virtual machines. Specifically, we present a set of optimizations that minimize the cost of transferring storage and virtual machine memory during migrations over low bandwidth and high latency Internet links. We evaluate our system on an operational cloud platform distributed across the continental US. During simultaneous migrations of four VMs between data centers in Texas and Illinois, CloudNet's optimizations reduce memory migration time by 65% and lower bandwidth consumption for the storage and memory transfer by 19GB, a 50% reduction.