An Eco Migration Algorithm of Virtual Machines in a Server Cluster

It is critical to reduce the electric energy consumption of servers in a cluster. In this paper, we discuss a migration approach to reducing the electric energy consumption of servers where a virtual machine with application processes migrates to a more energy-efficient server. In this paper, we consider a homogeneous cluster. We newly propose an ISEAM2H algorithm. Here, a virtual machine on a host server is selected to perform a process issued by an application and a guest server to which a virtual machine migrates is found so that the total electric energy consumption of servers can be minimized. In the evaluation, we show the total electric energy consumption and active time of the servers and the average execution time of processes can be reduced in the ISEAM2H algorithm.

In our previous studies, the dynamic virtual machine migration (DVMM) algorithm is proposed to reduce the total electric energy consumption of servers by migrating virtual machines to more energy-efficient servers. Here, virtual machines are dynamically created and dropped depending on number of processes. However, the more number of virtual machines are created, the more number of virtual machines are dropped. Servers consume the electric energy to drop virtual machines. In this paper, we propose an IDVMM (improved DVMM) algorithm to decrease the number of virtual machines to be dropped. In the evaluation, we show not only the total electric energy consumption and active time of servers but also the average execution time of processes can be reduced in the IDVMM algorithm compared with other algorithms.

In order to realize green society, we take the process migration approach to reducing the total electric energy consumption of clusters by migrating virtual machines. In our previous studies, the DM (Dynamic Migration) algorithm is proposed where virtual machines are dynamically resumed and suspended and a group of virtual machines on a host server migrate to a guest server so that the total energy consumption of the host and guest servers can be reduced. In this paper, we newly propose a DGM (Dynamic Group Migration) algorithm. Here, there is a seed virtual machine on each server. A virtual machine is created as a clone of the seed virtual machine so that the number of processes on each virtual machine is kept fewer. In the evaluation, we show the total electric energy consumption of servers can be more reduced in the DGM algorithm compared with other algorithms.

Bu çalışmanın amacı Türkiye’de yenilenebilir elektrik enerjisi üretimine etki eden faktörleri araştırmaktır. Bu amaçla 1998-2014 yılları arası Türkiye’nin yenilenebilir elektrik enerjisi üretimi, toplam elektrik enerjisi tüketimi, toplam elektrik enerjisi tüketimi içerisinde yenilenebilir enerji tüketiminin payı ve kişi başına Gayri Safi Yurt İçi Hâsıla yıllık büyüme oranı verileri Dünya Bankası verilerinden elde edilmiştir. OLS tahmin modeli kullanılarak yapılan analiz sonucunda Türkiye’de toplam elektrik enerjisi tüketiminin, toplam elektrik enerjisi tüketimi içerisinde yenilenebilir enerji tüketiminin payının ve kişi başına GSYH’nin yenilenebilir elektrik enerjisi üretimine anlamlı ve pozitif yönde katkı sağladığı bulunmuştur. Yenilenebilir elektrik enerjisi üretimi ile kişi başına GSYH büyüme hızı arasında pozitif yönde çift yönlü nedensellik bulunmuştur. Dahası, toplam elektrik enerjisi tüketimi değişkeni ile toplam elektrik enerjisi tüketimi içerisinde yenilenebilir enerji tüketiminin payının değişkenleri hem yenilenebilir elektrik enerjisi üretimini hem de kişi başına GSYH büyüme hızı değişkenlerini tek yönlü olarak pozitif yönde etkilemektedir.

The total electric energy consumption of a server cluster to perform application processes on multiple virtual machines has to be reduced to realize energy-efficient information systems. In our previous studies, the ECLB (energy consumption laxity based) algorithm to allocate computation type application processes (computation processes) to virtual machines in a server cluster is proposed so that the total electric energy consumption of the server cluster can be reduced. Here, each virtual machine is bounded to one thread on a CPU in a server and computation processes are performed by using only one thread bounded to the virtual machine even if some threads are not used in the server. In this paper, the improved ECLB (IECLB) algorithm is proposed to furthermore reduce the total electric energy consumption of a server cluster by allocating unused threads to each virtual machine to perform computation processes. We evaluate the IECLB algorithm in terms of the total electric energy consumption of a server cluster compared with the ECLB algorithm.

In order to realize eco-society, we have to reduce the electric energy consumed by servers in a cluster in addition to satisfying performance requirements. Virtual machines are now widely used to support applications with virtual computation service in clusters like cloud computing systems. Here, a virtual machine on a host server can migrate to a guest server while processes are being performed on the virtual machine. For example, if the host server of a virtual machine is heavily loaded and a guest server is less loaded, the virtual machine migrates to the guest server. In our previous studies, the EAMV (Energy-Aware Migration of Virtual machines) algorithm is proposed where a virtual machine migrates to a guest server which is expected to consume smaller electric energy to perform processes on the virtual machines. Here, the termination time of each process on each virtual machine is estimated. It is not easy to obtain the state of each process and take time to calculate the expected termination time. In this paper, a virtual machine is considered to be a unit of computation. Termination time of a virtual machine, i.e. time when every process is to terminate on the virtual machine'C is estimated by considering the virtual machines.

Scalable, high performance, and fault-tolerant distributed applications are realized with virtual machines in server cluster systems. Application processes are performed on virtual machines in each server. Processing load of virtual machines to perform application processes has to be balanced in a server cluster system to satisfy the application requirements like response time. On the other hand, a server cluster system consumes a large amount of electric energy since multiple servers consume electric energy to perform application processes. It is critical to discuss how to reduce the total electric energy consumption of a server cluster to perform application processes on virtual machines. In our previous studies, the transmission model and power consumption model of a server to perform communication processes on multiple virtual machines are proposed. In this paper, we newly propose the transmission energy consumption laxity based (TECLB) algorithm to allocate communication processes to virtual machines in a server cluster based on the proposed transmission model and power consumption model of a server so that the total energy consumption of a server cluster can be reduced. We evaluate the TECLB algorithm in terms of the total energy consumption of a server cluster and transmission time of each process compared with the basic round-robin (RR) algorithm. The evaluation results show the average total energy consumption of a server cluster is maximumly reduced to 9% of the RR algorithm.

In order to fault-tolerantly perform an application process in a server cluster, the process is replicated on multiple servers. Even if some replicas of the process is faulty, e.g. due to stop-fault of the host server, the process can be successfully performed as long as some number of replicas are operational. The more number of replicas of a process are performed, the more reliably and availably the process can be performed but the more electric energy is consumed. In this paper, we make clear what software components each replica of a process includes. We newly propose new replication schemes which are composed of a primary replica and more types of secondary replicas so that the total electric energy consumption can be reduced while the reliability and availability of each process can be increased. In the evaluation, we show the electric energy consumption of servers can be reduced in the replication schemes compared with the traditional active and passive replication schemes.

In Infrastructure-as-a-Service (IaaS) clouds, users manage the systems in virtual machines (VMs) called user VMs through remote management systems (RMSes). To allow users to manage their VMs during failures inside the VMs, IaaS usually provides out-of-band remote management. This management is performed indirectly via an RMS server in a privileged VM called the management VM. However, it is discontinued when a user VM is migrated. This is because an RMS server in the management VM at the source host is terminated on VM migration. Even worse, pending data is lost between an RMS client and a user VM. In this paper, we propose D-MORE for continuing out-of-band remote management across VM migration. D-MORE provides a privileged and migratable VM called DomR and performs out-of-band remote management of a user VM via DomR. During VM migration, it synchronously co-migrates DomR and its target VM and transparently maintains the connections between an RMS client, DomR, and its target VM. We have implemented D-MORE in Xen and confirmed that a remote user could manage his VM via DomR after the VM has been migrated. Our experiments showed that input data was not lost during VM migration and the overhead of D-MORE was acceptable.

Lighting performance and electrical energy consumption of a virtual window prototype

It is critical to reduce the electric energy consumed in server clusters in order to realize eco society. In our previous studies, a server is selected to perform a process by estimating the termination time of every current process and then the electric energy consumption of servers. However, it is not easy and takes time to collect the state of each process and estimate the termination time of each process. In this paper, we propose SLEA (simple locally energy-aware) and SGEA (simple globally energy-aware) algorithms to select a server where only the number of processes on each server is used. In the evaluation, we show the electric energy consumption and active time of the servers and the average execution time of processes can be reduced in the SLEA and SGEA algorithms.

Impact of Factors Affecting Pre-copy Virtual Machine Migration Technique for Cloud Computing

Live Virtual Machine (VM) migration has significantly improved the flexibility of modern Data Centers (DC). However, seamless live migration of a VM between geo-distributed DCs faces several challenges due to difficulties in preserving the network configuration after the migration paired with a large network convergence time. Although SDN-based approaches can speed up network convergence time, these techniques have two limitations. First, they typically react to the new topology by installing new flow rules once the migration is finished. Second, because the WAN is typically not under SDN control, they result in sub-optimal routing thus severely degrading the network performance once the VM is attached at the new location. In this paper, we identify networking challenges for VM migration across geo-distributed DCs. Based on those observations, we design a novel long-haul VM migration scheme that overcomes those limitations. First, instead of reactively restoring connectivity after the migration, our SDN-based approach proactively restores flows across the WAN towards the new location with the help of EVPN and VXLAN overlay technologies. Second, the SDN controller accelerates the network convergence by announcing the migration to other controllers using MP-BGP control plane messages. Finally, the SDN controller resolves the sub-optimal routing problem that arises as a result of migration implementing a distributed anycast gateway. We implement our approach as extensions to the OpenDaylight controller. Our evaluation shows that our approach outperforms existing approaches in reducing the downtime by 400 ms and increasing the application performance up to 12 times.

SummaryThis paper focus on providing a secure and trustworthy solution for virtual machine (VM) migration within an existing cloud provider domain, and/or to the other federating cloud providers. The infrastructure‐as‐a‐service (IaaS) cloud service model is mainly addressed to extend and complement the previous Trusted Computing techniques for secure VM launch and VM migration case. The VM migration solution proposed in this paper uses a Trust_Token based to guarantee that the user VMs can only be migrated and hosted on a trustworthy and/or compliant cloud platforms. The possibility to also check the compliance of the cloud platforms with the pre‐defined baseline configurations makes our solution compatible with an existing widely accepted standards‐based, security‐focused cloud frameworks like FedRAMP. Our proposed solution can be used for both inter‐ and intra‐cloud VM migrations. Different from previous schemes, our solution is not dependent on an active (on‐line) trusted third party; that is, the trusted third party only performs the platform certification and is not involved in the actual VM migration process. We use the Tamarin solver to realize a formal security analysis of the proposed migration protocol and show that our protocol is safe under the Dolev‐Yao intruder model. Finally, we show how our proposed mechanisms fulfill major security and trust requirements for secure VM migration in cloud environments.

The electric power consumed by servers has to be reduced in a cluster in order to realize eco society. We take a macro level approach to reducing the total electric energy consumption of servers to perform application processes in a server cluster. Servers are now equipped with multi-core CPUs. In this paper, we discuss a multi-level power consumption (MLPC) model of a server with a multi-core CPU. Here, the power consumption of a server depends on the number of active cores and active threads where at least one application process is performed. We also discuss a multi-level computation (MLC) model which gives the expected execution time of a process which is concurrently performed with other processes on a server with a multi-core CPU. Based on the MLPC model and the MLC model, we discuss an energy-aware (EA) algorithm to select a server for each process requested by a client in a cluster so as to reduce the total electric energy consumption while satisfying deadline requirements of the processes. We evaluate the EA algorithm and show not only the total energy consumption but also the average execution time of each process is reduced in the EA algorithm compared with the round-robin (RR) and random (RD) algorithms.