Hiroki Kataoka

Power Consumption and Computation Models of a Server with a Multi-core CPU and Experiments

The power consumption of servers has to be reduced in a cluster to realize eco society. In this paper, we discuss power consumption models of servers. We take a macro level approach to reducing the total power consumption of servers to perform application processes. A server is equipped with a multi-core CPU. Through measuring electric power consumed by types of servers to perform application processes, we newly propose 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 computation model which gives the expected execution time of a process on a server. Based on the MLPC model and the computation model, we discuss an energy-aware (EA) selection algorithm to select a server for each process requested by a client in a cluster so that the total electric energy consumption can be reduced. We evaluate the EA algorithm and show the total energy consumption is reduced in the EA algorithm compared with round-robin and random algorithms.

Multi-level Computation and Power Consumption Models

It is critical to reduce the electric power consumed by servers in a cluster in order to realize eco society. In the multi-level power consumption (MLPC) model of a server with a multi-core CPU, the power consumption of the server depends on the number of active cores and active threads where at least one application process is performed. In our previous studies, we discuss the energy-aware (EA) selection algorithm to select a server for each request process. Here, a server which is expected to consume the minimum electric energy is selected in a cluster. A server consumes the basic electric power even if no process is performed. The ratio of the basic energy consumption to the total electric energy consumption is large, e.g. 40 to 50 %. In this paper, we newly propose a globally energy-aware (GEA) algorithm to select a server for each process in a cluster. Here, not only the total electric energy consumption of the servers but also the ratio of basic electric energy consumed by servers to the total energy consumption can be reduced. We evaluate the GEA algorithm and show not only the total energy consumption of the servers but also the average execution time of processes are reduced in the GEA algorithm compared with the EA, round-robin (RR), and random (RD) algorithms.

Evaluation of Energy-Aware Server Selection Algorithms

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.

Energy-Aware Server Selection Algorithms in a Scalable Cluster

It is critical to reduce the electric energy consumed in information systems, especially server clusters. In this paper, we extend the multi-level power consumption (MLPC) model and the multi-level computation (MLC) model to a server with multiple CPUs. In this paper, we newly propose a totally energy-aware (TEA) algorithm to select a server for a process in a cluster. Here, servers in a cluster are first classified into subclusters. Each subcluster is characterized in terms of the electric power and computation rate. One server is randomly selected in each subcluster. Then, one server is selected so that the expected electric energy is minimum in the selected servers. We evaluate the TEA algorithm and show not only the total electric energy consumption of the servers but also the average execution time of processes are reduced in the TEA algorithm compared with other algorithms.

Energy-Aware Clusters of Servers for Storage and Computation Applications

It is now critical to reduce electric energy consumed in a cluster of servers, especially scalable systems like cloud computing systems. In clusters, most application processes like web applications use not only CPU resources but also files and databases. In this paper, we consider storage processes which read and write data in files in addition to computation processes. We propose a PCS model (power consumption model for a storage server) which shows how much electric power a server consumes to perform storage and computation processes. We also propose a CS model (a computation model for storage server) which shows how long it is expected to take to perform storage processes and computation processes. By using the PCS and CS models, we propose a local energy-aware (LEA) algorithm to select a server for a request process in a cluster so that the total electric energy consumption of the servers can be reduced. We evaluate the LEA algorithm in terms of total electric energy consumption of the servers. We show the electric energy consumed by servers to perform computation and storage processes can be reduced in the LEA algorithm.

Energy-Efficient Virtualisation of Threads in a Server Cluster

It is critical to reduce the electric power consumed by servers in a cluster in order to realize eco society. In our previous studies, the multi-level power consumption (MLPC) model of a server with a multi-thread CPU, the power consumption of the server is proposed and the globally energy-aware (GEA) algorithm is discussed to select a server for each process in a cluster. Here, not only the total electric energy consumption of all the servers but also the ratio of the basic electric energy consumed by the servers to the total electric energy consumption can be reduced in a cluster. However, the GEA algorithm is not scalable since every server is checked to find a server for each process in a cluster. In this paper, we newly propose a scalable energy-aware (SEA) algorithm to select a server for a process. Here, some number of servers are first randomly selected in a cluster and one server is then selected in the selected servers by the GEA algorithm. We evaluate the SEA algorithm and show not only the total electric energy consumption of the servers but also the average execution time of processes are reduced in the SEA algorithm compared with other algorithms.

Simple Energy-Aware Algorithms for Selecting a Server in a Scalable Cluster

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.

Multi-level power consumption model and energy-aware server selection algorithm

Electric power consumed by servers has to be reduced in a cluster in order to realise eco society. We take a macro-level approach to reducing the total electric energy consumption of servers to perform application processes. In this paper, we discuss a Multi-Level Power Consumption (MLPC) model of a server with a multi-thread CPU and a Multi-Level Computation (MLC) model. Based on the MLPC and the MLC models, we newly propose an energy-aware (EA) algorithm to select a server for each process requested by a client in a cluster to reduce the total electric energy consumption of the cluster while satisfying deadline requirements of the processes. We evaluate the EA algorithm and show that not only the total electric energy consumption of servers but also the average execution time of each process are reduced in the EA algorithm compared with the round-robin (RR) and random (RD) algorithms.

Energy-aware Server Selection Algorithms for Storage and Computation Processes

Application processes like Web applications use not only CPU but also storages like HDD. In our previous studies, the algorithms to select a server in a cluster are proposed to energy-efficiently perform processes which use either CPU or storages. In this paper, we consider a more general type of process which does both the computation and accesses to storages. In this paper, we newly propose LEAG and GEAG algorithms to select servers to perform general processes in a cluster so that the total electric energy consumption of the servers can be reduced. We evaluate the LEAG and GEAG algorithms in terms of total electric energy consumption of the servers and average execution time of the processes. We show the electric energy consumed by servers can be reduced in the LEAG and GEAG algorithms.

Simple Energy-efficient Server Selection Algorithm in a Scalable Cluster

It is critical to reduce the electric energy consumed in server clusters. In our previous studies, the MLPCM and MLCM models are proposed with LEA and MEA server selection algorithms. Here, a server is selected to perform a process by estimating the termination time of every current process. However, it takes time to collect the state of each process and estimate the termination time of each process. In this paper, we propose a simple energy-aware (PEA) algorithm to select a server where only state information on number of processes on each server is used. In the evaluation, we show the computation complexity of the PEA algorithm is O(1), smaller than the other algorithms while the total electric energy consumption of the servers of the PEA algorithm is almost the same as the MEA algorithm and is smaller than the others.