Information systems based on the cloud computing model and peer-to-peer (P2P) model are now getting popular. In the cloud computing model, a cloud of servers support thin clients with various types of service like Web pages and databases. On the other hand, every computer is peer and there is no centralized coordinator in the P2P model. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems to realize eco-society. In this paper, we consider a Web type of application on P2P overlay networks. First, we discuss a model for showing how much each server peer consumes electric power to perform Web requests from client peers. Then, we discuss algorithms for a client peer to select a server peer in a collection of server peers so that the total power consumption can be reduced while some constraint like deadline one is satisfied. Lastly, we evaluate the algorithms in terms of the total power consumption and throughput compared with traditional round robin algorithms.

Information systems are composed of various types of computers interconnected in networks. In addition, information systems are being shifted from the traditional client-server model to the peer-to-peer (P2P) model. P2P systems are scalable and fully distributed without any centralized coordinator. Here, it is getting more significant to discuss how to reduce the total electric power consumption of computers in addition to developing distributed algorithms to minimize the computation time and memory space. In this paper, we do not discuss microlevel models like the hardware specifications of computers like low-energy CPUs. We rather discuss a macrolevel model to show the relation of the amount of computation and the total power consumption of multiple peer computers to perform Web types of application processes. We also discuss algorithms for allocating a process to a computer so that the deadline constraint is satisfied and the total power consumption is reduced.

A wireless sensor network (WSN)consisting of a large number of tiny sensors can be an effective tool for gathering data in diverse kinds of environments. The data collected by each sensor is communicated to the base station, which forwards the data to the end user. Clustering is introduced to WSNs because it has proven to be an effective approach to provide better data aggregation and scalability for large WSNs. Clustering also conserves the limited energy resources of the sensors. This paper synthesises existing clustering algorithms news's and highlights the challenges in clustering.

In information systems, applications are required to be realized in the digital ecosystems, high performance systems, and scalable systems. In these applications, a client first selects a server in a cluster of servers and issues a request to the server. The request is performed as a process in the server. In this paper, we consider a computation process which mainly consumes the central processing unit (CPU) resources. Cooling devices, such as fans, consume the electric power in addition to the CPU of a server. The rotation speed of a fan is revved up in servers in order to decrease the temperature of a server. Thus, the total power consumption of a server depends on not only computational devices such as a CPU, but also cooling devices. We discuss an extended simple power consumption (ESPC) model of a server with a multicore CPU and cooling devices to perform computation type processes. It is critical to discuss how to select a server for each request issued by a client in order to not only achieve performance objectives, but also reduce the total power consumption of a system based on the ESPC model. An improved power consumption laxity-based (IPCLB) algorithm for selecting a server is proposed in this paper, where the minimum power to be consumed is used to perform the process. We evaluate the ESPC model and IPCLB algorithm in terms of power consumption and elapse time.

In order to realize green ecosocieties, the total electric power consumption of computers and networks is required to be reduced. In applications on distributed systems, clients issue service requests to servers and then servers send replies to clients. Here, we discuss how much electric power a server consumes since the power consumption of a client is neglectable compared with a server. We classify applications into transaction- and communication-based ones. A server mainly consumes CPU resources to perform the transaction-based applications. On the other hand, a server consumes communication resources to transmit a large volume of data to a client in communication-based applications. In our previous studies, the power consumption laxity-based and extended power consumption-based algorithms are proposed to select one of servers so that the total power consumption of servers is reduced for transaction- and communication-based applications, respectively. However, most applications are mixed types, i.e., composed of both the transaction and communication processing modules. Hence, we consider the mixed types of applications in this paper. First, we integrate the power consumption models of transaction- and communication-based applications into a modified simple power consumption (MSPC) model of a server. Based on the MSPC model, we propose an algorithm to select one of servers for mixed types of applications so that the total power consumption of servers can be reduced. We show the power consumption of servers can be reduced in the algorithm through the simulation.