Energy-efficient dynamic clusters of servers

The electric power consumed by servers has to be reduced in order to realize green societies. We consider computation (CP) and storage (ST) types of application processes performed on servers in this paper, where CPU and storage drives are mainly consumed, respectively. The authors proposed the storage-based power consumption (SBPC) model and storage-based computation (SBCP) model to perform ST and CP processes on a server. Here, the power consumption rate Et( ) [W] of a server st at time depends on types of processes which are concurrently performed but is independent of the number of the processes on the server st in the SBPC model. The average execution time of a CP process depends on the numbers of CP processes concurrently performed and the average execution time of an ST process depends on the number of concurrent ST and CP processes in the SBCP model. By using the SBPC and SBCP models, we enhance the energy aware (EA) algorithm for selecting a server for each CP/ST process so that not only the execut n time of each process but also the power consumption of servers can be reduced. We show the evaluation of the EA algorithm in terms of the total power consumption and average execution time. Keywords-Power consumption models, Energy-aware (EA) selection algorithm, Storage-based power consumption (SBPC) model, Storage-based computation (SBCP) model.

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.

Homomorphic encryption for smart grids has been investigated in many studies. It is possible to estimate the total power consumption in an area without knowing the consumption data of individual households. In the case of demand response (DR), it is important to calculate the total electric power consumption in an area because DR reports are published accordingly to reduce peak power consumption when the demand is high. However, the published data may reveal private information about residents, such as the timings of specific activities (leaving from and returning home), and device details. To overcome this problem, we propose a method specialized to enable energy providers to securely share electric power consumption data. The proposed method uses a self-organizing map (SOM), which is an unsupervised learning method. In order to share power consumption data while preserving privacy, the SOM is shared without the raw data. In this framework, a target accuracy of nearly 3% is achieved, while actual data are not published by any company.

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.

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. The P2P systems are scalable and fully distributed without any centralized coordination. It is getting more significant to discuss how to reduce the total electric power consumption of computers in information systems in addition to developing distributed algorithms to minimize the computation time. In this paper, we do not discuss the micro level like the hardware specification of each computer. We discuss a model to show the relation of the computation and the total power consumption of multiple peer computers to perform Web types of processes at macro level. 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.

Since the existing RSA Algorithm needs long key sizes, requires big memory spaces and also a long execution time, it seems necessary to make some changes or taking some necessary precautions during the implementation of RSA in order to provide maximum security level. In this paper, three experiments were developed in order to examine the execution time of the processes of encryption and decryption and comparing the results together to elicit the improved points and discussing them. Moreover, some programming techniques will be used in order to speed up the process of encryption or decryption. The results indicate that the execution time of encryption process was improved by 14% in experiment 3 by using some techniques to speed up the process, the decryption process was improved as well by 22%.

This paper presents a general framework for determining average program execution times and their variance, based on the program's interval structure and control dependence graph. Average execution times and variance values are computed using frequency information from an optimized counter-based execution profile of the program.

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.

In order to realize reliable and energy-aware server clusters, application processes have to be reliably and energy-efficiently performed in presence of server faults. In one way, an application process can be redundantly performed on multiple servers. In the improved redundant power consumption laxity-based (IRPCLB) algorithm which is proposed in our previous studies, since each application process is redundantly performed on more than one server, the reliability and availability of the cluster increase. However, the larger amount of electric power is consumed than non-redundant execution. In the IRPCLB algorithm, once a process terminates on a server, meaningless redundant processes on the other servers are forced to terminate in order to reduce the total power consumption. In this paper, we newly discuss the extended IRPCLB (EIRPCLB) algorithm to furthermore reduce the total power consumption of a server cluster. Here, in addition to forcing meaningless processes to terminate, each server is made reduce the consumption of computation resource to perform a redundant process by differentiating starting time of each redundant process. We evaluate the EIRPCLB algorithm in terms of total power consumption of a cluster and the average response time and number of servers to perform each process compared with the IRPCLB algorithm.

Task scheduling is an essential component of any distributed system because it routes tasks to appropriate resources for execution, such as grids, clouds, and peer-to-peer networks. Common scheduling algorithms include downsides, such as high temporal complexity, non-simultaneous processing of input tasks, and longer program execution times. Exploration-based scheduling algorithms prioritize tasks using a variety of methods, resulting in long execution times on heterogeneous distributed computing systems. As a result, task prioritization becomes a bottleneck in such systems. It is appropriate to prioritize tasks with the shortest execution time using faster algorithms. The genetic algorithm (GA) is one of the evolutionary approaches used to solve complex problems quickly. This paper proposes a parallel GA with a MapReduce architecture for scheduling jobs on cloud computing with various priority queues. The fundamental aim of this study is to employ a MapReduce architecture to minimize the total execution time of the task scheduling process in the cloud computing environment. The proposed method accomplishes task scheduling in two stages: first, the GA was used in conjunction with heuristic techniques to assign tasks to processors, and then the GA was used in conjunction with the MapReduce framework to assign jobs to processors. In our experiments, we consider heterogeneous resources that differ in their ability to execute various tasks, as well as running a job on different resources with varying execution durations. The results show that the proposed method outperforms other algorithms such as particle swarm optimization, whale optimization algorithm, moth-flame optimization, and intelligent water drops.

Behavior of a system using the diurnal range of atmosphere temperature in the summer is characterized by numerical simulation. The proposed system consists of a radiator, a thermal energy storage tank, and a fan-coil cooler. The storage tank is fully stratified. The performance of the system is compared with the case when the storage tank is fully mixed. Parameters for the calculation are assumed from the actual weather and characteristics of the appliances in Japan. Total consumption of electric power is used for evaluating the system performance. From the calculation, it is found that there are optimum conditions to decrease the consumption of electric power as well as the case of mixed tank system. When the radiator is operated during some limited hours, the total consumption of electric power becomes smaller than that when the radiator is continuously operated all night. The consumption decreases as the capacity of the thermal energy storage tank increases. There are also most effective conditions in the flow state of the pumps and the operating temperature of the fan-coil cooler. With a stratified tank, the total consumption of electric power is decreased in the maximum by about 3% compared with mixed one. This system has a possibility to save energy or make the uneven consumption of electric power uniform throughout a 24-hour period.

The RC-5 algorithm is an algorithm with the encryption method using the symmetric method and processing in the form of a cipher block, the same keywords are used for the encryption and decryption processes. The testing and analysis phase consists of a). Testing the execution time of the key generation process (set up key) b). Testing the success of the encryption and decryption process on the client-server integration method c). Testing the success of the encryption and decryption process using the file operation method d). Comparison of overhead/file size before and after passing through the system. The testing phase and execution time analysis are carried out when using the file operation method where the test will be divided into 3 parts, namely the execution time for the key setup process, the encryption process, and the decryption process. Testing the success of the encryption and decryption process on the client-server integration method is divided into 2 parts, namely testing the encryption process and the decryption process. In testing the encryption process, the characters that will be sent to the server will be encrypted first with a key to becoming a ciphertext in the form of integer data. The test results obtained are 1). The execution time for key generation (set up key) is very fast, around 9-10 ns. 2). In the encryption and decryption process, the execution time depends on the size of the plaintext file. The larger the plaintext file size, the longer the execution time. Where for a file with a size of 1 kb requires an execution time of 50.6 ms in the encryption process. 3). There is no difference between the size of the file before entering encryption and after decryption. Where for files with a size of 500 bytes before entering the encryption process, a file of 500 bytes will be generated from the decryption process.

In autonomous robots, the hardware and the algorithms are limited by the power constraints of the robots. Increasing algorithm efficiency can be considered as a way to deal with power constraints. Considering the processing load of Visual SLAM methods, which are frequently used in autonomous robots and become increasingly widespread, there is a need to investigate the effects of methods on power consumption. In this study, a performance comparison of ORB-SLAM2, which is a feature-based Visual SLAM method, DSO, and LDSO, which are direct Visual SLAM methods was conducted. We perform the comparison on ICL-NUIM, KITTI, and EUROC datasets using NVIDIA Jetson TX1 hardware. Central Processor Unit (CPU) usage, Graphics Processor Unit (GPU) power consumption, and total power consumption were taken into account since hardware and power constraints are very important issue for autonomous systems. Results showed that ORB-SLAM2 is the most efficient method for GPU load and power consumption. GPU power consumption and total power consumption are higher than expected for the DSO method compared to CPU load. The computational cost of Loop-Closure is seen clearly when comparing the DSO and LDSO methods.

Efficient power consumption, intelligent power monitoring and interactive control of power consumption in smart homes are the key factors of sustainable development, reduction of power demand peaks and activities related to avoidance of blackout effects.The highlight of the proposed system is its ability to control maximum consuming power of the home appliances (HAs) and keep the total power consumption below a certain limit and allowing the home user more flexibility to operate their HAs based on HA priority.The proposed smart power management system consists of smart electric sensors (SESs) and power provisioning controller (PPC).The PPC and SES are concurrently used to control and maintain stability and complete power waveform behavior examination.However, we need to reassign an amount of consuming power from one HA to another when the ON/OFF status of HA changes.This paper offers a system to control the maximum power consumption considering heterogeneous HAs with different response speed.In order to guarantee the maximum consuming power limit, we compute the limited suggested power level for HAs.Simulation results show the effectiveness of our system in managing maximum power consumption.

The advancement of multicore systems demands applications with more threads. In order to facilitate this demand, parallel programming models such as message passing interface (MPI) are developed. By using such models, the execution time and the power consumption can be reduced significantly. However, the performance of MPI programming depends on the total number of threads and the number of processing cores in the system. In this work, we experimentally study the impact of Open MPI and POSIX Thread (Pthread) implementations on performance and power consumption of multicore systems. Data dependent (like heat conduction on 2D surface) and data independent (like matrix multiplication) applications are used with high performance hardware in the experiments. Simulation results suggest that both implementations of more threads running in a system with more cores have potential to reduce the execution time with negligible or little increase in total power consumption. It is observed that the performance of MPI implementation varies (due to the dynamic communication overhead among the processing cores).