Energy-efficient response time management for embedded databases

2016 ◽  
Vol 53 (2) ◽  
pp. 228-253 ◽  
Author(s):  
Woochul Kang ◽  
Jaeyong Chung
Keyword(s):  
Response Time ◽  
Time Management ◽  
Energy Efficient ◽  
Embedded Databases

scholarly journals Low-Complexity Run-time Management of Concurrent Workloads for Energy-Efficient Multi-Core Systems

2020 ◽  
Vol 10 (3) ◽  
pp. 25
Author(s):  
Ali Aalsaud ◽  
Fei Xia ◽  
Ashur Rafiev ◽  
Rishad Shafik ◽  
Alexander Romanovsky ◽  
...  
Keyword(s):  
Time Management ◽  
Energy Efficient ◽  
Low Cost ◽  
Low Complexity ◽  
Optimization Approach ◽  
System Configuration ◽  
Efficient System ◽  
Time Control ◽  
Time Optimization ◽  
Run Time

Contemporary embedded systems may execute multiple applications, potentially concurrently on heterogeneous platforms, with different system workloads (CPU- or memory-intensive or both) leading to different power signatures. This makes finding the most energy-efficient system configuration for each type of workload scenario extremely challenging. This paper proposes a novel run-time optimization approach aiming for maximum power normalized performance under such circumstances. Based on experimenting with PARSEC applications on an Odroid XU-3 and Intel Core i7 platforms, we model power normalized performance (in terms of instruction per second (IPS)/Watt) through multivariate linear regression (MLR). We derive run-time control methods to exploit the models in different ways, trading off optimization results with control overheads. We demonstrate low-cost and low-complexity run-time algorithms that continuously adapt system configuration to improve the IPS/Watt by up to 139% compared to existing approaches.

RE-STORM: REAL-TIME ENERGY EFFICIENT DATA ANALYSIS ADAPTING STORM PLATFORM

2016 ◽  
Vol 78 (10) ◽  
Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M.
Keyword(s):  
Energy Efficiency ◽  
Big Data ◽  
Response Time ◽  
Energy Efficient ◽  
Data Stream ◽  
High Performance ◽  
High Energy ◽  
Streaming Data ◽  
Stream Computing ◽  
High Energy Efficiency

It is necessary to model an energy efficient and stream optimization towards achieve high energy efficiency for Streaming data without degrading response time in big data stream computing. This paper proposes an Energy Efficient Traffic aware resource scheduling and Re-Streaming Stream Structure to replace a default scheduling strategy of storm is entitled as re-storm. The model described in three parts; First, a mathematical relation among energy consumption, low response time and high traffic streams. Second, various approaches provided for reducing an energy without affecting response time and which provides high performance in overall stream computing in big data. Third, re-storm deployed energy efficient traffic aware scheduling on the storm platform. It allocates worker nodes online by using hot-swapping technique with task utilizing by energy consolidation through graph partitioning. Moreover, re-storm is achieved high energy efficiency, low response time in all types of data arriving speeds.it is suitable for allocation of worker nodes in a storm topology. Experiment results have been demonstrated the comparing existing strategies which are dealing with energy issues without affecting or reducing response time for a different data stream speed levels. Finally, it shows that the re-storm platform achieved high energy efficiency and low response time when compared to all existing approaches.

scholarly journals PARMA: Parallelization-Aware Run-Time Management for Energy-Efficient Many-Core Systems

2020 ◽  
Vol 69 (10) ◽  
pp. 1507-1518
Author(s):  
Mohammed A. Noaman Al-hayanni ◽  
Ashur Rafiev ◽  
Fei Xia ◽  
Rishad Shafik ◽  
Alexander Romanovsky ◽  
...  
Keyword(s):  
Time Management ◽  
Energy Efficient ◽  
Run Time ◽  
Many Core

Energy Efficient Real-Time Distributed Communication Architectures for Military Tactical Communication Systems

2015 ◽  
pp. 35-82
Author(s):  
Bora Karaoglu ◽  
Tolga Numanoglu ◽  
Bulent Tavli ◽  
Wendi Heinzelman
Keyword(s):  
Cloud Computing ◽  
Response Time ◽  
Real Time ◽  
Energy Efficient ◽  
Communication Systems ◽  
Mobile Cloud Computing ◽  
Mobile Cloud ◽  
Communication Architectures ◽  
Distributed Communication ◽  
Communication Modes

For military communication systems, it is important to achieve robust and energy efficient real-time communication among a group of mobile users without the support of a pre-existing infrastructure. Furthermore, these communication systems must support multiple communication modes, such as unicast, multicast, and network-wide broadcast, to serve the varied needs in military communication systems. One use for these military communication systems is in support of real-time mobile cloud computing, where the response time is of utmost importance; therefore, satisfying real-time communication requirements is crucial. In this chapter, we present a brief overview of military tactical communications and networking (MTCAN). As an important example of MTCAN, we present the evolution of the TRACE family of protocols, describing the design of the TRACE protocols according to the tactical communications and networking requirements. We conclude the chapter by identifying how the TRACE protocols can enable mobile cloud computing within military communication systems.

Task scheduling and resource allocation of seasonal requests of users in cloud using NMKA and CM-GA techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
S. Prathiba ◽  
Sharmila Sankar
Keyword(s):  
Resource Allocation ◽  
Response Time ◽  
Task Scheduling ◽  
Energy Efficient ◽  
Feature Reduction ◽  
Streaming Data ◽  
Frequent Itemset ◽  
Process Time ◽  
Content Type ◽  
Cauchy Mutation

Purpose The purpose of this paper is to provide energy-efficient task scheduling and resource allocation (RA) in cloud data centers (CDC). Design/methodology/approach Task scheduling and RA is proposed in this paper for cloud environment, which schedules the user’s seasonal requests and allocates resources in an optimized manner. The proposed study does the following operations: data collection, feature extraction, feature reduction and RA. Initially, the online streaming data of seasonal requests of multiple users were gathered. After that, the features are extracted based on user requests along with the cloud server, and the extracted features are lessened using modified principal component analysis. For RA, the split data of the user request is identified and that data is pre-processed by computing closed frequent itemset along with entropy values. After that, the user requests are scheduled using the normalized K-means algorithm (NKMA) centered on the entropy values. Finally, the apt resources are allotted to that scheduled task using the Cauchy mutation-genetic algorithm (CM-GA). The investigational outcomes exhibit that the proposed study outruns other existing algorithms in respect to response time, execution time, clustering accuracy, precision and recall. Findings The proposed NKMA and CM-GA technique’s performance is analyzed by comparing them with the existing techniques. The NKMA performance is analyzed with KMA and Fuzzy C-means regarding Prc (Precision), Rca (Recall), F ms (f measure), Acr (Accuracy)and Ct (Clustering Time). The performance is compared to about 500 numbers of tasks. For all tasks, the NKMA provides the highest values for Prc, Rca, Fms and Acr, takes the lowest time (Ct) for clustering the data. Then, the CM-GA optimization for optimally allocating the resource in the cloud is contrasted with the GA and particle swarm optimization with respect to Rt (Response Time), Pt (Process Time), Awt (Average Waiting Time), Atat (Average Turnaround Time), Lcy (Latency) and Tp (Throughput). For all number of tasks, the proposed CM-GA gives the lowest values for Rt, Pt, Awt, Atat and Lcy and also provides the highest values for Tp. So, from the results, it is known that the proposed technique for seasonal requests RA works well and the method optimally allocates the resources in the cloud. Originality/value The proposed approach provides energy-efficient task scheduling and RA and it paves the way for the development of effective CDC.

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