Energy-aware adaptive offloading of soft real-time jobs in mobile edge clouds

AbstractWe present a model for measuring the impact of offloading soft real-time jobs over multi-tier cloud infrastructures. The jobs originate in mobile devices and offloading strategies may choose to execute them locally, in neighbouring devices, in cloudlets or in infrastructure cloud servers. Within this specification, we put forward several such offloading strategies characterised by their differential use of the cloud tiers with the goal of optimizing execution time and/or energy consumption. We implement an instance of the model using Jay, a software framework for adaptive computation offloading in hybrid edge clouds. The framework is modular and allows the model and the offloading strategies to be seamlessly implemented while providing the tools to make informed runtime offloading decisions based on system feedback, namely through a built-in system profiler that gathers runtime information such as workload, energy consumption and available bandwidth for every participating device or server. The results show that offloading strategies sensitive to runtime conditions can effectively and dynamically adjust their offloading decisions to produce significant gains in terms of their target optimization functions, namely, execution time, energy consumption and fulfilment of job deadlines.

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Joint Optimization Offloading Strategy of Execution Time and Energy Consumption of Mobile Edge Computing

The International Arab Journal of Information Technology ◽

10.34028/iajit/18/5/114 ◽

2021 ◽

Vol 18 (5) ◽

Author(s):

Qingzhu Wang ◽

Xiaoyun Cui

Keyword(s):

Energy Consumption ◽

Mobile Devices ◽

Execution Time ◽

Optimal Solution ◽

Computation Offloading ◽

Joint Optimization ◽

Random Strategy ◽

Fitness Value ◽

Cloud Server ◽

Time And Energy

As mobile devices become more and more powerful, applications generate a large number of computing tasks, and mobile devices themselves cannot meet the needs of users. This article proposes a computation offloading model in which execution units including mobile devices, edge server, and cloud server. Previous studies on joint optimization only considered tasks execution time and the energy consumption of mobile devices, and ignored the energy consumption of edge and cloud server. However, edge server and cloud server energy consumption have a significant impact on the final offloading decision. This paper comprehensively considers execution time and energy consumption of three execution units, and formulates task offloading decision as a single-objective optimization problem. Genetic algorithm with elitism preservation and random strategy is adopted to obtain optimal solution of the problem. At last, simulation experiments show that the proposed computation offloading model has lower fitness value compared with other computation offloading models.

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Worst-case energy consumption minimization based on interference analysis and bank mapping in multicore systems

International Journal of Distributed Sensor Networks ◽

10.1177/1550147716686969 ◽

2017 ◽

Vol 13 (2) ◽

pp. 155014771668696

Author(s):

Zhihua Gan ◽

Zhimin Gu ◽

Hai Tan ◽

Mingquan Zhang ◽

Jizan Zhang

Keyword(s):

Energy Consumption ◽

Real Time ◽

Multicore Systems ◽

Worst Case ◽

Mapping Algorithm ◽

Fine Grained ◽

Energy Consumption Reduction ◽

Deadline Constraints ◽

Core Mapping ◽

The Impact

Energy is a scarce resource in real-time embedded systems due to the fact that most of them run on batteries. Hence, the designers should ensure that the energy constraints are satisfied in addition to the deadline constraints. This necessitates the consideration of the impact of the interference due to shared, low-level hardware resources such as the cache on the worst-case energy consumption of the tasks. Toward this aim, this article proposes a fine-grained approach to analyze the bank-level interference (bank conflict and bus access interference) on real-time multicore systems, which can reasonably estimate runtime interferences in shared cache and yield tighter worst-case energy consumption. In addition, we develop a bank-to-core mapping algorithm for reducing bank-level interference and improving the worst-case energy consumption. The experimental results demonstrate that our approach can improve the tightness of worst-case energy consumption by 14.25% on average compared to upper-bound delay approach. The bank-to-core mapping provides significant benefits in worst-case energy consumption reduction with 7.23%.

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Modeling Multi-Site Computation Offloading in Unreliable Cloud Environments

10.32920/ryerson.14660922.v1 ◽

2021 ◽

Author(s):

Marzieh Ranjbar Pirbasti

Keyword(s):

Energy Consumption ◽

Response Time ◽

Mobile Device ◽

Fault Injection ◽

Computation Offloading ◽

Failure Rates ◽

Recovery Mechanisms ◽

Efficient Allocations ◽

Cloud Servers ◽

Time And Energy

Offloading heavy computations from a mobile device to cloud servers can reduce the power consumption of the mobile device and improve the response time of mobile applications. However, the gains of offloading can be significantly affected by failures of cloud servers and network links. In this thesis, we propose a fault-aware, multi-site computation offloading model capable of finding efficient allocations of tasks to resources. Our model reduces both response time and energy consumption by incorporating the effect of failures and recovery mechanisms for various offloading allocations. In addition, we create a fault-injection framework to evaluate an allocation under various failure rates and recovery mechanisms. The experiments carried out with our fault-injection framework demonstrate that our fault-aware model can determine an allocation—based on the type of failures, failure rates, and the employed recovery mechanisms—that improves both response time and lower energy consumption compared to model without failures.

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Energy-Aware Real-Time Task Scheduling for Heterogeneous Multiprocessors with Particle Swarm Optimization Algorithm

Mathematical Problems in Engineering ◽

10.1155/2014/287475 ◽

2014 ◽

Vol 2014 ◽

pp. 1-9 ◽

Cited By ~ 13

Author(s):

Weizhe Zhang ◽

Hucheng Xie ◽

Boran Cao ◽

Albert M. K. Cheng

Keyword(s):

Particle Swarm Optimization ◽

Energy Consumption ◽

Real Time ◽

Task Scheduling ◽

Particle Swarm ◽

Energy Aware ◽

Swarm Optimization ◽

Heterogeneous Multiprocessors ◽

Time Task ◽

Feasible Solutions

Energy consumption in computer systems has become a more and more important issue. High energy consumption has already damaged the environment to some extent, especially in heterogeneous multiprocessors. In this paper, we first formulate and describe the energy-aware real-time task scheduling problem in heterogeneous multiprocessors. Then we propose a particle swarm optimization (PSO) based algorithm, which can successfully reduce the energy cost and the time for searching feasible solutions. Experimental results show that the PSO-based energy-aware metaheuristic uses 40%–50% less energy than the GA-based and SFLA-based algorithms and spends 10% less time than the SFLA-based algorithm in finding the solutions. Besides, it can also find 19% more feasible solutions than the SFLA-based algorithm.

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An Energy-Aware Real-Time VM-Provisioning Framework For Heterogeneous Data Centres

10.32920/17139047.v1 ◽

2021 ◽

Author(s):

Salam Ismaeel

Keyword(s):

Energy Consumption ◽

Real Time ◽

Power Efficiency ◽

Large Scale ◽

Virtual Machines ◽

Heterogeneous Data ◽

Energy Aware ◽

Time Dynamic ◽

Data Centre ◽

Data Centres

<div>Increasing power efficiency is one of the most important operational factors for any data centre providers. In this context, one of the most useful approaches is to reduce the number of utilized Physical Machines (PMs) through optimal distribution and re-allocation of Virtual Machines (VMs) without affecting the Quality of Service (QoS). Dynamic VMs provisioning makes use of monitoring tools, historical data, prediction techniques, as well as placement algorithms to improve VMs allocation and migration. Consequently, the efficiency of the data centre energy consumption increases.</div><div>In this thesis, we propose an efficient real-time dynamic provisioning framework to reduce energy in heterogeneous data centres. This framework consists of an efficient workload preprocessing, systematic VMs clustering, a multivariate prediction, and an optimal Virtual Machine Placement (VMP) algorithm. Additionally, it takes into consideration VM and user behaviours along with the existing state of PMs. The proposed framework consists of a pipeline successive subsystems. These subsystems could be used separately or combined to improve accuracy, efficiency, and speed of workload clustering, prediction and provisioning purposes.<br></div><div>The pre-processing and clustering subsystems uses current state and historical workload data to create efficient VMs clusters. Efficient VMs clustering include less consumption resources, faster computing and improved accuracy. A modified multivariate Extreme Learning Machine (ELM)-based predictor is used to forecast the number of VMs in each cluster for the subsequent period. The prediction subsystem takes users’ behaviour into consideration to exclude unpredictable VMs requests.<br></div><div>The placement subsystem is a multi-objective placement algorithm based on a novel Machine Condition Index (MCI). MCI represents a group of weighted components that is inclusive of data centre network, PMs, storage, power system and facilities used in any data centre. In this study it will be used to measure the extent to which PM is deemed suitable for handling the new and/or consolidated VM in large scale heterogeneous data centres. It is an efficient tool for comparing server energy consumption used to augment the efficiency and manageability of data centre resources.</div><div> The proposed framework components separately are tested and evaluated with both synthetic and realistic data traces. Simulation results show that proposed subsystems can achieve efficient results as compared to existing algorithms. <br></div>

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What goes on behind closed doors?

International Journal of Sustainability in Higher Education ◽

10.1108/ijshe-02-2015-0027 ◽

2016 ◽

Vol 17 (4) ◽

pp. 451-470 ◽

Cited By ~ 15

Author(s):

Nicole Sintov ◽

Ellen Dux ◽

Agassi Tran ◽

Michael Orosz

Keyword(s):

Energy Consumption ◽

Real Time ◽

Carbon Dioxide Emissions ◽

Group Identification ◽

Environmental Behaviors ◽

Content Type ◽

Individual Level ◽

Electricity Use ◽

Reduce Energy Consumption ◽

The Impact

Purpose The purpose of this paper was to evaluate the impact of a competition-based intervention combining high-resolution electricity feedback, incentives, information and prompts on college dormitory residents’ energy consumption and participation in demand response events. The authors also investigated changes in individual-level pro-environmental behaviors and examined psychosocial correlates of behavior change. Design/methodology/approach Residents of 39 suites in a freshman residence hall competed against one another to reduce energy consumption and win prizes as part of a three-week competition. Feedback was provided in near real-time at the suite-level via an interactive touch-screen kiosk. Participants also completed baseline and follow-up surveys. Findings Electricity use among all suites was approximately 6.4 per cent lower during the competition period compared to baseline, a significant reduction. Additionally, participants reported engaging in various pro-environmental behaviors significantly more frequently during the competition relative to baseline. Changes in pro-environmental behavior were associated with changes in level of group identification and perceived social norms. Practical implications In three weeks, dormitory residents saved 3,158 kWh of electricity compared to baseline – the equivalent of more than 3,470 pounds of carbon dioxide emissions. The findings provide evidence that real-time feedback, combined with incentives, information and prompts, can motivate on-campus residents to reduce energy consumption. Originality/value The authors contribute to a limited body of evidence supporting the effectiveness of dorm energy competitions in motivating college students to save energy. In addition, the authors identified individual-level behavioral and psychosocial changes made during such an intervention. University residential life planners may also use the results of this research to inform student programming.

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Energy-Efficient Task Partitioning for Real-Time Scheduling on Multi-Core Platforms

Computers ◽

10.3390/computers10010010 ◽

2021 ◽

Vol 10 (1) ◽

pp. 10

Author(s):

Manal A. El Sayed ◽

El Sayed M. Saad ◽

Rasha F. Aly ◽

Shahira M. Habashy

Keyword(s):

Energy Consumption ◽

Real Time ◽

Bin Packing ◽

Shared Resources ◽

Energy Aware ◽

Task Partitioning ◽

Worst Case ◽

Real Time Scheduling ◽

Static Partitioning ◽

Time Scheduling

Multi-core processors have become widespread computing engines for recent embedded real-time systems. Efficient task partitioning plays a significant role in real-time computing for achieving higher performance alongside sustaining system correctness and predictability and meeting all hard deadlines. This paper deals with the problem of energy-aware static partitioning of periodic, dependent real-time tasks on a homogenous multi-core platform. Concurrent access of the tasks to shared resources by multiple tasks running on different cores induced a higher blocking time, which increases the worst-case execution time (WCET) of tasks and can cause missing the hard deadlines, consequently resulting in system failure. The proposed blocking-aware-based partitioning (BABP) algorithm aims to reduce the overall energy consumption while avoiding deadline violations. Compared to existing partitioning strategies, the proposed technique achieves more energy-saving. A series of experiments test the capabilities of the suggested algorithm compared to popular heuristics partitioning algorithms. A comparison was made between the most used bin-packing algorithms and the proposed algorithm in terms of energy consumption and system schedulability. Experimental results demonstrate that the designed algorithm outperforms the Worst Fit Decreasing (WFD), Best Fit Decreasing (BFD), and Similarity-Based Partitioning (SBP) algorithms of bin-packing algorithms, reduces the energy consumption of the overall system, and improves schedulability.

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Energy Efficient and Real-Time Remote Sensing in AI-Powered Drone

Mobile Information Systems ◽

10.1155/2021/6650053 ◽

2021 ◽

Vol 2021 ◽

pp. 1-8

Author(s):

Bongjae Kim ◽

Jinman Jung ◽

Hong Min ◽

Junyoung Heo

Keyword(s):

Remote Sensing ◽

Energy Consumption ◽

Data Analysis ◽

Real Time ◽

Energy Efficient ◽

Urban Areas ◽

Computation Offloading ◽

Time Constraints ◽

Time Data ◽

Real Time Data

Remote sensing using drones has the advantage of being able to quickly monitor large areas such as rivers, oceans, mountains, and urban areas. In the case of applications dealing with large sensing data, it is not possible to send data from a drone to the server online, so it must be copied to the server offline after the end of the flight. However, online transmission is essential for applications that require real-time data analysis. The existing computation offloading scheme enables online transmission by processing large amounts of data in a drone and transferring it to the server, but without consideration for real-time constraints. We propose a novel computation offloading scheme which considers real-time constraints while minimizing the energy consumption of drones. Experimental results showed that the proposed scheme satisfied real-time constraints compared to the existing computation offloading scheme. Furthermore, the proposed technique showed that real-time constraints were satisfied even in situations where delays occurred on the server due to the processing of requests from multiple drones.

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