Energy-aware task assignment for mobile cyber-enabled applications in heterogeneous cloud computing

https://doi.org/10.1016/j.jpdc.2017.08.001Get rights and content

Highlights

  • We propose a novel approach for reducing the computation energy costs for heterogeneous MES in cloud systems. Our algorithm can intelligently assign the tasks to on-premise cores or remote cloud servers within an adaptive time period.

  • We present a method of the adjustment that is designed to transfer sub-optimal solutions to optimal solutions at a high success rate.

  • We propose a feasible solution to the proposed task assignment problem for heterogeneous MES that is a NP-hard problem. The proposed approach can be used in other application scenarios.

Abstract

Recent remarkable growth of mobile computing has led to an exceptional hardware upgrade, including the adoption of the multiple core processors. Along with this trend, energy consumptions are becoming greater when the computation capacity or workload grows. As one of the solutions, using cloud computing can mitigate energy costs due to the centralized computation. However, simply offloading the workloads to the remote side cannot efficiently reduce the energy consumptions when the energy costs caused by wireless communications are greater than that of on mobile devices. In this paper, we focus on the energy-saving problem and consider the energy wastes when tasks are assigned to remote cloud servers or heterogeneous core processors. Our solution aims to reduce the total energy cost of the mobile heterogeneous embedded systems by a novel task assignment to heterogeneous cores and mobile clouds. The proposed model is called Energy-Aware Heterogeneous Cloud Management (EA-HCM) model and the main algorithm is Heterogeneous Task Assignment Algorithm (HTA2). Our experimental evaluations have proved that our approach is effective to save energy when deploying heterogeneous embedded systems in mobile cloud systems.

Introduction

Contemporary mobile technologies have been enabling cyber-enabled systems to become a ubiquitous existence changing people’s lives in multiple dimensions, from smart phones to mobile vehicular systems [16], [25], [30]. Different multimedia systems have brought many new or strengthened accesses to the computing resources. As an emerging technology, Mobile Embedded Systems (MES) with Cyber-Enabled Applications (CEA) havebecome a mainstream of mobile computing that balance the high-performance and the cost. This change has been empowered by the deployments of cloud computing in recent years [8], [12]. Many cloud-based solutions support the offloads of the heavy workloads to the remote cloud servers, by which the energy consumptions are reduced [14], [19]. Nevertheless, most contemporary approaches are confronting the contradictions deriving from the wireless communications and energy consumptions, since the local energy costs are generally less than that of the wireless data transmissions [6]. A fixed working mode that offloads jobs to the cloud-based servers cannot satisfy the requirement of the energy-saving. Multiple factors need to be addressed when creating the strategy of offloading tasks to clouds, such as heterogeneous computing capacities and energy consumptions in different phases.

Moreover, contemporary energy consumptions on MES are facing a variety of challenges, from app executions to wireless communications [9], [23]. For instance, smart phones can not only deliver the wireless calls but also provide a mobile app platform. Heterogeneous computing systems enable mobile devices to achieve a high performance of computations, such as synchronously assigning a chain of tasks to multiple cores [21]. Nevertheless, the energy costs go up when the large sized tasks are loaded, which may require less energy while some tasks are offloaded to the clouds [11]. Therefore, an optimized deterministic mechanism of task assignments can aid the system to reduce the total energy costs when the energy requirements are varied between local and remote executions.

Focusing on this urgent demand, we propose a novel model, named Energy-Aware Heterogeneous Cloud Management (EA-HCM) model, in order to achieve the high performance computation capacity by using the reduced energy. Fig. 1 represents the architecture of EA-HCM model, by which the input tasks are dynamically assigned to heterogeneous cores and remote processors. This model is an extension of Gai et al.’s [10] work. Distinguishing prior work, this model further emphasizes the role of cyber-enabled applications that empower the task assignment for mobile embedded systems in heterogeneous cloud computing. The core algorithm creating near-optimal solutions is proposed in this model. In addition, as shown in the figure, the assignment uses the estimated energy costs to reduce the total energy consumption, which is controlled by the cyber-enabled applications. The expected goal is creating the plan of the task assignment that can reduce the total energy consumption while considering the both computation and communication energy costs.

Furthermore, we emphasize the implementation of the cyber-enabled application in our proposed model. The reason for using cyber-enabled applications is that it enables feasible configurations on various mobile devices for meeting different demands of data collections and task distributions. In line with the cyber-enabled applications, we propose our algorithm, Heterogeneous Task Assignment Algorithm (HTA2), in order to achieve our designed goal. The algorithm aims to dynamically assign the tasks to various cores and remote clouds. The tasks assignment is based on the cost mapping and our proposed optimal alternatives and adjustments. The algorithm consists of two steps: (1) generate a sub-optimal solution, and (2) improve the obtained sub-optimal solution and try to transfer it to the optimal solution via a set of adjustments. Our approach is an attempt of achieving high performance heterogeneous MES with lower-level energy requirements.

The main contributions of this paper include the following:

  • 1.

    We propose a novel approach for reducing the computation energy costs for heterogeneous MES in cloud systems. Our algorithm can intelligently assign the tasks to on-premises cores or remote cloud servers within an adaptive time period. Distinguishing from prior work, the approach proposed by this work considers all available computing resources the individual options for task assignment, such that each cloud source is an objective for task scheduling.

  • 2.

    We present a method of the adjustment for generating near-optimal solutions, which is designed to transfer sub-optimal solutions to optimal solutions at a high success rate.

  • 3.

    We propose an adaptive solution to the task assignment problem for heterogeneous MES, which has been proved as an NP-hard problem. The proposed approach can be used in other application scenarios.

The remainder of this paper is organized by the following sections. We accomplish a survey of cloud resource management in Section 2. Next, a motivational example is represented in Section 3. Moreover, we explain the mechanism and system definitions in Section 4. Furthermore, in Section 5, we illustrate the main algorithm proposed for our model. In addition, we display and analyze partial experimental results in Section 6. Finally, we give the conclusions in Section 7.

Section snippets

Related work

Previous research have explored a variety of dimensions in the field of cloud resource management and task scheduling provisions.

First, formulating a proper resource provision strategy was a research direction in demand. Rodriguez and Buyya [20] investigated the issue of resource provisioning and scheduling strategy for meeting the demands of resource management in the cloud environment. This approach used meta-heuristic optimization technique to minimize the total operation costs when the

Motivational example

We represent a simple example to explain the basic mechanism of our proposed scheme in this section. The motivation of this example is that the computation workload of many current mobile applications are becoming heavier along with the growth of the functionality. Multi-core and remote cores in the cloud provide mobile devices with alternatives for reducing the cost of the computing resources. In this paper, we define that a cost can refer to any required computing resource for completing

Concepts and the proposed model

We define the system definitions and the main concepts in this section.

Algorithms

This section introduces our main algorithm used in theEA-HCM model. The algorithm entitled the Heterogeneous Task Assignment Algorithm (HTA2) is designed to obtain the optimal solution by adjusting the sub-optimal solution. This algorithm was represented in our prior work [10], which was an adoptable approach of solving EMPHC problem. We further extend the algorithms used for data preprocessing, which are required by HTA2. Section 5.1 represents the extended algorithms of data preprocessing in

Experiment and the results

We describe our experiment configurations in Section 6.1 and illustrate a few experimental results, analyses, and findings in Section 6.2.

Conclusions

This paper focused on the issue of task mitigations by using heterogeneous MES in cloud computing and aimed to reduce the total energy consumption by using cyber-enabled applications to produce optimal task assignment plans. The proposed model, EA-HCM, was designed to solve the energy minimization problem on heterogeneous computing that is an NP-hard problem. The main algorithm used in this model was HTA2 that was proposed for producing optimal task assignments at a high success rate. Our

Acknowledgment

The second author’s work is partially supported by National Natural Science Foundation of China, #61672358.

Keke Gai holds degrees from Nanjing University of Science and Technology (B.Eng.), The University of British Columbia (MET) and Lawrence Technological University (M.B.A. and M.S.). He is currently pursuing his Ph.D. at Department of Computer Science at Pace University, New York, USA. Keke Gai has published more than 60peer-reviewed journals or conference papers, 20+ journal papers (including ACM/IEEE Transactions), and 40+ conference papers. He has been granted three IEEE Best Paper Awards

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    Keke Gai holds degrees from Nanjing University of Science and Technology (B.Eng.), The University of British Columbia (MET) and Lawrence Technological University (M.B.A. and M.S.). He is currently pursuing his Ph.D. at Department of Computer Science at Pace University, New York, USA. Keke Gai has published more than 60peer-reviewed journals or conference papers, 20+ journal papers (including ACM/IEEE Transactions), and 40+ conference papers. He has been granted three IEEE Best Paper Awards (IEEE SSC’16, IEEE CSCloud’, IEEE BigDataSecurity’15) and two IEEE Best Student Paper Awards (SmartCloud’16, HPCC’16) by IEEE conferences in recent years. His paper about cloud computing has been ranked as the “Most Downloaded Articles” of Journal of Network and Computer Applications (JNCA). He is involved in a number of professional/academic associations, including ACM and IEEE. Currently, he is serving as a Secretary/Treasurer of IEEE STC (Special Technical Community) in Smart Computing at IEEE Computer Society. His research interests include mobile cloud computing, cyber security, combinatorial optimization, business process modeling, enterprise architecture, and Internet computing.

    Meikang Qiu received the BE and ME degrees from Shanghai Jiao Tong University and received Ph.D. degree of Computer Science from University of Texas at Dallas. Currently, he is a Distinguished Professor at Shenzhen University, China and an Adjunct Professor at Columbia University. He is an IEEE Senior member and ACM Senior member. He is the Chair of IEEE Smart Computing Technical Committee. His research interests include Cyber Security, Cloud Computing, Smarting Computing, Intelligent Data, Embedded systems, etc. A lot of novel results have been produced and most of them have already been reported to research community through high-quality journal and conference papers. He has published 14 books, 400 peer-reviewed journal and conference papers (including 180+ journal articles, 220+ conference papers, 60+ IEEE/ACM Transactions papers), and 3 patents. He has won ACM Transactions on Design Automation of Electrical Systems (TODAES) 2011 Best Paper Award. His paper about cloud computing has been published in JPDC (Journal of Parallel and Distributed Computing, Elsevier) and ranked #1 in Top Hottest 25 Papers of JPDC 2012. His papers published in IEEE Transactions on Computers and Journal of Computer and System Science (Elsevier) have been recognized as Highly Cited Papers in 2016 and 2017. He has won another 10+ Conference Best Paper Awards in recent years. Currently he is an associate editor of 10+ international journals, including IEEE Transactions on Computer and IEEE Transactions on Cloud Computing. He is the General Chair/Program Chair of a dozens of IEEE/ACM international conferences, such as IEEE HPCC, IEEE TrustCom, IEEE CSCloud, and IEEE BigDataSecurity. He has given 100+ talks all over the world, including Oxford, Princeton, Stanford, and Yale University. He has won Navy Summer Faculty Award in 2012 and Air Force Summer Faculty Award in 2009. His research is supported by US government such as NSF, Air Force, Navy and companies such as GE, Nokia, TCL, and Cavium.

    Hui Zhao received the B.E. and M.S. degrees from Xi’an Technology University, Shanxi and Henan University, Henan, China, in 2000 and 2008, respectively. He is a Ph.D. student at the Seidenberg School of Computer Science and Information Systems of Pace University. He is currently an associate professor in the Software school of Henan University.

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    M. Qiu is with Shenzhen University, China and Columbia University, USA.

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