Pub Date : 2022-11-15DOI: 10.1109/TSUSC.2022.3222409
Charalampos Marantos;Lazaros Papadopoulos;Christos P. Lamprakos;Konstantinos Salapas;Dimitrios Soudris
Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.
{"title":"Bringing Energy Efficiency Closer to Application Developers: An Extensible Software Analysis Framework","authors":"Charalampos Marantos;Lazaros Papadopoulos;Christos P. Lamprakos;Konstantinos Salapas;Dimitrios Soudris","doi":"10.1109/TSUSC.2022.3222409","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3222409","url":null,"abstract":"Green, sustainable and energy-aware computing terms are gaining more and more attention during the last years. The increasing complexity of Internet of Things (IoT) applications makes energy efficiency an important requirement, imposing new challenges to software developers. Software tools capable of providing energy consumption estimations and identifying optimization opportunities are critical during all the phases of application development. This work proposes a novel framework that targets the energy efficiency at application development level. The proposed framework is implemented as a single user-friendly tool-flow, providing a variety of useful features, such as the estimation of the energy consumption without the need of executing the application on the targeted IoT devices and the estimation of potential gains by GPU acceleration on modern heterogeneous IoT architectures. The proposed methodology provides several novel contributions, such as the combination of static analysis and dynamic instrumentation approaches in order to exploit the advantages of both. The framework is evaluated on widely used benchmarks, achieving increased estimation accuracy (more than 90% for similar architectures and more than 72% for the potential use of the GPU). The effectiveness of the framework is further demonstrated using two industrial use-cases achieving an energy reduction from 91% up to 98%.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Many applications require simple controllers that continuously run the same application. These applications are often found in battery operated embedded systems that require to be ultra-low power (ULP) and are very price sensitive. Some examples include IoT devices of different nature and medical devices. Currently, these systems rely on off-the-shelf general-purpose microprocessors. One of the problems of using these processors, is that not all of the resources are needed for a specific application. Furthermore, because of the regularity of the workloads running on these systems there is a large opportunity to optimize the processor by pruning those unused resources to achieve lower area (cost) and power. Moreover, these processors can be specified at the behavioral level and use High-Level Synthesis (HLS) to generate an efficient Register Transfer Level (RTL) description. This opens a window to additional optimizations as the processor implementation is fully re-optimized during the HLS process. Also, many applications running on these embedded systems tolerate imprecise outputs. These include image processing and digital signal processing (DSP) applications. This opens the door to further optimizations in the context of approximate computing. To address these issues, this work presents a methodology to customize a behavioral RISC processor automatically for a given workload such that its area and power are significantly reduced as compared to the original, general-purpose processor. First, generating a bespoke processor that leads to the exact output as compared to the original general-purpose one and then by approximating it allowing a certain level of error at the output. Compared to previous work that customizes a given processor at the gate netlist only, our proposed method shows significant benefits. In particular, this work shows that raising the level of abstraction reduces the area and power by 78.3% and 70.1% for the exact solution on average, and further reduces the area by an additional 10.0% and 16.5% for the approximate version tolerating a maximum of 10% and 20% output errors respectively.
{"title":"Application Specific Approximate Behavioral Processor","authors":"Qilin Si;Prattay Chowdhury;Rohit Sreekumar;Benjamin Carrion Schafer","doi":"10.1109/TSUSC.2022.3222117","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3222117","url":null,"abstract":"Many applications require simple controllers that continuously run the same application. These applications are often found in battery operated embedded systems that require to be ultra-low power (ULP) and are very price sensitive. Some examples include IoT devices of different nature and medical devices. Currently, these systems rely on off-the-shelf general-purpose microprocessors. One of the problems of using these processors, is that not all of the resources are needed for a specific application. Furthermore, because of the regularity of the workloads running on these systems there is a large opportunity to optimize the processor by pruning those unused resources to achieve lower area (cost) and power. Moreover, these processors can be specified at the behavioral level and use High-Level Synthesis (HLS) to generate an efficient Register Transfer Level (RTL) description. This opens a window to additional optimizations as the processor implementation is fully re-optimized during the HLS process. Also, many applications running on these embedded systems tolerate imprecise outputs. These include image processing and digital signal processing (DSP) applications. This opens the door to further optimizations in the context of approximate computing. To address these issues, this work presents a methodology to customize a behavioral RISC processor automatically for a given workload such that its area and power are significantly reduced as compared to the original, general-purpose processor. First, generating a bespoke processor that leads to the exact output as compared to the original general-purpose one and then by approximating it allowing a certain level of error at the output. Compared to previous work that customizes a given processor at the gate netlist only, our proposed method shows significant benefits. In particular, this work shows that raising the level of abstraction reduces the area and power by 78.3% and 70.1% for the exact solution on average, and further reduces the area by an additional 10.0% and 16.5% for the approximate version tolerating a maximum of 10% and 20% output errors respectively.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339533","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-11DOI: 10.1109/TSUSC.2022.3221444
Ziyi Zhao;Jiliang Li;Zhou Su;Yuyi Wang
Smart contracts are programs running on Ethereum, whose deployment and use require gas. Gas measures the cost of performing specific operations as an index designed to quantify the computing power consumption. Existing unoptimized smart contracts make contract developers and users spend extra gas. To save gas and optimize smart contracts, this paper proposes a new tool named GaSaver for automatically detecting gas-expensive patterns based on Solidity source code. Specifically, we first identify 12 gas-expensive patterns in smart contracts and classify them into three categories: storage-related, judgment-related, and loop-related. Then, we deploy gas-expensive patterns and group them into three levels according to gas waste degree. By conducting extensive experiments on real data sets, we find that 89.68 $%$ of the 1172 smart contracts suffer from gas-expensive patterns, 94.27 $%$ of 1100 new smart contracts are gas-expensive, and 80.56 $%$ of 72 widely used smart contracts are affected. Finally, the experiment results show that the proposed GaSaver can effectively optimize smart contracts. Besides, the proportion of gas-expensive cases in widely used smart contracts is lower than that in the newly released smart contracts.
{"title":"GaSaver: A Static Analysis Tool for Saving Gas","authors":"Ziyi Zhao;Jiliang Li;Zhou Su;Yuyi Wang","doi":"10.1109/TSUSC.2022.3221444","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3221444","url":null,"abstract":"Smart contracts are programs running on Ethereum, whose deployment and use require gas. Gas measures the cost of performing specific operations as an index designed to quantify the computing power consumption. Existing unoptimized smart contracts make contract developers and users spend extra gas. To save gas and optimize smart contracts, this paper proposes a new tool named GaSaver for automatically detecting gas-expensive patterns based on Solidity source code. Specifically, we first identify 12 gas-expensive patterns in smart contracts and classify them into three categories: storage-related, judgment-related, and loop-related. Then, we deploy gas-expensive patterns and group them into three levels according to gas waste degree. By conducting extensive experiments on real data sets, we find that 89.68\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of the 1172 smart contracts suffer from gas-expensive patterns, 94.27\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of 1100 new smart contracts are gas-expensive, and 80.56\u0000<inline-formula><tex-math>$%$</tex-math></inline-formula>\u0000 of 72 widely used smart contracts are affected. Finally, the experiment results show that the proposed GaSaver can effectively optimize smart contracts. Besides, the proportion of gas-expensive cases in widely used smart contracts is lower than that in the newly released smart contracts.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421233","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of generators. However, distortion of measurement results of synchronous generator operation parameters is inevitable due to various reasons, such as device failure and operating environment interference and so on. Meanwhile, it is hard to transmit gigantic volumes of data to the information center due to limited communication bandwidth. To tackle these challenges, this article proposes a dynamic state estimation method for synchronous generators with event-triggered scheme. The proposed method first establishes a non-linear model to describe the dynamics of generators. Then, a measure-based event-triggering scheme is adopted to schedule the data transmission from the sensor to estimator, thus reducing communication pressure and enhanced resource utilization. Finally, an improved regularized particle filter (IRPF) algorithm is designed to guarantee the estimation performance. To this end, the genetic algorithm is used to optimize the particles sampled by regularized particle filter algorithm, which can solve particle exhaustion problem. The CEPRI7 system is used to verify the performance of the proposed method.
{"title":"Dynamic State Estimation for Synchronous Generator With Communication Constraints: An Improved Regularized Particle Filter Approach","authors":"Xingzhen Bai;Feiyu Qin;Leijiao Ge;Lin Zeng;Xinlei Zheng","doi":"10.1109/TSUSC.2022.3221090","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3221090","url":null,"abstract":"Accurate acquisition of real-time electromechanical dynamic states of synchronous generators plays an essential role in power systems. The phasor measurement units (PMUs) are widely used in data acquisition of synchronous generator operation parameters, which can capture the dynamic responses of generators. However, distortion of measurement results of synchronous generator operation parameters is inevitable due to various reasons, such as device failure and operating environment interference and so on. Meanwhile, it is hard to transmit gigantic volumes of data to the information center due to limited communication bandwidth. To tackle these challenges, this article proposes a dynamic state estimation method for synchronous generators with event-triggered scheme. The proposed method first establishes a non-linear model to describe the dynamics of generators. Then, a measure-based event-triggering scheme is adopted to schedule the data transmission from the sensor to estimator, thus reducing communication pressure and enhanced resource utilization. Finally, an improved regularized particle filter (IRPF) algorithm is designed to guarantee the estimation performance. To this end, the genetic algorithm is used to optimize the particles sampled by regularized particle filter algorithm, which can solve particle exhaustion problem. The CEPRI7 system is used to verify the performance of the proposed method.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339456","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In the cloud computing context, group data sharing, which is highly convenient for people who intend to share data with multiple members of a group, has attracted extensive research attention. However, the question of how data security might be ensured in this scenario, particularly as regards the security of group session keys, still remains an open one. To support secure group data sharing in cloud computing, it is essential to design a key agreement protocol characterized by both high flexibility and efficiency. Accordingly, this paper proposes a key agreement protocol based on Latin Square to reinforce the security of group data sharing. This protocol supports an arbitrary number of members to participate in a group and make equal contributions to generating a common session key among themselves. Compared with previous works, both the communication and the computational complexity in this protocol are significantly reduced. In addition, the services of authentication, key confirmation and fault tolerance are provided, enabling the protocol to resist different types of attacks. The results of both theoretical and experimental analysis indicate that the proposed protocol can efficiently support secure cloud data sharing for large-scale groups.
{"title":"A Novel Key Agreement Protocol Applying Latin Square for Cloud Data Sharing","authors":"Jian Shen;Tao Zhang;Yi Jiang;Tianqi Zhou;Tiantian Miao","doi":"10.1109/TSUSC.2022.3221125","DOIUrl":"10.1109/TSUSC.2022.3221125","url":null,"abstract":"In the cloud computing context, group data sharing, which is highly convenient for people who intend to share data with multiple members of a group, has attracted extensive research attention. However, the question of how data security might be ensured in this scenario, particularly as regards the security of group session keys, still remains an open one. To support secure group data sharing in cloud computing, it is essential to design a key agreement protocol characterized by both high flexibility and efficiency. Accordingly, this paper proposes a key agreement protocol based on Latin Square to reinforce the security of group data sharing. This protocol supports an arbitrary number of members to participate in a group and make equal contributions to generating a common session key among themselves. Compared with previous works, both the communication and the computational complexity in this protocol are significantly reduced. In addition, the services of authentication, key confirmation and fault tolerance are provided, enabling the protocol to resist different types of attacks. The results of both theoretical and experimental analysis indicate that the proposed protocol can efficiently support secure cloud data sharing for large-scale groups.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75272676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-28DOI: 10.1109/TSUSC.2022.3217970
Yu Luo;Lina Pu;Chun-Hung Liu
Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of Things (IoT). By collecting renewable energy such as solar or wind energy from the environment, a sustainable cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. In the real world, the density of sustainable energy can vary significantly over time. Therefore, the SEC cloudlet needs to dynamically adjust the clock frequency to balance energy consumption and computing latency. In this paper, we consider the limited energy storage of the cloudlet and the dynamic intensity of renewable energy, and then develop offline optimal CPU frequency scaling policies that (a) maximize the computing power of the cloudlet within a certain period of time, and (b) minimize the execution time given tasks offloaded to the cloudlet. An optimal tightest string policy is proposed to solve the optimization problem. In addition, a dynamic programming (DP) based suboptimal solution is introduced to simplify the practical implementation. How to design an online CPU frequency management strategy is also briefly discussed.
{"title":"CPU Frequency Scaling Optimization in Sustainable Edge Computing","authors":"Yu Luo;Lina Pu;Chun-Hung Liu","doi":"10.1109/TSUSC.2022.3217970","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3217970","url":null,"abstract":"Sustainable edge computing (SEC) is a promising technology that can reduce energy consumption and computing latency for the mobile Internet of Things (IoT). By collecting renewable energy such as solar or wind energy from the environment, a sustainable cloudlet outside the electric grid can provide powerful computing capabilities for resource-constrained mobile IoT devices. In the real world, the density of sustainable energy can vary significantly over time. Therefore, the SEC cloudlet needs to dynamically adjust the clock frequency to balance energy consumption and computing latency. In this paper, we consider the limited energy storage of the cloudlet and the dynamic intensity of renewable energy, and then develop offline optimal CPU frequency scaling policies that (a) maximize the computing power of the cloudlet within a certain period of time, and (b) minimize the execution time given tasks offloaded to the cloudlet. An optimal tightest string policy is proposed to solve the optimization problem. In addition, a dynamic programming (DP) based suboptimal solution is introduced to simplify the practical implementation. How to design an online CPU frequency management strategy is also briefly discussed.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-25DOI: 10.1109/TSUSC.2022.3217014
Keqin Li
Mobile edge computing with device-edge-cloud fusion provides a new type of heterogeneous computing environment. We consider task scheduling with device-edge-cloud fusion (without energy concern) and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems. The main contributions of the paper are summarized as follows. We design three heuristic algorithms for task scheduling with device-edge-cloud fusion and prove an asymptotic performance bound. We design one heuristic algorithm for energy-constrained task scheduling with device-edge-cloud fusion, which solves the two subproblems of task scheduling and power allocation in an interleaved way. We derive lower bounds for the optimal solutions for both task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion, so that the performance of our heuristic algorithms can be compared with that of an optimal algorithm. We experimentally evaluate the performance of our heuristic algorithms and find that the performance of our heuristic algorithms are very close to that of optimal algorithms. To the best of our knowledge, this is the first paper which studies task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems and conducts analytical performance evaluation.
{"title":"Design and Analysis of Heuristic Algorithms for Energy-Constrained Task Scheduling With Device-Edge-Cloud Fusion","authors":"Keqin Li","doi":"10.1109/TSUSC.2022.3217014","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3217014","url":null,"abstract":"Mobile edge computing with device-edge-cloud fusion provides a new type of heterogeneous computing environment. We consider task scheduling with device-edge-cloud fusion (without energy concern) and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems. The main contributions of the paper are summarized as follows. We design three heuristic algorithms for task scheduling with device-edge-cloud fusion and prove an asymptotic performance bound. We design one heuristic algorithm for energy-constrained task scheduling with device-edge-cloud fusion, which solves the two subproblems of task scheduling and power allocation in an interleaved way. We derive lower bounds for the optimal solutions for both task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion, so that the performance of our heuristic algorithms can be compared with that of an optimal algorithm. We experimentally evaluate the performance of our heuristic algorithms and find that the performance of our heuristic algorithms are very close to that of optimal algorithms. To the best of our knowledge, this is the first paper which studies task scheduling with device-edge-cloud fusion and energy-constrained task scheduling with device-edge-cloud fusion as combinatorial optimization problems and conducts analytical performance evaluation.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/TSUSC.2022.3216350
Kazi Main Uddin Ahmed;Math H. J. Bollen;Manuel Alvarez
The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers’ failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.
{"title":"A Stochastic Approach to Determine the Optimal Number of Servers for Reliable and Energy Efficient Operation of Data Centers","authors":"Kazi Main Uddin Ahmed;Math H. J. Bollen;Manuel Alvarez","doi":"10.1109/TSUSC.2022.3216350","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3216350","url":null,"abstract":"The increasing demand of the data center's computational capacity in recent years has introduced new data center operational challenges among others to maintain the service level agreements (SLA) and quality of services (QoS), while at the same time limiting energy consumption. In this paper, a stochastic operational risk assessment approach is presented that estimates the required number of spare servers in a data center considering the risk of servers’ failure in operation since servers define the computational capability of a data center. A reliability index called “risk of computational resource commitment (RCRC)” is introduced that quantifies the probability of having insufficient spare servers due to failures during the operational lead time, and the complement of the RCRC shows the ability of the resources to maintain SLA of a data center. The failure rates of the servers are obtained using a Monte Carlo Simulation with the failure data, published by Google in 2019. The analysis shows that the RCRC reduces with the increasing number of spare servers, while it also stresses the energy efficiency of the data center. The RCRC index could be used in data center operation to avoid overprovisioning of the servers and to limit the number of spare servers in the data center, while creating a suitable balance between QoS and energy consumption of the data centers.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50339565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-21DOI: 10.1109/TSUSC.2022.3216461
Jing Bi;Kaiyi Zhang;Haitao Yuan;Jia Zhang
As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, named P article swarm optimization based on G enetic L earning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs’ association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.
{"title":"Energy-Efficient Computation Offloading for Static and Dynamic Applications in Hybrid Mobile Edge Cloud System","authors":"Jing Bi;Kaiyi Zhang;Haitao Yuan;Jia Zhang","doi":"10.1109/TSUSC.2022.3216461","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3216461","url":null,"abstract":"As a promising paradigm, mobile edge computing (MEC) provides cloud resources in a network edge to offer low-latency services to mobile devices (MDs). MEC addresses the limited resource and energy issues of MDs by deploying edge servers, which are often located in small base stations. It is a big challenge, however, as how to dynamically connect resource-limited MDs to nearby edge servers, and reduce total energy consumption by MDs, small base stations and a cloud data center (CDC) all in a hybrid system. To tackle the challenge, this work provides an intelligent computation offloading method for both static and dynamic applications among entities in such a hybrid system. The minimization problem of total energy consumption is first formulated as a typical mixed integer non-linear program. An improved meta-heuristic optimization algorithm, named \u0000<underline>P</u>\u0000article swarm optimization based on \u0000<underline>G</u>\u0000enetic \u0000<underline>L</u>\u0000earning (PGL), is tailored to solve the problem. PGL synergistically take advantage of both the fast convergence of particle swarm optimization, and the global search ability of genetic algorithm. It jointly optimizes task offloading of heterogeneous applications, bandwidth allocation of wireless channels, MDs’ association with small base stations and/or a cloud datacenter, and computing resource allocation of MDs. Numerical results with real-life system configurations prove that PGL outperforms several state-of-the-art peers in terms of total energy consumption of the hybrid system.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50421236","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Network-on-Chips (NoCs) are the standard on-chip communication fabrics for connecting cores, caches, and memory controllers in multi/many-core systems. With the increase in communication load introduced by emerging parallel computing applications, on-chip communication is becoming more costly than computation in terms of energy consumption. This paper contributes to existing research on approximate communication by proposing a slack-aware packet approximation technique to reduce the energy consumed by NoCs for sustainable parallel computation. The proposed approximation technique lowers both the execution time and NoC power consumption by reducing the packet size based on slack. The slack is the number of cycles by which a packet can be delayed in the network with no effect on execution time. Thus, low-slack packets are considered critical to system performance, and prioritizing these packets during the transmission will significantly reduce execution time. The proposed technique includes a slack-aware control policy to identify low-slack packets and accelerates these packets using two packet approximation mechanisms, namely, an in-network approximation (INAP) and a network interface approximation (NIAP). INAP mechanism prioritizes low-slack packets during the arbitration phase of the router by approximating packets with high-slack. NIAP mechanism reduces the latency of the network links and switch traversals by truncating data for the low-slack packets. An approximate network interface and router are implemented to support the proposed technique with lightweight packet approximation hardware for lower power consumption and execution time. Cycle-accurate simulations using the AxBench and PARSEC benchmark suites show that the proposed approximate communication technique achieves reductions of up to 24% in execution time and 38% in energy consumption with 1.1% less accuracy loss on average compared to existing approximate communication techniques.
{"title":"Slack-Aware Packet Approximation for Energy-Efficient Network-on-Chips","authors":"Yuechen Chen;Ahmed Louri;Shanshan Liu;Fabrizio Lombardi","doi":"10.1109/TSUSC.2022.3213469","DOIUrl":"https://doi.org/10.1109/TSUSC.2022.3213469","url":null,"abstract":"Network-on-Chips (NoCs) are the standard on-chip communication fabrics for connecting cores, caches, and memory controllers in multi/many-core systems. With the increase in communication load introduced by emerging parallel computing applications, on-chip communication is becoming more costly than computation in terms of energy consumption. This paper contributes to existing research on approximate communication by proposing a slack-aware packet approximation technique to reduce the energy consumed by NoCs for sustainable parallel computation. The proposed approximation technique lowers both the execution time and NoC power consumption by reducing the packet size based on slack. The slack is the number of cycles by which a packet can be delayed in the network with no effect on execution time. Thus, low-slack packets are considered critical to system performance, and prioritizing these packets during the transmission will significantly reduce execution time. The proposed technique includes a slack-aware control policy to identify low-slack packets and accelerates these packets using two packet approximation mechanisms, namely, an in-network approximation (INAP) and a network interface approximation (NIAP). INAP mechanism prioritizes low-slack packets during the arbitration phase of the router by approximating packets with high-slack. NIAP mechanism reduces the latency of the network links and switch traversals by truncating data for the low-slack packets. An approximate network interface and router are implemented to support the proposed technique with lightweight packet approximation hardware for lower power consumption and execution time. Cycle-accurate simulations using the AxBench and PARSEC benchmark suites show that the proposed approximate communication technique achieves reductions of up to 24% in execution time and 38% in energy consumption with 1.1% less accuracy loss on average compared to existing approximate communication techniques.","PeriodicalId":13268,"journal":{"name":"IEEE Transactions on Sustainable Computing","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2022-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50296552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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