{"title":"On the Cost of Near-Perfect Wear Leveling in Flash-Based SSDs","authors":"B. V. Houdt","doi":"10.1145/3576855","DOIUrl":"https://doi.org/10.1145/3576855","url":null,"abstract":"","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"8 1","pages":"1-22"},"PeriodicalIF":0.6,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"64062245","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Tianming Zhao, Wei Li, Bo Qin, Ling Wang, Albert Y. Zomaya
Many mission- and time-critical cyber-physical systems deploy an isolated power system for their power supply. Under extreme conditions, the power system must process critical missions by maximizing the Pulsed Power Load (PPL) utility while maintaining the normal loads in the cyber-physical system. Optimal operation requires careful coordination of PPL deployment and power supply processes. In this work, we formulate the coordination problem for maximizing PPL utility under available resources, capacity, and demand constraints. The coordination problem has two scenarios for different use cases, fixed and general normal loads. We develop an exact pseudo-polynomial time dynamic programming algorithm for each scenario with a proven guarantee to produce an optimal coordination schedule. The performance of the algorithms is also experimentally evaluated, and the results agree with our theoretical analysis, showing the practicality of the solutions.
{"title":"Pulsed Power Load Coordination in Mission- and Time-critical Cyber-physical Systems","authors":"Tianming Zhao, Wei Li, Bo Qin, Ling Wang, Albert Y. Zomaya","doi":"10.1145/3573197","DOIUrl":"https://doi.org/10.1145/3573197","url":null,"abstract":"Many mission- and time-critical cyber-physical systems deploy an isolated power system for their power supply. Under extreme conditions, the power system must process critical missions by maximizing the Pulsed Power Load (PPL) utility while maintaining the normal loads in the cyber-physical system. Optimal operation requires careful coordination of PPL deployment and power supply processes. In this work, we formulate the coordination problem for maximizing PPL utility under available resources, capacity, and demand constraints. The coordination problem has two scenarios for different use cases, fixed and general normal loads. We develop an exact pseudo-polynomial time dynamic programming algorithm for each scenario with a proven guarantee to produce an optimal coordination schedule. The performance of the algorithms is also experimentally evaluated, and the results agree with our theoretical analysis, showing the practicality of the solutions.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"8 1","pages":"1 - 27"},"PeriodicalIF":0.6,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47480155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Nitish K. Panigrahy, Thirupathaiah Vasantam, P. Basu, D. Towsley, A. Swami, K. Leung
Distributed load balancing is the act of allocating jobs among a set of servers as evenly as possible. The static interpretation of distributed load balancing leads to formulating the load-balancing problem as a classical balls-and-bins problem with jobs (balls) never leaving the system and accumulating at the servers (bins). While most of the previous work in the static setting focus on studying the maximum number of jobs allocated to a server or maximum load, little importance has been given to the implementation cost, or the cost of moving a job/data to/from its allocated server, for such policies. This article designs and evaluates server proximity aware static load-balancing policies with a goal to reduce the implementation cost. We consider a class of proximity aware Power of Two (POT) choice-based assignment policies for allocating jobs to servers, where both jobs and servers are located on a two-dimensional Euclidean plane. In this framework, we investigate the tradeoff between the implementation cost and load-balancing performance of different allocation policies. To this end, we first design and evaluate a Spatial Power of two (sPOT) policy in which each job is allocated to the least loaded server among its two geographically nearest servers. We provide expressions for the lower bound on the asymptotic expected maximum load on the servers and prove that sPOT does not achieve classical POT load-balancing benefits. However, experimental results suggest the efficacy of sPOT with respect to expected implementation cost. We also propose two non-uniform server sampling-based POT policies that achieve the best of both implementation cost and load-balancing performance. We then extend our analysis to the case where servers are interconnected as an n-vertex graph G(S, E). We assume each job arrives at one of the servers, u, chosen uniformly at random from the vertex set S. We then assign each job to the server with minimum load among servers u and v where v is chosen according to one of the following two policies: (i) Unif-POT(k): Sample a server v uniformly at random from k-hop neighborhood of u; (ii) InvSq-POT(k): Sample a server v from k-hop neighborhood of u with probability proportional to the inverse square of the distance between u and v. An extensive simulation over a wide range of topologies validates the efficacy of both the policies. Our simulation results show that both policies consistently produce a load distribution that is much similar to that of a classical POT. Depending on topology, we observe the total variation distance to be of the order of 0.002–0.08 for both the policies while achieving a 8%–99% decrease in implementation cost as compared to the classical POT.
{"title":"On the Analysis and Evaluation of Proximity-based Load-balancing Policies","authors":"Nitish K. Panigrahy, Thirupathaiah Vasantam, P. Basu, D. Towsley, A. Swami, K. Leung","doi":"10.1145/3549933","DOIUrl":"https://doi.org/10.1145/3549933","url":null,"abstract":"Distributed load balancing is the act of allocating jobs among a set of servers as evenly as possible. The static interpretation of distributed load balancing leads to formulating the load-balancing problem as a classical balls-and-bins problem with jobs (balls) never leaving the system and accumulating at the servers (bins). While most of the previous work in the static setting focus on studying the maximum number of jobs allocated to a server or maximum load, little importance has been given to the implementation cost, or the cost of moving a job/data to/from its allocated server, for such policies. This article designs and evaluates server proximity aware static load-balancing policies with a goal to reduce the implementation cost. We consider a class of proximity aware Power of Two (POT) choice-based assignment policies for allocating jobs to servers, where both jobs and servers are located on a two-dimensional Euclidean plane. In this framework, we investigate the tradeoff between the implementation cost and load-balancing performance of different allocation policies. To this end, we first design and evaluate a Spatial Power of two (sPOT) policy in which each job is allocated to the least loaded server among its two geographically nearest servers. We provide expressions for the lower bound on the asymptotic expected maximum load on the servers and prove that sPOT does not achieve classical POT load-balancing benefits. However, experimental results suggest the efficacy of sPOT with respect to expected implementation cost. We also propose two non-uniform server sampling-based POT policies that achieve the best of both implementation cost and load-balancing performance. We then extend our analysis to the case where servers are interconnected as an n-vertex graph G(S, E). We assume each job arrives at one of the servers, u, chosen uniformly at random from the vertex set S. We then assign each job to the server with minimum load among servers u and v where v is chosen according to one of the following two policies: (i) Unif-POT(k): Sample a server v uniformly at random from k-hop neighborhood of u; (ii) InvSq-POT(k): Sample a server v from k-hop neighborhood of u with probability proportional to the inverse square of the distance between u and v. An extensive simulation over a wide range of topologies validates the efficacy of both the policies. Our simulation results show that both policies consistently produce a load distribution that is much similar to that of a classical POT. Depending on topology, we observe the total variation distance to be of the order of 0.002–0.08 for both the policies while achieving a 8%–99% decrease in implementation cost as compared to the classical POT.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"7 1","pages":"1 - 27"},"PeriodicalIF":0.6,"publicationDate":"2022-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42045120","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Performance models of server systems, based on layered queues, may be very complex. This is particularly true for cloud-based systems based on microservices, which may have hundreds of distinct components, and for models derived by automated data analysis. Often only a few of these many components determine the system performance, and a smaller simplified model is all that is needed. To assist an analyst, this work describes a focused model that includes the important components (the focus) and aggregates the rest in groups, called dependency groups. The method Focus-based Simplification with Preservation of Tasks described here fills an important gap in a previous method by the same authors. The use of focused models for sensitivity predictions is evaluated empirically in the article on a large set of randomly generated models. It is found that the accuracy depends on a “saturation ratio” (SR) between the highest utilization value in the model and the highest value of a component excluded from the focus; evidence suggests that SR must be at least 2 and must be larger to evaluate larger model changes. This dependency was captured in an “Accurate Sensitivity Hypothesis” based on SR, which can be used to indicate trustable sensitivity results.
{"title":"Focused Layered Performance Modelling by Aggregation","authors":"Farhana Islam, D. Petriu, M. Woodside","doi":"10.1145/3549539","DOIUrl":"https://doi.org/10.1145/3549539","url":null,"abstract":"Performance models of server systems, based on layered queues, may be very complex. This is particularly true for cloud-based systems based on microservices, which may have hundreds of distinct components, and for models derived by automated data analysis. Often only a few of these many components determine the system performance, and a smaller simplified model is all that is needed. To assist an analyst, this work describes a focused model that includes the important components (the focus) and aggregates the rest in groups, called dependency groups. The method Focus-based Simplification with Preservation of Tasks described here fills an important gap in a previous method by the same authors. The use of focused models for sensitivity predictions is evaluated empirically in the article on a large set of randomly generated models. It is found that the accuracy depends on a “saturation ratio” (SR) between the highest utilization value in the model and the highest value of a component excluded from the focus; evidence suggests that SR must be at least 2 and must be larger to evaluate larger model changes. This dependency was captured in an “Accurate Sensitivity Hypothesis” based on SR, which can be used to indicate trustable sensitivity results.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"7 1","pages":"1 - 23"},"PeriodicalIF":0.6,"publicationDate":"2022-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42589837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Niloofar Bayat, Richard T. B. Ma, V. Misra, D. Rubenstein
An objective of network neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of zero-rating, remains controversial. Zero-rating refers to the practice of providing free Internet access to some users under certain conditions, which usually concurs with differentiation among users or content providers. Even though some countries (India, Canada) have banned zero-rating, others have either taken no stance or explicitly allowed it (South Africa, Kenya, U.S.). In this article, we model zero-rating between Internet service providers and content providers (CPs) to better understand the conditions under which offering zero-rating is preferred, and who gains in utility. We develop a formulation in which providers’ incomes vary, from low-income startups to high-income incumbents, where their decisions to zero-rate are a variation of the traditional prisoner’s dilemma game. We find that if zero-rating is permitted, low-income CPs often lose utility, whereas high-income CPs often gain utility. We also study the competitiveness of the CP markets via the Herfindahl Index. Our findings suggest that in most cases the introduction of zero-rating reduces competitiveness.
{"title":"Big Winners and Small Losers of Zero-rating","authors":"Niloofar Bayat, Richard T. B. Ma, V. Misra, D. Rubenstein","doi":"10.1145/3539731","DOIUrl":"https://doi.org/10.1145/3539731","url":null,"abstract":"An objective of network neutrality is to design regulations for the Internet and ensure that it remains a public, open platform where innovations can thrive. While there is broad agreement that preserving the content quality of service falls under the purview of net neutrality, the role of differential pricing, especially the practice of zero-rating, remains controversial. Zero-rating refers to the practice of providing free Internet access to some users under certain conditions, which usually concurs with differentiation among users or content providers. Even though some countries (India, Canada) have banned zero-rating, others have either taken no stance or explicitly allowed it (South Africa, Kenya, U.S.). In this article, we model zero-rating between Internet service providers and content providers (CPs) to better understand the conditions under which offering zero-rating is preferred, and who gains in utility. We develop a formulation in which providers’ incomes vary, from low-income startups to high-income incumbents, where their decisions to zero-rate are a variation of the traditional prisoner’s dilemma game. We find that if zero-rating is permitted, low-income CPs often lose utility, whereas high-income CPs often gain utility. We also study the competitiveness of the CP markets via the Herfindahl Index. Our findings suggest that in most cases the introduction of zero-rating reduces competitiveness.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"7 1","pages":"1 - 24"},"PeriodicalIF":0.6,"publicationDate":"2022-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42152848","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper describes the results of our measurement-based study, conducted on an Intel Core i7 processor running the SPEC CPU2017 benchmark suites, that evaluates the impact of dynamic voltage frequency scaling (DVFS) on performance (P), energy efficiency (EE), and their product (PxEE). The results indicate that the default DVFS-based power management techniques heavily favor performance, resulting in poor energy efficiency. To remedy this problem, we introduce, implement, and evaluate four DVFS-based power management techniques driven by the following metrics derived from the processor's performance monitoring unit: (i) the total pipeline slot stall ratio (FS-PS), (ii) the total cycle stall ratio (FS-TS), (iii) the total memory-related cycle stall ratio (FS-MS), and (iv) the number of last level cache misses per kilo instructions (FS-LLCM). The proposed techniques linearly map these metrics onto the available processor clock frequencies. The experimental evaluation results show that the proposed techniques significantly improve EE and PxEE metrics compared to the existing approaches. Specifically, EE improves from 44% to 92%, and PxEE improves from 31% to 48% when all the benchmarks are considered together. Furthermore, we find that the proposed techniques are particularly effective for a class of memory-intensive benchmarks – they improve EE from 121% to 183% and PxEE from 100% to 141%. Finally, we elucidate the advantages and disadvantages of each of the proposed techniques and offer recommendations on using them.
{"title":"PMU-Events-Driven DVFS Techniques for Improving Energy Efficiency of Modern Processors","authors":"Ranjan Hebbar, A. Milenković","doi":"10.1145/3538645","DOIUrl":"https://doi.org/10.1145/3538645","url":null,"abstract":"This paper describes the results of our measurement-based study, conducted on an Intel Core i7 processor running the SPEC CPU2017 benchmark suites, that evaluates the impact of dynamic voltage frequency scaling (DVFS) on performance (P), energy efficiency (EE), and their product (PxEE). The results indicate that the default DVFS-based power management techniques heavily favor performance, resulting in poor energy efficiency. To remedy this problem, we introduce, implement, and evaluate four DVFS-based power management techniques driven by the following metrics derived from the processor's performance monitoring unit: (i) the total pipeline slot stall ratio (FS-PS), (ii) the total cycle stall ratio (FS-TS), (iii) the total memory-related cycle stall ratio (FS-MS), and (iv) the number of last level cache misses per kilo instructions (FS-LLCM). The proposed techniques linearly map these metrics onto the available processor clock frequencies. The experimental evaluation results show that the proposed techniques significantly improve EE and PxEE metrics compared to the existing approaches. Specifically, EE improves from 44% to 92%, and PxEE improves from 31% to 48% when all the benchmarks are considered together. Furthermore, we find that the proposed techniques are particularly effective for a class of memory-intensive benchmarks – they improve EE from 121% to 183% and PxEE from 100% to 141%. Finally, we elucidate the advantages and disadvantages of each of the proposed techniques and offer recommendations on using them.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"7 1","pages":"1 - 31"},"PeriodicalIF":0.6,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47197635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Claudio Rubattu, F. Palumbo, S. Bhattacharyya, M. Pelcat
In embedded and cyber-physical systems, the design of a desired functionality under constraints increasingly requires parallel execution of a set of tasks on a heterogeneous architecture. The nature of such parallel systems complicates the process of understanding and predicting performance in terms of response time. Indeed, response time depends on many factors related to both the functionality and the target architecture. State-of-the-art strategies derive response time by examining the operations required by each task for both processing and accessing shared resources. This procedure is often followed by the addition or elimination of potential interference due to task concurrency. However, such approaches require an advanced knowledge of the software and hardware details, rarely available in practice. This work presents an alternative “top-down” strategy, called PathTracer, aimed at understanding software response time and extending the cases in which it can be analyzed and estimated. PathTracer leverages on dataflow-based application representation and response time estimation of signal processing applications mapped on heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs). Experimental results demonstrate that PathTracer provides (i) information on the nature of the application (work-dominated, span-dominated, or balanced parallel), and (ii) response time modeling which can reach high accuracy when performed post-execution, leading to prediction errors with average and standard deviation under 5% and 3% respectively.
{"title":"PathTracer: Understanding Response Time of Signal Processing Applications on Heterogeneous MPSoCs","authors":"Claudio Rubattu, F. Palumbo, S. Bhattacharyya, M. Pelcat","doi":"10.1145/3513003","DOIUrl":"https://doi.org/10.1145/3513003","url":null,"abstract":"In embedded and cyber-physical systems, the design of a desired functionality under constraints increasingly requires parallel execution of a set of tasks on a heterogeneous architecture. The nature of such parallel systems complicates the process of understanding and predicting performance in terms of response time. Indeed, response time depends on many factors related to both the functionality and the target architecture. State-of-the-art strategies derive response time by examining the operations required by each task for both processing and accessing shared resources. This procedure is often followed by the addition or elimination of potential interference due to task concurrency. However, such approaches require an advanced knowledge of the software and hardware details, rarely available in practice. This work presents an alternative “top-down” strategy, called PathTracer, aimed at understanding software response time and extending the cases in which it can be analyzed and estimated. PathTracer leverages on dataflow-based application representation and response time estimation of signal processing applications mapped on heterogeneous Multiprocessor Systems-on-a-Chip (MPSoCs). Experimental results demonstrate that PathTracer provides (i) information on the nature of the application (work-dominated, span-dominated, or balanced parallel), and (ii) response time modeling which can reach high accuracy when performed post-execution, leading to prediction errors with average and standard deviation under 5% and 3% respectively.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"6 1","pages":"1 - 30"},"PeriodicalIF":0.6,"publicationDate":"2022-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44899266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theodoros Giannakas, A. Giovanidis, T. Spyropoulos
Controlling the network cost by delivering popular content to users, as well as improving streaming quality and overall user experience, have been key goals for content providers (CP) in recent years. While proposals to improve performance, through caching or other mechanisms (DASH, multicasting, etc.) abound, recent works have proposed to turn the problem on its head and complement such efforts. Instead of trying to reduce the cost to deliver every possible content to a user, a potentially very expensive endeavour, one could leverage omnipresent recommendations systems to nudge users towards the content of low(er) network cost, regardless of where this cost is coming from. In this paper, we focus on this latter problem, namely optimal policies for “Network Friendly Recommendations” (NFR). A key contribution is the use of a Markov Decision Process (MDP) framework that offers significant advantages, compared to existing works, in terms of both modeling flexibility as well as computational efficiency. Specifically we show that this framework subsumes some state-of-the-art approaches, and can also optimally tackle additional, more sophisticated setups. We validate our findings with real traces that suggest up to almost 2X in cost performance, and 10X in computational speed-up compared to recent state-of-the-art works.
{"title":"MDP-based Network Friendly Recommendations","authors":"Theodoros Giannakas, A. Giovanidis, T. Spyropoulos","doi":"10.1145/3513131","DOIUrl":"https://doi.org/10.1145/3513131","url":null,"abstract":"Controlling the network cost by delivering popular content to users, as well as improving streaming quality and overall user experience, have been key goals for content providers (CP) in recent years. While proposals to improve performance, through caching or other mechanisms (DASH, multicasting, etc.) abound, recent works have proposed to turn the problem on its head and complement such efforts. Instead of trying to reduce the cost to deliver every possible content to a user, a potentially very expensive endeavour, one could leverage omnipresent recommendations systems to nudge users towards the content of low(er) network cost, regardless of where this cost is coming from. In this paper, we focus on this latter problem, namely optimal policies for “Network Friendly Recommendations” (NFR). A key contribution is the use of a Markov Decision Process (MDP) framework that offers significant advantages, compared to existing works, in terms of both modeling flexibility as well as computational efficiency. Specifically we show that this framework subsumes some state-of-the-art approaches, and can also optimally tackle additional, more sophisticated setups. We validate our findings with real traces that suggest up to almost 2X in cost performance, and 10X in computational speed-up compared to recent state-of-the-art works.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"13 3","pages":"1 - 29"},"PeriodicalIF":0.6,"publicationDate":"2022-02-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41294070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Popular wireless network simulators have few available propagation models for outdoor Internet of Things applications. Of the available models, only a handful use real terrain data, yet an inaccurate propagation model can skew the results of simulations. In this article, we present TerrainLOS, a low-overhead propagation model for outdoor Internet of Things applications that uses real terrain data to determine whether two nodes can communicate. To the best of our knowledge, TerrainLOS is the first terrain-aware propagation model that specifically targets outdoor IoT deployments and that uses height maps to represent terrain. In addition, we present a new terrain classification method based on terrain “roughness,” which allows us to select a variety of terrain samples to demonstrate how TerrainLOS can capture the effects of terrain on communication. We also propose a technique to generate synthetic terrain samples based on “roughness.” Furthermore, we implemented TerrainLOS in the COOJA-Contiki network simulation/emulation platform, which targets IoT deployments and uses TerrainLOS to evaluate how often a network is fully connected based on the roughness of terrain, as well as how two popular power-aware routing protocols, RPL and ORPL, perform when terrain is considered.
{"title":"Modeling Communication over Terrain for Realistic Simulation of Outdoor Sensor Network Deployments","authors":"Sam Mansfield, K. Veenstra, K. Obraczka","doi":"10.1145/3510306","DOIUrl":"https://doi.org/10.1145/3510306","url":null,"abstract":"Popular wireless network simulators have few available propagation models for outdoor Internet of Things applications. Of the available models, only a handful use real terrain data, yet an inaccurate propagation model can skew the results of simulations. In this article, we present TerrainLOS, a low-overhead propagation model for outdoor Internet of Things applications that uses real terrain data to determine whether two nodes can communicate. To the best of our knowledge, TerrainLOS is the first terrain-aware propagation model that specifically targets outdoor IoT deployments and that uses height maps to represent terrain. In addition, we present a new terrain classification method based on terrain “roughness,” which allows us to select a variety of terrain samples to demonstrate how TerrainLOS can capture the effects of terrain on communication. We also propose a technique to generate synthetic terrain samples based on “roughness.” Furthermore, we implemented TerrainLOS in the COOJA-Contiki network simulation/emulation platform, which targets IoT deployments and uses TerrainLOS to evaluate how often a network is fully connected based on the roughness of terrain, as well as how two popular power-aware routing protocols, RPL and ORPL, perform when terrain is considered.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"6 1","pages":"1 - 22"},"PeriodicalIF":0.6,"publicationDate":"2021-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47320693","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.
{"title":"Adversarial Deep Learning for Online Resource Allocation","authors":"Bingqian Du, Zhiyi Huang, Chuan Wu","doi":"10.1145/3494526","DOIUrl":"https://doi.org/10.1145/3494526","url":null,"abstract":"Online algorithms are an important branch in algorithm design. Designing online algorithms with a bounded competitive ratio (in terms of worst-case performance) can be hard and usually relies on problem-specific assumptions. Inspired by adversarial training from Generative Adversarial Net and the fact that the competitive ratio of an online algorithm is based on worst-case input, we adopt deep neural networks (NNs) to learn an online algorithm for a resource allocation and pricing problem from scratch, with the goal that the performance gap between offline optimum and the learned online algorithm can be minimized for worst-case input. Specifically, we leverage two NNs as the algorithm and the adversary, respectively, and let them play a zero sum game, with the adversary being responsible for generating worst-case input while the algorithm learns the best strategy based on the input provided by the adversary. To ensure better convergence of the algorithm network (to the desired online algorithm), we propose a novel per-round update method to handle sequential decision making to break complex dependency among different rounds so that update can be done for every possible action instead of only sampled actions. To the best of our knowledge, our work is the first using deep NNs to design an online algorithm from the perspective of worst-case performance guarantee. Empirical studies show that our updating methods ensure convergence to Nash equilibrium and the learned algorithm outperforms state-of-the-art online algorithms under various settings.","PeriodicalId":56350,"journal":{"name":"ACM Transactions on Modeling and Performance Evaluation of Computing Systems","volume":"6 1","pages":"1 - 25"},"PeriodicalIF":0.6,"publicationDate":"2021-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43210152","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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