Pub Date : 2015-04-14DOI: 10.1109/SustainIT.2015.7101382
Jiva N. Bagale, John P. T. Moore, Antonio D. Kheirkhahzadeh, Yasmine Z. Rosunally
Wireless data transmission over sensor networks is known to consume a significant share of energy compared to data computation on the device itself. Thus data compression techniques have been used to reduce the data size at the cost of extra processing time on the device. This paper investigates the energy consumption trade-off of XML compression and data communication on embedded devices with low-bandwidth network.
{"title":"Energy consumption trade-offs for XML compression on embedded devices","authors":"Jiva N. Bagale, John P. T. Moore, Antonio D. Kheirkhahzadeh, Yasmine Z. Rosunally","doi":"10.1109/SustainIT.2015.7101382","DOIUrl":"https://doi.org/10.1109/SustainIT.2015.7101382","url":null,"abstract":"Wireless data transmission over sensor networks is known to consume a significant share of energy compared to data computation on the device itself. Thus data compression techniques have been used to reduce the data size at the cost of extra processing time on the device. This paper investigates the energy consumption trade-off of XML compression and data communication on embedded devices with low-bandwidth network.","PeriodicalId":229673,"journal":{"name":"2015 Sustainable Internet and ICT for Sustainability (SustainIT)","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124826188","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}
Pub Date : 2015-04-14DOI: 10.1109/SUSTAINIT.2015.7101374
Filipe Quintal, N. Nunes, Valentina Nisi
This paper presents our efforts in understanding the long-term effects of eco-feedback devices. Our work argues that lack of long term in the wild studies is a missed opportunity to understand how these devices are used after the novelty wears off. We build on previous work that showed a significant decrease in user awareness after a short period with a relapse in consumption, with the presented studies we aimed to investigate if new forms of feedback could overcome this issue, maintaining the users awareness for longer periods of time.
{"title":"Exploring the dimensions of eco-feedback in the wild","authors":"Filipe Quintal, N. Nunes, Valentina Nisi","doi":"10.1109/SUSTAINIT.2015.7101374","DOIUrl":"https://doi.org/10.1109/SUSTAINIT.2015.7101374","url":null,"abstract":"This paper presents our efforts in understanding the long-term effects of eco-feedback devices. Our work argues that lack of long term in the wild studies is a missed opportunity to understand how these devices are used after the novelty wears off. We build on previous work that showed a significant decrease in user awareness after a short period with a relapse in consumption, with the presented studies we aimed to investigate if new forms of feedback could overcome this issue, maintaining the users awareness for longer periods of time.","PeriodicalId":229673,"journal":{"name":"2015 Sustainable Internet and ICT for Sustainability (SustainIT)","volume":"222 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122047834","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}
Pub Date : 2015-04-14DOI: 10.1109/SustainIT.2015.7101367
Damiano Rapone, D. Sabella, Maurizio Fodrini
This paper focuses on techniques or actions that the network operator should consider for the mobile network's evolution towards 5G from a sustainability point of view. To this end, the energy consumption of the Radio Access Network (RAN) is assessed when considering different load conditions (obtained by means of actual daily traffic profiles extracted from the live network) and different yearly traffic forecasts. An overview of different "what-if" scenarios towards 2020 and beyond is provided and energy consumption of the network is assessed by considering evolutionary Power Models (PMs) of future Base Stations (BSs) related to different Radio Access Technologies (RATs). Together with evolutionary PMs, many Energy Efficiency (EE) features that may be implemented within the network are also considered, with particular focus on EE features evaluated at higher time scale (from minutes to years), i.e., related to progressive network renewal with traffic steering options and legacy RATs' phase-off policies. These have been evaluated by using a software tool fed by actual traffic profiles, aiming at assessing EE performance at network level, by also considering the European Telecommunications Standards Institute (ETSI) EE specification ES 203 228, which is based on homogeneous clusters' evaluations that may be extrapolated at country level, providing useful information on the possible evolutions of the mobile networks in the view of future 5G systems.
{"title":"Energy efficiency solutions for the mobile network evolution towards 5G: an operator perspective","authors":"Damiano Rapone, D. Sabella, Maurizio Fodrini","doi":"10.1109/SustainIT.2015.7101367","DOIUrl":"https://doi.org/10.1109/SustainIT.2015.7101367","url":null,"abstract":"This paper focuses on techniques or actions that the network operator should consider for the mobile network's evolution towards 5G from a sustainability point of view. To this end, the energy consumption of the Radio Access Network (RAN) is assessed when considering different load conditions (obtained by means of actual daily traffic profiles extracted from the live network) and different yearly traffic forecasts. An overview of different \"what-if\" scenarios towards 2020 and beyond is provided and energy consumption of the network is assessed by considering evolutionary Power Models (PMs) of future Base Stations (BSs) related to different Radio Access Technologies (RATs). Together with evolutionary PMs, many Energy Efficiency (EE) features that may be implemented within the network are also considered, with particular focus on EE features evaluated at higher time scale (from minutes to years), i.e., related to progressive network renewal with traffic steering options and legacy RATs' phase-off policies. These have been evaluated by using a software tool fed by actual traffic profiles, aiming at assessing EE performance at network level, by also considering the European Telecommunications Standards Institute (ETSI) EE specification ES 203 228, which is based on homogeneous clusters' evaluations that may be extrapolated at country level, providing useful information on the possible evolutions of the mobile networks in the view of future 5G systems.","PeriodicalId":229673,"journal":{"name":"2015 Sustainable Internet and ICT for Sustainability (SustainIT)","volume":"47 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121100746","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}
Pub Date : 2015-04-14DOI: 10.1109/SustainIT.2015.7101359
L. Cherkasova, A. Zhang
For most businesses, backup is a daily operation that needs to reliably protect diverse digital assets distributed across the enterprise. Efficiently processing ever increasing amounts of data residing on multiple desktops, servers, laptops, etc., and providing dynamic recovery capabilities becomes a high priority task for many IT departments. Driven by the advances in cloud computing and Software as a Service (SaaS) delivery model, IT departments are transitioning from providing highly customized services to offering on demand services which can be requested and canceled instantly. Backup service providers must be able to react efficiently to on-demand requests and cannot afford labor intensive resource planning and manual adjustments of schedules. Our goal is to automate the design of a backup schedule that minimizes the overall completion time for a given set of backup jobs. This problem can be formulated as a resource constrained scheduling problem where a set of n jobs should be scheduled on m machines with given capacities. In this work, we compare the outcome of the integer programming formulation with a heuristic-based job scheduling algorithm, called FlexLBF. The FlexLBF schedule produces close to optimal results (reducing backup time 20%-60%) while carrying no additional computing overhead and scaling well to efficiently process large datasets compared to the IP-based solution. Moreover, FlexLBF can be easily analyzed in a simulation environment to further tune a backup server configuration for achieving given performance objectives while saving power (up to additional 50% in our experiments). It helps to avoid guess-based configuration efforts by system administrators and significantly increase the quality and reliability of implemented solutions.
{"title":"Optimizing QoS, performance, and power efficiency of backup services","authors":"L. Cherkasova, A. Zhang","doi":"10.1109/SustainIT.2015.7101359","DOIUrl":"https://doi.org/10.1109/SustainIT.2015.7101359","url":null,"abstract":"For most businesses, backup is a daily operation that needs to reliably protect diverse digital assets distributed across the enterprise. Efficiently processing ever increasing amounts of data residing on multiple desktops, servers, laptops, etc., and providing dynamic recovery capabilities becomes a high priority task for many IT departments. Driven by the advances in cloud computing and Software as a Service (SaaS) delivery model, IT departments are transitioning from providing highly customized services to offering on demand services which can be requested and canceled instantly. Backup service providers must be able to react efficiently to on-demand requests and cannot afford labor intensive resource planning and manual adjustments of schedules. Our goal is to automate the design of a backup schedule that minimizes the overall completion time for a given set of backup jobs. This problem can be formulated as a resource constrained scheduling problem where a set of n jobs should be scheduled on m machines with given capacities. In this work, we compare the outcome of the integer programming formulation with a heuristic-based job scheduling algorithm, called FlexLBF. The FlexLBF schedule produces close to optimal results (reducing backup time 20%-60%) while carrying no additional computing overhead and scaling well to efficiently process large datasets compared to the IP-based solution. Moreover, FlexLBF can be easily analyzed in a simulation environment to further tune a backup server configuration for achieving given performance objectives while saving power (up to additional 50% in our experiments). It helps to avoid guess-based configuration efforts by system administrators and significantly increase the quality and reliability of implemented solutions.","PeriodicalId":229673,"journal":{"name":"2015 Sustainable Internet and ICT for Sustainability (SustainIT)","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2015-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121859576","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：