Implementing energy saving algorithms for Ethernet link aggregates with ONOS

During the last few years, there has been plenty of research for reducing energy consumption in telecommunication infrastructure. However, many of the proposals remain unim-plemented due to the lack of flexibility in legacy networks. In this paper we demonstrate how the software defined networking (SDN) capabilities of current networking equipment can be used to implement some of these energy saving algorithms. In particular, we developed an ONOS application to realize an energy-aware traffic scheduler to a bundle link made up of Energy Efficient Ethernet (EEE) links between two SDN switches. We show how our application is able to dynamically adapt to the traffic characteristics and save energy by concentrating the traffic on as few ports as possible. This way, unused ports remain in Low Power Idle (LPI) state most of the time, saving energy.Comment: 8 pages, 10 figure

Study the Effect of The Announcement Traffic Indication Messages Window ATIM Size on the Performance of the Ad-hoc Networks in Power Saving Mode PSM

The researches were interested in power saving in IEEE 802.11 standard in both of infrastructure networks and ad-hoc networks, where the power saving is one of the critical issues affecting wireless local area networks WLANs performance, especially ad-hoc networks, because each mobile device operates on the power of its limited battery. In ad-hoc networks which are called the independent basic service set IBSS  the power is saved by power saving mode PSM algorithm which depends on dividing the time into a number of  Beacon  intervals BIs, these intervals begin with a small period called announcement traffic indication messages window  ATIM period during which all the nodes are in the active mode. In this article, we study the effect of the announcement traffic indication messages window ATIM size on the performance of the  wireless networks in terms of power consumption and  throughput of the wireless network through several scenarios in the power saving mode PSM that differs in terms of the number of nodes used in the wireless network and the size of ATIM window. اهتم الباحثون بموضوع توفير الطاقة في معيار IEEE 802.11  في كل من الشبكات ذات البنية التحتيةInfrastructure  وشبكات Ad-hoc حيث يعد موضوع توفير الطاقة من القضايا الحرجة والمؤثرة على أداء الشبكات المحلية  اللاسلكية WLANs وخصوصا في شبكات Ad-hoc وذلك لأن كل جهاز جوال يعمل بالاعتماد على طاقة بطاريته المحدودة. يتم توفير الطاقة في شبكات Ad-hoc والتي تسمى مجموعة الخدمة الأساسية المستقلة IBSS باستخدام خوارزمية وضع توفير الطاقة PSM التي تعتمد على تقسيم الزمن إلى عدد من الفواصل الزمنية لإطار المرشد اللاسلكيBIs وتبدأ هذه الفواصل بفترة زمنية صغيرة للإعلان عن أطر التحكم تسمى بفترة نافذة رسائل إعلان إشارات المرور ATIM  تكون خلالها كافة العقد في وضع النشاط. تم في هذه المقالة دراسة تأثير اختلاف حجم نافذة ATIM على أداء الشبكة اللاسلكية من ناحية الطاقة المستهلكة ومردود الشبكة اللاسلكية وذلك من خلال عدة سيناريوهات في وضع توفير الطاقة PSM تختلف فيما بينها من ناحية عدد العقد المستخدمة في الشبكة اللاسلكية وحجم نافذة  ATIM

تحسين آلية التحكم في إدارة نمط توفير الطّاقة

إنَّ خوارزميةَ توفيرِ الطّاقة الافتراضيّة، التّي تتبّناها الهواتف الذّكية من أجلِ الحدِّ من استهلاكِ الطّاقة الناتج عن استعمالِ النظامِ الفرعي المضمّن Wi-Fi، هي نمط توفير الطّاقة القياسي، الذّي طرحهُ البروتوكول 802.11. يتصف تنفيذ النمط القياسي بعدّةَ سلبياتٍ لعلَّ أبرزها عدم تمكّنهِ منَ الاستفادةِ من نموذجِ حركةِ المعطيات، لإنقاصِ مُستوى استهلاكِ الطّاقة، بالإضافةِ إلى عدمِ مرونتهِ من حيث السّماح للمستخدمين بالتحكمِ في زمنِ تأخيرِ الرّزم. اقترحَ هذا البحث تحسيناً للخوارزميةِ القياسية، بحيث تمَّ العمل على تلافي ما سبقَ ذكرهُ، بالإضافةِ إلى مراعاةِ قيدِ الطّاقة على اعتبار أنَّ Wi-Fi ما هي إلاّ جزء من نظامٍ مضمّنٍ أكبر، يجب أن يؤدي وظائفَ أُخرى. تمَّ التحقّق منَ الخوارزميةِ المُقترحة ((EDPSM عن طريقِ مقارنةِ أدائِها معَ النمطِ القياسي، باستخدامِ المُحاكي NS2، وذلكَ وفقاً لمجموعةٍ من البارامتراتِ المؤثرةِ على الأداء. The default power saving algorithm adopted by smartphones in order to reduce power consumption resulting from the use of embedded subsystem Wi-Fi, is the standard power save mode which put forward by the 802.11 protocol. Implementation of the standard mode characterizes several disadvantages, the most notably is not being able to take advantage of the data traffic model, to reduce the level of power consumption, in addition to the lack of flexibility in terms of allowing users to control with the delay time of packets. This research suggested an improvement to the standard algorithm, so that it has been working to avoid the foregoing, in addition to taking into account the limitation of power because that Wi-Fi is only part of a larger embedded system should achieve other functions. The verification of the proposed algorithm (EDPSM) is done by comparing its performance with the standard mode, using the simulator NS2, and according to a set of parameters affecting the performance

Optimising WLANs Power Saving: Context-Aware Listen Interval

Energy is a vital resource in wireless computing systems. Despite the increasing popularity of Wireless Local Area Networks (WLANs), one of the most important outstanding issues remains the power consumption caused by Wireless Network Interface Controller (WNIC). To save this energy and reduce the overall power consumption of wireless devices, a number of power saving approaches have been devised including Static Power Save Mode (SPSM), Adaptive PSM (APSM), and Smart Adaptive PSM (SAPSM). However, the existing literature has highlighted several issues and limitations in regards to their power consumption and performance degradation, warranting the need for further enhancements. This thesis proposes a novel Context-Aware Listen Interval (CALI), in which the wireless network interface, with the aid of a Machine Learning (ML) classification model, sleeps and awakes based on the level of network activity of each application. We focused on the network activity of a single smartphone application while ignoring the network activity of applications running simultaneously. We introduced a context-aware network traffic classification approach based on ML classifiers to classify the network traffic of wireless devices in WLANs. Smartphone applications’ network traffic reflecting a diverse array of network behaviour and interactions were used as contextual inputs for training ML classifiers of output traffic, constructing an ML classification model. A real-world dataset is constructed, based on nine smartphone applications’ network traffic, this is used firstly to evaluate the performance of five ML classifiers using cross-validation, followed by conducting extensive experimentation to assess the generalisation capacity of the selected classifiers on unseen testing data. The experimental results further validated the practical application of the selected ML classifiers and indicated that ML classifiers can be usefully employed for classifying the network traffic of smartphone applications based on different levels of behaviour and interaction. Furthermore, to optimise the sleep and awake cycles of the WNIC in accordance with the smartphone applications’ network activity. Four CALI power saving modes were developed based on the classified output traffic. Hence, the ML classification model classifies the new unseen samples into one of the classes, and the WNIC will be adjusted to operate into one of CALI power saving modes. In addition, the performance of CALI’s power saving modes were evaluated by comparing the levels of energy consumption with existing benchmark power saving approaches using three varied sets of energy parameters. The experimental results show that CALI consumes up to 75% less power when compared to the currently deployed power saving mechanism on the latest generation of smartphones, and up to 14% less energy when compared to SAPSM power saving approach, which also employs an ML classifier