Distributed Heuristic Algorithm for Migration and Replication of Self-organized Services in Future Networks

أصبحت شبكات الاتصالات المحمولة في الوقت الحاضر جزءًا متأصلاً في حياتنا اليومية من خلال الكميات الهائلة من البيانات التي يتم تناقلها عبر أجهزة الاتصال، مما يقود إلى تحديات جديدة. وسيتم حسب مؤشر سيسكو للشبكات، توصيل أكثر من 29.3 بليون جهاز عبر  الشبكة خلال العام 2023.من الواضح أن البنى التحتية الموجودة في الشبكات الحالية لن تكون قادرة على دعم جميع البيانات التي يتم تبادلها بسبب عرض الحزمة المحدود وكلفة عمليات الإرسال والمعالجة. ومن أجل التعامل مع هذه المشكلات، يجب أن تحقق شبكات الاتصالات المحمولة المستقبلية متطلبات عالية من أجل إنقاص كمية البيانات المنقولة وتقليل زمن الوصول وكلفة عمليات المعالجة. تتمثّل إحدى التحديّاتِ العلميّةِ الهامّة ضمن هذا السياق في التوضيع المثالي للخدماتِ ذاتيّة التّأقلمِ.تمّ في هذه الورقة البحثية تقديم خوارزمية استدلالية لتوضيع الخدمات في الشبكات المستقبلية. تحقق هذه الخوارزميّة التوضيع المثالي لنسخ الخدمات من خلال مراقبة الحمل داخل عقدة المخدم وجوارها، واختيار العقدة التي يتمّ تلقّي الحمل الأكبر منها، ونسخ الخدمة أو تهجيرها إليها بناءً على معايير محددة، فتصبح بالتالي المسافة التي تعبرها الطلبات الواردة من العقد الزبائن صغيرة قدر الإمكان بسبب توضيع الخدمات في مواقع قريبة منها. تمّ الإثبات أنّ الخوارزميّة المقترحة من قبلنا تحقّق أداءً محسّنًا من ناحية تلبية الخدمات خلال زمن أقصر، وعرض حزمة أصغر وبالتالي كلفة اتصال أقلّ. أُجريت مقارنة بين هذه الخوارزمية وكل من نموذج الزبون-مخدّم التقليدي وخوارزميّة التوضيع العشوائي. أثبتت النتائج التجريبية أنّ الخوارزميّة الاستدلاليّة تتفوّق على الطرق الأخرى وتحقّق الأداء الأمثل من أجل شبكاتٍ بأحجامٍ مختلفة وسيناريوهات بأحمالٍ متنوعة.Nowadays, the mobile communication networks have become a consistent part of our everyday life by transforming huge amount of data through communicating devices, that leads to new challenges. According to the Cisco Networking Index, more than 29.3 billion networked devices will be connected to the network during the year 2023. It is obvious that the existing infrastructures in current networks will not be able to support all the generated data due to the bandwidth limits, processing and transmission overhead. To cope with these issues, future mobile communication networks must achieve high requirements to reduce the amount of transferred data, decrease latency and computation costs. One of the essential challenging tasks in this subject area is the optimal self-organized service placement. In this paper a heuristic-based algorithm for service placement in future networks was presented. This algorithm achieves the ideal placement of services replicas by monitoring the load within the server and its neighborhood, choosing the node that contributes with the highest received load, and finally replicating or migrating the service to it based on specific criteria, so that the distance of requests coming from clients becomes as small as possible because of placing services within nearby locations. It was proved that our proposed algorithm achieves an improved performance by meeting the services within a shorter time, a smaller bandwidth, and thus a lower communication cost. It was compared with the traditional client-server approach and the random placement algorithm. Experimental results showed that the heuristic algorithm outperforms other approaches and meets the optimal performance with different network sizes and varying load scenarios

Self organising cloud cells: a resource efficient network densification strategy

Network densification is envisioned as the key enabler for 2020 vision that requires cellular systems to grow in capacity by hundreds of times to cope with unprecedented traffic growth trends being witnessed since advent of broadband on the move. However, increased energy consumption and complex mobility management associated with network densifications remain as the two main challenges to be addressed before further network densification can be exploited on a wide scale. In the wake of these challenges, this paper proposes and evaluates a novel dense network deployment strategy for increasing the capacity of future cellular systems without sacrificing energy efficiency and compromising mobility performance. Our deployment architecture consists of smart small cells, called cloud nodes, which provide data coverage to individual users on a demand bases while taking into account the spatial and temporal dynamics of user mobility and traffic. The decision to activate the cloud nodes, such that certain performance objectives at system level are targeted, is carried out by the overlaying macrocell based on a fuzzy-logic framework. We also compare the proposed architecture with conventional macrocell only deployment and pure microcell-based dense deployment in terms of blocking probability, handover probability and energy efficiency and discuss and quantify the trade-offs therein

Using Machine Learning for Handover Optimization in Vehicular Fog Computing

Smart mobility management would be an important prerequisite for future fog computing systems. In this research, we propose a learning-based handover optimization for the Internet of Vehicles that would assist the smooth transition of device connections and offloaded tasks between fog nodes. To accomplish this, we make use of machine learning algorithms to learn from vehicle interactions with fog nodes. Our approach uses a three-layer feed-forward neural network to predict the correct fog node at a given location and time with 99.2 % accuracy on a test set. We also implement a dual stacked recurrent neural network (RNN) with long short-term memory (LSTM) cells capable of learning the latency, or cost, associated with these service requests. We create a simulation in JAMScript using a dataset of real-world vehicle movements to create a dataset to train these networks. We further propose the use of this predictive system in a smarter request routing mechanism to minimize the service interruption during handovers between fog nodes and to anticipate areas of low coverage through a series of experiments and test the models' performance on a test set