Business-driven policy optimization for service management

The performance of services offered by network operators has a direct impact on its reputation, on its revenue due to new customer subscriptions, and also on penalties that can apply when services are not provided to an acceptable quality level. Previous research on business-oriented network and service optimization has mainly focused on optimizing individual business indicators, such as profit and revenue, in isolation without analyzing the effect on network configurations and the subsequent impact on other indicators. Given that different business objectives are usually incompatible, a single network configuration cannot optimize them simultaneously. Determining the configuration and the associated trade-offs that satisfy multiple objectives is a complex task. This paper addresses this gap and presents a framework that derives policy configurations that optimize the business value of the network infrastructure. We describe a methodology to quantify business functions considering the dynamics of network events, the dynamics of end-user service usage, the nature of the business indicators, and their relationships with the underlying control methods. The proposed approach addresses the complexity of the target problem through a surrogate-based optimization approach properly tailored to match our application domain needs. We evaluate the effectiveness of the proposed approach through experimentation in a simulation environment we developed over OPNET

Adaptive and business-driven service placement in federated Cloud computing environments

he emergence of large-scale federated Cloud computing environments and of dynamic resource pricing schemes presents interesting saving opportunities for service providers, that could dynamically change the placement of IT service components in order to reduce their bills. However, that calls for smart management solutions able to respond to pricing changes by dynamically reconfiguring IT service component placement in federated Cloud environments so to enforce highlevel business objectives defined by the service providers. This paper proposes a novel adaptive and business-driven IT service component reconfiguration solution based on what-if scenario analysis and on genetic-algorithm optimization. Our solution is able to model complex Cloud computing IT services and to evaluate their performance in a wide range of alternative configurations, by also detecting the optimal placement for their components. The paper presents the experimental evaluation of our framework in a realistic scenario that consists of a 2-tier service architecture with real-world pricing schemes. The results demonstrate the effectiveness of our solution and the suitability of business-driven IT management techniques for the optimal placement of service components in federated Clouds

Optimasi Rute Trafik Data Dan Destinasi Pada Jaringan Bergerak Maritim Menggunakan Algoritma Blind Search Dan Swarm Intelligence

Maraknya illegal fishing di perairan Indonesia sangat berpengaruh pada keamanan negara dan sumber daya laut kita. Mengacu pada data dari Kementerian Kelautan dan Perikanan tahun 2012 bahwa jumlah kapal di bawah 30 GT mendominasi 98% dari keseluruhan jumlah kapal tangkap ikan di Indonesia. Kapal-kapal tangkap ini tidak memiliki kewajiban melengkapi dengan peralatan sistem pemantauan berbasis satelit. Sedangkan kapal asing dengan bobot kecil 20- 30 GT sudah dilengkapi sistem ini, sehingga mereka dengan mudah mendapatkan informasi tentang lokasi penangkapan ikan (rumpon, fish aggreagting device (FAD)). Oleh karena itu, khusus kapal tangkap ikan < 30 GT, perlu mendapatkan perhatian, khususnya untuk sharing informasi antar kapal dan destinasi menuju FAD. Optimasi rute trafik data dan destinasi FAD perlu dilakukan untuk memudahkan kapal menuju FAD dengan mempertimbangkan jarak dan kondisi cuaca dan tinggi gelombang di FAD. Metode pendekatan optimasi metode Swarm Intelligence (SI) banyak ditawarkan untuk menyelesaikan permasalahan tersebut. Metode optimasi seperti Gossip dan Genetic algorithm (GA) telah banyak digunakan untuk mendapatkan solusi terbaik. Usulan optimasi rute trafik data Breadth fixed gossip (BFG) dan PSO untuk jaringan dinamis ditujukan untuk menentukan rute terpilih berdasarkan pertimbangan jarak dan konektifitas dengan kapal lainnya. Algoritma optimasi rute trafik data BFG merupakan hybrid algoritma breadth first search, model fixed radius dan Gossip. Sedangkan optimasi rute destinasi FAD diusulkan menggunakan algoritma firefly dan GA. Dengan menggabungkan kedua algoritma optimasi rute, maka dibangun optimasi rute trafik data dan destinasi lokasi tangkap ikan sekaligus yaitu: BFG-G dan PSO-G. Pengujian berupa simulasi dilakukan untuk mengetahui tingkat keberhasilan menentukan rute trafik data dan lokasi FAD. Sedangkan pengujian komputasi didasarkan pada kompleksitas waktu, keakurasian, kecepatan konvergen dan jumlah relai yang diperlukan untuk mencapai kapal tujuan. ================================================================================================================== The rise of illegal fishing in Indonesian ocean is very influential on the country security and marine resources. Referring to data from the Ministry of Marine Affairs and Fisheries in 2012 that the number of ships under 30 GT dominates 98% of the total number of fishing vessels in Indonesia. These fishing vessels have no obligation to equip with the satellite-based monitoring system. While foreign ships with a small weight of 20-30 GT already equipped this system, hence they easily get information about the location of fishing (rumpon, fish aggregating device (FAD)). Therefore, the fishing vessels <30 GT, need to get attention, especially for sharing information between ships and destinations to FAD. Optimization of data traffic routes and FAD destinations needs to be done to facilitate the ship to FAD by considering the distance and weather conditions and wave height in FAD. An approach optimization method of Swarm Intelligence (SI) is widely offered to solve the problem. Optimization methods such as Gossip and Genetic algorithm (GA) have been widely used to get the best solution. The proposed optimization of Breadth fixed gossip (BFG) data traffic route and PSO for a dynamic network are intended to determine the selected route based on consideration of distance and connectivity with other vessels. BFG traffic route optimization algorithm is a hybrid algorithm of breadth first search, fixed radius model, and Gossip. While FAD route destination optimization is proposed using firefly and GA algorithm. By combining the two route optimization algorithms, the optimization of data traffic and FAD routes are BFGG and PSO-G. The simulations are performed to determine the success rate determine the route of data traffic and FAD location. While computational testing is based on the complexity of time, accuracy, convergent speed and the number of relays required to reach the destination ship in determining data traffic

Adaptive and business-driven service placement in federated Cloud computing environments

The emergence of large-scale federated Cloud computing environments and of dynamic resource pricing schemes presents interesting saving opportunities for service providers, that could dynamically change the placement of IT service components in order to reduce their bills. However, that calls for smart management solutions able to respond to pricing changes by dynamically reconfiguring IT service component placement in federated Cloud environments so to enforce highlevel business objectives defined by the service providers. This paper proposes a novel adaptive and business-driven IT service component reconfiguration solution based on what-if scenario analysis and on genetic-algorithm optimization. Our solution is able to model complex Cloud computing IT services and to evaluate their performance in a wide range of alternative configurations, by also detecting the optimal placement for their components. The paper presents the experimental evaluation of our framework in a realistic scenario that consists of a 2-tier service architecture with real-world pricing schemes. The results demonstrate the effectiveness of our solution and the suitability of business-driven IT management techniques for the optimal placement of service components in federated Clouds