A new Co-evolutionary Algorithm Based on Constraint Decomposition

in IPDPS (2017)

Self-Regulated Multi-criteria Decision Analysis: An Autonomous Brokerage-Based Approach for Service Provider Ranking in the Cloud

in 9th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2017), December 11-14, Hong Kong China. (2017)

The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias ... [more ▼]

The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias if the customer has insufficient domain knowledge. He/she may exclude relevant or include irrelevant criterion termed as ’misspecification of criterion’. This causes structural uncertainty within the MCDA leading to selection of suboptimal service provider by online broker. To cater such issue, we propose a self-regulated MCDA, which uses notion of factor analysis from the field of statistics. Two QoS based datasets were used for evaluation of proposed model. The prior dataset i.e., feedback from customers, was compiled using leading review websites such as Cloud Hosting Reviews, Best Cloud Computing Providers, and Cloud Storage Reviews and Ratings. The later dataset i.e., feedback from servers, was generated from Cloud brokerage architecture that was emulated using high performance computing (HPC) cluster at University of Luxembourg (HPC @ Uni.lu). The results show better performance of proposed model as compared to its counterparts in the field. The beneficiary of the research would be enterprises that view insufficient domain knowledge as a limiting factor for acquisition of Cloud services. [less ▲]

Target Tracking Optimization of UAV Swarms Based on Dual-Pheromone Clustering

in CYBCONF 2017-12-13 09:39:53 +0000 2017-12-13 09:39:53 +0000 (2017)

Min_c: Heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention

in Programming and Computer Software (2017), 43(3), 204-215

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking ... [more ▼]

In this paper, we address energy-aware online scheduling of jobs with resource contention. We propose an optimization model and present new approach to resource allocation with job concentration taking into account types of applications and heterogeneous workloads that could include CPU-intensive, diskintensive, I/O-intensive, memory-intensive, network-intensive, and other applications. When jobs of one type are allocated to the same resource, they may create a bottleneck and resource contention either in CPU, memory, disk or network. It may result in degradation of the system performance and increasing energy consumption. We focus on energy characteristics of applications, and show that an intelligent allocation strategy can further improve energy consumption compared with traditional approaches. We propose heterogeneous job consolidation algorithms and validate them by conducting a performance evaluation study using the Cloud Sim toolkit under different scenarios and real data. We analyze several scheduling algorithms depending on the type and amount of information they require. [less ▲]

Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers

in Service Performance Pattern Analysis and Prediction of Commercially Available Cloud Providers (2016, December 12)

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible ... [more ▼]

The knowledge of service performance of cloud providers is essential for cloud service users to choose the cloud services that meet their requirements. Instantaneous performance readings are accessible, but prolonged observations provide more reliable information. However, due to technical complexities and costs of monitoring services, it may not be possible to access the service performance of cloud provider for longer time durations. The extended observation periods are also a necessity for prediction of future behavior of services. These predictions have very high value for decision making both for private and corporate cloud users, as the uncertainty about the future performance of purchased cloud services is an important risk factor. Predictions can be used by specialized entities, such as cloud service brokers (CSBs) to optimally recommend cloud services to the cloud users. In this paper, we address the challenge of prediction. To achieve this, the current service performance patterns of cloud providers are analyzed and future performance of cloud providers are predicted using to the observed service performance data. It is done using two automatic predicting approaches: ARIMA and ETS. Error measures of entire service performance prediction of cloud providers are evaluated against the actual performance of the cloud providers computed over a period of one month. Results obtained in the performance prediction show that the methodology is applicable for both short- term and long-term performance prediction. [less ▲]

Proceedings of the 8th IEEE International Conference on Cloud Computing Technology and Science (ClouCom 2016)

Book published by IEEE Computer Society (2016)

CloudCom is the premier conference on Cloud Computing worldwide, attracting researchers, developers, users, students and practitioners from the fields of big data, systems architecture, services research ... [more ▼]

CloudCom is the premier conference on Cloud Computing worldwide, attracting researchers, developers, users, students and practitioners from the fields of big data, systems architecture, services research, virtualization, security and privacy, high performance computing, always with an emphasis on how to build cloud computing platforms with real impact. The conference is co-sponsored by the Institute of Electrical and Electronics Engineers (IEEE), is steered by the Cloud Computing Association, and draws on the excellence of its world-class Program Committee and its participants. The 8th IEEE International Conference on Cloud Computing Technology and Science (CloudCom 2016) was held in the city of Luxembourg on 12-15 December 2016 [less ▲]

Game-Theoretic Recruitment of Sensing Service Providers for Trustworthy Cloud-Centric Internet-of-Things (IoT) Applications

in IEEE Global Communications Conference (GLOBECOM) Workshops: Fifth International Workshop on Cloud Computing Systems, Networks, and Applications (CCSNA) (2016, December)

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications ... [more ▼]

Widespread use of connected smart devices that are equipped with various built-in sensors has introduced the mobile crowdsensing concept to the IoT-driven information and communication applications. Mobile crowdsensing requires implicit collaboration between the crowdsourcer/recruiter platforms and users. Additionally, users need to be incentivized by the crowdsensing platform because each party aims to maximize their utility. Due to the participatory nature of data collection, trustworthiness and truthfulness pose a grand challenge in crowdsensing systems in the presence of malicious users, who either aim to manipulate sensed data or collaborate unfaithfully with the motivation of maximizing their income. In this paper, we propose a game-theoretic approach for trustworthiness-driven user recruitment in mobile crowdsensing systems that consists of three phases: i) user recruitment, ii) collaborative decision making on trust scores, and iii) badge rewarding. Our proposed framework incentivizes the users through a sub-game perfect equilibrium (SPE) and gamification techniques. Through simulations, we show that the platform utility can be improved by up to the order of 50\% while the average user utility can be increased by at least 15\% when compared to fully-distributed and user-centric trustworthy crowdsensing. [less ▲]

From Random Process to Chaotic Behavior in Swarms of UAVs

in DIVANet '16 Proceedings of the 6th ACM Symposium on Development and Analysis of Intelligent Vehicular Networks and Applications (2016, November)

Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance ... [more ▼]

Unmanned Aerial Vehicles (UAVs) applications have seen an important increase in the last decade for both military and civilian applications ranging from fire and high seas rescue to military surveillance and target detection. While this technology is now mature for a single UAV, new methods are needed to operate UAVs in swarms, also referred to as fleets. This work focuses on the mobility management of one single autonomous swarm of UAVs which mission is to cover a given area in order to collect information. Several constraints are applied to the swarm to solve this problem due to the military context. First, the UAVs mobility must be as unpredictable as possible to prevent any UAV tracking. However the Ground Control Station (GCS) operator(s) still needs to be able to forecast the UAVs paths. Finally, the UAVs are autonomous in order to guarantee the mission continuity in a hostile environment and the method must be distributed to ensure fault-tolerance of the system. To solve this problem, we introduce the Chaotic Ant Colony Optimization to Coverage (CACOC) algorithm that combines an Ant Colony Optimization approach (ACO) with a chaotic dynamical system. CACOC permits to obtain a deterministic but unpredictable system. Its performance is compared to other state-of-the art models from the literature using several coverage-related metrics, i.e. coverage rate, recent coverage and fairness. Numerical results obtained by simulation underline the performance of our CACOC method: a deterministic method with unpredictable UAV trajectories that still ensures a high area coverage. [less ▲]

Metaheuristic Based Clustering Algorithms for Biological Hypergraphs

in Proceedings of META’2016, 6th International Conference on Metaheuristics and Nature Inspired computing (2016, October 27)

Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and ... [more ▼]

Hypergraphs are widely used for modeling and representing relationships between entities, one such field where their application is prolific is in bioinformatics. In the present era of big data, sizes and complexity of these hypergraphs grow exponentially, it is impossible to process them manually or even visualize their interconnectivity superficially. A common approach to tackle their complexity is to cluster similar data nodes together in order to create a more comprehensible representation. This enables similarity discovery and hence, extract hidden knowledge within the hypergraphs. Several state-of-the-art algorithms have been proposed for partitioning and clustering of hypergraphs. Nevertheless, several issues remain unanswered, improvement to existing algorithms are possible, especially in scalability and clustering quality. This article presents a concise survey on hypergraph-clustering algorithms with the emphasis on knowledge-representation in systems biomedicine. It also suggests a novel approach to clustering quality by means of cluster-quality metrics which combines expert knowledge and measurable objective distances in existing biological ontology. [less ▲]

Smart Probabilistic Fingerprinting for Indoor Localization over Fog Computing Platforms

in IEEE 5th International Conference on Cloud Networking (CloudNet), Pisa, Italy (2016, October)

Indoor navigation and localization are becoming fundamental services nowadays. WiFi-based solutions such as FingerPrinting (FP) are the most widely adopted techniques for positioning and provide better ... [more ▼]

Indoor navigation and localization are becoming fundamental services nowadays. WiFi-based solutions such as FingerPrinting (FP) are the most widely adopted techniques for positioning and provide better results if compared to other approaches. It requires to compare WiFi Received Signal Strength (RSS) with an pre-computed radio map called fingerprint. The recently proposed Smart Probabilistic FingerPrinting (P-FP) algorithm reduces the computational complexity of the traditional FP approach without any accuracy detriment. On the other hand, fog computing has emerged as a new promising paradigm in the recent years, which extends traditional mobile cloud computing capabilities towards the edge of the network and enables location- aware services. In this paper we propose to offload Smart P-FP computation over a fog platform exploiting a novel distributed algorithm. Performance evaluation validates the effectiveness of the proposed approach with the analysis of: i) the amount of power saved and ii) the efficiency of candidate selection process for offloading. Having 2 or more devices in the vicinity contributing to the computation makes offloading beneficial from a power standpoint. The offloading effectiveness increases with the number of devices willing to contribute and the amount of data to be transferred. Power savings can be as high as 80% if compared with local computation. [less ▲]