• About
    I am an assistant professor of Wireless Networks and Communications at King Saud University , SA, and an Associate Fellow of the UK Higher Education Academy. I received my PhD from School of Engineering and Design, Brunel University, UK. I have many publications in international journals and conferences. My research interests include Cloud and Grid Computing, Bio-inspired Engineering, Wireless and P2P Networks. https://www.linkedin.com/in/hebakurdi
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    Assistant Professor
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    Projects (2)
    This project aims at designing, implementing and evaluating a scheduler for hybrid IaaS clouds. The scheduler will be of a non-clairvoyant scheduling policy, hence being able to deal with realistic demands, where incoming requests for computing resources are unpredictable in terms of timing and nature and running environments are dynamically changing. This is considered as a great improvement over current cloud schedulers as they are of clairvoyant policies assuming availability or predictability of information about incoming jobs and running environments. Clairvoyant policies drastically limit schedulers flexibility in managing cloud infrastructure and considerably increase their running time overheads in order to collect information about jobs and underlying infrastructures or make reasonable prediction about them. The proposed scheduler should be integrated easily with any cloud infrastructure management system, hence it will be developed as open source software making it freely available for utilization and improvement. It has the potential to help in providing public authorities, municipalities, institutions, administrations and businesses with an elaborated tool that is cost effective to seamlessly harness and efficiently utilize resources of their hybrid IaaS clouds. This is important specially in the Kingdom of Saudi Arabia where government’s operations as well as all other sectors are becoming highly dependent upon ICT.
    Research Items
    Reputation and trust are dominant challenges in peer-to-peer (P2P) networks in general and peer-to-peer sensor network (P2PWSN) in particular. Numerous efforts have been devoted to developing reputation management systems that can alleviate the difficulties in finding trustworthy communication partners in P2P networks. There are two main approaches in this field: peer-based reputation systems and message-based reputation systems, each of which has its own advantages and limitations. In this paper, we propose a new reputation system for P2PWSN, AuthenticPeer, which utilizes techniques from both approaches in a way that maximizes their advantages and overcomes their limitations. The proposed system has been tested thoroughly in various simulated P2PWSN environments under various number of files and common threat models. Experimental results illustrate enhanced performance of AuthenticPeer reputation system when compared to two P2P reputation systems, EigenTrust and Incremental EigenTrust, in terms of success rate of good users and fraction of inauthentic downloads, specifically with the threat models: individual malicious, malicious collective, camouflaged collective, and malicious spies, regardless of the number of files in the networks.
    The growth of cloud computing has led to uneconomical energy consumption in data processing, storage, and communications. This is unfriendly to the environment, because of the carbon emissions. Therefore, green IT is required to save the environment. The green cloud computing (GCC) approach is part of green IT; it aims to reduce the carbon footprint of datacenters by reducing their energy consumption. The GCC is a broad and exciting field for research. A plethora of research has emerged aiming to support the GCC vision by improving the utilization of computing resources from different aspects, such as: software optimization, hardware optimization, and network optimization techniques. This paper overviews the approaches to GCC and classifies them. Such a classification assists in comparisons between GCC approaches by identifying the key implementation approaches and the issues related to each.
    Environmental problems are a global issue that everyone should contribute to minimize. As it is difficult for people in charge alone to locate all the cases of the environmental hazards and to address them on time, this paper proposes a cloud based mobile application with a user friendly interface that allows citizens to help their government make their city a better place by reporting environmental violence. The aim is to help the responsible agencies have easy and quick access to notifications provided by the community about environmental issues, so they can be addressed promptly. We choose to customize the mobile application to Riyadh City, the capital of Saudi Arabia. However, the software is generic and can be customized to any other city.
    The visible success of the Peer to Peer (P2P) paradigm is associated with many challenges in finding trustworthy peers as reliable communication partners. Reputation management systems are emerging in the face of these challenges. The EigenTrust reputation management system is among the most known and successful reputation systems. On the other hand, a main drawback of this system is its reliance on a set of pre-trusted peers which causes nodes to centre around them. As a consequence, other peers are ranked low despite being honest, marginalizing their effect in the system. To tackle this problem, this paper proposed enhancing the EigenTrust algorithm by giving peers with high reputation values (honest peers) a role in calculating the global reputation of other peers. Rather than solely depending on the static group of pre-trusted peers, the proposed algorithm, HonestPeer, selects the most reputable nodes, honest peers, dynamically based on the quality of the provided files. This makes HonestPeer more robust to the increase in the number of files and nodes in the system. Through simulation, it has been shown that HonestPeer has successfully maintained higher success rate and lower percentage of inauthentic downloads when compared to the original algorithm.
    Multi-channel MAC protocols have recently obtained considerable attention in wireless networking research because they promise to increase capacity of wireless networks significantly by exploiting multiple frequency bands. In this paper, we do a comparison between IEEE 802.11 and IEEE 802.15.4 and investigate the performance between both using simulations conducted in NS2. This investigation will allow us to determine the feasibility in having IEEE 802.11 being considered as a future medium for wireless sensor networks operating in a multichannel environment at high data rate with streaming data that would be a challenge for IEEE 802.15.4. More so IEEE 802.15.4 will be facing severe challenge to operate in the 2.4GHz frequency band when the IEEE 802.11n becomes popular, operating within the same frequency band. We demonstrate through simulations that IEEE 802.11 perform better with high data rate, streaming constant bit rate and at longer range comparing to 802.15.4 which operates better with small data size at much shorter range. The outcome from this paper will be valuable for our future work in designing a multichannel MAC protocol for contention-based 802.11 WSN.
    Service composition is an evolving approach that increases the number of applications of cloud computing by reusing existing services. However, the available methods focus on generating composite services from a single cloud, which limits the benefits that are derived from other clouds. This paper proposes a novel COMbinatorial optimization algorithm for cloud service COMposition (COM2) that can efficiently utilize multiple clouds. The proposed algorithm ensures that the cloud with the maximum number of services will always be selected before other clouds, which increases the possibility of fulfilling service requests with minimal overhead. The experimental results demonstrate that the COM2 successfully competes with previous multiple cloud service composition algorithms by examining a small number of services—which directly relates to execution time—without compromising the number of combined clouds.
    Nowadays, Cloud Computing is emerging as a replacement for traditional physical hardware computing. Infrastructure-as-a-Service (IaaS) is one of the fundamental cloud computing models, where users can request virtual resources with various capabilities whenever needed. Haizea is a well-known open source virtual machine scheduler for IaaS clouds, offering four types of leases, i.e. requests for virtual machines: immediate, Best-effort (BE), Dead Line Sensitive (DLS) and Advanced Reservations (AR). One of Haizea disadvantages is that BE leases will be preempted whenever their resources are required by AR or Immediate leases. Thus, when the system has a high number of AR or immediate lease requests, BE leases will wait for a long time, or even forever, resulting in what is known as the starvation problem. To prevent this problem, some policies suggest rejecting AR lease requests if BE leases have been waiting for a long time. However, this would result in decreased resource utilization and customer satisfaction. Therefore, this paper proposes an enhancement to Haizea scheduler that reduces the starvation problem and request rejection rate, while increasing resource utilization of the cloud infrastructure. Simulation results demonstrated that the proposed algorithm has outperformed pure Haizea in all of the aforementioned performance measures.
    Quality of Service (QoS) support in private clouds is a challenging process because of the limitations of available resources and the high rate of received jobs, which leads to an NP hard scheduling problem. In private clouds, resource owners are usually interested in maximizing their resource utilization and completion rates while minimizing the turnaround time of their jobs, which complicates the scheduling problem even more. Haizea is an eminent cloud scheduler that offers high performance in terms of job turnaround time and completion rate. However, Haizea, and cloud schedulers in general, suffer from low resource utlization. Additionally, cloud schedulers usually consider only end users’ demands, while providers’ demands are entirely neglected. This is because an infinite pool of resources is assumed, which is difficult to achieve and simply not true in private clouds. Conversely, Condor, the eminent High Throughput Computing (HTP) scheuler, is known for addressing these shortcomings by formulating owner's and user's requirements as a logical expression evaluated based on the context which result is high resource utilization. Unfortunatly, this comes with the price of long execution time. As each of Haizea and Condor has its own advantages and limitations, in this paper, we propose a hybrid Haizea and Condor approach (HHCS) which utilizes techniques from both schedulers in a way that maximizes their advantages and overcomes their limitations. The proposed approach has been tested thoroughly in a simulated private cloud environment under various numbers of nodes and jobs. Experimental results illustrated an enhanced performance in terms of resources utilization without compromising the job turnaround time or the job completion rate.
    Nowadays, videos are extensively used in different aspects of our lives such as entertainment, education and social networking. Therefore, video sharing is expected to dramatically grow in the future. There are important factors that affect the way of how to transmit videos over the clouds such as encoding techniques and compressions. In this paper, we study how distance affects QoS of real time applications such as video streaming services by means of simulation. The results show that distance has a significant impact on response time and packet end to end variation. However, throughput is not affected by far distances. In addition, the impact varies from one application to another as well for the same application.
    Evacuation process is a critical problem, in which the evacuation time is an important issue. This paper presents an evacuation plan generator using the Genetic Algorithm (GA) to reduce the overall evacuation time. The proposed work guides the evacuees to suitable exit doors based on the entered facility configuration. The results obtained show that the overall evacuation time was reduced using our GA plan generator.
    Resource discovery in unstructured peer-to-peer (P2P) networks is a challenging problem. With the absence of structure, developing a good and simple search algorithm is a key issue. Many algorithms have already emerged to address this issue, but there are always trade-offs between various performance measures. In this paper, two hybrid algorithms for unstructured P2P networks are proposed. The first is a combination of Flooding and Random Walk approaches, while the second combines Flooding with Ran- dom Walk with Neighbours Table. The performance of each algorithm was investigated in a simulated unstructured P2P network under variable conditions. Simulation results showed that hybrid algorithms provide the most balanced performance regarding the average number of hops, average search time and number of failures when compared to the basic resource discovery algorithms.
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