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Advances and commoditization of media generation devices enable capturing and sharing of any special event by multiple attendees. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud-based Multi-View Crowdsourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. The CMVCS is a complex system having many research challenges. In this paper, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming. Moreover, we propose a Fairness-Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large-scale system (Twitch). This work was supported by NPRP through the Qatar National Research Fund (a member of Qatar Foundation) under Grant 8-519-1-108. Scopus

Recently, the growing demand for rich multimedia content such as Video on Demand (VoD) has made the data transmission from content delivery networks (CDN) to end-users quite challenging. Edge networks have been proposed as an extension to CDN networks to alleviate this excessive data transfer through caching and to delegate the computation tasks to edge servers. To maximize the caching efficiency in the edge networks, different Mobile Edge Computing (MEC) servers assist each others to effectively select which content to store and the appropriate computation tasks to process. In this paper, we adopt a collaborative caching and transcoding model for VoD in MEC networks. However, unlike other models in the literature, different chunks of the same video are not fetched and cached in the same MEC server. Instead, neighboring servers will collaborate to store and transcode different video chunks and consequently optimize the limited resources usage. Since we are dealing with chunks caching and processing, we propose to maximize the edge efficiency by studying the viewers watching pattern and designing a probabilistic model where chunks popularities are evaluated. Based on this model, popularity-aware policies, namely Proactive caching policy (PcP) and Cache replacement Policy (CrP), are introduced to cache only highest probably requested chunks. In addition to PcP and CrP, an online algorithm (PCCP) is proposed to schedule the collaborative caching and processing. The evaluation results prove that our model and policies give better performance than approaches using conventional replacement policies. This improvement reaches up to 50% in some cases., Submitted to International Conference on Wireless Communications and Networking (WCNC) 2019

Video streaming has become a leading consumer of network resources in the last decade. Despite considerable developments, video content providers still face major challenges, which include minimizing data transfer from Content Delivery Network (CDN) or origin servers, CDN cost, and video startup delays. Recent edge computing technologies, such as Mobile Edge Computing (MEC) introduces new opportunities for Radio Access Networks (RANs) by providing computing and storage resources at the Mobile Base Stations (MBSs). Caching and processing videos at the edge networks relieve excessive data transfers over the backhaul links and minimize the viewers perceived delay. Collaborative caching and processing strategies have been proposed to efficiently utilize the edge resources, where neighboring MEC servers share the cached videos. However, such strategies introduce new challenges due to excessive backhaul links utilization for video sharing and limited resources. We propose a collaborative joint caching and processing strategy using the X2 network interface for sharing video data among multiple caches. Our design aims to minimize: (a) backhaul links usage for sharing video data, (b) network usage in transferring data from the CDN, (c) the viewer perceived delay, and (d) CDN cost. We also propose to fetch the higher bitrate version video from the origin/CDN servers and transcode it to the requested version on the fly to effectively use the Adaptive Bit Rate (ABR) streaming and online transcoding. This joint caching and processing approach is formulated as a minimization problem, subject to storage, processing, and bandwidth constraints. We also propose an online greedy algorithm that controls video transcoding, sharing using the X2 or backhaul links, and manages video caching and removing at the edge caches. Simulation results prove a better performance of our proposed algorithm compared to the recent edge caching approaches in terms of cost, average delay, cache removal, and cache hit ratio for different configurations. - 2019 Elsevier Ltd This publication was made possible by NPRP grant 8-519-1-108 from the Qatar National Research Fund (a member of Qatar Foundation). The findings achieved herein are solely the responsibility of the author(s). Appendix A Scopus

Benefitting from large-scale training datasets and the complex training network, Convolutional Neural Networks (CNNs) are widely applied in various fields with high accuracy. However, the training process of CNNs is very time-consuming, where large amounts of training samples and iterative operations are required to obtain high-quality weight parameters. In this paper, we focus on the time-consuming training process of large-scale CNNs and propose a Bi-layered Parallel Training (BPT-CNN) architecture in distributed computing environments. BPT-CNN consists of two main components: (a) an outer-layer parallel training for multiple CNN subnetworks on separate data subsets, and (b) an inner-layer parallel training for each subnetwork. In the outer-layer parallelism, we address critical issues of distributed and parallel computing, including data communication, synchronization, and workload balance. A heterogeneous-aware Incremental Data Partitioning and Allocation (IDPA) strategy is proposed, where large-scale training datasets are partitioned and allocated to the computing nodes in batches according to their computing power. To minimize the synchronization waiting during the global weight update process, an Asynchronous Global Weight Update (AGWU) strategy is proposed. In the inner-layer parallelism, we further accelerate the training process for each CNN subnetwork on each computer, where computation steps of convolutional layer and the local weight training are parallelized based on task-parallelism. We introduce task decomposition and scheduling strategies with the objectives of thread-level load balancing and minimum waiting time for critical paths. Extensive experimental results indicate that the proposed BPT-CNN effectively improves the training performance of CNNs while maintaining the accuracy. This research is partially funded by the National Key R&D Program of China (Grant No. 2016YFB0200201), the National Outstanding Youth Science Program of National Natural Science Foundation of China (Grant No. 61625202), the International Postdoctoral Exchange Fellowship Program (Grant No. 2018024), and the China Postdoctoral Science Foundation funded project (Grant No. 2018T110829). This work is also supported in part by NSF through grants IIS-1526499, IIS-1763325, CNS-1626432, and NSFC 61672313. Scopus

Cloud computing services have gained tremendous popularity and widespread adoption due to their flexible and on-demand nature. Cloud computing services are hosted in Cloud Data Centers (CDC) that deploy thousands of computation, storage, and communication devices leading to high energy utilization and carbon emissions. Renewable energy resources replace fossil fuels based grid energy to effectively reduce carbon emissions of CDCs. Moreover, waste heat generated from electronic components can be utilized in absorption based cooling systems to offset cooling costs of data centers. However, data centers need to be located at ideal geographical locations to reap benefits of renewable energy and waste heat recovery options. Modular Data Centers (MDC) can enable energy as a location paradigm due to their shippable nature. Moreover, workload can be transferred between intelligently placed geographically dispersed data centers to utilize renewable energy available elsewhere with virtual machine migration techniques. However, adoption of aforementioned sustainability techniques and technologies opens new challenges, such as, intermittency of power supply from renewable resources and higher capital costs. In this paper, we examine sustainable CDCs from various aspects to survey the enabling techniques and technologies. We present case studies from both academia and industry that demonstrate favorable results for sustainability measures in CDCs. Moreover, we discuss state-of-the-art research in sustainable CDCs. Furthermore, we debate the integration challenges and open research issues to sustainable CDCs.

Covert channels are widely considered as a major risk of information leakage in various operating systems, such as desktop, cloud, and mobile systems. The existing works of modeling covert channels have mainly focused on using finite state machines (FSMs) and their transforms to describe the process of covert channel transmission. However, a FSM is rather an abstract model, where information about the shared resource, synchronization, and encoding/decoding cannot be presented in the model, making it difficult for researchers to realize and analyze the covert channels. In this paper, we use the high-level Petri Nets (HLPN) to model the structural and behavioral properties of covert channels. We use the HLPN to model the classic covert channel protocol. Moreover, the results from the analysis of the HLPN model are used to highlight the major shortcomings and interferences in the protocol. Furthermore, we propose two new covert channel models, namely: (a) two channel transmission protocol (TCTP) model and (b) self-adaptive protocol (SAP) model. The TCTP model circumvents the mutual inferences in encoding and synchronization operations; whereas the SAP model uses sleeping time and redundancy check to ensure correct transmission in an environment with strong noise. To demonstrate the correctness and usability of our proposed models in heterogeneous environments, we implement the TCTP and SAP in three different systems: (a) Linux, (b) Xen, and (c) Fiasco.OC. Our implementation also indicates the practicability of the models in heterogeneous, scalable and flexible environments.

Recent growth in the processing and memory resources of mobile devices has fueled research within the field of mobile virtualization. Mobile virtualization enables multiple persona on a single mobile device by hosting heterogeneous operating systems (OSs) concurrently. However, adding a virtualization layer to resource-constrained mobile devices with real-time requirements can lead to intolerable performance overheads. Hardware virtualization extensions that support efficient virtualization have been incorporated in recent mobile processors. Prior to hardware virtualization extensions, virtualization techniques that are enabled by performance prohibitive and resource consuming software were adopted for mobile devices. Moreover, mobile virtualization solutions lack standard procedures for device component sharing and interfacing between multiple OSSs. The objective of this article is to survey software- and hardware-based mobile virtualization techniques in light of the recent advancements fueled by the hardware support for mobile virtualization. Challenges and issues faced in virtualization of CPU, memory, I/O, interrupt, and network interfaces are highlighted. Moreover, various performance parameters are presented in a detailed comparative analysis to quantify the efficiency of mobile virtualization techniques and solutions.

The phonon variations, photoluminescence emission and electrical properties were systematically studied in Er3+ doped Bi3YO6 (Bi3Y1−xErxO6) oxide-ion conductors. The unit cell parameters (a and V) undergo a first increase and subsequent decrease trend with x, which is attributed to the fluctuations of the O occupation at 48i site. Five fundamental and new phonon mode splitting by Er3+ were observed by reflective FT-IR Reflectance spectroscopy, suggesting the locally structural distortion associated with the Er3+ octahedral occupancy. The Bi3Y1−xErxO6 ceramics of x = 0.2 exhibits the most intensive photoluminescence emission and a smallest activation energy of 1.1 eV. The high oxide-ion conductivity (>0.02 S/cm at 600 °C) was achieved, which can rival the most studied Yttria-stabilized zirconia (YSZ) system. The conductivities for all the Bi3Y1−xErxO6 ceramics present a curvature above 550 °C and a sudden decrease at x = 0.4. These changes in the conductivity were described intern of lattice vibrations for given oxide-ion systems.

It is crucial to provide compatible treatment schemes for a disease according to various symptoms at different stages. However, most classification methods might be ineffective in accurately classifying a disease that holds the characteristics of multiple treatment stages, various symptoms, and multi-pathogenesis. Moreover, there are limited exchanges and cooperative actions in disease diagnoses and treatments between different departments and hospitals. Thus, when new diseases occur with atypical symptoms, inexperienced doctors might have difficulty in identifying them promptly and accurately. Therefore, to maximize the utilization of the advanced medical technology of developed hospitals and the rich medical knowledge of experienced doctors, a Disease Diagnosis and Treatment Recommendation System (DDTRS) is proposed in this paper. First, to effectively identify disease symptoms more accurately, a Density-Peaked Clustering Analysis (DPCA) algorithm is introduced for disease-symptom clustering. In addition, association analyses on Disease-Diagnosis (D-D) rules and Disease-Treatment (D-T) rules are conducted by the Apriori algorithm separately. The appropriate diagnosis and treatment schemes are recommended for patients and inexperienced doctors, even if they are in a limited therapeutic environment. Moreover, to reach the goals of high performance and low latency response, we implement a parallel solution for DDTRS using the Apache Spark cloud platform. Extensive experimental results demonstrate that the proposed DDTRS realizes disease-symptom clustering effectively and derives disease treatment recommendations intelligently and accurately.

Protein interactions constitute the fundamental building block of almost every life activity. Identifying protein communities from Protein-Protein Interaction (PPI) networks is essential to understand the principles of cellular organization and explore the causes of various diseases. It is critical to integrate multiple data resources to identify reliable protein communities that have biological significance and improve the performance of community detection methods for large-scale PPI networks. In this paper, we propose a Multi-source Learning based Protein Community Detection (MLPCD) algorithm by integrating Gene Expression Data (GED) and a parallel solution of MLPCD using cloud computing technology. To effectively discover the biological functions of proteins that participating in different cellular processes, GED under different conditions is integrated with the original PPI network to reconstruct a Weighted-PPI (WPPI) network. To flexibly identify protein communities of different scales, we define community modularity and functional cohesion measurements and detect protein communities from WPPI using an agglomerative method. In addition, we respectively compare the detected communities with known protein complexes and evaluate the functional enrichment of protein function modules using Gene Ontology annotations. Moreover, we implement a parallel version of the MLPCD algorithm on the Apache Spark platform to enhance the performance of the algorithm for large-scale realistic PPI networks. Extensive experimental results indicate the superiority and notable advantages of the MLPCD algorithm over the relevant algorithms in terms of accuracy and performance., Comment: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2018

Network congestion poses significant impact on application performance and network throughput in Network-on-Chip (NoC) based systems. Efficient core mapping can significantly reduce the network contention and end-to-end latency leading to improved application performance in NoC based multicore systems. In this work, we propose a Congestion-Aware (CA) core mapping heuristic based on betweenness centrality metric. The proposed CA algorithm optimizes core mapping using betweenness centrality of links to alleviate congestion from highly loaded NoC links. We use modified betweenness centrality metric to identify highly loaded NoC links that are more prone to congestion. In contrast to traditional betweenness centrality metric, which is generally used to measure the structural/static characteristics of the system, the adapted betweenness centrality metric utilizes the volume of communication traversing through the edges (NoC links) to capture the operational and dynamic characteristics of the system. The experimental results demonstrate that our proposed algorithm achieved significantly lower average channel load and end-to-end latency compared to the baseline First Fit (FF) and Nearest Neighbor (NN) core mapping algorithms. Particularly, CA algorithm achieved up to 46% and 12% lower channel load and end-to-end latency compared to FF algorithm, respectively. Moreover, proposed algorithm exhibits an average gain of 32% in terms of reduced network energy consumption compared to the baseline configuration. 2016 Elsevier B.V. Scopus

Multi-view videos are composed of multiple video streams captured simultaneously using multiple cameras from various angles (different viewpoints) of a scene. Multi-view videos offer more appealing and realistic view of the scene leading to higher user satisfaction and enjoyment. However, displaying realistic and live multiview scenes captured from a limited view-points faces multiple challenges, including excessive number of precise synchronization of many cameras, color differences among cameras, large bandwidth, computation and storage requirements, and complex encoding. current multi-view video setups are very limited and based in studios. We propose a novel system to collect individual video streams (views) captured for the same event by multiple attendees, and combine them into multi-view videos, where viewers can watch the event from various angles, taking crowdsourced media streaming to a new immersive level. The proposed system is called Cloud based Multi-View Crowdsourced Streaming (CMVCS), and it delivers multiple views of an event to viewers at the best possible video representation based on each viewer's available bandwidth. CMVCS is a complex system having many research challenges. In this study, we focus on resource allocation of the CMVCS system. The objective of the study is to maximize the overall viewer satisfaction by allocating available resources to transcode views in an optimal set of representations, subject to computational and bandwidth constraints. We choose the video representation set to maximize QoE using Mixed Integer Programming (MIP). Moreover, we propose a Fairness Based Representation Selection (FBRS) heuristic algorithm to solve the resource allocation problem efficiently. We compare our results with optimal and Top-N strategies. The simulation results demonstrate that FBRS generates near optimal results and outperforms the state-of-the-art Top-N policy, which is used by a large scale system (Twitch). Moreover, we consider region based distributed datacenters to minimize the overall end-to-end latency. To further enhance the viewers’ satisfaction level and Quality of Experience (QoE), we propose an edge based cooperative caching and online transcoding strategy to minimize the delay and backhaul bandwidth consumption. Our main research contributions are: We present the design and architecture of a Cloud based Multi-View Crowdsourced Streaming (CMVCS) system that allows viewers to experience the captured events from various angles. We propose a QoE metric to determine the overall user satisfaction based on the received view representation, viewers’ bandwidth capability, and end-to-end latency between viewer and transcoding site. We formulate a Mixed Integer Programming (MIP) optimization problem for multi-region distributed resource allocation to choose the optimal set of views and representations to maximize QoE in constrained settings. We propose a fairness based heuristic algorithm to find near optimal resource allocation efficiently. We propose an edge computing based video caching and online transcoding strategy to minimize delay and backhaul network consumption. We use multiple real-world traces to simulate various scenarios and show the efficiency of the proposed solution.

One of the major challenges in the high performance computing (HPC) clusters is intelligent power management to improve energy efficiency. The key contribution of the presented work is the modeling of a Power Aware Job Scheduler (PAJS) for HPC clusters, such that the: (a) threshold voltage is adjusted judiciously to achieve energy efficiency and (b) response time is minimized by scaling the supply voltage. The PAJS considers the symbiotic relationship between power and performance and caters the optimization of the both, simultaneously. The key novelty in our work is utilization of the dynamic threshold-voltage scaling (DTVS) for the reduction of cumulative power utilized by each node in the cluster. Moreover, to enhance the performance of the resource scheduling strategies in this work, independent tasks within a job are scheduled to most suitable computing nodes (CNs). This paper analyzes and compares eight scheduling techniques in terms of energy consumption and makespan. Primarily, the most suitable dynamic voltage scaling (DVS) level adhering to the deadline is identified for each of the CNs by the scheduling heuristics. Afterwards, the DTVS is employed to scale down the static, as well as dynamic power by regulating the supply and bias voltages. Finally, the per node threshold scaling is used attain power saving. Our simulation results affirm that the proposed methodology significantly reduces the energy consumption using the DTVS.

The recent concept of "Health Insurance Marketplace" introduced to facilitate the purchase of health insurance by comparing different insurance plans in terms of price, coverage benefits, and quality designates a key role to the health insurance providers. Currently, the web based tools available to search for health insurance plans are deficient in offering personalized recommendations based on the coverage benefits and cost. Therefore, anticipating the users' needs we propose a cloud based framework that offers personalized recommendations about the health insurance plans. We use the Multi-attribute Utility Theory (MAUT) to help users compare different health insurance plans based on coverage and cost criteria, such as: (a) premium, (b) co-pay, (c) deductibles, (d) co-insurance, and (e) maximum benefit offered by a plan. To overcome the issues arising possibly due to the heterogeneous data formats and different plan representations across the providers, we present a standardized representation for the health insurance plans. The plan information of each of the providers is retrieved using the Data as a Service (DaaS). The framework is implemented as Software as a Service (SaaS) to offer customized recommendations by applying a ranking technique for the identified plans according to the user specified criteria. We present a cloud based health insurance plan recommendation system.We propose a standard ontological representation for all the health insurance plans.An algorithm to determine the similarities between the user requirements and plans is presented.We propose a ranking technique based on the Multi-attribute Utility Theory (MAUT).

Video streaming and computer games are among the most popular and highest bandwidth consuming media in the Internet. Video contents consume around 70% of the total bandwidth usage in the Internet today. Advancements in media generation tools, high processing power, and high speed connectivity have enabled generation of live, interactive, multi-view media generation. Cognitive assisted and online multi-player gaming have unlocked new horizons for gaming experience. However, such interactive gaming, and multi-view and 360-degree view videos, are currently limited by delay intolerance and excessive bandwidth usage. Edge computing is the name of a set of new technologies, such as cloudlets, micro data centers, fog, and mobile edge computing. It aims to provide storage and computational resources near to user at the network edge, to minimize latency and response time. Edge computing is foreseen as a significant enabler of Internet of Every Thing (IoET) era by extending the cloud services and resources at the end of the network to deliver very low latency and real-time communication. It can provide significant services to video and gaming applications and enable new stream of interactive multimedia era. In this paper, we highlight some of the potentials and prospects of edge computing for interactive media, and present some preliminary works in the area. We shed light on how edge computing can be used to tackle various challenges faced by todays interactive media application. We also present the benefits of using edge computing to save cost, bandwidth, and energy in multimedia applications, video streaming, and transcoding. Qatar National Research Fund,QNRF,Qatar Foundation,QF Scopus

With the emergence of the big data age, the issue of how to obtain valuable knowledge from a dataset efficiently and accurately has attracted increasingly attention from both academia and industry. This paper presents a Parallel Random Forest (PRF) algorithm for big data on the Apache Spark platform. The PRF algorithm is optimized based on a hybrid approach combining dataparallel and task-parallel optimization. From the perspective of data-parallel optimization, a vertical data-partitioning method is performed to reduce the data communication cost effectively, and a data-multiplexing method is performed is performed to allow the training dataset to be reused and diminish the volume of data. From the perspective of task-parallel optimization, a dual parallel approach is carried out in the training process of RF, and a task Directed Acyclic Graph (DAG) is created according to the parallel training process of PRF and the dependence of the Resilient Distributed Datasets (RDD) objects. Then, different task schedulers are invoked for the tasks in the DAG. Moreover, to improve the algorithm's accuracy for large, high-dimensional, and noisy data, we perform a dimension-reduction approach in the training process and a weighted voting approach in the prediction process prior to parallelization. Extensive experimental results indicate the superiority and notable advantages of the PRF algorithm over the relevant algorithms implemented by Spark MLlib and other studies in terms of the classification accuracy, performance, and scalability. With the expansion of the scale of the random forest model and the Spark cluster, the advantage of the PRF algorithm is more obvious. Scopus

The main objective of this empirical study is to determine the affect of leadership behavior with the organizational innovative techniques and managerial practices on the businesses’ growth in Pakistan particularly in textile sector. Firstly, the study discusses the relationship between leadership behavior and organizational innovation, between organizational behavior and managerial practices. Furthermore, this relationship is also measured between organizational innovation and business growth and between managerial practices and business growth. Secondly, the study investigates that these relationship are strongly reinforced in the firm with the support of theoretical evidences made but not determined theoretically in the prior literature. Managerial practices take place in textile community for interaction in which organizational cognition is developed and expanded dynamically between the cognitive and behavioral changes. Organizations with greater organizational acquisition and practices create a learning network that will become easier to learn that what they use to learn and to initiate, enabling the firm to preserve its effective and competitive aversion as a textile technological center. Nomothetic research method is applied to test the hypothesis, developed for this study. The research examined a sample of one hundred ten textile firms with sample unit of senior managers, executives, administrative and other-level managers. A research model is formulated for the study and hypotheses are tested with the use of structural equation model. This study purposes that the leaders of firms initiate a transformational role, which facilitate organizational innovation and managerial performances and consequently enhance the business growth especially in textile firms.

Data centers are growing exponentially (in number and size) to accommodate the escalating user and application demands. Likewise, the concerns about the environmental impacts, energy needs, and electricity cost of data centers are also growing. Network infrastructure being the communication backbone of the data center plays a pivotal role in the data center’s scalability, performance, energy consumption, and cost. Research community is endeavoring hard to overcome the challenges faced by the legacy Data Center Networks (DCNs). Serious efforts have been made to handle the problems in various DCN areas. This survey presents significant insights to the state-of-the-art research conducted pertaining to the DCN domain along with a detailed discussion of the energy efficiency aspects of the DCNs. The authors explored: (a) DCN architectures (electrical, optical, and hybrid), (b) network traffic management and characterization, (c) DCN performance monitoring, (d) network-aware resource allocation, (e) DCN experimentation techniques, and (f) energy efficiency. The survey presents an overview of the ongoing research in the broad domain of DCNs and highlights the challenges faced by the DCN research community.

Next-generation datacenters (DCs) built on virtualization technologies are pivotal to the effective implementation of the cloud computing paradigm. To deliver the necessary services and quality of service, cloud DCs face major reliability and robustness challenges.

The information and communication technology (ICT) sector has grown exponentially in the recent years. An essential component of the ICT organizations is constituted by the data centers that are densely populated with redundant servers and communicational links to ensure the provision of 99.99 % availability of services; a fact responsible for the heavy energy consumption by data centers. For energy economy, the redundant elements can be powered off based on the current workload within the data center. We propose a Data Center-wide Energy-Efficient Resource Scheduling framework (DCEERS) that schedules data center resources according to the current workload of the data center. Minimum subset of resources to service the current workload are calculated by solving the Minimum Cost Multi Commodity Flow (MCMCF) using the Benders decomposition algorithm. The Benders decomposition algorithm is scalable: it solves the MCMCF problem in linear time for large data center environments. Our simulated results depict that DCEERS achieves better energy efficiency than the previous data center resource scheduling methodologies.

Effective patient queue management to minimize patient wait delays and patient overcrowding is one of the major challenges faced by hospitals. Unnecessary and annoying waits for long periods result in substantial human resource and time wastage and increase the frustration endured by patients. For each patient in the queue, the total treatment time of all the patients before him is the time that he must wait. It would be convenient and preferable if the patients could receive the most efficient treatment plan and know the predicted waiting time through a mobile application that updates in real time. Therefore, we propose a Patient Treatment Time Prediction (PTTP) algorithm to predict the waiting time for each treatment task for a patient. We use realistic patient data from various hospitals to obtain a patient treatment time model for each task. Based on this large-scale, realistic dataset, the treatment time for each patient in the current queue of each task is predicted. Based on the predicted waiting time, a Hospital Queuing-Recommendation (HQR) system is developed. HQR calculates and predicts an efficient and convenient treatment plan recommended for the patient. Because of the large-scale, realistic dataset and the requirement for real-time response, the PTTP algorithm and HQR system mandate efficiency and low-latency response. We use an Apache Spark-based cloud implementation at the National Supercomputing Center in Changsha to achieve the aforementioned goals. Extensive experimentation and simulation results demonstrate the effectiveness and applicability of our proposed model to recommend an effective treatment plan for patients to minimize their wait times in hospitals. Scopus

Data centers are experiencing a remarkable growth in the number of interconnected servers. Being one of the foremost data center design concerns, network infrastructure plays a pivotal role in the initial capital investment and ascertaining the performance parameters for the data center. Legacy data center network (DCN) infrastructure lacks the inherent capability to meet the data centers growth trend and aggregate bandwidth demands. Deployment of even the highest-end enterprise network equipment only delivers around 50% of the aggregate bandwidth at the edge of network. The vital challenges faced by the legacy DCN architecture trigger the need for new DCN architectures, to accommodate the growing demands of the ‘cloud computing’ paradigm. We have implemented and simulated the state of the art DCN models in this paper, namely: (a) legacy DCN architecture, (b) switch-based, and (c) hybrid models, and compared their effectiveness by monitoring the network: (a) throughput and (b) average packet delay. The presented analysis may be perceived as a background benchmarking study for the further research on the simulation and implementation of the DCN-customized topologies and customized addressing protocols in the large-scale data centers. We have performed extensive simulations under various network traffic patterns to ascertain the strengths and inadequacies of the different DCN architectures. Moreover, we provide a firm foundation for further research and enhancement in DCN architectures. Copyright ？ 2012 John Wiley & Sons, Ltd.

Energy consumption of the Information and Communication Technology (ICT) sector has grown exponentially in recent years. A major component of the today’s ICT is constituted by the data centers which have experienced an unprecedented growth in their size and population, recently. The Internet giants like Google, IBM and Microsoft house large data centers for cloud computing and application hosting. Many studies, on energy consumption of data centers, point out to the need to evolve strategies for energy efficiency. Due to large-scale carbon dioxide (\(\mathrm{CO}_2\)) emissions, in the process of electricity production, the ICT facilities are indirectly responsible for considerable amounts of green house gas emissions. Heat generated by these densely populated data centers needs large cooling units to keep temperatures within the operational range. These cooling units, obviously, escalate the total energy consumption and have their own carbon footprint. In this survey, we discuss various aspects of the energy efficiency in data centers with the added emphasis on its motivation for data centers. In addition, we discuss various research ideas, industry adopted techniques and the issues that need our immediate attention in the context of energy efficiency in data centers.

This chapter contains sections titled: Introduction Problem Formulation Proposed Algorithms Simulations, Results, and Discussion Related Works Conclusion References ]]>

The Information and Communication Technology sector is considered to be a major consumer of energy and has become an active participant in Green House Gas emissions. Lots of efforts have been devoted to make network infrastructure and network protocols power-aware and green. Among these efforts, Adaptive Link Rate (ALR) is one of the most widely discussed approaches. This survey highlights the most recent ALR approaches with a brief taxonomy and the state of the art.

Wireless mesh networks (WMNs) have gained considerable popularity in recent times thanks to their self-healing, self-organizing, and self-configuring nature. Because of their ability to provide high throughput and minimum packet delay, WMNs are considered to be favorable for broadband applications. For such applications, WMNs employ multiple channels, which give rise to issues like channel assignment, load balancing, and interference avoidance. Most of these issues fall into two broad categories, namely routing and channel assignment. For routing, we propose a novel proactive protocol, the stable-path multi-channel routing protocol (SMRP). Our proposed solution, to address channel assignment, is the extended level-based channel assignment (ELCA) scheme. SMRP is designed to work in combination with ELCA in order to minimize interference and balance the load among the underlying nodes. Simulation results show enhanced throughput and minimal packet delay as compared with the contemporary schemes. Copyright © 2011 John Wiley & Sons, Ltd.

Cloud computing has experienced enormous popularity and adoption in many areas, such as research, medical, web, and e-commerce. Providers, like Amazon, Google, Microsoft, and Yahoo have deployed their cloud services for use. Cloud computing pay-as-you-go model, on demand scaling, and low maintenance cost has attracted many users. The widespread adoption of cloud paradigm upshots various challenges. The legacy data center and cloud architectures are unable to handle the escalating user demands. Therefore, new data center network architectures, policies, protocols and topologies are required. However, new solutions must be tested thoroughly, before deployment within a real production environment. As the experimentation and testing is infeasible in the production environment and real cloud setup, therefore, there is an indispensable need for simulation tools that provide ways to model and test applications, and estimate cost, performance, and energy consumption of services and application within cloud environment. Simulation tools providing cloud simulation environments currently are limited in terms of features and realistic cloud setups, focus on a particular problem domain, and require tool-specific modeling, which can be frustrating and time consuming. This paper aims to provide a detailed comparison of various cloud simulators, discuss various offered features, and highlight their strengths and limitations. Moreover, we also demonstrate our work on a new cloud simulator "Nutshell", which offers realistic cloud environments and protocols. The Nutshell is designed to diminish flaws and limitations of available cloud simulators, by offering: (a) multiple datacenter network architectures, like three-tier, fat-tree, and dcell, (b) fine grained network details, (c) realistic cloud traffic patterns, (d) congestion control strategies and analysis, (e) energy consumption, (f) cost estimation, and (g) data center monitoring and analysis. Flexibility to stretch the architectures to simulate smart city IT infrastructure.

Cloud computing is emerging computational paradigm that provide resources to perform complex tasks. Large datacenters are used to facilitate the incoming tasks by providing resources, such as CPU, memory, storage, and network bandwidth. Datacenters offers hosting and processing of complex tasks and services, where servers and cooling systems consume huge amount of energy. Excessive amount of energy consumption results in large power bills and Green House gases (GHG) emissions. Substantial amount of energy can be saved by powering down servers that are idle. Various authors have come up with energy efficient solutions that try to minimize overall energy consumption. One set of energy-efficient solutions is based on best fit decreasing (BFD) algorithm. In this paper, we evaluate the performance of existing energy efficient BFD algorithms based on various workloads and migration techniques. Moreover, considering the significance of Service Level Agreement (SLA), we introduce SLA-awareness in traditional BFD algorithm to minimize the SLA violation. We present the analysis and observations for each of the considered techniques based on total energy consumption, average SLA violations, and SLA performance degradation due to migration.

The performance demands of modern computing applications have enormously accelerated the power density of on-chip devices. Consequently, not only the operational budget has increased exponentially, but also the temperature has experienced an alarming increase rate. The high chip temperatures pose serious threat to devices reliability. The aforementioned challenges necessitate the requirement of realizing efficient mapping methodologies to overcome resource exploitation. We present a convex optimization model that maximizes the power savings while satisfying the task completion deadline and maintaining chip temperature within acceptable bounds. Optimization is achieved by varying the frequency of operation of the core. The salient feature of the devised approach is the bounds of operation for the desired objectives. The acceptable operation limit manifests the elasticity of the computing resources in an autonomic manner to achieve a Pareto front for the optimized solutions. To validate our proposed method, extensive simulations are performed on a variable workload.

Data centers constitute the foundations of cloud computing. Increased adoption of cloud computing in various sectors of life necessitates the data center growth. Cloud providers are facing numerous challenges in data centers design and operation. This chapter presents the modeling and simulation of the major data center network architectures using ns-3. The chapter details the three major DCN architectures and their implementation details.

With the availability of low cost, on demand, and pay-as-you-go model based utility computing services offered by clouds, multiple businesses consider moving their services to the cloud. Typically, the clouds comprise of geographically distributed data centers connected through a high speed network. Most of the research and development is focused on cloud services, applications, and security issues; however, very limited effort has been devoted to address energy efficiency, scalability, and high-speed inter and intra-data center communication. We present CloudNetSim++, a modeling and simulation toolkit to facilitate simulation of distributed data center architectures, energy models, and high speed data centers' communication network. The CloudNetSim++ is designed to allow researchers to incorporate their custom protocols and, applications, to analyze under realistic data center architectures with network traffic patterns. CloudNetSim++ is the first cloud computing simulator that uses real network physical characteristics to model distributed data centers. CloudNetSim++ provides a generic framework that allows users to define SLA policy, scheduling algorithms, and modules for different components of data centers without worrying about low level details with ease and minimum effort.

The concerns about environmental impacts, electricity cost, and energy needs of data centers are mounting. These concerns have given significant impetus to consider energy efficiency as one of the major data center design parameters for researchers and IT executives. Tremendous growth in the size and computing demands of data centers mandate scalable, fault tolerant, and energy efficient network infrastructure within data centers. In the recent years, new Data Center Network (DCN) architectures have aptly proposed a viable solution to the issues posed by the legacy DCNs. New DCN architectures are required to be energy efficient and energy proportional, besides dealing with other identified design drawbacks, such as scalability, fault tolerance, end-to-end bandwidth, and non-agility. In this article, we will deliberate on the various DCN architectures, their inherent problems, and the potentials to achieve energy efficiency. We will also shed light on myriad techniques that may be suitable to make the DCN architectures more energy efficient and energy proportional. Finally, we will outline future directions.

With the advent of High Performance Computing (HPC) in the large-scale parallel computational environment, job scheduling and resource allocation techniques are required to deliver the Quality of Service (QoS) and resource management. Therefore, job scheduling on a large-scale parallel system has been studied to: (a) minimize the queue time and response time, and (b) maximize the overall system utilization. We compare and analyze thirteen job scheduling policies to analyze their behavior. The set of job scheduling policies include: (a) priority-based policies, (b) first fit, (c) backfilling techniques, and (d) window-based policies. All of the policies are extensively simulated and compared. A real data center workload comprised of 22385 jobs is used for simulation. We analyze the: (a) queue time, (b) response time, and (c) slowdown ratio to evaluate the policies. Moreover, we present a comprehensive workload characterization that can be used as a tool for optimizing system's performance and for scheduler design. We investigate four categories of the workload characteristics including: (a) Narrow, (b) Wide, (c) Short, and (d) Long for detailed analysis of the schedulers' performance. This study highlights the strengths and weakness of various job scheduling polices and helps to choose an appropriate job scheduling policy in a given scenario.

In this paper, we develop four heuristics to deal with the nominal channel allocation in cellular networks. We provide the theoretical bound on the number of channels for voice and data services. The four heuristics are compared using varying traffic distributions.

This paper proposes a robust greedy algorithm and four of its variants for the resource path finding problem in distributed networks. In contrast to the existing solutions that rely on a single minimum cost path for each request, the proposed algorithm makes use of finding "robust" paths for each request within the network. We give a mathematical definition of robustness for the resource path finding problem in distributed networks. The four proposed variants are then compared with each other and with a traditional "non-robust" path finding algorithm. The simulation results show interesting improvement in solution quality when robustness is incorporated into the path finding algorithm for distributed networks.

Internet is one of the most extensively used communications medium in the world, which makes the whole world a global village. The Internet is growing at an annualized rate of 18% and now has one billion users. Second billion users will follow in the next ten years. Unfortunately there is very little work done in order to use the IP network in the field of emergency services. These services are mostly based on PSTN (public switched telephone network). In this paper we enhance the previous work that has been done in this regard and tried to improved the performance by bringing some changes in its architecture, which ensures the LI (location Information) stipulation which is a the most challenging aspect using IP flat address model. We have used the concept of DHCP relay (dynamic host configuration protocol) in order to ensure the LI from the caller side. Along with this, our technique also simplifies the architecture of the system.

Machine Translation, (hereafter in this document referred to as the "MT") faces a lot of complex problems from its origination. Extracting multiword expressions is also one of the complex problems in MT. Finding multiword expressions during translating a sentence from English into Urdu, through existing solutions, takes a lot of time and occupies system resources. We have designed a simple relational data approach, in which we simply set a bit in dictionary (database) for multiword, to find and handle multiword expression. This approach handles multiword efficiently., {"references":["I.A. Sag, T. Baldwin, F. Bond, A. Copestake, D. Flickinger. 2001,\n\"Multi-word Expressions: A Pain in the Neck for NLP\", LinGO Working\nPaper No. 2001-03. Stanford University, CA.","Scott S. L. Piao, Paul Rayson, Dawn Archer, Andrew Wilson, Tony\nMcEnery, \"Extracting Multiword Expression Using a Semantic Tagger\",\nLancaster University.","Ann Copestake, Fabre Lambeau, Aline Villavicecio, Francis Bond,\nTimothy Baldwin, Ivan A.Sag, Dan Flickinger, \"Multiword Expressions:\nlinguistic precision and reusability\", University of Cambridge Computer\nLaboratory,William Gates Building, JJ Thomson Avenue, Cambridge,\nCB3 0FD, UK. NTT Communication Science Labortries, Hikari Dai,\nSeiko-cho,Soraku-gun, Kioto 619-0237, JAPAN.","Z. Pervez, S. Khan, F. Mustafa, M. Mahmood, U. Hasan, \"Pharasal\nConsolidation Algorithm For Part Of Speech Tags In Machine\nTranslation From English To Urdu\", National University of Science and\nTechnology, Rawalpindi Pakistan.","Sarmad Hussein. \"Letter-to-Sound Conversion for Urdu Text-to-Speech\nSystem\". Center for Research in Urdu Language Processing, National\nUniversity of Computer and Emerging Sciences, Lahore, Pakistan.","T. Rahman (2002). \"Language Ideology and Power: Language Learning\nAmong the Muslims of Pakistan and North India\", Oxford University\nPress, Karachi, Pakistan.","Ethnologue, 13th Edition.","T. Mitamura, E. Nyberg, E. Torrejon, D. Svoboda, A.Brunner and K.\nBaker. \"Pronominal Anaphora Resolution in the Kantoo Multilingual\nMachine Translation System\", Proceedings of the 9th International\nConference on Theoretical and Methodological Issues in Machine\nTranslation. Keihanna, Japan, Mar 2002.","AltaVista Babelfish. URL: http://babelfish.altavista.com\n[10] Google Language Tool.\nURL: http://www.google.com.pk/language_tools\n[11] Z. Pervez, S. Khan, F. Mustafa, M. Mahmood, U. Hasan, \"Pharasal\nConsolidation Algorithm for Part Of Speech Tags In Machine\nTranslation from English to Urdu\", NUST Institute of Information\nTechnology, National University of Sciences and Technology."]}

In this paper we analyze different TCP models and propose a hybrid solution that can significantly improve the performance of network when reordering of packets or short term congestion occurs. The key idea here is to delay congestion response by dynamic DUPTHRESH adjustment so that local recovery can be done, new transmission should be after every two DUPACK to minimize receiver buffer requirement and at the same time maintains basis ACK clocking. We also introduce linear decrease algorithm for CWND adjustment. The Simulation results show that our model can achieve significant performance improvement as compare to previous work with minimal overhead. We carried out a simple modification to the existing solutions therefore it is easy to deploy.

Data centers being an architectural and functional block of cloud computing are integral to the Information and Communication Technology (ICT) sector. Cloud computing is rigorously utilized by various domains, such as agriculture, nuclear science, smart grids, healthcare, and search engines for research, data storage, and analysis. A Data Center Network (DCN) constitutes the communicational backbone of a data center, ascertaining the performance boundaries for cloud infrastructure. The DCN needs to be robust to failures and uncertainties to deliver the required Quality of Service (QoS) level and satisfy Service Level Agreement (SLA). In this paper, we analyze robustness of the state-of-the-art DCNs. Our major contributions are: (a) we present multi-layered graph modeling of various DCNs; (b) we study the classical robustness metrics considering various failure scenarios to perform a comparative analysis; (c) we present the inadequacy of the classical network robustness metrics to appropriately evaluate the DCN robustness; and (d) we propose new procedures to quantify the DCN robustness. Currently, there is no detailed study available centering the DCN robustness. Therefore, we believe that this study will lay a firm foundation for the future DCN robustness research.

Data Centers (DCs) are experiencing a tremendous growth in the number of hosted servers. Aggregate bandwidth requirement is a major bottleneck to data center performance. New Data Center Network (DCN) architectures are proposed to handle different challenges faced by current DCN architecture. In this paper we have implemented and simulated two promising DCN architectural models, namely switch-based and hybrid models, and compared their effectiveness by monitoring the network throughputs and average packet latencies. The presented analysis may be a background for the further studies on the simulation and implementation of the DCN customized topologies, and customized addressing protocols in the large-scale data centers.