Cost-Effective Resource Allocation and Throughput Maximization in Mobile Cloudlets and Distributed Clouds

With the advance in communication networks and the use explosion of mobile devices, distributed clouds consisting of many small and medium datacenters in geographical locations and cloudlets defined as "mini" datacenters are envisioned as the next-generation cloud computing platform. In particular, distributed clouds enable disaster-resilient and scalable services by scaling the services into multiple datacenters, while cloudlets allow pervasive and continuous services with low access delay by further enabling mobile users to access the services within their proximity. To realize the promises provided by distributed clouds and mobile cloudlets, it is urgently to optimize various system performance of distributed clouds and cloudlets, such as system throughput and operational cost by developing efficient solutions. In this thesis, we aim to devise novel solutions to maximize the system throughput of mobile cloudlets, and minimize the operational costs of distributed clouds, while meeting the resource capacity constraints and users' resource demands. This however poses great challenges, that is, (1) how to maximize the system throughput of a mobile cloudlet, considering that a mobile cloudlet has limited resources to serve energy-constrained mobile devices, (2) how to efficiently and effectively manage and evaluate big data in distributed clouds, and (3) how to efficiently allocate the resources of a distributed cloud to meet the resource demands of various users. Existing studies mainly focused on implementing systems and lacked systematic optimization methods to optimize the performance of distributed clouds and mobile cloudlets. Novel techniques and approaches for performance optimization of distributed clouds and mobile cloudlets are desperately needed. To address these challenges, this thesis makes the following contributions. We firstly study online request admissions in a cloudlet with the aim of maximizing the system throughput, assuming that future user requests are not known in advance. We propose a novel admission cost model to accurately model dynamic resource consumption, and devise efficient algorithms for online request admissions. We secondly study a novel collaboration- and fairness-aware big data management problem in a distributed cloud to maximize the system throughput, while minimizing the operational cost of service providers, subject to resource capacities and users' fairness constraints, for which, we propose a novel optimization framework and devise a fast yet scalable approximation algorithm with an approximation ratio. We thirdly investigate online query evaluation for big data analysis in a distributed cloud to maximize the query acceptance ratio, while minimizing the query evaluation cost. For this problem, we propose a novel metric to model the costs of different resource consumptions in datacenters, and devise efficient online algorithms under both unsplittable and splittable source data assumptions. We fourthly address the problem of community-aware data placement of online social networks into a distributed cloud, with the aim of minimizing the operational cost of the cloud service provider, and devise a fast yet scalable algorithm for the problem, by leveraging the close community concept that considers both user read rates and update rates. We also deal with social network evolutions, by developing a dynamic evaluation algorithm for the problem. We finally evaluate the performance of all proposed algorithms in this thesis through experimental simulations, using real and/or synthetic datasets. Simulation results show that the proposed algorithms significantly outperform existing algorithms

Efficient Virtual Network Embedding Via Exploring Periodic Resource Demands

Cloud computing built on virtualization technologies promises provisioning elastic computing and communication resources to enterprise users. To share cloud resources efficiently, embedding virtual networks of different users to a distributed cloud consisting of multiple data centers (a substrate network) poses great challenges. Motivated by the fact that most enterprise virtual networks usually operate on long-term basics and have the characteristics of periodic resource demands, in this paper we study the virtual network embedding problem by embedding as many virtual networks as possible to a substrate network such that the revenue of the service provider of the substrate network is maximized, while meeting various Service Level Agreements (SLAs) between enterprise users and the cloud service provider. For this problem, we propose an efficient embedding algorithm by exploring periodic resource demands of virtual networks, and employing a novel embedding metric that models the workloads on both substrate nodes and communication links if the periodic resource demands of virtual networks are given; otherwise, we propose a prediction model to predict the periodic resource demands of these virtual networks based on their historic resource demands. We also evaluate the performance of the proposed algorithms by experimental simulation. Experimental results demonstrate that the proposed algorithms outperform existing algorithms, improving the revenue from 10% to 31%

Performance of steel frames with new lightweight composite infill walls under curvature ground deformation

In this paper, the structural performance of steel frames with novel lightweight composite infill walls is experimentally and numerically investigated under curvature ground deformation, which is a common consequence of ground mining activities that can cause significant effects on structures and buildings in these areas. A new structural form that combines steel frames and lightweight composite infill walls has recently been used; its performance under curvature ground deformation is of great interest but still not entirely clear. This study compares the mechanical behavior of the open-frame, the closed-frame with mudsill, and the closed-frame with infill walls, through experimental testing under positive and negative curvature ground deformations. Structural responses such as basement counterforce, additional strains at different key locations, and effects of mudsill and infill walls are evaluated. In addition, 3D finite element models are established to simulate the performance of the tested samples and are validated by comparing the results against those from experiments. After validation, the numerical model is applied to a few complex structures incorporating the composite infill walls to investigate their structural performance under both positive and negative curvature ground deformation. It has been found that steel frames with the new composite infill walls can considerably increase the stiffness of structures in resisting ground deformation and re-distribute the loads amongst the beam and column members in the frame. Failure modes for the structures can also be changed by shifting the most dangerous ones from the upper part of the frame to the lower part. Moreover, it has been found that the vertical force of the infill walls is more sensitive to curvature ground deformation than the horizontal force. Further, the influence of the infill wall on the column is more significant, in comparison to that on the beam of the frame

Enabling multicast slices in edge networks

Telecommunication networks are undergoing a disruptive transition towards distributed mobile edge networks with virtualized network functions (VNFs) (e.g., firewalls, Intrusion Detection Systems (IDSs), and transcoders) within the proximity of users. This transition will enable network services, especially IoT applications, to be provisioned as network slices with sequences of VNFs, in order to guarantee the performance and security of their continuous data and control flows. In this paper we study the problems of delay-aware network slicing for multicasting traffic of IoT applications in edge networks. We first propose exact solutions by formulating the problems into Integer Linear Programs (ILPs). We further devise an approximation algorithm with an approximation ratio for the problem of delay-aware network slicing for a single multicast slice, with the objective to minimize the implementation cost of the network slice subject to its delay requirement constraint. Given multiple multicast slicing requests, we also propose an efficient heuristic that admits as many user requests as possible, through exploring the impact of a non-trivial interplay of the total computing resource demand and delay requirements. We then investigate the problem of delay-oriented network slicing with given levels of delay guarantees, considering that different types of IoT applications have different levels of delay requirements, for which we propose an efficient heuristic based on Reinforcement Learning (RL). We finally evaluate the performance of the proposed algorithms through both simulations and implementations in a real test-bed. Experimental results demonstrate that the proposed algorithms is promising

A “built-up” composite film with synergistic functionalities on Mg–2Zn–1Mn bioresorbable stents improves corrosion control effects and biocompatibility

Control of premature corrosion of magnesium (Mg) alloy bioresorbable stents (BRS) is frequently achieved by the addition of rare earth elements. However, limited long-term experience with these elements causes concerns for clinical application and alternative methods of corrosion control are sought after. Herein, we report a “built-up” composite film consisting of a bottom layer of MgF2 conversion coating, a sandwich layer of a poly (1, 3-trimethylene carbonate) (PTMC) and 3-aminopropyl triethoxysilane (APTES) co-spray coating (PA) and on top a layer of poly (lactic-co-glycolic acid) (PLGA) ultrasonic spray coating to decorate the rare earth element-free Mg–2Zn–1Mn (ZM21) BRS for tailoring both corrosion resistance and biological functions. The developed “built-up” composite film shows synergistic functionalities, allowing the compression and expansion of the coated ZM21 BRS on an angioplasty balloon without cracking or peeling. Of special importance is that the synergistic corrosion control effects of the “built-up” composite film allow for maintaining the mechanical integrity of stents for up to 3 months, where complete biodegradation and no foreign matter residue were observed about half a year after implantation in rabbit iliac arteries. Moreover, the functionalized ZM21 BRS accomplished re-endothelialization within one month

Study on effects of mudsill and infilled wallboard on response of steel frame under curvature surface deformation

The structural performance of steel frames with novel lightweight composite infilled wallboard is experimentally and numerically investigated under the curvature surface deformation. This study compares the mechanical behavior for the open-frame, the closed-frame with mudsill and the closed-frame with infilled wallboard, through experiment and finite element analysis, under the positive and negative curvature surface deformations respectively. The structural responses such as basement counterforce, additional strains at different key locations and the effects of mudsill and infilled wallboard are evaluated. It has been found that the steel frames with the new composite infilled wallboard can considerably increase the stiffness of the structures in resisting surface deformation and re-distribute the loads amongst the beam and column members in the frame. The force transmission effect of the mudsill accelerated the changes in the additional strain at column bottom. Frame beam is the main component that bears the curvature surface deformation. Mudsill has a consistent stress pattern with the frame beam and can significantly influence additional strain of the frame beam due to its stress sharing effect. The infilled wallboard is connected with the column-beam members and thereby influences the additional strain of the frame column significantly

Identity-aware attribute recognition via real-time distributed inference in mobile edge clouds

With the development of deep learning technologies, attribute recognition and person re-identification (re-ID) have attracted extensive attention and achieved continuous improvement via executing computing-intensive deep neural networks in cloud datacenters. However, the datacenter deployment cannot meet the real-time requirement of attribute recognition and person re-ID, due to the prohibitive delay of backhaul networks and large data transmissions from cameras to datacenters. A feasible solution thus is to employ mobile edge clouds (MEC) within the proximity of cameras and enable distributed inference. In this paper, we design novel models for pedestrian attribute recognition with re-ID in an MEC-enabled camera monitoring system. We also investigate the problem of distributed inference in the MEC-enabled camera network. To this end, we first propose a novel inference framework with a set of distributed modules, by jointly considering the attribute recognition and person re-ID. We then devise a learning-based algorithm for the distributions of the modules of the proposed distributed inference framework, considering the dynamic MEC-enabled camera network with uncertainties. We finally evaluate the performance of the proposed algorithm by both simulations with real datasets and system implementation in a real testbed. Evaluation results show that the performance of the proposed algorithm with distributed inference framework is promising, by reaching the accuracies of attribute recognition and person identification up to 92.9% and 96.6% respectively, and significantly reducing the inference delay by at least 40.6% compared with existing methods

HierFedML: aggregator placement and UE assignment for hierarchical federated learning in mobile edge computing

Federated learning (FL) is a distributed machine learning technique that enables model development on user equipments (UEs) locally, without violating their data privacy requirements. Conventional FL adopts a single parameter server to aggregate local models from UEs, and can suffer from efficiency and reliability issues – especially when multiple users issue concurrent FL requests . Hierarchical FL consisting of a master aggregator and multiple worker aggregators to collectively combine trained local models from UEs is emerging as a solution to efficient and reliable FL. The placement of worker aggregators and assignment of UEs to worker aggregators plays a vital role in minimizing the cost of implementing FL requests in a Mobile Edge Computing (MEC) network. Cost minimization associated with joint worker aggregator placement and UE assignment problem in an MEC network is investigated in this work. An optimization framework for FL and an approximation algorithm with an approximation ratio for a single FL request is proposed. Online worker aggregator placements and UE assignments for dynamic FL request admissions with uncertain neural network models, where FL requests arrive one by one without the knowledge of future arrivals, is also investigated by proposing an online learning algorithm with a bounded regret. The performance of the proposed algorithms is evaluated using both simulations and experiments in a real testbed with its hardware consisting of server edge servers and devices and software built upon an open source hierarchical FedML (HierFedML) environment. Simulation results show that the performance of the proposed algorithms outperform their benchmark counterparts, by reducing the implementation cost by at least 15% per FL request. Experimental results in the testbed demonstrate the performance gain using the proposed algorithms using real datasets for image identification and text recognition applications

A Novel Cold-Regulated Cold Shock Domain Containing Protein from Scallop Chlamys farreri with Nucleic Acid-Binding Activity

Background: The cold shock domain (CSD) containing proteins (CSDPs) are one group of the evolutionarily conserved nucleic acid-binding proteins widely distributed in bacteria, plants, animals, and involved in various cellular processes, including adaptation to low temperature, cellular growth, nutrient stress and stationary phase. Methodology: The cDNA of a novel CSDP was cloned from Zhikong scallop Chlamys farreri (designated as CfCSP) by expressed sequence tag (EST) analysis and rapid amplification of cDNA ends (RACE) approach. The full length cDNA of CfCSP was of 1735 bp containing a 927 bp open reading frame which encoded an N-terminal CSD with conserved nucleic acids binding motif and a C-terminal domain with four Arg-Gly-Gly (RGG) repeats. The CSD of CfCSP shared high homology with the CSDs from other CSDPs in vertebrate, invertebrate and bacteria. The mRNA transcripts of CfCSP were mainly detected in the tissue of adductor and also marginally detectable in gill, hepatopancreas, hemocytes, kidney, mantle and gonad of healthy scallop. The relative expression level of CfCSP was up-regulated significantly in adductor and hemocytes at 1 h and 24 h respectively after low temperature treatment (P,0.05). The recombinant CfCSP protein (rCfCSP) could bind ssDNA and in vitro transcribed mRNA, but it could not bind dsDNA. BX04, a cold sensitive Escherichia coli CSP quadruple-deletion mutant, was used to examine the cold adaptation ability of CfCSP. After incubation at 17uC for 120 h, the strain of BX04 containing the vector pINIII showed growth defect and failed to form colonies, while strain containing pINIII-CSPA or pINIII