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Cloud computing offers a cost-effective and elastic computing paradigm that facilitates large scale data storage and analytics. By deploying virtualization technologies in the datacenter, cloud enables efficient resource management and isolation for various big data applications. Since the hotspots (i.e., overloaded machines) can degrade the performance of these applications, virtual machine migration has been utilized to perform load balancing in the datacenters to eliminate hotspots and guarantee Service Level Agreements (SLAs). However, the previous load balancing schemes make migration decisions based on deterministic resource demand estimation and workload characterization, without considering their stochastic properties. By studying real world traces, we show that the resource demand and workload of virtual machines are highly dynamic and bursty, which can cause these schemes to make inefficient migrations for load balancing. To address this problem, in this paper we propose a stochastic load balancing scheme which aims to provide probabilistic guarantee against the resource overloading with virtual machine migration, while minimizing the total migration overhead. Our scheme effectively addresses the prediction of the distribution of resource demand and the multidimensional resource requirements with stochastic characterization. Moreover, as opposed to the previous works that measure the migration cost without considering the network topology, our scheme explicitly takes into account the distance between the source physical machine and the destination physical machine for a virtual machine migration. The trace-driven experiments show that our scheme outperforms the previous schemes in terms of SLA violation and the migration cost.

Background: Liver cancer is considered one of the prevalent malignancies, of which most are hepatocellular carcinoma (HCC). Pyroptosis is a form of inflammation that can mediate cellular programmed death. Long non-coding RNA (lncRNA) is one of the non-coding RNAs which performs powerful biological functions, and they exert an irreplaceable role in the regulation of many biological processes. However, pyroptosis-related lncRNAs expression and their correlation to the HCC patient prognosis remain to be investigated.Methods: Using The Cancer Genome Atlas (TCGA) database, we built a pyroptosis-related multi-lncRNA signature to predict the HCC patient prognosis. The least absolute shrinkage and selection operator (LASSO) Cox regression method was used to construct a pyroptosis-associated lncRNA signature to classify all HCC patients into high- and low-risk groups.Results: The low-risk group patients have a better prognosis. We identified the risk score as an independently predictor in HCC patients. Furthermore, chemotherapeutic drug sensitivity (gemcitabine, doxorubicin, mitomycin C and sorafenib), and immune cell expression (NK cells resting, Mast cells resting, M0 macrophages, Monocytes and CD8 T cells) showed significantly different infiltration degree between the two groups. We identified SC-5608, anisomycin and vancomycin as possible potential drug targets for the high-risk group patients. We performed enrichment analysis for the differential genes in the two groups of patients. Intriguingly, the result showed that targeting the wnt/β-catenin signal pathway could help to develop treatment strategies for patients with HCC. Conclusions: In conclusively, we found a signature of pyroptosis-related lncRNA for predicting independent HCC patient prognosis. The study provides new insights for reliably and accurately predicting prognosis and developing therapeutic strategies for HCC patients.

To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. The unique features of clouds pose formidable challenges to achieving effective and efficient load balancing. First, VMs in clouds use different resources (e.g., CPU, bandwidth, memory) to serve a variety of services (e.g., high performance computing, web services, file services), resulting in different overutilized resources in different PMs. Also, the overutilized resources in a PM may vary over time due to the time-varying heterogeneous service requests. Second, there is intensive network communication between VMs. However, previous load balancing methods statically assign equal or predefined weights to different resources, which lead to degraded performance in terms of speed and cost to achieve load balance. Also, they do not strive to minimize the VM communications between PMs. We propose a Resource Intensity Aware Load balancing method (RIAL). For each PM, RIAL dynamically assigns different weights to different resources according to their usage intensity in the PM, which significantly reduces the time and cost to achieve load balance and avoids future load imbalance. It also tries to keep frequently communicating VMs in the same PM to reduce bandwidth cost, and migrates VMs to PMs with minimum VM performance degradation. We also propose an extended version of RIAL with three additional algorithms. First, it optimally determines the weights for considering communication cost and performance degradation due to VM migrations. Second, it has a more strict migration triggering algorithm to avoid unnecessary migrations while still satisfying Service Level Objects (SLOs). Third, it conducts destination PM selection in a decentralized manner to improve scalability. Our extensive trace-driven simulation results and real-world experimental results show the superior performance of RIAL compared to other load balancing methods.

Previous resource provisioning strategies in cloud datacenters allocate physical resources to virtual machines (VMs) based on the predicted resource utilization pattern of VMs. The pattern for VMs of a job is usually derived from historical utilizations of multiple VMs of the job. We observed that these utilization curves are usually misaligned in time, which would lead to resource over-prediction and hence over-provisioning. Since this resource utilization misalignment problem has not been revealed and studied before, in this paper, we study the VM resource utilization from public datacenter traces and Hadoop benchmark jobs to verify the commonness of the utilization misalignments. Then, to reduce resource over-provisioning, we propose three VM resource utilization pattern refinement algorithms to improve the original generated pattern by lowering the cap of the pattern, reducing cap provision duration and varying the minimum value of the pattern. We then extend these algorithms to further improve the resource efficiency by considering periodical resource demand patterns that have multiple pulses in a pattern. These algorithms can be used in any resource provisioning strategy that considers predicted resource utilizations of VMs of a job. We then adopt these refinement algorithms in an initial VM allocation mechanism and test them in trace-driven experiments and real-world testbed experiments. The experimental results show that each improved mechanism can increase resource utilization, and reduce the number of PMs needed to satisfy tenant requests. Also, our extended refinement algorithms are effective in improving resource efficiency of the refinement algorithms.

In cloud datacenters, effective resource provisioning is needed to maximize the energy efficiency and utilization of cloud resources while guaranteeing the service-level agreement (SLA) for tenants. To address this need, we propose an initial virtual machine (VM) allocation mechanism (called CompVM) that consolidates complementary VMs with spatial/ temporal awareness. Complementary VMs are the VMs whose total demand of each resource dimension (in the spatial space) nearly reaches their host’s capacity during VM lifetime period (in the temporal space). Based on our observation of the existence of VM resource utilization patterns, the mechanism predicts the resource utilization patterns of VMs. Based on the predicted patterns, it coordinates the requirements of different resources and consolidates complementary VMs in the same physical machine (PM). This mechanism reduces the number of PMs needed to provide VM service, hence increases energy efficiency and resource utilization, and also reduces the number of VM migrations and SLA violations. We further propose a utilization variation-based mechanism, a correlation coefficient-based mechanism, and a VM group-based mechanism to match the complementary VMs in order to enhance the VM consolidation performance. Simulation based on two real traces and real-world testbed experiments shows that CompVM significantly reduces the number of PMs used, SLA violations, and VM migrations of the previous resource provisioning strategies. The results also show the effectiveness of the enhancement mechanisms in improving the performance of the basic CompVM.

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical hosts and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new physical machines (PMs). A heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm is proposed, which exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.

To provide robust infrastructure as a service, clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of service level agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time and energy) and delay by quickly identifying VMs and destination PMs to migrate. We further propose enhancement methods for higher performance. First, we propose a cloud profit oriented reward system in the MDP model so that when the MDP tries to maximize the rewards for load balance, it concurrently improves the actual profit of the datacenter. Second, we propose a new MDP model, which considers the actions for both migrating a VM out of a PM and migrating a VM into a PM, in order to reduce the overhead and improve the effectiveness of load balancing. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency, and long-term load balance maintenance. Our experimental results also show the effectiveness of our proposed enhancement methods.

Hepatocellular carcinoma (HCC) remains one of the most common and aggressive human cancers worldwide. Accumulating evidences indicate that non-coding RNAs are critical regulators implicated in various physiological processes including HCC development. Long non-coding RNA (lncRNA) MYCN opposite-strand (MYCNOS) was reported to be up-regulated in several human cancers, yet its role in HCC progression is still elusive. In the present study, MYCNOS was up-regulated in both HCC tissues and cell lines, and elevated MYCNOS expression was correlated to shorter survival time of HCC patients. We knocked down MYCNOS expression using short hairpin RNAs specifically targeting MYCNOS. MYCNOS knockdown significantly inhibited proliferation in HCC cells in vitro accompanied by exacerbated cell apoptosis; it also suppressed tumor growth in mouse model in vivo. Besides, the migration and invasion of HCC cells were remarkably inhibited after MYCNOS knockdown. In addition, MYCNOS acted as a negative regulator of miR-340 in HCC cells, and all effects of MYCNOS knockdown were abrogated by further miR-340 inhibition. We also discovered that oncogene phosphatidylinositol-3, 4, 5-trisphosphate-dependent Rac exchange factor 2 (PREX2) was a downstream target of miR-340, and PREX2 expression was positively correlated to that of MYCNOS in HCC tissues. In conclusion, our findings demonstrated that MYCNOS knockdown inhibited HCC progression through regulating miR-340.

Extracellular communication in the tumor microenvironment is critical. Results of qRT-PCR show that circ-0051443 is significantly lower in the plasma exosomes and tissues from patients with hepatocellular carcinoma (HCC) than healthy controls. Compared with the producer cells, circ-0051443 is mainly packaged into exosomes. A receiver operating characteristic curve (ROC) shows that the patients with HCC can be distinguished from the controls by exosomal circ-0051443. The role of exosomal circ-0051443 in HCC was determined by animal and cell analyses. Circ-0051443 is transmitted from normal cells to HCC cells via exosomes and suppresses the malignant biological behaviors by promoting cell apoptosis and arresting the cell cycle. Exosomal circ-0051443 decreases the weight and volume of the xenograft tumors in nude mice via BAK1 upregulation in these tumors. BAK1 expression is mediated by exosomal circ-0051443 through competitive bound to miR-331-3p. Therefore, exosomal circ-0051443 can serve as a predictor and potential therapeutic target for HCC.

In the present study, we aimed to search for dysregulated lnRNAs in Hepatocellular carcinoma (HCC) tissues, and analyze the relationship of its expression level with the clinicopathological feature and patient prognosis. The biological function of FLVCR1-AS1, the identified lncRNA, in the process of HCC development, and progression was investigated in vitro and in vivo. The underlying molecular mechanism was further explored. We determined FLVCR1-AS1 expression in HCC tissues and peri-tumor tissues by bioinformatic analysis, qRT-PCR, Northern blot and in situ hybridization. The relationship between FLVCR1-AS1 expression level and prognosis was determined by analyzing clinical samples. The effects of FLVCR1-AS1 knockdown on HCC cell proliferation, apoptosis, migration, and invasion were investigated by CCK8, FACS, and tanswell assay, respectively. Tumor xenograft model was used to determine the influence of down-regulated FLVCR1-AS1 on tumor growth and metastasis. lncRNA FLVCR1-AS1 was extremely up-regulated in HCC tissues and cell lines. FLVCR1-AS1 expression level was positively correlated with tumor severity. FLVCR1-AS1 knockdown remarkably inhibited HCC cell proliferation, migration, and invasion in vitro and in vivo while induced cell apoptosis. In mechanism, FLVCR1-AS1 acted as a competitive endogenous RNAs to sponge miR-513c which targeted the mRNA of MET for degradation. By directly sponging miR-513c, FLVCR1-AS1 increased MET expression in HCC, and then promoted HCC progression. It was demonstrated that FLVCR1-AS1 played a positive role in HCC development and progression according to the study in its mechanism, function and clinical manifestation, so that it could be expected to become a new target in HCC prevention and treatment.

Previous resource provisioning strategies in cloud datacenters allocate physical resources to virtual machines (VMs) based on the predicted resource utilization pattern of VMs. The pattern for VMs of a job is usually derived from historical utilizations of multiple VMs of the job. We observed that these utilization curves are usually misaligned in time, which would lead to resource over-prediction and hence over-provisioning. Since this resource utilization misalignment problem has not been revealed and studied before, in this paper, we study the VM resource utilization from public datacenter traces to verify the existence of the utilization misalignments. Then, to reduce resource over-provisioning, we propose three VM resource utilization pattern refinement algorithms to improve the original generated pattern by lowering the cap of the pattern, reducing cap provision duration and varying the minimum value of the pattern. These algorithms can be used in any resource provisioning strategy that considers predicted resource utilizations of VMs of a job. We then adopt these refinement algorithms in an initial VM allocation mechanism and test them in trace-driven experiments and real-world cluster experiments. The experimental results show that each improved mechanism can increase resource efficiency up to 74%, and reduce the number of PMs needed to satisfy tenant requests up to 47% while conforming the SLO requirement.

Demand-prediction based resource provisioning schemes help assure service level objectives (SLO) in cloud systems. We notice that if a provisioning scheme does not exclude bursts from historical resource demands in normal demand prediction or always uses a large padding to correct under-prediction, it will lead to resource over-provisioning and low resource utilization. To improve the previous schemes, in this paper, we present a Resource-efficient Predictive Resource Provisioning system in clouds (RPRP) that excludes bursts in demand prediction and has algorithms to specifically handle bursts to avoid resource over-provisioning. Rather than setting padding to a possibly high value, RPRP has a load-dependent padding algorithm that adaptively determines padding based on predicted demands. To handle bursts, RPRP embodies a responsive padding algorithm that adaptively adjusts padding to recover from both under-provisioning and over-provisioning. We implemented RPRP on top of Xen and conducted both trace-driven simulation and real-world testbed experiments. The experimental results show that RPRP achieves higher resource utilization, more accurate demand predictions, and fewer SLO violations than previous schemes.

In cloud datacenters, multiple Virtual Machines (VMs) are co-located in a Physical Machine (PM) to serve different applications. Prior VM consolidation methods for cloud datacenters schedule VMs mainly based on resource (CPU and memory) constraints in PMs but neglect serious shared Last Level cache contention between VMs. This may cause severe VM performance degradation due to cache thrashing and starvation for VMs. Current cache contention aware VM consolidation strategies either estimate cache contention by coarse VM classification for each individual VM without considering co-location and (or) require the aid of hardware to monitor the online miss rate of each VM. Therefore, these strategies are insufficiently accurate and (or) difficult to adopt for VM consolidation in clouds. In this paper, we formalize the problem of cache contention aware VM placement and migration in cloud datacenters using integer linear programming. We then propose a cache contention aware VM placement and migration algorithm (CacheVM). It estimates the total cache contention degree of co-locating a given VM with a group of VMs in a PM based on the cache stack distance profiles of the VMs. Then, it places the VM to the PM with the minimum cache contention degree and chooses the VM from a PM that generates the maximum cache contention degree to migrate out. We implemented CacheVM and its comparison methods on a supercomputing cluster. Trace-driven simulation and real-testbed experiments show that CacheVM outperforms other methods in terms of the number of cache misses, execution time and throughput.

In cloud systems, achieving high resource utilization and low Service Level Objective (SLO) violation rate are important to the cloud provider for high profit. For this purpose, recently, some methods have been proposed to predict allocated but unused resources and reallocate them to long-running service jobs. However, the accuracy of their prediction method relies on the existence of patterns in jobs' resource utilization. Therefore, these methods cannot be used for short-lived jobs, which usually do not have certain patterns but exhibit frequent fluctuations in resource requirements. Also, these methods may result in resource fragmentation and lead to low resource utilization because they neglect job resource intensity in multi-resource allocation and may allocate much more resources to jobs. To handle this problem, we propose a Cooperative Opportunistic Resource Provisioning scheme (CORP) for short-lived jobs. CORP uses the deep learning method to predict the amount of temporarily-unused resource of each short-lived job. It also predicts the fluctuations of the amount of unused resource using Hidden Markov Model, and adjusts the predicted amount for the peak and valley of unused resource, and dynamically allocates the corrected amount of resource to jobs. Further, CORP uses a job packing strategy by leveraging complementary jobs' requirements on different resource types and allocates such jobs to the same VM to fully utilize unused resources, which increases resource utilization. Extensive experimental results based on a real cluster and Amazon EC2 show that CORP achieves high resource utilization and low SLO violation rate compared to previous resource provisioning schemes.

Functioning as an intermediary between cloud tenants and providers, cloud service brokerages (CSBs) bring about great benefits to the cloud market. To maximize its own profit, a CSB is faced with a challenge: how to reserve servers and distribute tenant demands to the reserved servers such that the total reservation cost is minimized while the reserved servers can satisfy the tenant service level agreement (SLA)? Demand prediction and demand allocation are two steps to solve this problem. However, previous demand prediction methods cannot accurately predict tenant demands since they cannot accurately estimate prediction errors and also assume the existence of seasonal periods of demands. Previous demand allocation methods only aim to minimize the number of reserved servers rather than the server reservation cost, which is more challenging. To solve this challenge, we propose a Probabilistic Demand Allocation system (PDA). It predicts demands and more accurate prediction errors without the assumption of the existence of seasonal periods. It then formulates a nonlinear programming problem and has a decentralized method to find the problem solution. In addition to overcoming the shortcomings in previous methods, PDA is novel in that rather than separately conducting the prediction and demand allocation, it considers prediction errors in demand allocation in order to allocate demands with offsetting prediction errors (e.g., −1 and +1) to the same server, which helps find the problem solution. Both simulation and real-world experimental results demonstrate the superior performance of our system in reducing servers' reservation cost.

We reported the preparation and properties of liposome-loaded polycaprolactone microspheres (LPMs) as a drug delivery system for controlling the release of flurbiprofen. LPMs were fabricated using double emulsion solvent extraction/evaporation method and characterized by scanning electron microscopy, Fourier transform infrared spectrum, X-ray diffraction, differential scanning calorimeter and UV–vis spectrum. The results suggest that LPMs have uniform sizes with pores on the external surface. Liposomes are intactly encapsulated in LPMs, which causes slight change of polycaprolactone from semi-crystalline to less-ordered amorphous. The concentrations of polycaprolactone and polyvinyl alcohol (PVA) and the amount of liposomes can affect the diameter, surface morphology and encapsulation efficiency of LPMs. The diameter of LPMs increases from 80 to 200 μm and the encapsulation efficiency of flurbiprofen in LPMs increases from 36.92% to 48.42% when the concentration of polycaprolactone increases from 0.15 to 0.6 g/mL. However, the larger amount of liposomes promotes the aggregation between emulsion droplets and causes more pores on the surface of LPMs, which leads to lower drug encapsulation efficiency. The presence of PVA stabilizes the emulsion droplets against coalescence. With the increase of PVA concentration, the diameter of LPMs decreases and the amount of flurbiprofen encapsulated in LPMs increases. In vitro release studies suggest the structure and morphology of LPMs have close relationship with drug release kinetics. The smaller LPMs with more porous surface have faster cumulative release rate.

The effect of tryptophan on the membrane stability was studied by using three artificial biological membranes including liposome, Langmuir monolayer and solid supported bilayer lipid membrane (s-BLM) as models. All the results indicate that the penetration of tryptophan can destabilize different artificial biological membranes. The diameter of liposome and the leakage of calcein from liposome increased with the increase of tryptophan concentration because the penetration of tryptophan was beneficial for dehydrating the polar head groups of lipids and the formation of fusion intermediates. π-A isotherms of lecithin on the subphase of tryptophan solution further confirm that tryptophan can penetrate into lipid monolayer and reduce the stability of lipid monolayer. When the concentration of tryptophan increased from 0 to 2 × 10 −3 mol L −1 , the limiting molecular area of lecithin increased from 110.5 to 138.5 A 2 , but the collapse pressure of the monolayer decreased from 47.6 to 42.3 mN m −1 , indicating the destabilization of lipid monolayer caused by the penetration of tryptophan. The resistance spectra of s-BLM demonstrate that the existence of tryptophan leads to the formation of some defects in s-BLM and the destabilization of s-BLM. The values of electron-transfer resistance and double layer capacitance respectively decreased from 5.765 × 10 6 Ω and 3.573 × 10 −8 F to 1.391 × 10 6 Ω and 3.340 × 10 −8 F when the concentration of tryptophan increased from 0 to 2 × 10 −3 mol L −1 . Correspondingly, the breakdown voltage of s-BLM decreased from 2.51 to 1.72 V.

An interaction of helicid with phosphatidylcholine liposome biomembrane was studied by transmission electron microscopy, UV–vis, fluorescence, Raman and 31P NMR spectra. The results indicate that most of helicid molecules associate with liposomes at their surface and some of them penetrate the liposomes and locate in the hydrophobic regions of the membrane. The distribution coefficient KD between liposome phases and aqueous phases is 13.5. The liposome becomes more dispersive and stable in the presence of helicid. The microenvironmental micropolarity and the microhydrophobicity of liposome membrane decrease with the increase of helicid concentration. The interaction of helicid molecules with liposome results in a slight decrease of the membrane longitudinal order, and an increase of the membrane lateral order. A model for the interaction of helicid with liposome biomembrane is proposed on the basis of the change of microenvironment parameters of liposome including the micropolarity, microhydrophobicity and membrane order. The change of microenvironment parameters results mainly from hydrogen bonding interaction between the hydroxyl groups of the pyranoside rings of helicid molecules and the polar head groups of phosphatidylcholine.

Vesicles have been prepared at a constant molar ratio of L-glutamic acid/1-aminodecane/ibuprofen/H2O. The fusion dynamics of the vesicles have been directly observed and followed by using optical microscopy and freeze fracture replication-electron microscopy technologies. With time increasing up to 25 minutes, the vesicles slowly fuse to form large stable vesicles. A membrane phase transition temperature is detected at 97°C.

The critical micelle concentration of aminodecane-glutamic acid (bio-active substance, C10G) is 1.6 × 10−2 mol L−1 using surface tension and conductivity measurements. The additions of ibuprofen, glucose and salt lead to the decrease of the critical micelle concentration. The nanospheres with a radius of 40–80 nm can be prepared in C10G/ibuprofen/H2O system. The stability of the nanospheres is about 180 days. Glucose, pH and NaCl all have the effects on the size and dispersion behaviors of the nanospheres.

The interaction and association between chitosan and hemoglobin (Hb) are studied by UV-vis and fluorescence spectroscopies, viscometry, circular dichroism, dynamic and static light scattering. Chitosan can obviously associate with Hb to form protein-chitosan complexes, which affects microstructure of Hb. The distance between the first association site of chitosan with 214-tryptophan residue in Hb is about 5.473 nm. The intrinsic UV-vis absorption and fluorescence intensities of Hb increase with an increase of chitosan concentration. The alpha-helix in Hb is drawn and changed into beta-sheet.

Reducing energy consumption has become an important task in cloud datacenters. Many existing scheduling approaches in cloud datacenters try to consolidate virtual machines (VMs) to the minimum number of physical machines (PMs) and hence minimize the energy consumption. VM live migration technique is used to dynamically consolidate VMs to as few PMs as possible; however, it introduces high migration overhead. Furthermore, the cost factor is usually not taken into account by existing approaches, which will lead to high payment cost for cloud users. In this paper, we aim to achieve energy reduction for cloud providers and payment saving for cloud users, and at the same time, without introducing VM migration overhead and without compromising deadline guarantees for user tasks. Motivated by the fact that some of the tasks have relatively loose deadlines, we can further reduce energy consumption by proactively postponing the tasks without waking up new PMs. In this paper, we propose a heuristic task scheduling algorithm called Energy and Deadline Aware with Non-Migration Scheduling (EDA-NMS) algorithm. EDA-NMS exploits the looseness of task deadlines and tries to postpone the execution of the tasks that have loose deadlines in order to avoid waking up new PMs. When determining the VM instant types, EDA-NMS selects the instant types that are just sufficient to guarantee task deadline to reduce user payment cost. The results of extensive experiments show that our algorithm performs better than other existing algorithms on achieving energy efficiency without introducing VM migration overhead and without compromising deadline guarantees.

As video streaming applications are deployed on the cloud, cloud providers are charged by ISPs for inter-data enter transfers under the dominant percentile-based charging models. In order to minimize the payment costs, existing works aim to keep the traffic on each link under the charging volume (i.e., 95th percentile traffic volume from the beginning of a charging period up to current time). However, these methods cannot fully utilize each link's available bandwidth capacity, and may increase the charging volumes. To further reduce the bandwidth payment cost by fully utilizing link bandwidth, we propose an economical and deadline-driven video flow scheduling system, called EcoFlow. Considering different video flows have different transmission deadlines, EcoFlow transmits videos in the order of their deadline tightness and postpones the deliveries of later-deadline videos to later time slots so that the charging volume at current time interval will not increase. The flows that are expected to miss their deadlines are divided into sub flows to be rerouted to other underutilized links in order to meet their deadlines without increasing charging volumes. Experimental results on Planet Lab and EC2 show that compared to existing methods, EcoFlow achieves the least bandwidth costs for cloud providers.

Functioning as an intermediary between tenants and cloud providers, cloud service brokerages (CSBs) can bring about great benefits to the cloud market. CSBs buy the cloud resources, i.e., servers, with lower prices from cloud providers and sell the resources to the tenants with higher prices. To maximize its own profit, a CSB may distribute tenants' requests to the clouds that waste energy resources. However, as energy costs of cloud computing have been increasing rapidly, there is a need for cloud providers to optimize energy efficiency while maintain high service level performance to tenants, not only for their own benefit but also for social welfares (e.g., protecting environment). Thus, for green cloud companies, two questions have arisen: 1) under what pricing policies from the cloud providers to the CSB, a profit-driven CSB is willing to minimize the total cloud energy cost while satisfy tenant demands and 2) how should a CSB distribute tenants' demands to achieve this objective? To address question 1), we find a pricing policy for cloud providers such that maximizing CSB's profit is equivalent to minimizing cloud providers' energy cost. To address question 2), we first devise a greedy solution, and then propose an approximation algorithm with a constant approximation ratio. Both simulation and real-world Amazon EC2 experimental results demonstrate the effectiveness of our pricing policy to incentivize CSBs to save energy for cloud providers and the superior performance of our algorithms in energy efficiency and resource utilizations in comparison with the previous algorithms.

Virtualization is a key technology for cloud data centers to implement infrastructure as a service (IaaS) and to provide flexible and cost-effective resource sharing. It introduces an additional layer of abstraction that produces resource utilization overhead. Disregarding this overhead may cause serious reduction of the monitoring accuracy of the cloud providers and may cause degradation of the VM performance. However, there is no previous work that comprehensively investigates the virtualization overhead. In this paper, we comprehensively measure and study the relationship between the resource utilizations of virtual machines (VMs) and the resource utilizations of the device driver domain, hypervisor and the physical machine (PM) with diverse workloads and scenarios in the Xen virtualization environment. We examine data from the real-world virtualized deployment to characterize VM workloads and assess their impact on the resource utilizations in the system. We show that the impact of virtualization overhead depends on the workloads, and that virtualization overhead is an important factor to consider in cloud resource provisioning. Based on the measurements, we build a regression model to estimate the resource utilization overhead of the PM resulting from providing virtualized resource to the VMs and from managing multiple VMs. Finally, our trace-driven real-world experimental results show the high accuracy of our model in predicting PM resource consumptions in the cloud datacenter, and the importance of considering the virtualization overhead in cloud resource provisioning.

Sodium-vanadate-doped ordered mesoporous carbon foams (V-MCFs) were prepared through an evaporation-induced self-assembly strategy. The resultant V-MCFs exhibit highly ordered mesostructure with sp...

To provide robust infrastructure as a service (IaaS), clouds currently perform load balancing by migrating virtual machines (VMs) from heavily loaded physical machines (PMs) to lightly loaded PMs. Previous reactive load balancing algorithms migrate VMs upon the occurrence of load imbalance, while previous proactive load balancing algorithms predict PM overload to conduct VM migration. However, both methods cannot maintain long-term load balance and produce high overhead and delay due to migration VM selection and destination PM selection. To overcome these problems, in this paper, we propose a proactive Markov Decision Process (MDP)-based load balancing algorithm. We handle the challenges of allying MDP in virtual resource management in cloud datacenters, which allows a PM to proactively find an optimal action to transit to a lightly loaded state that will maintain for a longer period of time. We also apply the MDP to determine destination PMs to achieve long-term PM load balance state. Our algorithm reduces the numbers of Service Level Agreement (SLA) violations by long-term load balance maintenance, and also reduces the load balancing overhead (e.g., CPU time, energy) and delay by quickly identifying VMs and destination PMs to migrate. Our trace-driven experiments show that our algorithm outperforms both previous reactive and proactive load balancing algorithms in terms of SLA violation, load balancing efficiency and long-term load balance maintenance.

The fabrication of core-shell structural nanosilica@liposome nanocapsules as a drug delivery vehicle is reported. SiO(2) nanoparticles are encapsulated within liposomes by a W/O/W emulsion approach to form supramolecular assemblies with a core of colloidal particles enveloped by a lipid bilayer shell. A nanosilica core provides charge compensation and architectural support for the lipid bilayer, which significantly improves their physical stability. A preliminary application of these core-shell nanocapsules for hemoglobin (Hb) delivery is described. Through the H-bonding interaction between the hydroxyl groups on nanosilicas and the amino nitrogens of Hb, Hb-SiO(2) nanocomplexes in which the saturated adsorption amount of Hb on SiO(2) is 0.47 g g(-1) are coated with lipids to generate core-shell Hb-SiO(2)@liposome nanocapsules with mean diameters of 60-500 nm and Hb encapsulation efficiency of 48.4-87.9%. Hb-SiO(2)@liposome supramolecular nanovehicles create a mode of delivery that stabilizes the encapsulated Hb and achieves long-lasting release, thereby improving the efficacy of the drug. Compared with liposome-encapsulated Hb and Hb-loaded SiO(2) particles, such core-shell nanovehicles show substantially enhanced release performance of Hb in vitro. This finding opens up a new window of liposome-based formulations as drug delivery nanovehicles for widespread pharmaceutical applications.

A novel liposome-encapsulated hemoglobin/silica nanoparticle (LEHSN) was fabricated by a water-in-oil-in-water (W/O/W) double emulsion approach. Bovine hemoglobin (Hb) was first adsorbed onto the surfaces of silica nanoparticles (SNs), and then the complex of Hb/SNs was encapsulated by liposome to form LEHSN which has a core-shell supramolecular structure. On the one hand, liposomes built a cell membrane-like environment for the controlled release of Hb. On the other hand, SNs which act as rigid core provide a supported framework for lecithin membrane, and enhance the stability of liposomes. In comparison with liposome-encapsulated Hb (LEH), LEHSN shows substantially enhanced stability and improved release property of Hb in vitro. This study highlights the potential of the novel LEHSN as an oxygen carrier for pharmaceutical applications.

Four non-zero-dispersion-shifted fibers with almost the same large effective area (A(eff)) and optimized dispersion properties are realized by novel index profile designing and modified vapor axial deposition and modified chemical vapor deposition processes. An A(eff) of greater than 71 μm(2) is obtained for the designed fibers. Three of the developed fibers with positive dispersion are improved by reducing the 1550 nm dispersion slope from 0.072 ps/nm(2)/km to 0.063 ps/nm(2)/km or 0.05 ps/nm(2)/km, increasing the 1550 nm dispersion from 4.972 ps/nm/km to 5.679 ps/nm/km or 7.776 ps/nm/km, and shifting the zero-dispersion wavelength from 1500 nm to 1450 nm. One of these fibers is in good agreement with G655D and G.656 fibers simultaneously, and another one with G655E and G.656 fibers; both fibers are beneficial to high-bit long-haul dense wavelength division multiplexing systems over S-, C-, and L-bands. The fourth developed fiber with negative dispersion is also improved by reducing the 1550 nm dispersion slope from 0.12 ps/nm(2)/km to 0.085 ps/nm(2)/km, increasing the 1550 nm dispersion from -4 ps/nm/km to -6.016 ps/nm/km, providing facilities for a submarine transmission system. Experimental measurements indicate that the developed fibers all have excellent optical transmission and good macrobending and splice performances.

The design of a miniature-sized turbidity sensor fabricated by laser direct writing was proposed and tested. A dual-beam dual-detector sensing structure was written by a 488 nm laser from UV curable optical polymer to form a 4 mm diameter turbidity sensing probe, with the fabrication process being shortened to a few seconds. Experimental tests on prototypes were conducted by using standard turbidity solutions, and the data were processed with a self-adapting neural network based on a single input single output algorithm. The scattering coefficient for normalized turbidity of the standards was obtained, and system accuracy was validated by an error analysis. Experimental results indicated that in the testing situation presented in this paper, the sensor was capable of responding to turbidity with a relative error of about 3%.

A novel design of multimode light power splitter is proposed and fabricated by using secondary asymmetric Y branches. An almost equally divided output among output terminals is obtained experimentally. Maskless laser direct writing technique is applied in the fabrication process to facilitate the formation of power splitters by ultraviolet curable polymer. The analysis of the performances of both four-port and eight-port devices shows that these two dividers obtain a power splitting uniformity of more than 95%.

A compact-size fiber optic accelerometer was designed to achieve both high resolution and wide dynamic range concurrently. An optical cavity, with its length modulated by a piezoelectric oscillator at audio frequency, is utilized to resolve the nanometer-level displacement of a silicon micro-mirror which serves as an inertial mass for acceleration sensing. Strain analysis of the proof-mass flexure structure by Cosmosworks was carried out to calculate responsivity and resonance frequency for a comparison with experimental measurement. The responsivity below the structural resonance frequency of 160 Hz reaches 36 dB re 1 rad/g. Performance measurement demonstrated that the accelerometer was able to resolve an acceleration of 48 ng Hz−1/2 with a dynamic range of 2 × 107.

In the title complex, [Cd(C2O4)(C12H8N2)]n, the CdII atom has a distorted octa­hedral coordination, defined by four O atoms from two symmetry-related oxalate ligands and by two N atoms from a bidentate 1,10-phenanthroline ligand. Each oxalate ligand bridges two CdII atoms, generating a zigzag chain structure propagating along [100]. The packing of the structure is consolidated by non-classical C—H⋯O hydrogen-bonding inter­actions.

We design and fabricate a novel optical accelerometer based on high-order diffraction beam interference with a built-in phase-generated carrier modulator. A proof-of-concept prototype is tested and achieves a resolution of 96 ng/√Hz with a dynamic range of 60 g. By employing optical interference between ±1 order diffraction beams from a grating translating perpendicular to an optical beam for acceleration sensing, the accelerometer realizes a wide dynamic range, while maintaining a high resolution. Compared with prior optical accelerometers, the interference in this structure is free from the effect of short coherence length or beam divergence, and a greater than ordinary dynamic range is obtained. The proposed design is also applicable to a MOEMS platform, offering a new thought in the design of high-performance MOEMS accelerometers.