Fine-Grained Resource Provisioning and Task Scheduling for Heterogeneous Applications in Distributed Green Clouds

An increasing number of enterprises have adopted cloud computing to manage their important business applications in distributed green cloud(DGC)systems for low response time and high cost-effectiveness in recent years.Task scheduling and resource allocation in DGCs have gained more attention in both academia and industry as they are costly to manage because of high energy consumption.Many factors in DGCs,e.g.,prices of power grid,and the amount of green energy express strong spatial variations.The dramatic increase of arriving tasks brings a big challenge to minimize the energy cost of a DGC provider in a market where above factors all possess spatial variations.This work adopts a G/G/1 queuing system to analyze the performance of servers in DGCs.Based on it,a single-objective constrained optimization problem is formulated and solved by a proposed simulated-annealing-based bees algorithm(SBA)to find SBA can minimize the energy cost of a DGC provider by optimally allocating tasks of heterogeneous applications among multiple DGCs,and specifying the running speed of each server and the number of powered-on servers in each GC while strictly meeting response time limits of tasks of all applications.Realistic databased experimental results prove that SBA achieves lower energy cost than several benchmark scheduling methods do.

Self-adaptive Bat Algorithm With Genetic Operations

Swarm intelligence in a bat algorithm(BA)provides social learning.Genetic operations for reproducing individuals in a genetic algorithm(GA)offer global search ability in solving complex optimization problems.Their integration provides an opportunity for improved search performance.However,existing studies adopt only one genetic operation of GA,or design hybrid algorithms that divide the overall population into multiple subpopulations that evolve in parallel with limited interactions only.Differing from them,this work proposes an improved self-adaptive bat algorithm with genetic operations(SBAGO)where GA and BA are combined in a highly integrated way.Specifically,SBAGO performs their genetic operations of GA on previous search information of BA solutions to produce new exemplars that are of high-diversity and high-quality.Guided by these exemplars,SBAGO improves both BA’s efficiency and global search capability.We evaluate this approach by using 29 widely-adopted problems from four test suites.SBAGO is also evaluated by a real-life optimization problem in mobile edge computing systems.Experimental results show that SBAGO outperforms its widely-used and recently proposed peers in terms of effectiveness,search accuracy,local optima avoidance,and robustness.

Heuristic Virtual Machine Allocation for Multi-Tier Ambient Assisted Living Applications in a Cloud Data Center

Cloud computing provides the essential infrastructure for multi-tier Ambient Assisted Living(AAL) applications that facilitate people's lives. Resource provisioning is a critically important problem for AAL applications in cloud data centers(CDCs). This paper focuses on modeling and analysis of multi-tier AAL applications, and aims to optimize resource provisioning while meeting requests' response time constraint. This paper models a multi-tier AAL application as a hybrid multi-tier queueing model consisting of an M/M/c queueing model and multiple M/M/1 queueing models. Then, virtual machine(VM) allocation is formulated as a constrained optimization problem in a CDC, and is further solved with the proposed heuristic VM allocation algorithm(HVMA). The results demonstrate that the proposed model and algorithm can effectively achieve dynamic resource provisioning while meeting the performance constraint.

Workload-aware request routing in cloud data center using software-defined networking

Large latency of applications will bring revenue loss to cloud infrastructure providers in the cloud data center. The existing controllers of software-defined networking architecture can fetch and process traffic information in the network. Therefore, the controllers can only optimize the network latency of applications. However, the serving latency of applications is also an important factor in delivered user-experience for arrival requests. Unintelligent request routing will cause large serving latency if arrival requests are allocated to overloaded virtual machines. To deal with the request routing problem, this paper proposes the workload-aware software-defined networking controller architecture. Then, request routing algorithms are proposed to minimize the total round trip time for every type of request by considering the congestion in the network and the workload in virtual machines(VMs). This paper finally provides the evaluation of the proposed algorithms in a simulated prototype. The simulation results show that the proposed methodology is efficient compared with the existing approaches.

Inherent mass transfer engineering of a Co,N co-doped carbon material towards oxygen reduction reaction

Oxygen reduction reaction (ORR) is an important process for the conversion and utilization of a wide range of renewable energy sources, and is critical for the shape of future energy scenario [1–10]. However, ORR is a complex four-electron transfer process and is kinetically sluggish. It is urgent to develop high-efficient electrocatalysts to solve this problem [11–15]. Up to now, precious metal-based catalysts such as Pt-based electrocatalysts have been widely studied and found to be one of the most efficient electrocatalysts for ORR. However, the high price and the small reserves limit their large-scale commercialization [10,16–23]. Therefore, in order to fulfill needs for the practical applications, it is necessary to develop low-cost electrocatalysts, also with high activity and great stability [19,24–28].

Yangxinshi Tablets Versus Trimetazidine on Exercise Tolerance After Percutaneous Coronary Intervention: Rationale and Design of the Double-blind, Randomized HEARTRIP Trial

Improving exercise tolerance is indisputably beneficial for long-term survival in patients treated with percutaneous coronary intervention(PCI).Although previous studies suggested that Yangxinshi tablets effectively improve exercise tolerance in patients with coronary heart disease,the evidence is limited due to the lack of high-quality randomized trials.The Effects of Yangxinshi Tablets on Exercise Tolerance Compared with Trimetazidine in Patients after PCI(HEARTRIP)trial is a multicenter,double-blind,double-dummy,active drug-controlled,randomized trial designed to test if the effects of Yangxinshi tablets on exercise tolerance are non-inferior to those of trimetazidine in patients undergoing PCI.A total of 668 patients who have undergone PCI for the first time and completed a cardiopulmonary exercise test(CPET)will be enrolled and randomly assigned,in a 1:1 ratio,to receive Yangxinshi tablets(3 tablets,3 times/d)plus trimetazidine-placebo or trimetazidine(20 mg,3 times/d)plus Yangxinshi-placebo for 24 weeks.The primary endpoint is metabolic equivalent of tasks(METs)measured by CPET at 24 weeks after randomization.The secondary endpoints include comprehensive CPET indicators,incidence of major adverse cardiac and cerebrovascular events,and depression(Patient Health Questionnaire-9),anxiety(Generalized Anxiety Disorder-7),and quality of life(Seattle Angina Questionnaire)scores.This study will appraise current clinical evidence on the beneficial effect of Yangxinshi tablets on improving exercise tolerance after PCI and may substantiate their use as an effective pharmacological option for cardiac rehabilitation patients.The HEARTRIP study protocol received approval from the ethics committee of the General Hospital of Northern Theater Command(Shenyang,China).The procedures set out in this protocol are in accordance with the principles of the Declaration of Helsinki and the Good Clinical Practice guidelines.The results will be published following the guidelines of the CONSORT statement in a peer-reviewed scientific journal(Trial registration number:NCT03809273).