A Reliable and Cost-Efficient Auto-Scaling System for Web Applications Using Heterogeneous Spot Instances

Cloud providers sell their idle capacity on markets through an auction-like mechanism to increase their return on investment. The instances sold in this way are called spot instances. In spite that spot instances are usually 90% cheaper than on-demand instances, they can be terminated by provider when their bidding prices are lower than market prices. Thus, they are largely used to provision fault-tolerant applications only. In this paper, we explore how to utilize spot instances to provision web applications, which are usually considered availability-critical. The idea is to take advantage of differences in price among various types of spot instances to reach both high availability and significant cost saving. We first propose a fault-tolerant model for web applications provisioned by spot instances. Based on that, we devise novel auto-scaling polices for hourly billed cloud markets. We implemented the proposed model and policies both on a simulation testbed for repeatable validation and Amazon EC2. The experiments on the simulation testbed and the real platform against the benchmarks show that the proposed approach can greatly reduce resource cost and still achieve satisfactory Quality of Service (QoS) in terms of response time and availability

An Efficient and Reliable Asynchronous Federated Learning Scheme for Smart Public Transportation

Since the traffic conditions change over time, machine learning models that predict traffic flows must be updated continuously and efficiently in smart public transportation. Federated learning (FL) is a distributed machine learning scheme that allows buses to receive model updates without waiting for model training on the cloud. However, FL is vulnerable to poisoning or DDoS attacks since buses travel in public. Some work introduces blockchain to improve reliability, but the additional latency from the consensus process reduces the efficiency of FL. Asynchronous Federated Learning (AFL) is a scheme that reduces the latency of aggregation to improve efficiency, but the learning performance is unstable due to unreasonably weighted local models. To address the above challenges, this paper offers a blockchain-based asynchronous federated learning scheme with a dynamic scaling factor (DBAFL). Specifically, the novel committee-based consensus algorithm for blockchain improves reliability at the lowest possible cost of time. Meanwhile, the devised dynamic scaling factor allows AFL to assign reasonable weights to stale local models. Extensive experiments conducted on heterogeneous devices validate outperformed learning performance, efficiency, and reliability of DBAFL

An Fe stabilized metallic phase of NiS2 for the highly efficient oxygen evolution reaction.

This work reports a fundamental study on the relationship of the electronic structure, catalytic activity and surface reconstruction process of Fe doped NiS2 (FexNi1-xS2) for the oxygen evolution reaction (OER). A combined photoemission and X-ray absorption spectroscopic study reveals that Fe doping introduces more occupied Fe 3d6 states at the top of the valence band and thereby induces a metallic phase. Meanwhile, Fe doping also significantly increases the OER activity and results in much better stability with the optimum found for Fe0.1Ni0.9S2. More importantly, we performed detailed characterization to track the evolution of the structure and composition of the catalysts after different cycles of OER testing. Our results further confirmed that the catalysts gradually transform into amorphous (oxy)hydroxides which are the actual active species for the OER. However, a fast phase transformation in NiS2 is accompanied by a decrease of OER activity, because of the formation of a thick insulating NiOOH layer limiting electron transfer. On the other hand, Fe doping retards the process of transformation, because of a shorter Fe-S bond length (2.259 Å) than Ni-S (2.400 Å), explaining the better electrochemical stability of Fe0.1Ni0.9S2. These results suggest that the formation of a thin surface layer of NiFe (oxy)hydroxide as an active OER catalyst and the remaining Fe0.1Ni0.9S2 as a conductive core for fast electron transfer is the base for the high OER activity of FexNi1-xS2. Our work provides important insight and design principle for metal chalcogenides as highly active OER catalysts

Auto-scaling and deployment of web applications in distributed computing clouds

© 2016 Dr. Chenhao QuCloud Computing, which allows users to acquire/release resources based on real-time demand from large data centers in a pay-as-you-go model, has attracted considerable attention from the ICT industry. Many web application providers have moved or plan to move their applications to Cloud, as it enables them to focus on their core business by freeing them from the task and the cost of managing their data center infrastructures, which are often over-provisioned or under-provisioned under a dynamic workload. Applications these days commonly serve customers from geographically dispersed regions. Therefore, to meet the stringent Quality of Service (QoS) requirements, they have to be deployed in multiple data centers close to the end customer locations. However, efficiently utilizing Cloud resources to reach high cost-efficiency, low network latency, and high availability is a challenging task for web application providers, especially when the service provider intends to deploy the application in multiple geographical distributed Cloud data centers. The problems, including how to identify satisfactory Cloud offerings, how to choose geographical locations of data centers so that the network latency is minimized, how to provision the application with minimum cost incurred, and how to guarantee high availability under failures and flash crowds, should be addressed to enable QoS-aware and cost-efficient utilization of Cloud resources. In this thesis, we investigated techniques and solutions for these questions to help application providers to efficiently manage deployment and provision of their applications in distributed computing Clouds. It extended the state-of-the-art by making the following contributions: 1. A hierarchical fuzzy inference approach for identifying satisfactory Cloud services according to individual requirements. 2. Algorithms for selection of multi-Cloud data centers and deployment of applications on them to minimize Service Level Objective (SLO) violations for web applications requiring strong consistency. 3. An auto-scaler for web applications that achieves both high availability and significant cost saving by using heterogeneous spot instances. 4. An approach that mitigates the impact of short-term application overload caused by either resource failures or flash crowds in any individual data center through geographical load balancing

A Cloud Trust Evaluation System using Hierarchical Fuzzy Inference System for Service Selection

Abstract—Cloud computing is an utility computing paradigm that allows users to flexibly acquire virtualized computing resources in a pay-as-you-go model. To realize the benefits of using cloud, users need to first select the suitable cloud services that can satisfy their applications ’ functional and non-functional requirements. However, this is a difficult task due to large number of available services, users ’ unclear requirements, and performance variations in cloud. In this paper, we propose a system that evaluates trust of clouds according to users ’ fuzzy Quality of Service (QoS) requirements and services ’ dynamic performances to facilitate service selection. We demonstrate the effectiveness and efficiency of our system through simulations and case studies

Auto-scaling web applications in clouds : a taxonomy and survey

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area

Mitigating impact of short-term overload on multi-cloud web applications through geographical load balancing

Managed by an auto-scaler in the clouds, applications may still be overloaded by sudden flash crowds or resource failures as the auto-scaler takes time to make scaling decisions and provision resources. With more cloud providers building geographically dispersed data centers, applications are commonly deployed in multiple data centers to better serve customers worldwide. In this case, instead of sufficiently over-provisioning each data center to prepare for occasional overloads, it is more cost-efficient to over-provision each data center a small amount of capacity and to balance the extra load among them when resources in any data center are suddenly saturated. In this paper, we present a decentralized system that timely detects short-term overload situations and autonomously handles them using geographical load balancing and admission control to minimize the resulted performance degradation. Our approach also includes a new algorithm that optimally distributes the excessive load to remote data centers causing minimum increase of overall response times. We developed a prototype and evaluated it on Amazon Web Services. The results show that our approach is able to maintain acceptable quality of service while greatly increase the number of requests served during overloading periods

SLO-aware deployment of web applications requiring strong consistency using multiple clouds

Geographically dispersed cloud data centers (DCs) enable web application providers to improve their services' response time and availability by deploying application replicas in multiple DCs. To allow applications requiring strong consistency to be deployed in multiple clouds, industry and academia have developed various scalable database systems that can guarantee strong inter-DC consistency with alleviated network overhead. For applications using these database systems, it is essential to take both the network latencies to the end users and the communication overhead of the databases into account when selecting the hosting DCs. In this paper, we study how to identify the satisfactory deployment plan (hosting DCs and request routing) considering SLO satisfaction, migration cost, and operational cost for applications using these databases. The proposed approach involves two steps. First, it searches the deployment plan with minimum amount of SLO violations using genetic algorithm when the application is first migrated to the clouds. Then it continuously optimizes the deployment in a certain time interval according to the changing workload and the current deployment plan. We illustrate how our approach works for the applications using two databases (Cassandra and Galera Cluster), and demonstrate the effectiveness of our approach through simulation studies using settings of two example applications (TPC-W and Twissandra). Our solution is extensible to applications using other database systems that have similar properties

Auto-scaling web applications in clouds : a taxonomy and survey

Web application providers have been migrating their applications to cloud data centers, attracted by the emerging cloud computing paradigm. One of the appealing features of the cloud is elasticity. It allows cloud users to acquire or release computing resources on demand, which enables web application providers to automatically scale the resources provisioned to their applications without human intervention under a dynamic workload to minimize resource cost while satisfying Quality of Service (QoS) requirements. In this article, we comprehensively analyze the challenges that remain in auto-scaling web applications in clouds and review the developments in this field. We present a taxonomy of auto-scalers according to the identified challenges and key properties. We analyze the surveyed works and map them to the taxonomy to identify the weaknesses in this field. Moreover, based on the analysis, we propose new future directions that can be explored in this area

Asynchronous Federated Learning on Heterogeneous Devices: A Survey

Federated learning (FL) is experiencing a fast booming with the wave of distributed machine learning. In the FL paradigm, the global model is aggregated on the centralized aggregation server according to the parameters of local models instead of local training data, mitigating privacy leakage caused by the collection of sensitive information. With the increased computing and communication capabilities of edge and IoT devices, applying FL on heterogeneous devices to train machine learning models becomes a trend. The synchronous aggregation strategy in the classic FL paradigm cannot effectively use the limited resource, especially on heterogeneous devices, due to its waiting for straggler devices before aggregation in each training round. Furthermore, the disparity of data spread on devices (i.e. data heterogeneity) in real-world scenarios downgrades the accuracy of models. As a result, many asynchronous FL (AFL) paradigms are presented in various application scenarios to improve efficiency, performance, privacy, and security. This survey comprehensively analyzes and summarizes existing variants of AFL according to a novel classification mechanism, including device heterogeneity, data heterogeneity, privacy and security on heterogeneous devices, and applications on heterogeneous devices. Finally, this survey reveals rising challenges and presents potentially promising research directions in this under-investigated field