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Edge computing is envisioned as a promising enabler to leverage computation capacities at the edge and address the issues faced in cloud computing. In this paper, we propose an advanced decision model to solve the computation offloading problem in edge computing. It utilizes the intrinsic hierarchical topology of the Internet and performs online scheduling in a decentralized manner to eliminate anticipated modeling. It also admits the fact that congestion can take place unexpectedly, for the possible reasons such as stale information. Our decision model is enhanced in two approaches to endow both sender and receiver (for one offloading action) with the ability of “dropping” the request, so as to avoid congestion. Random Early Detection(RED) algorithm is incorporated. The results of simulation demonstrate that our decision model can handle workloads well and these two enhancements are effective.

Tian, J.J.; Liu, Y.J., and Yin, Z.D., 2020. Relationship between coastal foreign direct investment & industrial upgrading of China under the condition of the Belt and Road initiative. In: Guido Aldana, P.A. and Kantamaneni, K. (eds.), Advances in Water Resources, Coastal Management, and Marine Science Technology. Journal of Coastal Research, Special Issue No. 104, pp. 695–699. Coconut Creek (Florida), ISSN 0749-0208.At present, the issuance of the relationship between foreign direct investment & the transformation and upgrading of domestic industrial structures has been controversial under the condition of the construction of the “Belt and Road” of China. Based on the data from 2008 to 2018, the VAR model is applied for studying the interrelationship between foreign direct investment in sixty four countries along the “Belt and Road” and the transformation and upgrading of domestic industrial structures according to the experience since China has joined in Word Trade Organization (“WTO”); and it is found that in this kind of countries, the direct investment is without any statistical significance on the industrial upgrading of China so far; nevertheless, the industrial restructuring of China still in turn accelerates the going out intention of companies and promotes direct investment in this kind of countries.

Background: Arteriosclerosis Obliterans (ASO) is an important cause for lower limb amputation. We aimed to compare the effects of interventional and conservative therapies on lower extremity ASO, and to analyze the risk factors for prognosis. Methods: Ninety-eight eligible patients were randomly divided into experimental (n = 50) and control groups (n = 48). Conservative therapy was conducted for control group, and experimental group was given conservative and interventional therapies. Their baseline clinical data, hospital stay length, claudication distance, Rutherford stage, diseased arteries, Ankle-Brachial Index (ABI), clinical outcomes and adverse events were recorded. Then they were divided into poor (n = 31) and good prognosis groups (n = 67), and risk factors were explored by univariate and multivariate analyses. Results: The hospital stay length of experimental group was significantly shorter than that of control group (p < 0.05). After treatment, Rutherford stage decreased, and vascular patency rate and total response rate rose in experimental group (p < 0.05). Claudication distance and ABI significantly increased in both groups compared with those before treatment, especially in experimental group (p < 0.05). The incidence rates of cardiovascular and cerebrovascular events, amputation and death were significantly lower in experimental group (p < 0.05). Age, conservative therapy, smoking, hypertension, diabetes mellitus, hyperlipidemia and fibrinogen were independent risk factors for poor prognosis (p < 0.05). Age of >70 years old and fibrinogen level of >4 g/L had greater effects on prognosis. Conclusion: Interventional therapy works well for lower extremity ASO, with high safety, easy recovery and mild complications. Poor prognosis is caused by old age, conservative therapy, smoking, hypertension, diabetes mellitus, hyperlipidemia and increased fibrinogen.

With the rapid development of cloud computing, more and more application providers are deploying their applications in the cloud in order to be free from the burden of system administration. Meanwhile, the world is full of information explosion, which makes some applications become hot in a short period of time unexpectedly. Thus these applications in cloud may encounter sudden traffic increment or decrement. Usually, developers use the alarm service or the auto scaling service provided by the cloud provider to tackle sudden traffic change. But these are reactive methods which have time latency and usually they only consider horizontal resizing of scaling group. In this paper, we propose PRMRAP: a proactive framework based on the prediction of resource amount to cope with sudden traffic change. Compared with post-action methods and existing proactive methods, we have lower time latency and we consider not only horizontal resizing but also vertical resizing of scaling group which makes our model quicker and much more cost-effective.

Because of the popularity of cloud computing, Cloud Service Providers (CSPs) can rent virtual machines (VMs) from Cloud Providers (CPs) conveniently. In our previous work, we proposed an autonomic and elastic resource scheduling framework, named AERS, which made full use of both proactive and reactive controllers in the field of dynamic resource provision and was integrated with an availability-aware and communication overhead optimized placement strategy. In this paper, we propose an enhanced version of AERS, named EAERS. It eliminates modeling the specific cloud application and instead determines the relationship between workloads and the number of virtual machines (VMs) through self-learning so that the whole scheduling can be carried out in a more transparent manner. Dynamic consolidation is also designed, implemented and integrated into EAERS. Experiments on OpenStack show that EAERS performs as expected.

The elasticity of cloud computing is able to help Cloud Application Providers (CAPs) adjust the number of rented virtual machines (VMs) for cloud applications according to actual varying demands while enforcing SLAs. In this paper, we design a framework called AERS (Autonomic and Elastic Resource Scheduling Framework). AERS makes full use of the profits of both proactive controllers and reactive controllers in the field of dynamic resource provision. It can not only adjust the number of available VMs for cloud applications with fluctuation of workloads but also react fast when cloud applications break SLAs. In addition, We build a model for the availability of cloud applications. Based on this model, we propose an availability-aware and communication overhead optimized placement strategy integrated in AERS to help CAPs choose proper availability zones for launching VMs. Experiments on Openstack show that AERS is able to provide VMs elastically while enforcing SLAs.