Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Elasticity is a fundamental property in cloud computing that has recently witnessed major developments This article reviews both classical and recent elasticity solutions and provides an overview of containerization, a new technological trend in lightweight virtualization It also discusses major issues and research challenges related to elasticity in cloud computing We comprehensively review and analyze the proposals developed in this field We provide a taxonomy of elasticity mechanisms according to the identified works and key properties Compared to other works in literature, this article presents a broader and detailed analysis of elasticity approaches and is considered as the first survey addressing the elasticity of containers

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Elasticity in Cloud Computing: State of the Art and Research Challenges

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Auto-scaling Techniques for Elastic Applications in Cloud Environments

Increasing popular big data applications bring about invaluable information, but along with challenges to industrial community and academia. Cloud computing with unlimited resources seems to be the way out. However, this panacea cannot play its role if we do not arrange fine allocation for cloud infrastructure resources. In this paper, we present a multi-objective optimization algorithm to trade off the performance, availability, and cost of Big Data application running on Cloud. After analyzing and modeling the interlaced relations among these objectives, we design and implement our approach on experimental environment. Finally, three sets of experiments show that our approach can run about 20 percent faster than traditional optimization approaches, and can achieve about 15 percent higher performance than other heuristic algorithms, while saving 4 to 20 percent cost.

Cloud Infrastructure Resource Allocation for Big Data Applications

Amazon Elastic Compute Cloud (EC2) provides a cloud computing service by renting out computational resources to customers (i.e., cloud users). The customers can dynamically provision virtual servers (i.e., computing instances) in EC2, and then the customers are charged by Amazon on a pay-per-use basis. EC2 offers three options to provision virtual servers, i.e., on-demand, reservation, and spot options. Each option has different price and yields different benefit to the customers. Spot price (i.e., price of spot option) could be the cheapest, however, the spot price is fluctuated and even more expensive than the prices of on-demand and reservation options due to supply-and-demand of available resources in EC2. Although the reservation and on-demand options have stable prices, their costs are mostly more expensive than that of spot option. The challenge is how the customers efficiently purchase the provisioning options under uncertainty of price and demand. To address this issue, two virtual server provisioning algorithms are proposed to minimize the provisioning cost for long- and short-term planning. Stochastic programming, robust optimization, and sample-average approximation are applied to obtain the optimal solutions of the algorithms. To evaluate the performance of the algorithms, numerical studies are extensively performed. The results show that the algorithms can significantly reduce the total provisioning cost incurred to customers.

Cost Minimization for Provisioning Virtual Servers in Amazon Elastic Compute Cloud

One of the key factors behind successful deployment of Cloud for on demand services is the optimal utilization of its virtual resources. A poorly managed cloud application may lead to huge cost which is even more than the cost of physical deployment. The most important issues in Cloud are the scalability and availability. A highly scalable deployment may lead to poor resource utilization whereas a low scalable deployment may lead to unavailability of services. This paper proposes architecture for optimal utilization of such resources considering both scalability and availability. The proposed architecture, named as Monitoring & Optimizing Virtual Resources (MOVR) architecture, manages and optimizes the usage of the resources required by a cloud application considering auto deployment, auto scaling and auto recovery of the provisioned resources for the application.

Optimizing the utilization of virtual resources in Cloud environment

In recent years most of the cloud computing community adopt an infrastructure centric approach to optimize the cost of cloud resources considering both Cloud Computing Service Users(CCSU) and Cloud Computing Service Providers(CCSP). In this paper we have brought up the concept of Application-centric Cloud. With Application-centric Cloud we propose an architecture to provide a generalized framework for auto deploying, sharing, providing scalability, robustness and availability of any cloud based application. Along with application independence, the framework also considers CCSP independence so that an application can use resources across providers.

Application-centric Cloud management

The waste tire was used as a precursor to prepare activated carbon, and the effect of different processing conditions on the prepared activated carbon was studied. Response surface methodology coupled with Box–Behnken Design (BBD) has been used to find the optimal experimental parameters for the preparation of activated carbon. Response surface methodology was implemented to evaluate the effects of activation temperature, activation time and impregnation ratio. BBD was used to determine the model for the different levels of activation temperature (400–600 °C), activation time (60–180 min) and potassium hydroxide-to-tire ratio (0.5–1.5). The obtained activated carbon was evaluated based on its ability to remove methylene blue dye from aqueous solution. The influential parameters that affected the quality of the prepared activated carbon were determined by analysis of variance based upon the response variable. The optimum condition obtained for the activated carbon preparation was at an activation temperature of 400 °C, the reaction time of 165 min and potassium hydroxide-to-tire ratio of 1.5. The regression equation obtained was validated which showed a good agreement with the predicted model with a maximum error of 1.5% and R2 value of 0.975.

Optimization of adsorbent derived from non-biodegradable waste employing response surface methodology toward the removal of dye solutions

Oil and gas production wells generate large volumes of water mixed with hydrocarbons (dispersed and dissolved), salts (ions), and solids. This ‘produced water’ (PW) is a waste stream that must be disposed of appropriately. The presence of toxic hydrocarbons and ions in PW makes it unsuitable for surface discharge or disposal in groundwater resources. Thus, PW is often injected into deep geological formations as a disposal method. However, the supply of global water sources is diminishing, and the demand for water in industrial, domestic, and agricultural use in water-stressed regions makes PW a potentially attractive resource. PW also contains valuable elements like lithium and rare earth elements, which are increasing in global demand. This review article provides an overview of constituents present in PW, current technologies available to remove and recover valuable elements, and a case study highlighting the costs and economic benefits of recovering these valuable elements. PW contains a promising source of valuable elements. Developing technologies, such as ceramic membranes with selective sorption chemistry could make elemental recovery economically feasible and turn PW from a waste stream into a multi-faceted resource.

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Treatment and Recovery of High-Value Elements from Produced Water

Development of Mango Peel Derived Activated Carbon-Nickel Nanocomposite as an Adsorbent towards Removal of Heavy Metal and Organic Dye Removal from Aqueous Solution

Anirban Ghosh

Papers

Optimizing the utilization of virtual resources in Cloud environment

Application-centric Cloud management

Optimization of adsorbent derived from non-biodegradable waste employing response surface methodology toward the removal of dye solutions

Treatment and Recovery of High-Value Elements from Produced Water

Development of Mango Peel Derived Activated Carbon-Nickel Nanocomposite as an Adsorbent towards Removal of Heavy Metal and Organic Dye Removal from Aqueous Solution