Abstract Application development paradigms and composition of technology services are decisively moving in the direction of hybrid and multi clouds. Enterprises are stitching new cloud‐native business models that leverage containerized multi‐tier microservice architecture, heterogeneity of cloud deployment models, and diversity of cloud providers. Scalability and resiliency are key components of this new world architecture, but these are also functions of the predictability of provisioning the underpinning compute instances on cloud. Thus, a major challenge to surmount before complex multicloud aware applications can be designed is the problem of unpredictable latencies associated with the provisioning of compute services on cloud. In the first part of this article, we develop a technique for time‐sensitive provisioning of virtual compute on demand, while also allowing deprovisioning on demand. Using the technique we propose, a cloud broker will be able to operate on a pool of reserved instances sourced from cloud providers and multiplex them profitably across cloud customers with associated provisioning time guarantees, but without usage commitment restrictions. We articulate this challenge in the form of theReserved Instance Allocation Problem(RIAP), which we first prove to be NP‐hard. We then design a heuristic‐based method to solve the intractable RIAP in polynomial time. We evaluate the effectiveness of our heuristic‐based mechanism through a combination of deep simulations and practical validation on mainstream public clouds. We demonstrate that our algorithm consistently yields a high profit‐to‐investment ratio for a broker who seeks to operate a commerce of virtual machines with time‐sensitive provisioning. In the second part of this article, we tackle the problem of unpredictable provisioning latencies in bare metal commerce. We build and evaluate an allocation model to calculate optimal supporting bare metal inventory to maximize cost‐sensitive fulfillment of bare metal provisioning requests in a time‐sensitive manner.

Containerized deployment of microservices has gained immense traction across industries. To meet demand, traditional cloud providers offer container-as-a-service, where selection of the container and containerization of workloads remain developer’s responsibility. This task is arduous for a developer since the choice of containers across different cloud providers is many. Furthermore, there does not exist any mechanism using which one can compare and contrast the capabilities of containers across different providers. In this scenario, we envisage the need for a smart cloud broker that can automatically deploy a chosen IT service into the best-fit container environment mapped to performance requirements, from among the set of available underpinning brokered container hosting systems spread across multiple cloud providers. We propose a novel fitness-aware containerization-as-a-service to achieve this. We show why a best-fit container selection process is operationally complex and time consuming, and how we heuristically prune the associated decision tree in two phases so that it becomes viable to implement this as an on-demand service. We propose a new metric called fitness quotient ($FQ$) to evaluate containers obtained from heterogeneous providers. We leverage machine learning techniques to inject automation into these two phases: unsupervised K-Means clustering in the first-level build-time phase to accurately classify IaaS cost and performance data, and polynomial regression during the second-level provisioning-time phase to discover relationships between SaaS performance and container strength. We also show that the utility of the framework that we propose is not limited to the container fitness use case that we analyze in this paper; rather it can be generalized to address a class of problems where overall time and cost complexity for provisioning-time decision making needs to be controlled under a given set of constraints.

A modern cloud service that is getting popular is the supply of bare metal servers on demand to consumers who need to run high-performance algorithms for short periods of time. However, provisioning-time latencies are unpredictable in bare metal commerce, primarily because suppliers cannot exploit virtualization levers to adjust and optimize capacity. This impacts bare metal service leverage because, in the face of indeterministic fulfillment times, consumers often pre-provision for peak demand, which goes against the fundamental tenets and advantages of cloud adoption. To address this, we advocate that a cloud service broker offers time-sensitive bare metal provisioning services by building and maintaining an inventory of bare metal servers. This, however, leads to inventory optimization and profit maximization problems that resemble what traditional supply chains face, but with characteristics unique to on-demand compute economics. In this paper, we build a brokered bare metal supply chain model that identifies impacting variables, their characteristics, and inter-relationships. We argue and demonstrate that, given complex inter-variable relationships and environmental uncertainties, simulation runs are needed to complete the model and yield recommendations to optimize inventory and maximize profits.

Cloud service brokerage is an emerging technology that attempts to simplify the consumption and operation of hybrid clouds. Today's cloud brokers attempt to insulate consumers from the vagaries of multiple clouds. To achieve the insulation, the modern cloud broker needs to disguise itself as the end-provider to consumers by creating and operating a virtual data center construct that we call a "meta-cloud", which is assembled on top of a set of participating supplier clouds. It is crucial for such a cloud broker to be considered a trusted partner both by cloud consumers and by the underpinning cloud suppliers. A fundamental tenet of brokerage trust is vendor neutrality. On the one hand, cloud consumers will be comfortable if a cloud broker guarantees that they will not be led through a preferred path. And on the other hand, cloud suppliers would be more interested in partnering with a cloud broker who promises a fair apportioning of client provisioning requests. Because consumer and supplier trust on a meta-cloud broker stems from the assumption of being agnostic to supplier clouds, there is a need for a test strategy that verifies the fairness of cloud brokerage. In this paper, we propose a calculus of fairness that defines the rules to determine the operational behavior of a cloud broker. The calculus uses temporal logic to model the fact that fairness is a trait that has to be ascertained over time; it is not a characteristic that can be judged at a per-request fulfillment level. Using our temporal calculus of fairness as the basis, we propose an algorithm to determine the fairness of a broker probabilistically, based on its observed request apportioning policies. Our model for the fairness of cloud broker behavior also factors in inter-provider variables such as cost divergence and capacity variance. We empirically validate our approach by constructing a meta-cloud from AWS, Azure and IBM, in addition to leveraging a cloud simulator. Our industrial engagements with large enterprises also validate the need for such cloud brokerage with verifiable fairness.

Summary The hybrid cloud idea is increasingly gaining momentum because it brings distinct advantages as a hosting platform for complex software systems. However, there are several challenges that need to be surmounted before hybrid hosting can become pervasive and penetrative. One main problem is to architecturally partition workloads across permutations of feasible cloud and non‐cloud deployment choices to yield the best‐fit hosting combination. Another is to predict the effort estimate to deliver such an advantageous hybrid deployment. In this paper, we describe a heuristic solution to address the said obstacles and converge on the ideal hybrid cloud deployment architecture, based on properties and characteristics of workloads that are sought to be hosted. We next propose a model to represent such a hybrid cloud deployment and demonstrate a method to estimate the effort required to implement and sustain that deployment. We also validate our model through dozens of case studies spanning several industry verticals and record results pertaining to how the industrial grouping of a software system can impact the aforementioned hybrid deployment model. Copyright © 2017 John Wiley & Sons, Ltd.