Microservices Architecture Enables DevOps: an Experience Report on Migration to a Cloud-Native Architecture

This article reports on experiences and lessons learned during incremental migration and architectural refactoring of a commercial mobile back end as a service to microservices architecture. It explains how the researchers adopted DevOps and how this facilitated a smooth migration

Migrating to Cloud-Native Architectures Using Microservices: An Experience Report

Migration to the cloud has been a popular topic in industry and academia in recent years. Despite many benefits that the cloud presents, such as high availability and scalability, most of the on-premise application architectures are not ready to fully exploit the benefits of this environment, and adapting them to this environment is a non-trivial task. Microservices have appeared recently as novel architectural styles that are native to the cloud. These cloud-native architectures can facilitate migrating on-premise architectures to fully benefit from the cloud environments because non-functional attributes, like scalability, are inherent in this style. The existing approaches on cloud migration does not mostly consider cloud-native architectures as their first-class citizens. As a result, the final product may not meet its primary drivers for migration. In this paper, we intend to report our experience and lessons learned in an ongoing project on migrating a monolithic on-premise software architecture to microservices. We concluded that microservices is not a one-fit-all solution as it introduces new complexities to the system, and many factors, such as distribution complexities, should be considered before adopting this style. However, if adopted in a context that needs high flexibility in terms of scalability and availability, it can deliver its promised benefits

SDN-based virtual machine management for cloud data centers

Software-Defined Networking (SDN) is an emerging paradigm to logically centralize the network control plane and automate the configuration of individual network elements. At the same time, in Cloud Data Centers (DCs), even though network and server resources converge over the same infrastructure and typically over a single administrative entity, disjoint control mechanisms are used for their respective management. In this paper, we propose a unified server-network control mechanism for converged ICT environments. We present a SDN-based orchestration framework for live Virtual Machine (VM) management where server hypervisors exploit temporal network information to migrate VMs and minimize the network-wide communication cost of the resulting traffic dynamics. A prototype implementation is presented and Mininet is used to evaluate the impact of diverse orchestration algorithms

Origin and Dynamical Evolution of Neptune Trojans - II: Long Term Evolution

We present results examining the fate of the Trojan clouds produced in our previous work. We find that the stability of Neptunian Trojans seems to be strongly correlated to their initial post-migration orbital elements, with those objects that survive as Trojans for billions of years displaying negligible orbital evolution. The great majority of these survivors began the integrations with small eccentricities (e < 0.2) and small libration amplitudes (A < 30 - 40{\deg}). The survival rate of "pre-formed" Neptunian Trojans (which in general survived on dynamically cold orbits (e < 0.1, i < 5 - 10{\deg})) varied between ~5 and 70%. By contrast, the survival rate of "captured" Trojans (on final orbits spread across a larger region of e-i element space) were markedly lower, ranging between 1 and 10% after 4 Gyr. Taken in concert with our earlier work, we note that planetary formation scenarios which involve the slow migration (a few tens of millions of years) of Neptune from an initial planetary architecture that is both resonant and compact (aN < 18 AU) provide the most promising fit of those we considered to the observed Trojan population. In such scenarios, we find that the current day Trojan population would number ~1% of that which was present at the end of the planet's migration, with the bulk being sourced from captured, rather than pre-formed objects. We note, however, that even those scenarios still fail to reproduce the currently observed portion of the Neptune Trojan population moving on orbits with e 20{\deg}. Dynamical integrations of the currently observed Trojans show that five out of the seven are dynamically stable on 4 Gyr timescales, while 2001 QR322, exhibits significant dynamical instability. The seventh Trojan object, 2008 LC18, has such large orbital uncertainties that only future studies will be able to determine its stability.Comment: 24 pages, 6 figures, accepted for publication in MNRAS (The abstract was shortened. Original version can be found in the pdf file

Securely Launching Virtual Machines on Trustworthy Platforms in a Public Cloud

In this paper we consider the Infrastructure-as-a-Service (IaaS) cloud model which allows cloud users to run their own virtual machines (VMs) on available cloud computing resources. IaaS gives enterprises the possibility to outsource their process workloads with minimal effort and expense. However, one major problem with existing approaches of cloud leasing, is that the users can only get contractual guarantees regarding the integrity of the offered platforms. The fact that the IaaS user himself or herself cannot verify the provider promised cloud platform integrity, is a security risk which threatens to prevent the IaaS business in general. In this paper we address this issue and propose a novel secure VM launch protocol using Trusted Computing techniques. This protocol allows the cloud IaaS users to securely bind the VM to a trusted computer configuration such that the clear text VM only will run on a platform that has been booted into a trustworthy state. This capability builds user confidence and can serve as an important enabler for creating trust in public clouds. We evaluate the feasibility of our proposed protocol via a full scale system implementation and perform a system security analysis

StackInsights: Cognitive Learning for Hybrid Cloud Readiness

Hybrid cloud is an integrated cloud computing environment utilizing a mix of public cloud, private cloud, and on-premise traditional IT infrastructures. Workload awareness, defined as a detailed full range understanding of each individual workload, is essential in implementing the hybrid cloud. While it is critical to perform an accurate analysis to determine which workloads are appropriate for on-premise deployment versus which workloads can be migrated to a cloud off-premise, the assessment is mainly performed by rule or policy based approaches. In this paper, we introduce StackInsights, a novel cognitive system to automatically analyze and predict the cloud readiness of workloads for an enterprise. Our system harnesses the critical metrics across the entire stack: 1) infrastructure metrics, 2) data relevance metrics, and 3) application taxonomy, to identify workloads that have characteristics of a) low sensitivity with respect to business security, criticality and compliance, and b) low response time requirements and access patterns. Since the capture of the data relevance metrics involves an intrusive and in-depth scanning of the content of storage objects, a machine learning model is applied to perform the business relevance classification by learning from the meta level metrics harnessed across stack. In contrast to traditional methods, StackInsights significantly reduces the total time for hybrid cloud readiness assessment by orders of magnitude