Designing a VM-level vertical scalability service in current cloud platforms: A new hope for wearable computers

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Public clouds are becoming ripe for enterprise adoption. Many companies, including large enterprises, are increasingly relying on public clouds as a substitute for, or a supplement to, their own computing infrastructures. On the other hand, cloud storage service has attracted over 625 million users. However, apart from the storage service, other cloud services, such as the computing service, have not yet attracted the end users’ interest for economic and technical reasons. Cloud service providers offers horizontal scalability to make their services scalable and economical for enterprises while it is still not economical for the individual users to use their computing services due to the lack of vertical scalability. Moreover, current virtualization technologies and operating systems, specifically the guest operating systems installed on virtual machines, do not support the concept of vertical scalability. In addition, network remote access protocols are meant to administer remote machines but they are unable to run the non-administrative tasks such as playing heavy games and watching high quality videos remotely in a way that makes the users feel as if they are sitting locally on their personal machines. On the other hand, the industry is yet unable to make efficient wearable computers a reality due to the limited size of the wearable devices, where it is infeasible to place efficient processors and big enough hard disks. This paper aims to highlight the need for the vertical scalability service and design the appropriate cloud, virtualization layer, and operating system services to incorporate vertical scalability in current cloud platforms in a way that will make it economically and technically efficient for the end users to use cloud virtual machines as if they are using their personal laptops. Through these services, the cloud takes wearable computing to the next stage and makes wearable computers a reality

Cloud Index Tracking: Enabling Predictable Costs in Cloud Spot Markets

Cloud spot markets rent VMs for a variable price that is typically much lower than the price of on-demand VMs, which makes them attractive for a wide range of large-scale applications. However, applications that run on spot VMs suffer from cost uncertainty, since spot prices fluctuate, in part, based on supply, demand, or both. The difficulty in predicting spot prices affects users and applications: the former cannot effectively plan their IT expenditures, while the latter cannot infer the availability and performance of spot VMs, which are a function of their variable price. To address the problem, we use properties of cloud infrastructure and workloads to show that prices become more stable and predictable as they are aggregated together. We leverage this observation to define an aggregate index price for spot VMs that serves as a reference for what users should expect to pay. We show that, even when the spot prices for individual VMs are volatile, the index price remains stable and predictable. We then introduce cloud index tracking: a migration policy that tracks the index price to ensure applications running on spot VMs incur a predictable cost by migrating to a new spot VM if the current VM's price significantly deviates from the index price.Comment: ACM Symposium on Cloud Computing 201

The state of SQL-on-Hadoop in the cloud

Managed Hadoop in the cloud, especially SQL-on-Hadoop, has been gaining attention recently. On Platform-as-a-Service (PaaS), analytical services like Hive and Spark come preconfigured for general-purpose and ready to use. Thus, giving companies a quick entry and on-demand deployment of ready SQL-like solutions for their big data needs. This study evaluates cloud services from an end-user perspective, comparing providers including: Microsoft Azure, Amazon Web Services, Google Cloud, and Rackspace. The study focuses on performance, readiness, scalability, and cost-effectiveness of the different solutions at entry/test level clusters sizes. Results are based on over 15,000 Hive queries derived from the industry standard TPC-H benchmark. The study is framed within the ALOJA research project, which features an open source benchmarking and analysis platform that has been recently extended to support SQL-on-Hadoop engines. The ALOJA Project aims to lower the total cost of ownership (TCO) of big data deployments and study their performance characteristics for optimization. The study benchmarks cloud providers across a diverse range instance types, and uses input data scales from 1GB to 1TB, in order to survey the popular entry-level PaaS SQL-on-Hadoop solutions, thereby establishing a common results-base upon which subsequent research can be carried out by the project. Initial results already show the main performance trends to both hardware and software configuration, pricing, similarities and architectural differences of the evaluated PaaS solutions. Whereas some providers focus on decoupling storage and computing resources while offering network-based elastic storage, others choose to keep the local processing model from Hadoop for high performance, but reducing flexibility. Results also show the importance of application-level tuning and how keeping up-to-date hardware and software stacks can influence performance even more than replicating the on-premises model in the cloud.This work is partially supported by the Microsoft Azure for Research program, the European Research Council (ERC) under the EUs Horizon 2020 programme (GA 639595), the Spanish Ministry of Education (TIN2015-65316-P), and the Generalitat de Catalunya (2014-SGR-1051).Peer ReviewedPostprint (author's final draft

Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud

With the advent of cloud computing, organizations are nowadays able to react rapidly to changing demands for computational resources. Not only individual applications can be hosted on virtual cloud infrastructures, but also complete business processes. This allows the realization of so-called elastic processes, i.e., processes which are carried out using elastic cloud resources. Despite the manifold benefits of elastic processes, there is still a lack of solutions supporting them. In this paper, we identify the state of the art of elastic Business Process Management with a focus on infrastructural challenges. We conceptualize an architecture for an elastic Business Process Management System and discuss existing work on scheduling, resource allocation, monitoring, decentralized coordination, and state management for elastic processes. Furthermore, we present two representative elastic Business Process Management Systems which are intended to counter these challenges. Based on our findings, we identify open issues and outline possible research directions for the realization of elastic processes and elastic Business Process Management.Comment: Please cite as: S. Schulte, C. Janiesch, S. Venugopal, I. Weber, and P. Hoenisch (2015). Elastic Business Process Management: State of the Art and Open Challenges for BPM in the Cloud. Future Generation Computer Systems, Volume NN, Number N, NN-NN., http://dx.doi.org/10.1016/j.future.2014.09.00

On the feasibility of collaborative green data center ecosystems

The increasing awareness of the impact of the IT sector on the environment, together with economic factors, have fueled many research efforts to reduce the energy expenditure of data centers. Recent work proposes to achieve additional energy savings by exploiting, in concert with customers, service workloads and to reduce data centers’ carbon footprints by adopting demand-response mechanisms between data centers and their energy providers. In this paper, we debate about the incentives that customers and data centers can have to adopt such measures and propose a new service type and pricing scheme that is economically attractive and technically realizable. Simulation results based on real measurements confirm that our scheme can achieve additional energy savings while preserving service performance and the interests of data centers and customers.Peer ReviewedPostprint (author's final draft