Enabling horizontal scalability in an open source enterprise services bus

Cloud computing is a recent paradigm which describes a new way of consuming and delivering IT Services. In the Platform as a Service (PaaS) model, an underlying infrastructure such as network, operative system or server is provided to the Cloud consumers for either deploying their own applications, or applications supplied by the Cloud provider. In effect, Cloud computing modifies how applications should be built, provided, and consumed, as they may provide or be totally exposed as services, or consume existing third party applications services. The main advantages in Cloud computing are related to dynamic scaling of resources which are able to adapt to changes based on demand of resources and the use of multi-tenancy techniques in order based on sharing of resources between different users towards achieving the economy of scale. The Enterprise Service Bus (ESB) is essential as an integration middleware between application and services within and between multiple Cloud infrastructures. Different communication protocols might be used by application services and it is therefore necessary to have a mediator between them. Several challenges might arise when using an ESB as communication mediator between applications in cloud when to scale in and scale out to optimize resource consumption. The number of ESB instances should vary depending on the load in the Cloud infrastructure. This can be achieved by dynamically scaling in and out multiple ESB instances which constitute the horizontal ESB cluster. In this Master Thesis we focus on enabling horizontal scalability support for an open source multi-tenant aware Enterprise Service Bus (ESB). The investigation is based on two possible scenarios for enabling horizontal scalability: interconnected vs. non interconnected ESB instances. Therefore, in this work we investigate their advantages, disadvantages, and possible challenges and solutions. Based on previous investigations, a realization approach for enabling multi-instance management of a multi-tenant aware ESB is provided

Resource Sharing for Multi-Tenant Nosql Data Store in Cloud

Thesis (Ph.D.) - Indiana University, Informatics and Computing, 2015Multi-tenancy hosting of users in cloud NoSQL data stores is favored by cloud providers because it enables resource sharing at low operating cost. Multi-tenancy takes several forms depending on whether the back-end file system is a local file system (LFS) or a parallel file system (PFS), and on whether tenants are independent or share data across tenants In this thesis I focus on and propose solutions to two cases: independent data-local file system, and shared data-parallel file system. In the independent data-local file system case, resource contention occurs under certain conditions in Cassandra and HBase, two state-of-the-art NoSQL stores, causing performance degradation for one tenant by another. We investigate the interference and propose two approaches. The first provides a scheduling scheme that can approximate resource consumption, adapt to workload dynamics and work in a distributed fashion. The second introduces a workload-aware resource reservation approach to prevent interference. The approach relies on a performance model obtained offline and plans the reservation according to different workload resource demands. Results show the approaches together can prevent interference and adapt to dynamic workloads under multi-tenancy. In the shared data-parallel file system case, it has been shown that running a distributed NoSQL store over PFS for shared data across tenants is not cost effective. Overheads are introduced due to the unawareness of the NoSQL store of PFS. This dissertation targets the key-value store (KVS), a specific form of NoSQL stores, and proposes a lightweight KVS over a parallel file system to improve efficiency. The solution is built on an embedded KVS for high performance but uses novel data structures to support concurrent writes, giving capability that embedded KVSs are not designed for. Results show the proposed system outperforms Cassandra and Voldemort in several different workloads

Teenustele orienteeritud ja tõendite-teadlik mobiilne pilvearvutus

Arvutiteaduses on kaks kõige suuremat jõudu: mobiili- ja pilvearvutus. Kui pilvetehnoloogia pakub kasutajale keerukate ülesannete lahendamiseks salvestus- ning arvutusplatvormi, siis nutitelefon võimaldab lihtsamate ülesannete lahendamist mistahes asukohas ja mistahes ajal. Täpsemalt on mobiilseadmetel võimalik pilve võimalusi ära kasutades energiat säästa ning jagu saada kasvavast jõudluse ja ruumi vajadusest. Sellest tulenevalt on käesoleva töö peamiseks küsimuseks kuidas tuua pilveinfrastruktuur mobiilikasutajale lähemale? Antud töös uurisime kuidas mobiiltelefoni pilveteenust saab mobiilirakendustesse integreerida. Saime teada, et töö delegeerimine pilve eeldab mitmete pilve aspektide kaalumist ja integreerimist, nagu näiteks ressursimahukas töötlemine, asünkroonne suhtlus kliendiga, programmaatiline ressursside varustamine (Web APIs) ja pilvedevaheline kommunikatsioon. Nende puuduste ületamiseks lõime Mobiilse pilve vahevara Mobile Cloud Middleware (Mobile Cloud Middleware - MCM) raamistiku, mis kasutab deklaratiivset teenuste komponeerimist, et delegeerida töid mobiililt mitmetele pilvedele kasutades minimaalset andmeedastust. Teisest küljest on näidatud, et koodi teisaldamine on peamisi strateegiaid seadme energiatarbimise vähendamiseks ning jõudluse suurendamiseks. Sellegipoolest on koodi teisaldamisel miinuseid, mis takistavad selle laialdast kasutuselevõttu. Selles töös uurime lisaks, mis takistab koodi mahalaadimise kasutuselevõttu ja pakume lahendusena välja raamistiku EMCO, mis kogub seadmetelt infot koodi jooksutamise kohta erinevates kontekstides. Neid andmeid analüüsides teeb EMCO kindlaks, mis on sobivad tingimused koodi maha laadimiseks. Võrreldes kogutud andmeid, suudab EMCO järeldada, millal tuleks mahalaadimine teostada. EMCO modelleerib kogutud andmeid jaotuse määra järgi lokaalsete- ning pilvejuhtude korral. Neid jaotusi võrreldes tuletab EMCO täpsed atribuudid, mille korral mobiilirakendus peaks koodi maha laadima. Võrreldes EMCO-t teiste nüüdisaegsete mahalaadimisraamistikega, tõuseb EMCO efektiivsuse poolest esile. Lõpuks uurisime kuidas arvutuste maha laadimist ära kasutada, et täiustada kasutaja kogemust pideval mobiilirakenduse kasutamisel. Meie peamiseks motivatsiooniks, et sellist adaptiivset tööde täitmise kiirendamist pakkuda, on tagada kasutuskvaliteet (QoE), mis muutub vastavalt kasutajale, aidates seeläbi suurendada mobiilirakenduse eluiga.Mobile and cloud computing are two of the biggest forces in computer science. While the cloud provides to the user the ubiquitous computational and storage platform to process any complex tasks, the smartphone grants to the user the mobility features to process simple tasks, anytime and anywhere. Smartphones, driven by their need for processing power, storage space and energy saving are looking towards remote cloud infrastructure in order to solve these problems. As a result, the main research question of this work is how to bring the cloud infrastructure closer to the mobile user? In this thesis, we investigated how mobile cloud services can be integrated within the mobile apps. We found out that outsourcing a task to cloud requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, asynchronous communication with the client, programmatically provisioning of resources (Web APIs) and cloud intercommunication. Hence, we proposed a Mobile Cloud Middleware (MCM) framework that uses declarative service composition to outsource tasks from the mobile to multiple clouds with minimal data transfer. On the other hand, it has been demonstrated that computational offloading is a key strategy to extend the battery life of the device and improves the performance of the mobile apps. We also investigated the issues that prevent the adoption of computational offloading, and proposed a framework, namely Evidence-aware Mobile Computational Offloading (EMCO), which uses a community of devices to capture all the possible context of code execution as evidence. By analyzing the evidence, EMCO aims to determine the suitable conditions to offload. EMCO models the evidence in terms of distributions rates for both local and remote cases. By comparing those distributions, EMCO infers the right properties to offload. EMCO shows to be more effective in comparison with other computational offloading frameworks explored in the state of the art. Finally, we investigated how computational offloading can be utilized to enhance the perception that the user has towards an app. Our main motivation behind accelerating the perception at multiple response time levels is to provide adaptive quality-of-experience (QoE), which can be used as mean of engagement strategy that increases the lifetime of a mobile app

Autonomous management of cost, performance, and resource uncertainty for migration of applications to infrastructure-as-a-service (IaaS) clouds

2014 Fall.Includes bibliographical references.Infrastructure-as-a-Service (IaaS) clouds abstract physical hardware to provide computing resources on demand as a software service. This abstraction leads to the simplistic view that computing resources are homogeneous and infinite scaling potential exists to easily resolve all performance challenges. Adoption of cloud computing, in practice however, presents many resource management challenges forcing practitioners to balance cost and performance tradeoffs to successfully migrate applications. These challenges can be broken down into three primary concerns that involve determining what, where, and when infrastructure should be provisioned. In this dissertation we address these challenges including: (1) performance variance from resource heterogeneity, virtualization overhead, and the plethora of vaguely defined resource types; (2) virtual machine (VM) placement, component composition, service isolation, provisioning variation, and resource contention for multitenancy; and (3) dynamic scaling and resource elasticity to alleviate performance bottlenecks. These resource management challenges are addressed through the development and evaluation of autonomous algorithms and methodologies that result in demonstrably better performance and lower monetary costs for application deployments to both public and private IaaS clouds. This dissertation makes three primary contributions to advance cloud infrastructure management for application hosting. First, it includes design of resource utilization models based on step-wise multiple linear regression and artificial neural networks that support prediction of better performing component compositions. The total number of possible compositions is governed by Bell's Number that results in a combinatorially explosive search space. Second, it includes algorithms to improve VM placements to mitigate resource heterogeneity and contention using a load-aware VM placement scheduler, and autonomous detection of under-performing VMs to spur replacement. Third, it describes a workload cost prediction methodology that harnesses regression models and heuristics to support determination of infrastructure alternatives that reduce hosting costs. Our methodology achieves infrastructure predictions with an average mean absolute error of only 0.3125 VMs for multiple workloads

MINIMIZATION OF RESOURCE CONSUMPTION THROUGH WORKLOAD CONSOLIDATION IN LARGE-SCALE DISTRIBUTED DATA PLATFORMS

The rapid increase in the data volumes encountered in many application domains has led to widespread adoption of parallel and distributed data management systems like parallel databases and MapReduce-based frameworks (e.g., Hadoop) in recent years. Use of such parallel and distributed frameworks is expected to accelerate in the coming years, putting further strain on already-scarce resources like compute power, network bandwidth, and energy. To reduce total execution times, there is a trend towards increasing execution parallelism by spreading out data across a large number of machines. However, this often increases the total resource consumption, and especially energy consumption, significantly because of process startup costs and other overheads (e.g., communication overheads). In this dissertation, we develop several data management techniques to minimize resource consumption through workload consolidation. In this dissertation, we introduce a key metric called query span, i.e., number of machines involved in the execution of a query or a job. In order to minimize the per query resource consumption we propose to minimize query span. To that end, we develop several workload-driven data partitioning and replica selection algorithms that attempt to minimize the average query span by exploiting the fact that most distributed environments need to use replication for fault tolerance. Extensive experiments on various datasets show that judicious data placement and replication can dramatically reduce the average query spans resulting in significant reductions in resource consumption. We show our results primarily on two applications, distributed data warehouse system and distributed information retrieval. In the first case, we show that minimizing average query spans can minimize overall resource consumption for a given workload and can also improve the performance of complex analytical queries. In the second case, our approach minimizes the overall search cost as well as effectively trades off search cost with load imbalance. The best case of resource efficiency for any underlying data processing system is achieved when the job or the query can be run efficiently on a single machine (i.e., query span=1). In the final part of dissertation, we discuss an in-memory MapReduce system optimized for performing complex analytics tasks on input data sizes that fit in a single machine's memory. We argue that systems like Hadoop that are designed to operate across a large number of machines are not optimal in performance for small and medium sized complex analytics tasks because of high startup costs, heavy disk activity, and wasteful checkpointing. We have developed a prototype runtime called HONE that is API compatible with standard (distributed) Hadoop. In other words, we can take existing Hadoop code and run it, without modification, on a multi-core shared memory machine. This allows us to take existing Hadoop algorithms and find the most suitable runtime environment for execution on datasets of varying sizes. Overall, in this dissertation, our key contributions in this work include identification of key metric query span and its relationship with overall resource consumption in scale-out architectures. We introduce several workload-aware techniques to optimize this key metric. We go on to demonstrate the effectiveness of query span minimization on different application scenarios. In order to take advantage of scale-up architectures effectively we develop novel in-memory MapReduce system HONE for single machine. Our thorough experiments on real and synthetic datasets demonstrate the efficacy of our proposed approaches