Cost/Performance Evaluation for Cloud Applications Using Simulation

Application latency is one significant user metric for evaluating the performance of online cloud applications. However, as applications are migrated to the cloud and deployed across a wide-area network, the application latency usually presents high variability over time. Among lots of subtleties that influence the latency, one important factor is relying on the Internet for application connectivity, which introduces a high degree of variability and uncertainty on user-perceived application latency. As a result, a key challenge faced by application designers is how to build consistently low-latency cloud applications with the large number of geo-distributed and latency-varying cloud components. In this paper, we propose a dynamic request routing framework, DR2, by taking full advantage of redundant components in the clouds to tolerate latency variability. In practice, many functionally-equivalent components have been already deployed redundantly for load balancing and fault tolerance, thus resulting in low additional overhead for DR2. To evaluate the performance of our approach, we conduct a set of experiments based on two large-scale real-world datasets and a synthetic dataset. The results show the effectiveness and efficiency of our approach.

The advances in technologies of cloud computing and mobile computing enable the newly emerging mobile cloud computing paradigm. Three approaches have been proposed for mobile cloud applications: 1) extending the access to cloud services to mobile devices; 2) enabling mobile devices to work collaboratively as cloud resource providers; 3) augmenting the execution of mobile applications on portable devices using cloud resources. In this paper, we focus on the third approach in supporting mobile data stream applications. More specifically, we study the computation partitioning, which aims at optimizing the partition of a data stream application between mobile and cloud such that the application has maximum speed/throughput in processing the streaming data. To the best of our knowledge, it is the first work to study the partitioning problem for mobile data stream applications, where the optimization is placed on achieving high throughput of processing the streaming data rather than minimizing the make span of executions in other applications. We first propose a framework to provide runtime support for the dynamic partitioning and execution of the application. Different from existing works, the framework not only allows the dynamic partitioning for a single user but also supports the sharing of computation instances among multiple users in the cloud to achieve efficient utilization of the underlying cloud resources. Meanwhile, the framework has better scalability because it is designed on the elastic cloud fabrics. Based on the framework, we design a genetic algorithm to perform the optimal partition. We have conducted extensive simulations. The results show that our method can achieve more than 2X better performance over the execution without partitioning.

For future generations to meet their needs, and to close the global inequality gap, we need to degrow. That is we need to reduce resource and energy consumption to bring the economy back into balance with the living world in a way that reduces inequality and improves human well-being (Hickel, 2020a,b). This transition has consequences for business, because instead of boosting sales companies need to encourage consumers to make do with less, avoiding build in obsolescence, extending product lives to slow disposal and replacement, focusing on satisfying ‘needs’ rather than ‘wants’ and reducing overall resource consumption through conscious changes in sales and marketing techniques, new revenue models and innovative technology solutions (Bocken & Short, 2016). Overall, we can say that companies have to rethink their business models, therefore I specifically aim to answer the following research question: what could a degrowth business model framework look like? Degrowth business models (DGMs) are supposed to serve the dual aim of (1) obeying planetary boundaries whilst simultaneously (2) contributing to reducing inequality and increasing well-being. That is companies need to develop value propositions that, on the one hand contribute to absolutely reducing resource and energy consumption, and on the other are aimed at production of protected needs (Di Giulio & Defila, 2021). Since degrowth is considered an authentic and legitimate interpretation of sustainable development, SDGs 12-16 can serve as proxies for obeying planetary boundaries, whilst the remaining SDGs (minus SDG8.1 -economic growth) can be regarded as proxies for well-being and reducing inequality.

In this paper study for users have unquestionably profited from the rapidly changing paradigm of cloud computing. It employs virtual machines instead of actual equipment to host, store, and network multiple components, and it charges per use. As the amount of data being stored grows, so does the importance of load balancing as a research area. One of the most difficult problems in cloud computing is distributing the ever-changing burden among the nodes in an efficient manner known as load balancing. There are numerous load balancing methods in use today that considerably improve the efficiency and management of resources. We use cloud analyst as a simulator in this article to compare techniques with a single broker policy that helped boost cloud and related application performance.

Every organization needs a viable business model. Strikingly, most of current literature is focused on business model design, whereas there is almost no attention for business model validation and implementation and related business model experimentation. The goal of the research as described in this paper is to develop a business model engineering tool for supporting business model management as a continuous design, validation and implementation cycle. The tool is applied to an online investment research startup in roll out and market phase. This paper describes the research as performed in a case study setting by focusing on the design, implementation and evaluation of the business model engineering tool. We also analyze the actual implementation and usage of the business model tool by the online investment research startup by focusing on the most critical actions related to actual business model implementation – i.e. actions with so-called ‘Lollapalooza tendencies’.

The increasing demand on execution of large-scale Cloud workflow applications which need a robust and elastic computing infrastructure usually lead to the use of high-performance Grid computing clusters. As the owners of Cloud applications expect to fulfill the requested Quality of Services (QoS) by the Grid environment, an adaptive scheduling mechanism is needed which enables to distribute a large number of related tasks with different computational and communication demands on multi-cluster Grid computing environments. Addressing the problem of scheduling large-scale Cloud workflow applications onto multi-cluster Grid environment regarding the QoS constraints declared by application's owner is the main contribution of this paper. Heterogeneity of resource types (service type) is one of the most important issues which significantly affect workflow scheduling in Grid environment. On the other hand, a Cloud application workflow is usually consisting of different tasks with the need for different resource types to complete which we call it heterogeneity in workflow. The main idea which forms the soul of all the algorithms and techniques introduced in this paper is to match the heterogeneity in Cloud application's workflow to the heterogeneity in Grid clusters. To obtain this objective a new bi-level advanced reservation strategy is introduced, which is based upon the idea of first performing global scheduling and then conducting local scheduling. Global-scheduling is responsible to dynamically partition the received DAG into multiple sub-workflows that is realized by two collaborating algorithms: (1) The Critical Path Extraction algorithm (CPE) which proposes a new dynamic task overall critically value strategy based on DAG's specification and requested resource type QoS status to determine the criticality of each task; and (2) The DAG Partitioning algorithm (DAGP) which introduces a novel dynamic score-based approach to extract sub-workflows based on critical paths by using a new Fuzzy Qualitative Value Calculation System to evaluate the environment. Local-scheduling is responsible for scheduling tasks on suitable resources by utilizing a new Multi-Criteria Advance Reservation algorithm (MCAR) which simultaneously meets high reliability and QoS expectations for scheduling distributed Cloud-base applications. We used the simulation to evaluate the performance of the proposed mechanism in comparison with four well-known approaches. The results show that the proposed algorithm outperforms other approaches in different QoS related terms.

The Internet-of-Things (IoT) is today one of the hypes in the technological world but despite the enormous attention and research investment, the clear business value is still hard to perceive. IoT deployments are costly to be installed, managed and maintained, and need to provide a very clear value to justify the investments. For another viewpoint, IoT technologies need to be proven before deployment, which implies the need to test and assess IoT solutions in real settings and involve the actual target users. And as such, this presents an opportunity to have IoT deployments with a clear business model mainly focused on real-life large-scale research and technological experimentation. This would mean having a sustainable IoT infrastructure in-place based on the provision of experimentation services and a trial environment to industry and research, which then could also present an opportunity to establish added-value (business) services. This is the exact idea of the flagship SmartSantander testbed facility and especially its major deployment in the city of Santander, Spain. The SmartSantander facility business model is built around experimentally-driven research and technology development thus attracting many experimenters from industry and European research projects. This model makes it possible to sustain an outstanding large-scale IoT deployment of around 12,000 sensors and on top of it the development of new the development of new services and applications especially targeting the needs of users (citizens, businesses, authorities) in smart-cities. This paper studies the business model of outstanding SmartSantander facility in order to provide a generic Business Model for IoT testbeds that can provide guidance and be adapted by owners (or owners to-be) wishing to exploit their IoT deployments as facilities supporting experimentation and trials of IoT solutions.

The advent of cloud computing has made access to computing infrastructure available to millions of users that face resource constraints. In the context of high performance computing (HPC), public cloud resources have emerged as a cost‐effective alternative to expensive on‐premises clusters. However, there are several challenges and limitations in adopting this approach. This paper proposes HPC@Cloud , a provider‐agnostic open‐source software toolkit that facilitates the migration, testing, and execution of HPC applications in public clouds. The toolkit takes advantage of various fault tolerance technologies to enable the use of inexpensive transient cloud infrastructure, commonly known as “spot” instances. Also, it features integration with singularity containers, allowing users to run complex applications on virtual HPC clusters in a portable and reproducible way. Finally, it provides a data‐based empirical approach to estimating cloud infrastructure costs for HPC workloads. The results obtained on two public cloud providers (AWS and Vultr) show that: (i) HPC@Cloud can efficiently build virtual HPC clusters on the cloud; (ii) the new adaptive fault tolerance strategy outperforms other existing strategies based on blocking restoration; (iii) the integration of singularity containers into HPC@Cloud improves the portability of HPC applications to public clouds with negligible performance penalty to the applications; (iv) the proposed cost prediction approach can estimate the cost of running the applications on AWS and Vultr with up to 93% accuracy on average.

Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka

Business model is at the heart of every business. It is the way a company creates, captures, and delivers value and it is a prerequisite for a strategy to be implemented throughout an organization. Traditionally, value was considered to be purely financial. However, with increasing global environmental, social, and political challenges, we need to create a more sustainable society where economic growth is decoupled from resource consumption. In order to enable the transition to circular economy, businesses are expected to play a significant role in the shift to a circular economy by implementing circular business models.

Resource Depletion Risk for Medical Equipment: Embracing the Circular Economy.

A resource elasticity framework for QoS-aware execution of cloud applications

This paper proposes a cloud application framework, which integrates both the development and production environment seamlessly. The framework consists of the client-side integrated development environment (IDE), and the server-side service portfolio and cloud controller. The IDE has requirement definition, architecture design and application prototyping tools, and it can simulate execution of large-scale applications in developers' PCs. The service portfolio incorporates data from these tools and enables automatic data sharing between them, thereby avoiding setback and redundancy. To accommodate the applications on the cloud, the cloud controller utilizes the resource structures designed in the IDE and generates virtual machines (VMs) from templates, in which a verified OS and middleware for large-scale data processing are packaged. The behavior of VMs and applications will be automatically monitored and catalogued as a guideline for the next project. With these comprehensive approaches, the system integration methods can be streamlined and the acceleration of development can be easily demonstrated.

Exploiting the computing power of the diversity of resources available on heterogeneous systems is mandatory but a very challenging task. The diversity of architectures, execution models and programming tools, together with disjoint address spaces and different computing capabilities, raise a number of challenges that severely impact on application performance and programming productivity. This problem is further compounded in the presence of data parallel irregular applications. This paper presents a framework that addresses development and execution of data parallel irregular applications in heterogeneous systems. A unified task-based programming and execution model is proposed, together with inter and intra-device scheduling, which, coupled with a data management system, aim to achieve performance scalability across multiple devices, while maintaining high programming productivity. Intra-device scheduling on wide SIMD/SIMT architectures resorts to consumer-producer kernels, which, by allowing dynamic generation and rescheduling of new work units, enable balancing irregular workloads and increase resource utilization. Results show that regular and irregular applications scale well with the number of devices, while requiring minimal programming effort. Consumer-producer kernels are able to sustain significant performance gains as long as the workload per basic work unit is enough to compensate overheads associated with intra-device scheduling. This not being the case, consumer kernels can still be used for the irregular application. Comparisons with an alternative framework, StarPU, which targets regular workloads, consistently demonstrate significant speedups. This is, to the best of our knowledge, the first published integrated approach that successfully handles irregular workloads over heterogeneous systems.

In Cloud Computing (CC) environment, load balancing refers to the process of optimizing resources of virtual machines. Load balancing in the CC environment is one of the analytical approaches utilized to ensure indistinguishable workload distribution and effective utilization of resources. This is because only by ensuring effective balance of dynamic workload results in higher user satisfaction and optimal allocation of resource, therefore improve cloud application performance. Moreover, a paramount objective of load balancing is task scheduling because surges in the number of clients utilizing cloud lead to inappropriate job scheduling. Hence, issues encircling task scheduling has to be addressed. In this work a method called, Ford Fulkerson and Newey West Regression-based Dynamic Load Balancing (FF-NWRDLB) in CC environment is proposed. The FF-NWRDLB method is split into two sections, namely, task scheduling and dynamic load balancing. First, Ford Fulkerson-based Task Scheduling is applied to the cloud user requested tasks obtained from Personal Cloud Dataset. Here, employing Ford Fulkerson function based on the flow of tasks, energy-efficient task scheduling is ensured. The execution of asymmetrical scientific applications can be smoothly influenced by an unbalanced workload distribution between computing resources. In this context load balancing signifies as one of the most significant solution to enhance utilization of resources. However, selecting the best accomplishing load balancing technique is not an insignificant piece of work. For example, selecting a load balancing model does not work in circumstances with dynamic behavior. In this context, a machine learning technique called, Newey West Regression-based dynamic load balancer is designed to balance the load in a dynamic manner at run time, therefore ensuring accurate data communication. The FF-NWRDLB method has been compared to recent algorithms that use the markov optimization and the prediction scheme to achieve load balancing. Our experimental results show that our proposed FF-NWRDLB method outperforms other state of the art schemes in terms of energy consumption, throughput, delay, bandwidth and task scheduling efficiency in CC environment.