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The use of multi-criteria decision analysis (MCDA) by online broker to rank different service providers in the Cloud is based upon criteria provided by a customer. However, such ranking is prone to bias if the customer has insufficient domain knowledge. He/she may exclude relevant or include irrelevant criterion termed as 'misspecification of criterion'. This causes structural uncertainty within the...
Nowadays smartphones are equipped with many sensors which applications can continuously invoke to acquire real-time sensor information, such as GPS tracking. Due to the resource-constrained nature of the smartphones, it is often beneficial if the processing of the sensor data is offloaded to a remote resource. However, the decision to offload the computation depends on a multitude of factors such...
In the era of Internet of Things (IoT), the concept of "cloudlet" was proposed, which can be considered as a "micro data center in a box or node" deployed at the edge of the mobile network. The integration of cloudlet and wireless mesh networks gives rise to the idea of "cloudlet mesh", which offers services to a large number of IoT applications simultaneously. With virtualized...
Decomposed cloud gaming is a novel paradigm to deliver gaming as a service to mobile terminals. In such a system, video games are decomposed into software components that are cognitive to players' behavior and execution environments in both cloud and mobile devices. However, in order to balance the workload distribution between cloud and mobile terminals in a real system, there are still a number...
In this paper we present Foggy, an architectural framework and software platform based on Open Source technologies. Foggy orchestrates application workload, negotiates resources and supports IoT operations for multi-tier, distributed, heterogeneous and decentralized Cloud Computing systems. Foggy is tailored for emerging domains such as 5G Networks and IoT, which demand resources and services to be...
Efficiently scheduling the workloads that are submitted to warehouse-scale clusters is not a trivial task. In these systems, the scheduler needs to deal with the heterogeneity in both the jobs that compose the workload, as well as in the servers that comprise the clusters. Moreover, placement constraints that either prevent or force jobs to be allocated in particular servers, makes the scheduler's...
High availability of containerized applications requires to perform robust storage of applications’ state. Since basic replication techniques are extremely costly at scale, storage space requirements can be reduced by means of erasure and/or repairing codes.In this paper we address storage regeneration using repair codes, a robust distributed storage technique with no need to fully restore the whole...
Modern web/mobile applications have a highly variable user load from different geographical locations that depends upon the time-of-day, flash crowds, and growth rate (high or low) of user load. Irrespective of server hosting location of the applications, users from different geographical regions need highly responsive applications because bad performance results in unhappy customers. A recent survey...
This paper analyzes, compares and looks at tradeoffs in different load distribution schemes for consistent hashing in DHTs(distributed hash tables). Different traffic patterns, including an adversarial pattern, were constructed to test the load distribution. A simulator was made for each load distribution scheme and the load on each node was recorded for each traffic pattern. It was shown that increasing...
Since the Internet is an aggregation of multiple ASes (Autonomous Systems), congestion control and utilization are not globally optimized. For example, it is not uncommon that a direct shortest route with low latency delivers less bandwidth than an alternative, long and roundabout route. Previous research has shown that geospatially distributed computing instances in commercial clouds offer users...
Cloud computing represents a paradigm shift in provisioning on-demand computational resources underpinned by data center infrastructure, which now constitutes 1.5% of worldwide energy consumption. Such consumption is not merely limited to operating IT devices, but encompasses cooling systems representing 40% total data center energy usage. Given the substantive complexity and heterogeneity of data...
In line with cloud computing emergence as the dominant enterprise computing paradigm, our conceptualization of the cloud computing reference architecture and service construction has also evolved. For example, to address the need for cost reduction and rapid provisioning, virtualization has moved beyond hardware to containers. More recently, serverless computing or Function-as-a-Service has been presented...
This paper presents the Data Science Model Curriculum (MC-DS) that is based on the Data Science Competence Framework and Data Science Body of Knowledge defined in EDISON Data Science Framework (EDSF). MC-DS follows a competence-based curriculum design approach grounded in the Data Science competences (CD-DS) defined in EDSF and correspondingly defined Learning Outcomes (LO). The DSBoK provides a basis...
DevOps teams have to consider many technology and platform aspects when developing, deploying and operating cloud based applications: application deployments need to work everywhere on different cloud platforms, identities need to come from anywhere, and networks need to connect to anyone. The CYCLONE middleware is a holistic middleware stack that allows deploying and managing cloud based applications...
Multi-tenancy is sharing a single application's resources to serve more than a single group of users (i.e. tenant). Cloud application providers are encouraged to adopt multi-tenancy as it facilitates increased resource utilization and ease of maintenance, translating into lower operational and energy costs. However, introducing multi-tenancy to a single-tenant application requires significant changes...
Nowadays, IoT data come from multiple sources from a large number of devices. To manage them, IoT frameworks rely on Big Data ecosystems hosted in the cloud to provide scalable storage systems and to achieve scalable processing. Although these ecosystems scale well to process large sizes of data, in many cases this is done naively. Many datasets, such as IoT Energy measurement data, consist, even...
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