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The rapidly increasing availability of healthcare data from multiple heterogeneous sources has spearheaded the adoption of data-driven approaches for improved clinical research, decision making, and patient management. The patient healthcare data are usually longitudinal and can be expressed as medical event sequences, where the events include clinical diagnosis, medications, laboratory reports, etc...
Leveraging Virtual Machine (VM) technologies to host multiple Web applications on the same physical machine can improve the resource utilization and thus save a cloud provider's provisioning cost. By allocating and scheduling virtual CPU (vCPU) resources for running VMs, a hosted Web application may achieve varying performances. Thus, when facing an end user with a specific SLA (Service Level Agreement)...
QoS aware service composition necessitates an effective pricing mechanism in regulating service providers in public cloud computing environments. However, due to the fact that service providers are usually autonomous, strategic and self-motivated, it is far from trivial to deal with the pricing issues between them. In this paper we formulate a non-cooperative service pricing game to understand the...
Soft resource allocation is an important factor of system configuration which plays a critical role in guaranteeing the performance of multi-tier web service systems. There is a tradeoff between real-time performance and resource consumption, and thus the real-time adjustment of soft resource allocation in response to dynamic workload is quite challenging. In this paper, we propose a real-time soft...
Current Infrastructure-as-a-Service (IaaS) clouds offer both on-demand and reservation instance purchasing options. Users can combine these two options dynamically to serve time-varying demands while minimizing their instance acquisition costs. However, when future demands are unknown, it is far from trivial for cloud users to make optimal instance purchasing decisions. To deal with this problem,...
Performance analysis is crucial to the successful development of cloud computing paradigm. And it is especially important for a cloud computing center serving parallelizable application jobs, for determining a proper degree of parallelism could reduce the mean service response time and thus improve the performance of cloud computing obviously. In this paper, taking the cloud based rendering service...
In multi-tier cloud service systems, performance evaluation relies on numerous experiments in order to collect key metrics such as resources usage. The approach may result in highly time-consuming in practice. In this paper, we propose an automated framework for performance tracking, data management and analysis to minimize human intervention in multi-tier cloud service systems. The framework support...
In social network analysis, we often need to predict new links, given some available evidence. This may, for instance, enable us to study user behavior and infer likely new interactions in the near future. Recently, a family of algorithms based on exchangeable graphs has proven effective for link prediction. The network is modeled as an exchangeable array, whose entries can flexibly be traced back...
Parallel application jobs account for a large population in current domain of cloud computing and Big Data processing services, whose execution time can be varied greatly with different runtime configurations. For efficiently scheduling resources and services to run parallel jobs, the ability to quickly and accurately estimate the performance of parallel applications is critical. Analytic predictive...
Mobile cloud computing is now emerging as a promising way to enlarge the capabilities of mobile devices by computation offloading. One of the critical challenges faced by the mobile cloud providers today is how to increase the profitability of their cloud services. In this paper, we deal with the problem of scheduling parallelizable computation jobs offloaded by mobile users in public cloud to maximize...
Standard recommender systems usually rely only on past user ratings as well as optional profiles of customers and products. In e-commerce settings, however, a more complete understanding of the corresponding bi-directional impact between the demands of customers and the supply capabilities of providers can be the key to success. This motivates us to design a recommendation model that explicitly reflects...
MOP (Multiobjective Optimization Problem) is a prevailing research field for its well-modeling on the decision-making dilemma in the real world. We present a distributed ACO (Ant Colony Optimization) algorithm based on a crowdsourcing model, with a few innovative strategies as enhancement, for continuous MOPs. The original MOP is expected to be decomposed into multiple single-objective subtasks, which...
With the rapid development of IaaS market, how to efficiently trade between cloud providers and users is becoming a new challenge which attracts huge attentions from both industry and academia. Compared with traditional fixed-price model, market-oriented trading mechanism such as auction demonstrates greater promise for resource pricing and allocation in clouds due to its adaptability and flexibility...
It is obvious that big data can bring us new opportunities to discover valuable information. Apparently, corresponding big datasets are powerful tools for scholars, which connect theoretical studies to reality. They can help scholars to evaluate their achievements and find new problems. In recent years, there has been a significant growth in research data repositories and registries. However, these...
In the case of a cloud-based remote control system such as SCADA (Supervisory Control and Data Acquisition) that enables users to collect data from cloud-connected machines deployed anywhere at any time. However, machine data models may not be updated in a timely manner after the devices are upgrades or modified. This leads to mismatches between the machine data and data models. A key obstacle of...
Development of the Smart City has produced much data with attributions of timestamp and location, but in some applications like investigation of the large bomb explosion in New York, the government takes precedence to investigate the relation data from New York city rather than the whole Country, which prompts us to do some research works in computing partial graph more fast. So we propose SpatialGraphx,...
Efficient resources utilization and better system performance are always two important objectives that service providers pursue to enjoy a maximum profit. In this paper, through analyzing experimental measurements, we study the performance impact of interdependent soft resources on an n-tier application benchmark - the RUBiS system. Soft resources are vital factors that influence hardware resources...
Soft resources, which are system software components that use hardware or synchronize the use of hardware, are playing a critical role in the performance of multi-tier web systems, and thus it is quite important to tune the soft resource allocation for using the limited hardware resources to obtain maximum effectiveness. In this paper, we integrate both theoretical and experimental studies to the...
With the development of intelligent manufacturing technology, it can be foreseen that time series data generated by smart devices will raise to an unprecedented level. For time series with high amount, high dimension and renewal speed characteristics, resulting in difficult data mining and presentation on the original time series data. This paper presented a piecewise linear representation based on...
Today, there has been a strong demand of distributed collaboration in design and manufacturing, due to the acceleration of economic globalization and the popularity of virtual enterprises (VE) model. Because of the characteristics of cloud computing, such as elasticity and on-demand computing, it is promising to deploy and execute collaborative workflows that contain multiple tasks and services such...
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