Invocation Frequency Research Articles

QoS Prediction for Mobile Edge Service Recommendation With Auto-Encoder

In the mobile edge computing environment, there are a large number of mobile edge services which are the carriers of various mobile intelligent applications. So how to recommend the most suitable candidate from such a huge number of available services is an urgent task, especially the recommendation task based on quality-of-service (QoS). In traditional service recommendation, collaborative filtering (CF) has been studied in academia and industry. However, due to the mobility of users and services, there exist several defects that limit the application of the CF-based methods, especially in an edge computing environment. The most important problem is the cold-start. In this paper, we propose an ensemble model which combines the model-based CF and neighborhood-based CF. Our approach has two phases, i.e., global features learning and local features learning. In the first phase, to alleviate the cold-start problem, we propose an improved auto-encoder which deals with sparse inputs by pre-computing an estimate of the missing QoS values and can obtain the effective hidden features by capturing the complex structure of the QoS records. In the second phase, to further improve prediction accuracy, a novel computation method is proposed based on Euclidean distance that aims to address the overestimation problem. We introduce two new concepts, common invocation factor and invocation frequency factor, in similarity computation. Then we propose three prediction models, containing two individual models and one hybrid model. The two individual models are proposed to utilize user similar neighbors and service similar neighbors, and the hybrid model is to utilize all neighbors. The experiments conducted in a real-world dataset show that our models can produce superior prediction results and are not sensitive to parameter settings.

How to Fillet a Penguin: Runtime Data Driven Partitioning of Linux Code

In many modern operating systems (OSs), there exists no isolation between different kernel components, i.e., the failure of one component can affect the whole kernel. While microkernel OSs introduce address space separation for large parts of the OS, their improved fault isolation comes at the cost of performance. Despite significant improvements in modern microkernels, monolithic OSs like Linux are still prevalent in many systems. To achieve fault isolation in addition to high performance and code reuse in these systems, approaches to move only fractions of kernel code into user mode have been proposed. These approaches solely rely on static code analyses for deciding which code to isolate, neglecting dynamic properties like invocation frequencies. We propose to augment static code analyses with runtime data to achieve better estimates of dynamic properties for common case operation. We assess the impact of runtime data on the decision what code to isolate and the impact of that decision on the performance of such “microkernelized” systems. We extend an existing tool chain to implement automated code partitioning for existing monolithic kernel code and validate our approach in a case study of two widely used Linux device drivers and a file system.

“Time–Location–Frequency”–aware Internet of things service selection based on historical records

The advertised quality of an Internet of things service is not always trustable due to the exaggerated quality propagation and dynamic network environment. Therefore, it is more trustable to evaluate the Internet of things service quality based on the historical execution records of service. However, an Internet of things service often has multiple historical records whose invocation time and location are different, which makes it necessary to weigh each historical record of an identical Internet of things service. Besides, for different candidate Internet of things services, their invocation frequencies are often varied, which may also affect the final service selection decision of target user. In view of the above two challenges, a novel service selection approach “Time–Location–Frequency”–aware Service Selection Approach is put forward in this article. In Time–Location–Frequency–aware Service Selection Approach, we first weigh each historical record of an Internet of things service, based on its service invocation time and location; afterward, we weigh each candidate Internet of things service based on its invocation frequency; finally, with the derived two kinds of weights, we evaluate each candidate Internet of things service and return the quality-optimal one to the target user. At last, through a set of experiments deployed on a real service quality data set WS-DREAM, we validate the feasibility of our proposal.

DCR: Double Component Ranking for Building Reliable Cloud Applications

Since cloud applications are usually large-scale, it is too expensive to enhance the reliability of all components for building highly reliable cloud applications. Therefore, we need to identify significant components which have great impact on the system reliability. FTCloud, an existing approach, ranks the components only considering the impact of component internal failures and ignoring error propagation. However, error propagation is also an important factor on the system reliability. To attack the problem, we propose an improved component ranking framework, named DCR, to identify significant components in cloud applications. DCR employs two individual algorithms to rank the components twice and determines a set of the most significant components based on the two ranking results. In addition, DCR does not require information of component invocation frequencies. Extensive experiments are provided to evaluate DCR and compare it with FTCloud. The experimental results show that DCR outperforms FTCloud in almost all cases.

Designing Fault Tolerance Strategy by Iterative Redundancy for Component-Based Distributed Computing Systems

Reliability is a critical issue for component-based distributed computing systems, some distributed software allows the existence of large numbers of potentially faulty components on an open network. Faults are inevitable in this large-scale, complex, distributed components setting, which may include a lot of untrustworthy parts. How to provide highly reliable component-based distributed systems is a challenging problem and a critical research. Generally, redundancy and replication are utilized to realize the goal of fault tolerance. In this paper, we propose a CFI (critical fault iterative) redundancy technique, by which the efficiency can be guaranteed to make use of resources (e.g., computation and storage) and to create fault-tolerance applications. When operating in an environment with unknown components’ reliability, CFI redundancy is more efficient and adaptive than other techniques (e.g., K-Modular Redundancy and N-Version Programming). In the CFI strategy of redundancy, the function invocation relationships and invocation frequencies are employed to rank the functions’ importance and identify the most vulnerable function implemented via functionally equivalent components. A tradeoff has to be made between efficiency and reliability. In this paper, a formal theoretical analysis and an experimental analysis are presented. Compared with the existing methods, the reliability of components-based distributed system can be greatly improved by tolerating a small part of significant components.

Component Ranking for Fault-Tolerant Cloud Applications

Cloud computing is becoming a mainstream aspect of information technology. More and more enterprises deploy their software systems in the cloud environment. The cloud applications are usually large scale and include a lot of distributed cloud components. Building highly reliable cloud applications is a challenging and critical research problem. To attack this challenge, we propose a component ranking framework, named FTCloud, for building fault-tolerant cloud applications. FTCloud includes two ranking algorithms. The first algorithm employs component invocation structures and invocation frequencies for making significant component ranking. The second ranking algorithm systematically fuses the system structure information as well as the application designers' wisdom to identify the significant components in a cloud application. After the component ranking phase, an algorithm is proposed to automatically determine an optimal fault-tolerance strategy for the significant cloud components. The experimental results show that by tolerating faults of a small part of the most significant components, the reliability of cloud applications can be greatly improved.

Model validation for embedded systems using formal method-aided simulation

Embedded systems are becoming increasingly common in our everyday lives. As technology progresses, these systems become more and more complex. At the same time, the systems must fulfil strict requirements on reliability and correctness. Informal validation techniques, such as simulation, suffer from the fact that they only examine a small fraction of the state space. Therefore simulation results cannot be 100% guaranteed. Formal techniques, on the other hand, suffer from state-space explosion and might not be practical for huge, complex systems due to memory and time limitations. A validation approach, based on simulation, which addresses some of the above problems is proposed. Formal methods, in particular, model checking, are used to aid, or guide, the simulation process in certain situations in order to boost coverage. The invocation frequency of the model checker is dynamically controlled by estimating certain parameters related to the simulation speed of the particular system at hand. These estimations are based on statistical data collected during the validation session, in order to minimise verification time, and at the same time, achieve reasonable coverage.