Abstract
An OS-online system called TransCom is based on a virtual storage system that supports heterogeneous services of the operating system and applications online. In TransCom, OS and software which run in the client are stored on the centralized servers, while computing tasks are carried out by the clients, so the server is the bottleneck of the system performance. This paper firstly analyzes the characteristics of its real usage workload and builds a queuing model to locate the system bottlenecks. According to the study, the disk is the primary bottleneck, so an optimal two-level cache arrangement policy is developed on both the server and the client, which aims to avoid most of the server disk accesses. LRU algorithm is used in the client-side cache. A cache management algorithm called Frequency-based Multi-Priority Queues (FMPQ) proposed in this paper is used in the server-side cache. Experiment results show that the appropriate cache arrangement can significantly improve the capability of the TransCom server.
Highlights
During the last decade, with the rapid advance in the embedded and mobile devices, the traditional general-purpose desktop computing is shifting toward the greatly heterogeneous and scalable cloud computing [1,2], which aims to offer novel pervasive services for users in right place, at right time and by right means with some kinds/ levels of smart or intelligent behaviours
The requests have a significant temporal locality characteristic in the client cache, so this section will not evaluate the performance of the client cache algorithm in TransCom client, and focus on the performance of Frequency-based Multi-Priority Queues (FMPQ) algorithm in the server cache
The performance of FBR algorithm is better than LRU, but it is always worse than FMPQ, in several cases the difference is very large
Summary
With the rapid advance in the embedded and mobile devices, the traditional general-purpose desktop computing is shifting toward the greatly heterogeneous and scalable cloud computing [1,2], which aims to offer novel pervasive services for users in right place, at right time and by right means with some kinds/ levels of smart or intelligent behaviours. From the scalable service perspective, these pervasive services are highly expected, that a smart ubiquitous computing platform should enable users to get different services via a single light-weight device and a same service via different types of devices. All of the current technologies cannot achieve the uneven conditioning services. In another word, users are often unable to select their desired service freely via the devices or platforms available to them. Due to the central storage of OSes and applications, the installation, maintenance, and managment are centralized, leaving the clients light-weighted.
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