Dispatching cloud based on the concept of cloud computing provides an efficient and feasible technology solution, which in order to ensure security, stability, economic and environmental operation in the future integrated bulk grid. This paper analyzed application services mode in detail for on-line analysis in dispatching cloud, designed a multi-level cooperative on-line computing architecture, and studied the encapsulation of computing service modules. The main topic focuses on the key technologies to realize multi-level cooperative on-line computation, which include computing tasks allocation mapping mechanism, decomposition-coordination computing service bus, computing service driven engine and data interaction service mechanism, etc. Finally, the computing service diagram is developed to achieve on-line analysis service and state estimation service instance is applied to respond user's calculation request. It is a useful exploration and provides reference with a scene of carrying out collaborative operation between data cloud, model cloud and computing cloud in an efficient way.

Cellular networks employ a specific radio resource management policy distinguishing them from wired and Wi-Fi networks. A lack of awareness of this important mechanism potentially leads to resource-inefficient mobile applications. We perform the first network-wide, large-scale investigation of a particular type of application traffic pattern called periodic transfers where a handset periodically exchanges some data with a remote server every t seconds. Using packet traces containing 1.5 billion packets collected from a commercial cellular carrier, we found that periodic transfers are very prevalent in today's smartphone traffic. However, they are extremely resource-inefficient for both the network and end-user devices even though they predominantly generate very little traffic. This somewhat counter-intuitive behavior is a direct consequence of the adverse interaction between such periodic transfer patterns and the cellular network radio resource management policy. For example, for popular smartphone applications such as Facebook, periodic transfers account for only 1.7% of the overall traffic volume but contribute to 30% of the total handset radio energy consumption. We found periodic transfers are generated for various reasons such as keep-alive, polling, and user behavior measurements. We further investigate the potential of various traffic shaping and resource control algorithms. Depending on their traffic patterns, applications exhibit disparate responses to optimization strategies. Jointly using several strategies with moderate aggressiveness can eliminate almost all energy impact of periodic transfers for popular applications such as Facebook and Pandora.

Despite the popularity of mobile applications, their performance and energy bottlenecks remain hidden due to a lack of visibility into the resource-constrained mobile execution environment with potentially complex interaction with the application behavior. We design and implement ARO, the mobile Application Resource Optimizer, the first tool that efficiently and accurately exposes the cross-layer interaction among various layers including radio resource channel state, transport layer, application layer, and the user interaction layer to enable the discovery of inefficient resource usage for smartphone applications. To realize this, ARO provides three key novel analyses: (i) accurate inference of lower-layer radio resource control states, (ii) quantification of the resource impact of application traffic patterns, and (iii) detection of energy and radio resource bottlenecks by jointly analyzing cross-layer information. We have implemented ARO and demonstrated its benefit on several essential categories of popular Android applications to detect radio resource and energy inefficiencies, such as unacceptably high (46%) energy overhead of periodic audience measurements and inefficient content prefetching behavior.

No abstract available.

The use of cellular data networks is increasingly popular as network coverage becomes more ubiquitous and many diverse user-contributed mobile applications become available. The growing cellular traffic demand means that cellular network carriers are facing greater challenges to provide users with good network performance and energy efficiency, while protecting networks from potential attacks. To better utilize their limited network resources while securing the network and protecting client devices the carriers have already deployed various network policies that influence traffic behavior. Today, these policies are mostly opaque, though they directly impact application designs and may even introduce network vulnerabilities.

Despite the tremendous growth in the cellular data network usage due to the popularity of smartphones, so far there is rather limited understanding of the network infrastructure of various cellular carriers. Understanding the infrastructure characteristics such as the network topology, routing design, address allocation, and DNS service configuration is essential for predicting, diagnosing, and improving cellular network services, as well as for delivering content to the growing population of mobile wireless users. In this work, we propose a novel approach for discovering cellular infrastructure by intelligently combining several data sources, i.e., server logs from a popular location search application, active measurements results collected from smartphone users, DNS request logs from a DNS authoritative server, and publicly available routing updates. We perform the first comprehensive analysis to characterize the cellular data network infrastructure of four major cellular carriers within the U.S. in our study.

In 3G cellular networks, the release of radio resources is controlled by inactivity timers. However, the timeout value itself, also known as the tail time, can last up to 15 seconds due to the necessity of trading off resource utilization efficiency for low management overhead and good stability, thus wasting considerable amount of radio resources and battery energy at user handsets. In this paper, we propose Tail Optimization Protocol (TOP), which enables cooperation between the phone and the radio access network to eliminate the tail whenever possible. Intuitively, applications can often accurately predict a long idle time. Therefore the phone can notify the cellular network on such an imminent tail, allowing the latter to immediately release radio resources. To realize TOP, we utilize a recent proposal of 3GPP specification called fast dormancy, a mechanism for a handset to notify the cellular network for immediate radio resource release. TOP thus requires no change to the cellular infrastructure and only minimal changes to smartphone applications. Our experimental results based on real traces show that with a reasonable prediction accuracy, TOP saves the overall radio energy (up to 17%) and radio resources (up to 14%) by reducing tail times by up to 60%. For applications such as multimedia streaming, TOP can achieve even more significant savings of radio energy (up to 60%) and radio resources (up to 50%).

3G cellular data networks have recently witnessed explosive growth. In this work, we focus on UMTS, one of the most popular 3G mobile communication technologies. Our work is the first to accurately infer, for any UMTS network, the state machine (both transitions and timer values) that guides the radio resource allocation policy through a light-weight probing scheme. We systematically characterize the impact of operational state machine settings by analyzing traces collected from a commercial UMTS network, and pinpoint the inefficiencies caused by the interplay between smartphone applications and the state machine behavior. Besides basic characterizations, we explore the optimal state machine settings in terms of several critical timer values evaluated using real network traces. Our findings suggest that the fundamental limitation of the current state machine design is its static nature of treating all traffic according to the same inactivity timers, making it difficult to balance tradeoffs among radio resource usage efficiency, network management overhead, device radio energy consumption, and performance. To the best of our knowledge, our work is the first empirical study that employs real cellular traces to investigate the optimality of UMTS state machine configurations. Our analysis also demonstrates that traffic patterns impose significant impact on radio resource and energy consumption. In particular, We propose a simple improvement that reduces YouTube streaming energy by 80% by leveraging an existing feature called fast dormancy supported by the 3GPP specifications.