MANAGING QUERY AND UPDATE TRANSACTIONS UNDER QUALITY CONTRACTS IN WEB-DATABASES

In modern Web-database systems, users typically perform read-only queries, whereas all write-only data updates are performed in the background, concurrently with queries.For most of these services to be successful and their users to be kept satisfied, two criteria need to be met: user requests must be answered in a timely fashion and must return fresh data. This is relatively easy when the system is lightly loaded and, as such, both queries and updates can be executed quickly. However, this goal becomes practically hard to achieve in real systems due to the high volumes of queries and updates, especially in periods of flash crowds. In this work, we argue it is beneficial to allow users to specify their preferences and let the system optimize towards satisfying user preferences, instead of simply improving the average case. We believe that this user-centric approach will empower the system to gracefully deal with a broader spectrum of workloads.Towards user-centric web-databases, we propose a Quality Contracts framework to help users express their preferences over multiple quality specifications. Moreover, we propose a suite of algorithms to effectively perform load balancing and scheduling for both queries and updates according to user preferences. We evaluate the proposed framework and algorithms through a simulation with real traces from disk accesses and from a stock information website. Finally, to increase the applicability of Quality Contracts enhanced Web-database systems, we propose an algorithm to help users adapt to the Web-database system behavior and maximize their query success ratio

Preference-Aware Query and Update Scheduling in Web-databases

Typical web-database systems receive read-only queries, that generate dynamic web pages as a response, and writeonly updates, that keep information up-to-date. Users expect short response times and low staleness. However, it may be extremely hard to apply all updates on time, i.e., keep zero staleness, and also get fast response times, especially in periods of bursty traffic. In this paper, we present the concept of Quality Contracts (QCs) which combines the two incomparable performance metrics: response time or Quality of Service (QoS), and staleness or Quality of Data (QoD). QCs allows individual users to express their preferences for the expected QoS and QoD of their queries by assigning “profit ” values. To maximize the total profit from submitted QCs, we propose an adaptive algorithm, called QUTS. QUTS addresses the problem of prioritizing the scheduling of updates over queries using a twolevel scheduling scheme that dynamically allocates CPU resources to updates and queries according to user preferences. We present the results of an extensive experimental study using real data (taken from a stock information web site), where we show that QUTS performs better than baseline algorithms under the entire spectrum of QCs; QUTS also adapts fast to changing workloads.

Abstract Node Mobility in Unstructured P2P Networks

P2P networks research is mainly focused on techniques for efficiently finding files to download from the network. Problems like churn and robustness have been thoroughly analyzed. Mobility in P2P networks is still an open topic. In this paper, we address the problem of deciding what to do in the case of node mobility. A node which knows that it is going to move in another part of the network wants to take the best decision what to do with its documents in the interest of the overall network performance. This problem is particularly challenging in the special case of a node which is the primary source for its documents, because of update costs and privacy issues. In this paper we identify possible decisions and circumstances in which a particular decision becomes the “best decision ” for the special case mentioned above. We also provide some experimental cases and results that compare the options with regard to different performance metrics.

Measuring Proximity on Graphs with Side Information

This paper studies how to incorporate side information (such as users ’ feedback) in measuring node proximity on large graphs. Our method (ProSIN) is motivated by the well-studied random walk with restart (RWR). The basic idea behind ProSIN is to leverage side information to refine the graph structure so that the random walk is biased towards/away from some specific zones on the graph. Our case studies demonstrate that ProSIN is well-suited in a variety of applications, including neighborhood search, center-piece subgraphs, and image caption. Given the potential computational complexity of ProSIN, we also propose a fast algorithm (Fast-ProSIN) that exploits the smoothness of the graph structures with/without side information. Our experimental evaluation shows that Fast-ProSIN achieves significant speedups (up to 49x) over straightforward implementations

UNIT: User-centric Transaction Management in Web-Database Systems

Web-database systems are nowadays an integral part of everybody’s life, with applications ranging from monitoring/trading stock portfolios, to personalized blog aggregation and news services, to personalized weather tracking services. For most of these services to be successful (and their users to be kept satisfied), two criteria need to be met: user requests must be answered in a timely fashion and using fresh data. This paper presents a framework to balance both requirements from the users’ perspective. Toward this, we propose a user satisfaction metric to measure the overall effectiveness of the Web-database system. We also provide a set of algorithms to dynamically optimize this metric, through query admission control and update frequency modulation. Finally, we present extensive experimental results which compare our proposed algorithms to the current state of the art and show that we outperform competitors under various workloads (generated based on real traces) and user requirements

Quality contracts for realtime enterprises

Abstract. Real-time enterprises rely on user queries being answered in a timely fashion and using fresh data. This is relatively easy when systems are lightly loaded and both queries and updates can be finished quickly. However, this goal becomes fundamentally hard to achieve due to the high volume of queries and updates in real systems, especially in periods of flash crowds. In such cases, systems typically try to optimize for the average case, treating all users, queries, and data equally. In this paper, we argue that it is more beneficial for real-time enterprises to have the users specify how to balance such a tradeoff between Quality of Service (QoS) and Quality of Data (QoD), in other words, “instructing” the system on how to best allocate resources to maximize the overall user satisfaction. Specifically, we propose Quality Contracts (QC) which is a framework based on the micro-economic paradigm and provides an intuitive and easy to use, yet very powerful way for users to specify their preferences for QoS and QoD. Beyond presenting the QC framework, we present results of applying it in two different domains: scheduling in real-time web-databases and replica selection in distributed query processing.