Optimizing storage performance in public cloud platforms

Cloud computing is an elastic computing model where users can lease computing and storage resources on demand from a remote infrastructure. It is gaining popularity due to its low cost, high reliability, and wide availability. With the emergence of public cloud storage platforms like Amazon, Microsoft, and Google, individual applications and enterprise storage are being deployed on Clouds. However, a serious impediment to its wider deployment is the relative lack of effective data management services. Our experiments, as well as industry reports, have shown that the performance and service-level agreement (SLA) cannot be guaranteed when the data is served over public Clouds. The relatively slow access to persistent data and large variability in cloud storage I/O performance can significantly degrade the performance of data-intensive applications. This paper addresses the issue of I/O performance fluctuation over public cloud platforms and we propose a middleware called CloudMW between the Cloud storage and clients to provide the storage services with better performance and SLA satisfaction. Some technologies, including data virtualization, data chunking, caching, and replication, are integrated into CloudMW to achieve a more stable and predictable performance, and permit flexible sharing of storage among the virtual machines (VMs). Experimental results based on Amazon Web Services (AWS) show that CloudMW is able to improve the stability and help provide better SLAs and data sharing for cloud storage.

Who Should Be Invited to My Party:A Size-Constrained k-Core Problem in Social Networks

In this paper,we investigate the problem of a size-constrained k-core group query (SCCGQ)in social networks, taking both user closeness and network topology into consideration.More specifically,SCCGQ intends to find a group of h users that has the highest social closeness while being a k-core.SCCGQ can be widely applied to event planning,task assignment,social analysis,and many other fields.In contrast to existing work on the k-core detection problem,which aims to find a k-core in a social network,SCCGQ not only focuses on k-core detection but also takes size constraints into consideration.Although the conventional k-core detection problem can be solved in linear time,SCCGQ has a higher complexity.To solve the problem of SCCGQ,we propose a Blast Scatter (BS)algorithm,which appoints the query node as the center to begin outward expansions via breadth search.In each outward expansion,BS finds a new center through a greedy strategy and then selects multiple neighbors of the center.To speed up the BS algorithm,we propose an advanced search algorithm,called Bounded Extension (BE).Specifically,BE combines an effective social distance pruning strategy and a tight upper bound of social closeness to prune the search space considerably.In addition,we propose an offiine social-aware index to accelerate the query processing.Finally,our experimental results demonstrate the efficiency and effectiveness of our proposed algorithms on large real-world social networks.