Search

Mobile cloud provides smart phone users with unprecedented opportunities to enjoy the abundant computing and storage resources of cloud computing. One viable scheme is to offload computational intensive applications to a mobile phone's agent in the cloud, which could be implemented as a thin virtual machine (VM), also termed as phone clone. Due to shared hardware components among co-resident VMs, a VM is subject to covert channel attacks and may potentially leak information to other VMs located in the same physical host. In this paper, we address two critical problems: how to allocate phone clones to minimize the risk of information leakage and how to migrate phone clones whenever the risk becomes higher than a given threshold. We design SWAP: a security aware provisioning and migration scheme for phone clones. Our solution utilizes the spatial and temporal features of phone clones, and by considering the online social connection of mobile users, we greatly simplify the search space of the optimal solution. Furthermore, we study the tradeoffs among security, cost, and load balancing in phone clone provisioning. We evaluate our solution using Reality Mining and Nodobo dataset. Experimental results indicate that our algorithms are nearly optimal for phone clone allocation and are effective in maintaining low security risk and minimizing the number of phone clone migrations.

One of the major problem in routing messages in an Opportunistic Network (OpNet) is the absence of a complete end-to-end path from the source to the destination. To deal with such adversity many routing protocols employ epidemic techniques to flood the network with multiple copies of a message. Although flooding many copies of a message in the network increases the chance of the message delivery, it can lead to a very high network overhead and can cause network congestion. On the other hand, the quota based routing protocols restrict the maximum number of copies of a message in the network by setting a replication factor. Such protocols maintain comparatively low overhead but suffer from low delivery ratio. In this paper, we introduce a novel quota based routing protocol, TTL Based Routing (TBR). TBR prioritizes both the schedule of messages to be forwarded to the neighbor and the schedule of messages to be dropped from the buffer. These priorities are based on massage time to live (TTL), message hop count, message replication count and message size. The TTL based message priority enhances the chance of message delivery by giving preference to the messages having the earliest deadline. Our simulation results show that, TBR achieves more than 10-15% higher delivery ratio than that of the available quota based routing protocols while incurring 10% to 15% less overhead. TBR not only matches the delivery ratio of flooding based routing protocols but also achieves better delivery ratio with 70% to 75% less overhead.

Virtual private networks (VPNs) provide a secure and reliable communication between customer sites over a shared network. With increase in number and size of VPNs, service providers need efficient provisioning techniques that adapt to customer demands. The recently proposed hose model for VPN alleviates the scalability problem of the pipe model by reserving for aggregate ingress and egress bandwidths instead of between every pair of VPN endpoints. Existing studies on quality of service guarantees in the hose model either deal only with bandwidth requirements or regard the delay limit as the main objective ignoring the bandwidth cost. In this work we propose a new approach to enhance the hose model to guarantee delay limits between endpoints while optimizing the provisioning cost. We connect VPN endpoints using a tree structure and our algorithm attempts to optimize the total bandwidth reserved on edges of the VPN tree. Further, we introduce a fast and efficient algorithm in finding the shared VPN tree to reduce the total provisioning cost compared to the results proposed in previous works. Our proposed approach takes into account the user preferences in meeting the delay limits and provisioning cost to find the optimal solution of resource allocation problem. Our simulation results indicate that the VPN trees constructed by our proposed algorithm meet maximum end-to-end delay limits while reducing the bandwidth requirements as compared to previously proposed algorithms.

Mobile Telecom Cloud (MTC) refers to cloud services provided by mobile telecommunication companies. Since mobile network operators support the last-mile Internet access to users, they have advantages over other cloud providers by providing users with better mobile connectivity and required quality of service (QoS). Compared to other third-party cloud (TPC) providers such as Amazon and Google, however, MTC providers usually have a much smaller scale of computing resources. This paper presents an approach for MTC providers to take advantage of both MTC and TPC so that they can offer better and cheaper cloud services to their customers. The opportunity comes from the discounts that TPC normally offers to group buyers as well as the better QoS that MTC can support. Our solution is to allow MTC providers to act as a brokerage, called MTC brokerage, to broker TPC cloud resources and integrate the resources reserved from TPC with those of their own MTC. We address the technical challenges of optimally allocating users' cloud requests to MTC and TPC data centres to meet users' QoS requirement with minimum cost. We also study the price range that can be profitable to a MTC brokerage. We then investigate the resource reservation problem with dynamic request changes. We evaluate our solution using real Google traces collected over a 29-day period from a Google cluster. The results show that our solution results in cost savings for both MTC providers and their customers.