Many emerging online stream processing services require the consideration of quality of service (QoS), which is highly dependent on the placement of services at various hosts. This paper investigates the QoS-aware placement problems of stream processing services under different contexts. On condition that the client demands are stable, the QoS-aware placement problem aiming to minimize the cost when servers are CPU-uncapacitated, is equivalent to the set cover problem, and can be solved by a greedy algorithm with approximation factor O (log n ), where n is the number of clients. However, when CPU capacity constraints on servers are taken into account, the QoS-aware placement problem cannot be approximated unless P=NP. Therefore, we propose two heuristic algorithms: (1) ISCA (Iterated Set Cover-based Algorithm) and (2) KBA (Knapsack-Based Algorithm). We also consider the placement problem of client demands increasing over time. Two objectives, called extension factor and system lifetime, are proposed for demand increment-blind and increment-aware models respectively. Both of them can be solved by extending ISCA and KBA . The experimental results show that ISCA and KBA have distinct effects on different demand sizes. ISCA is more efficient when client demands are relatively small, while KBA performs better for larger demands.

This paper focuses on optimizing the virtual machine (VM) placement to reduce the bandwidth usage in modern virtualization based data centers. Existing solutions require all the traffic rates between any pair of VMs to be known beforehand, which may incur significant overhead or even be impossible. To reduce such overhead, we adopt the product traffic pattern model to characterize the traffic rates. In this model, each VM is associated with an activity level, and the normalized traffic rate between each pair of VMs is approximated by the product of their activity levels. Specifically, we consider the VM placement problem with product traffic in data centers that follow the Clique or VL2 architecture. We then present an optimal solution for the special case where all the physical machines (PMs) connect to the same switch. By extending the intrinsic idea of such solution, we further develop a simple yet efficient algorithm for the general case. We prove its optimality in a common case where the PMs are homogeneous, and also derive its approximation ratio in the general case. Finally, we demonstrate via simulations that the performance of our algorithm is near-optimal under practical settings.

Context-aware systems provide proactive services for users based on environment contexts reasoning. This paper proposes context-aware systems, and addresses the important issues of modeling and evolutionary context rules. Service modeling is based on ontology technology to describe context-aware systems, and service reasoning rules are constantly extracted and evolved using tough-set theory by accommodating new data to evolutionary mining.

Broadcast is a fundamental operation in wireless ad hoc networks (WANETs). To design efficient broadcast protocols, one of the most important concerns is to reduce broadcast redundancy. In conventional WANETs where nodes are always active, due to the broadcast nature of wireless medium, minimizing broadcast redundancy is equivalent to finding a Minimum Connected Dominating Set (MCDS). However, this is not true for uncoordinated duty-cycled WANETs, where each node periodically switches between active and sleep states, and can only hear messages when it is active. In this paper, we investigate the minimum redundancy broadcast problem in uncoordinated duty-cycled WANETs. We first show that by modifying the conventional CDS-based approaches properly, a constant-approximation broadcast algorithm (MCA) can be obtained. We then propose a hierarchical CDS-based algorithm (HCA), improving the best known approximation ratio from 20 to 13.67. Both algorithms are distributed, and with low time and message complexities. Simulation results show that our algorithms achieve about 5%-30% performance improvement over the state-of-the art scheme.

Service composition is a useful technology to achieve dynamic requests. But in cloud, the cloud provider should guarantee the QoS of the user request as well. Previous work investigated how to select proper available replicas (the similar functional services located on different physical nodes) to effectively achieve the composite services. However, as the requirements for some service grow, even the optimal selection strategy could not satisfy all the QoS requests, if it is only based on the existed replicas. In this case, cloud provider should deploy more replicas to meet the growing requests. Some existing literatures have addressed the service redeployment problem, aiming to optimize the overall or average performance; however, few of them can guarantee QoS of each request. This paper investigates QoS-aware service redeployment problem (SRP), with objective to minimize the redeployment cost. We show that, it is NP-hard to decide whether there exists a feasible solution of SRP. Thus we propose a novel heuristic algorithm SRA, which can find a solution such that most of the requests can be satisfied, while the deployment cost is minimized. Experimental results show that our approach is effective and efficient.

Service composition is a useful technique to assemble light, independent services to meet the complicated and dynamic requirements. Previous research has addressed the quality-of-service (QoS) aware composition path selection problem. However, as the requests growing, the selected service composition path may violate the QoS requirements. In this case, more service replicas should be deployed on suitable nodes to improve the QoS. But which service component should be selected and where these service replicas should be deployed is a challenge. In this paper, we make a deep study on this service replication problem. We give a detailed description of service replication triggering time. And then, we propose LDCS ( L ongest D elay Service C omponent S election) to select the bottle-neck service component by evaluating the real-time performance of all these components. Finally, we employ MACP ( M aximum A vailable C apacity P ath) algorithm to select a suitable node to deploy this service replica. Simulation results approve that our approach is effective and efficient.