Max-k-Cut and correlation clustering are fundamental graph partitioning problems. For a graph $G=(V,E)$ with $n$ vertices, the methods with the best approximation guarantees for Max-k-Cut and the Max-Agree variant of correlation clustering involve solving SDPs with $\mathcal{O}(n^2)$ constraints and variables. Large-scale instances of SDPs, thus, present a memory bottleneck. In this paper, we develop simple polynomial-time Gaussian sampling-based algorithms for these two problems that use $\mathcal{O}(n+|E|)$ memory and nearly achieve the best existing approximation guarantees. For dense graphs arriving in a stream, we eliminate the dependence on $|E|$ in the storage complexity at the cost of a slightly worse approximation ratio by combining our approach with sparsification.

In recent times, there is a paradigm shift to cloud services that offer on-demand computer system resources, especially data storage and computing power. The main reason for the shift is that it removes the user's active participation to perform computationally intensive tasks. However, current cloud-based services incur high user latency as being deployed very far from the user. One alternative solution to the traditional cloud-based paradigm is drone-based edge computing. In drone edge computing, drones are located near the user and deployed to provide data offload services. There have been many works that have addressed the issue of efficient task assignment in edge devices. This paper presents a concrete analytical performance model for drone cloudlet networks and factors that influence the service response time to the user. The results can be helpful for network administrators to make the current edge computing paradigm faster, more robust and, cost-effective.

We consider a content distribution network (CDN) in which data hubs or servers are established in multiple locations to cater to local demands. The distributions of data to these hubs along with related network design problems (such as hub location and user assignment) are the key decision problems to consider to minimize the total routing cost. A new model for allocation of segments is introduced in Sen, Krishnamoorthy, Rangaraj and Narayanan, Comput Oper 62 (2015), 282–295, in which local preferences guide the database partitioning process, and the servers are fully connected to each other. In this article, we develop a simulated annealing (SA) approach (referred to as SA‐mesh) to solve this problem and compare its performance with the corresponding mixed‐integer linear programming (MILP) formulation. We also formulate a much harder variant of the problem in which servers are interconnected by a tree. We develop a SA algorithm (referred to as SA‐tree) for this variant, in which a local search is incorporated to find a suboptimal tree backbone. We use a customized data structure based on linked lists to represent a solution in our algorithms. This enables our algorithms to scale to much larger instances of the problem. We use optimal solutions and the benchmarks obtained by CPLEX to justify the performance of our algorithms. © 2016 Wiley Periodicals, Inc. NETWORKS, Vol. 68(1), 4–22 2016