Abstract—Many cloud services are running on geographically distributed datacenters for better reliability and performance. We consider the emerging problem of joint request mapping and response routing with distributed datacenters in this paper. We formulate the problem as a general workload management optimization. A utility function is used to capture various performance goals, and the location diversity of electricity and bandwidth costs are realistically modeled. To solve the large-scale optimization, we develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). Following a decomposition-coordination approach, our algorithm allows for a parallel implementation in a datacenter where each server solves a small sub-problem. The solutions are coordinated to find an optimal solution to the global problem. Our algorithm converges to near optimum within tens of iterations, and is insensitive to step sizes. We empirically evaluate our algorithm based on real-world workload traces and latency measurements, and demonstrate its effectiveness compared to conventional methods. I.

Abstract — Cloud computing has attracted significant attention due to the increasing demand for low-cost, high performance, and energy-efficient computing. In this large-scale, heterogeneous, multi-user environment of a cloud system, profit maximization for the cloud service provider (CSP) is a key objective. In this paper, the problem of global optimization of the cloud system operation (in the sense of lowering operation costs by maximizing energy efficiency, while satisfying user deadlines defined in the Service Level Agreements) is addressed from the perspective of the CSP. The modeling of the workload dictates viable approaches toward cloud operation optimization. Of the two current models: independent batch requests and task graphs with dependencies, we adopt the later. This fine-grained treatment of workloads provides many opportunities for energy and performance optimizations, thus enabling the CSP to meet user deadlines at lower operation costs. However, these optimizations require additional efforts in terms of resource provisioning, virtual machine placement, and task scheduling. Such issues are addressed in a holistic fashion in the proposed framework. In this cloud environment, users can construct their own services and applications based on the available set of virtual machines, but are relieved from the burden of resource provisioning and task scheduling. The CSP will then capitalize on the data parallelisms in each user workload, effectively manage the collective user requests, and apply custom optimizations to create a global energy cost and deadline-aware cloud platform. I.

Abstract—Advances in future computing to support emerging sensor applications are becoming more important as the need to better utilize computation and communication resources and make them energy efficient. As a result, it is predicted that intelligent devices and networks, including mobile wireless sensor networks (MWSN), will become the new interfaces to support future applications. In this paper, we pro-pose a novel approach to minimize energy consumption of processing an application in MWSN while satisfying a certain completion time requirement. Specifically, by introducing the concept of cooperation, the logics and related computation tasks can be optimally partitioned, offloaded and executed with the help of peer sensor nodes, thus the proposed solution can be treated as a joint optimization of computing and networking resources. Moreover, for a network with multiple mobile wireless sensor nodes, we propose energy efficient cooperation node selection strategies to offer a tradeoff between fairness and energy consumption. Our performance analysis is supplemented by simulation results to show the significant energy saving of the proposed solution. Index Terms—Edge and cloud computing, mobile wireless sensor networks, Cooperation 1

Abstract—Large-scale Internet applications, such as content distribution networks, are deployed in a geographically dis-tributed manner and emit massive amounts of carbon footprint at the data center. To provide uniform low access latencies, Cisco has introduced Fog computing as a new paradigm which can transform the network edge into a distributed computing infrastructure for applications. Fog nodes are geographically distributed and the deployment size at each location reflects the regional demand for the application. Thus, we need to control the fraction of user traffic to data center to maximize the social welfare. In this paper, we consider the emerging problem of joint resource allocation and minimizing carbon footprint problem for video streaming service in Fog computing. To solve the large-scale optimization, we develop a distributed algorithm based on the proximal algorithm and alternating direction method of multipliers (ADMM). The numerical results show that our algorithm converges to near optimum within fifteen iterations, and is insensitive to step sizes. I.