Socially Trusted Collaborative Edge Computing in Ultra Dense Networks

Small cell base stations (SBSs) endowed with cloud-like computing capabilities are considered as a key enabler of edge computing (EC), which provides ultra-low latency and location-awareness for a variety of emerging mobile applications and the Internet of Things. However, due to the limited computation resources of an individual SBS, providing computation services of high quality to its users faces significant challenges when it is overloaded with an excessive amount of computation workload. In this paper, we propose collaborative edge computing among SBSs by forming SBS coalitions to share computation resources with each other, thereby accommodating more computation workload in the edge system and reducing reliance on the remote cloud. A novel SBS coalition formation algorithm is developed based on the coalitional game theory to cope with various new challenges in small-cell-based edge systems, including the co-provisioning of radio access and computing services, cooperation incentives, and potential security risks. To address these challenges, the proposed method (1) allows collaboration at both the user-SBS association stage and the SBS peer offloading stage by exploiting the ultra dense deployment of SBSs, (2) develops a payment-based incentive mechanism that implements proportionally fair utility division to form stable SBS coalitions, and (3) builds a social trust network for managing security risks among SBSs due to collaboration. Systematic simulations in practical scenarios are carried out to evaluate the efficacy and performance of the proposed method, which shows that tremendous edge computing performance improvement can be achieved.Comment: arXiv admin note: text overlap with arXiv:1010.4501 by other author

Scheduling of data-intensive workloads in a brokered virtualized environment

Providing performance predictability guarantees is increasingly important in cloud platforms, especially for data-intensive applications, for which performance depends greatly on the available rates of data transfer between the various computing/storage hosts underlying the virtualized resources assigned to the application. With the increased prevalence of brokerage services in cloud platforms, there is a need for resource management solutions that consider the brokered nature of these workloads, as well as the special demands of their intra-dependent components. In this paper, we present an offline mechanism for scheduling batches of brokered data-intensive workloads, which can be extended to an online setting. The objective of the mechanism is to decide on a packing of the workloads in a batch that minimizes the broker's incurred costs, Moreover, considering the brokered nature of such workloads, we define a payment model that provides incentives to these workloads to be scheduled as part of a batch, which we analyze theoretically. Finally, we evaluate the proposed scheduling algorithm, and exemplify the fairness of the payment model in practical settings via trace-based experiments

Support Service for Reciprocal Computational Resource Sharing in Wireless Community Networks

In community networks, individuals and local organizations from a geographic area team up to create and run a community-owned IP network to satisfy the community's demand for ICT, such as facilitating Internet access and providing services of local interest. Most current community networks use wireless links for the node interconnection, applying off-the-shelf wireless equipment. While IP connectivity over the shared network infrastructure is successfully achieved, the deployment of applications in community networks is surprisingly low. To address the solution of this problem, we propose in this paper a service to incentivize the contribution of computing and storage as cloud resources to community networks, in order to stimulate the deployment of services and applications. Our final goal is the vision that in the long term, the users of community networks will not need to consume applications from the Internet, but find them within the wireless community network

ERA: A Framework for Economic Resource Allocation for the Cloud

Cloud computing has reached significant maturity from a systems perspective, but currently deployed solutions rely on rather basic economics mechanisms that yield suboptimal allocation of the costly hardware resources. In this paper we present Economic Resource Allocation (ERA), a complete framework for scheduling and pricing cloud resources, aimed at increasing the efficiency of cloud resources usage by allocating resources according to economic principles. The ERA architecture carefully abstracts the underlying cloud infrastructure, enabling the development of scheduling and pricing algorithms independently of the concrete lower-level cloud infrastructure and independently of its concerns. Specifically, ERA is designed as a flexible layer that can sit on top of any cloud system and interfaces with both the cloud resource manager and with the users who reserve resources to run their jobs. The jobs are scheduled based on prices that are dynamically calculated according to the predicted demand. Additionally, ERA provides a key internal API to pluggable algorithmic modules that include scheduling, pricing and demand prediction. We provide a proof-of-concept software and demonstrate the effectiveness of the architecture by testing ERA over both public and private cloud systems -- Azure Batch of Microsoft and Hadoop/YARN. A broader intent of our work is to foster collaborations between economics and system communities. To that end, we have developed a simulation platform via which economics and system experts can test their algorithmic implementations