A Distributed Orchestration Algorithm for Edge Computing Resources with Guarantees

Edge Computing brings flexibility and scalability of virtualization technologies at the edge of the network, enabling service providers to deploy new applications over a richer network infrastructure. However, the coexistence of such variety of applications on the same infrastructure exacerbates the already challenging problem of coordinating resource allocation while preserving the resource assignment optimality. In fact, (i) each application can potentially require different optimization criteria due to their heterogeneous requirements, and (ii) we may not count on a centralized orchestrator due to the highly dynamic nature of edge networks. To solve this problem, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different applications running over a common edge infrastructure.We designed DRAGON to guarantee both a bound on convergence time and an optimal (1-1/e)-approximation with respect to the Pareto optimal resource assignment. We evaluate convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to traditional orchestration approaches

A Service-Defined Approach for Orchestration of Heterogeneous Applications in Cloud/Edge Platforms

Edge Computing is moving resources toward the network borders, thus enabling the deployment of a pool of new applications that benefit from the new distributed infrastructure. However, due to the heterogeneity of such applications, specific orchestration strategies need to be adopted for each deployment request. Each application can potentially require different optimization criteria and may prefer particular reactions upon the occurrence of the same event. This paper presents a Service- Defined approach for orchestrating cloud/edge services in a distributed fashion, where each application can define its own orchestration strategy by means of declarative statements, which are parsed into a Service-Defined Orchestrator (SDO). Moreover, to coordinate the coexistence of a variety of SDOs on the same infrastructure while preserving the resource assignment optimality, we present DRAGON, a Distributed Resource AssiGnment and OrchestratioN algorithm that seeks optimal partitioning of shared resources between different actors. We evaluate the advantages of our novel Service-Defined orchestration approach over some representative edge use cases, as well as measure convergence and performance of DRAGON on a prototype implementation, assessing the benefits compared to conventional orchestration approaches

Server Assignment with Time-Varying Workloads in Mobile Edge Computing

Mobile Edge Computing (MEC) has emerged as a viable technology for mobile operators to push computing resources closer to the users so that requests can be served locally without long-haul crossing of the network core, thus improving network efficiency and user experience. In MEC, commodity servers are deployed in the edge to form a distributed network of mini datacenters. A consequential task is to partition the user cells into groups, each to be served by an edge server, to maximize the offloading to the edge. The conventional setting for this problem in the literature is: (1) assume that the interaction workload between two cells has a known interaction rate, (2) compute a partition optimized for these rates, for example, by solving a weighted-graph partitioning problem, and (3) for a time-varying workload, incrementally re-compute the partition when the interaction rates change. This setting is suitable only for infrequently changing workloads. The operational and computation costs of the partition update can be expensive and it is difficult to estimate interaction rates if they are not stable for a long period. Hence, this dissertation is motivated by the following questions: is there an efficient way to compute just one partition, no update needed, that is robust for a highly time-varying workload? Especially, what if we do not know the interaction rates at any time? By ``robust , we mean that the cost to process the workload at any given time remains small despite unpredictable workload increases. Another consideration is geographical awareness. The edge servers should be geographically close to their respective user cells for maximizing the benefits of MEC. This dissertation presents novel solutions to address these issues. The theoretical findings are substantiated by evaluation studies using real-world data

Edge Assignment and Data Valuation in Federated Learning

Federated Learning (FL) is a recent Machine Learning method for training with private data separately stored in local machines without gathering them into one place for central learning. It was born to address the following challenges when applying Machine Learning in practice: (1) Communication cost: Most real-world data that can be useful for training are locally collected; to bring them all to one place for central learning can be expensive, especially in real-time learning applications when time is of the essence, for example, predicting the next word when texting on a smartphone; and (2) Privacy protection: Many applications must protect data privacy, such as those in the healthcare field; the private data can only be seen by its local owner and as such the learning may only use a content-hiding representation of this data, which is much less informative. To fulfill FL’s promise, this dissertation addresses three important problems regarding the need for good training data, system scalability, and uncertainty robustness: 1. The effectiveness of FL depends critically on the quality of the local training data. We should not only incentivize participants who have good training data but also minimize the effect of bad training data on the overall learning procedure. The first problem of my research is to determine a score to value a participant’s contribution. My approach is to compute such a score based on Shapley Value (SV), a concept of cooperative game theory for profit allocation in a coalition game. In this direction, the main challenge is due to the exponential time complexity of the SV computation, which is further complicated by the iterative manner of the FL learning algorithm. I propose a fast and effective valuation method that overcomes this challenge. 2. On scalability, FL depends on a central server for repeated aggregation of local training models, which is prone to become a performance bottleneck. A reasonable approach is to combine FL with Edge Computing: introduce a layer of edge servers to each serve as a regional aggregator to offload the main server. The scalability is thus improved, however at the cost of learning accuracy. The second problem of my research is to optimize this tradeoff. This dissertation shows that this cost can be alleviated with a proper choice of edge server assignment: which edge servers should aggregate the training models from which local machines. Specifically, I propose an assignment solution that is especially useful for the case of non-IID training data which is well-known to hinder today’s FL performance. 3. FL participants may decide on their own what devices they run on, their computing capabilities, and how often they communicate the training model with the aggregation server. The workloads incurred by them are therefore time-varying, and unpredictably. The server capacities are finite and can vary too. The third problem of my research is to compute an edge server assignment that is robust to such dynamics and uncertainties. I propose a stochastic approach to solving this problem