On Uncoordinated Service Placement in Edge-Clouds

Edge computing has emerged as a new paradigm to bring cloud applications closer to users for increased performance. ISPs have the opportunity to deploy private edge-clouds in their infrastructure to generate additional revenue by providing ultra-low latency applications to local users. We envision a rapid increase in the number of such applications for “edge” networks in the near future with virtual/augmented reality (VR/AR), networked gaming, wearable cognitive assistance, autonomous driving and IoT analytics having already been proposed for edge- clouds instead of the central clouds to improve performance. This raises new challenges as the complexity of the resource allocation problem for multiple services with latency deadlines (i.e., which service to place at which node of the edge-cloud in order to satisfy the latency constraints) becomes significant. In this paper, we propose a set of practical, uncoordinated strategies for service placement in edge-clouds. Through extensive simulations using both synthetic and real-world trace data, we demonstrate that uncoordinated strategies can perform comparatively well with the optimal placement solution, which satisfies the maximum amount of user requests

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

A Hierarchical Receding Horizon Algorithm for QoS-driven control of Multi-IaaS Applications

Cloud Computing is emerging as a major trend in ICT industry. However, as with any new technology, new major challenges lie ahead, one of them con- cerning the resource provisioning. Indeed, modern Cloud applications deal with a dynamic context that requires a continuous adaptation process in order to meet sat- isfactory Quality of Service (QoS) but even the most titled Cloud platform provide just simple rule-based tools; the rudimentary autoscaling mechanisms that can be carried out may be unsuitable in many situations as they do not prevent SLA vio- lations, but only react to them. In addition, these approaches are inherently static and cannot catch the dynamic behavior of the application. This situation calls for advanced solutions designed to provide Cloud resources in a predictive and dy- namic way. This work presents capacity allocation algorithms, whose goal is to minimize the total execution cost, while satisfying some constraints on the average response time of Cloud based applications. We propose a receding horizon con- trol technique, which can be employed to handle multiple classes of requests. An extensive evaluation of our solution against an Oracle with perfect knowledge of the future and well-known heuristics presented in the literature is provided. The analysis shows that our solution outperforms the heuristics producing results very close to the optimal ones, and reducing the number of QoS violations (in the worst case we violated QoS constraints for only 8 minutes over a day versus up to 260 minutes of other approaches). Furthermore, a sensitivity analysis over two differ- ent time scales indicates that finer grained time scales are more appropriate for spiky workloads, whereas smooth traffic conditions are better handled by coarser grained time scales. Our analytical results are validated through simulation, which shows also the impact on our solution of Cloud environment random perturbations. Finally, experiments on a prototype environment demonstrate the effectiveness of our approach under real workloads

Enhancing Mobile Capacity through Generic and Efficient Resource Sharing

Mobile computing devices are becoming indispensable in every aspect of human life, but diverse hardware limits make current mobile devices far from ideal for satisfying the performance requirements of modern mobile applications and being used anytime, anywhere. Mobile Cloud Computing (MCC) could be a viable solution to bypass these limits which enhances the mobile capacity through cooperative resource sharing, but is challenging due to the heterogeneity of mobile devices in both hardware and software aspects. Traditional schemes either restrict to share a specific type of hardware resource within individual applications, which requires tremendous reprogramming efforts; or disregard the runtime execution pattern and transmit too much unnecessary data, resulting in bandwidth and energy waste.To address the aforementioned challenges, we present three novel designs of resource sharing frameworks which utilize the various system resources from a remote or personal cloud to enhance the mobile capacity in a generic and efficient manner. First, we propose a novel method-level offloading methodology to run the mobile computational workload on the remote cloud CPU. Minimized data transmission is achieved during such offloading by identifying and selectively migrating the memory contexts which are necessary to the method execution. Second, we present a systematic framework to maximize the mobile performance of graphics rendering with the remote cloud GPU, during which the redundant pixels across consecutive frames are reused to reduce the transmitted frame data. Last, we propose to exploit the unified mobile OS services and generically interconnect heterogeneous mobile devices towards a personal mobile cloud, which complement and flexibly share mobile peripherals (e.g., sensors, camera) with each other