Mobile Augmented Reality (MAR) applications face performance challenges due to their high computational demands and need for low-latency responses. Traditional approaches like on-device storage or reactive data fetching from the cloud often result in limited augmented reality (AR) experiences. Edge caching, which caches AR objects closer to the user, provides a promising solution. However, existing edge caching approaches do not consider AR-specific features such as AR object sizes, user interactions, user’s field of view and physical location in a coherent manner. This paper investigates how to further optimize edge caching by employing AR-aware prefetching techniques. We present ASTRA, a prefetching framework tailored for mobile augmented reality edge caches. It integrates object associations derived from user interaction patterns with spatial awareness based on the user’s physical location and field of view. This approach employs an association factor per object that considers the recency of object co-access; and a lazy fetching strategy that prioritizes prefetching only when the user is in close proximity to the virtual objects. Furthermore, ASTRA incorporates an adaptive tuning algorithm for minimum support in association rule generation to minimize the computation overhead, making it a distinct and effective solution for enhancing user experience in AR applications by ensuring timely virtual object availability.Through extensive evaluation using both synthetic and real-world workloads, we demonstrate that ASTRA significantly improves cache hit rates compared to current prefetching algorithms, achieving gains in hit rate of upto 35% and end-to-end latency by upto 14%. Further, we demonstrate that the adaptive tuning algorithm that automatically tunes minimum support further improves the hit rate of ASTRA by 10%. Our findings demonstrate the potential of ASTRA to substantially enhance the user experience in MAR applications by ensuring the timely availability of virtual objects.

Modern mobile CPU software pose challenges for conventional instruction cache replacement policies due to their complex runtime behavior causing high reuse distance between executions of the same instruction.Mobile code commonly suffers from large amounts of stalls in the CPU frontend and thus starvation of the rest of the CPU resources.Complexity of these applications and their code footprint are projected to grow at a rate faster than available on-chip memory due to power and area constraints, making conventional hardware-centric methods for managing instruction caches to be inadequate.We present a novel software-hardware co-design approach called TRRIP (Temperature-based Re-Reference Interval Prediction) that enables the compiler to analyze, classify, and transform code based on "temperature" (hot/cold), and to provide the hardware with a summary of code temperature information through a well-defined OS interface based on using code page attributes.TRRIP's lightweight hardware extension employs code temperature attributes to optimize the instruction cache replacement policy resulting in the eviction rate reduction of hot code.TRRIP is designed to be practical and adoptable in real mobile systems that have strict feature requirements on both the software and hardware components.TRRIP can reduce the L2 MPKI for instructions by 26.5% resulting in geomean speedup of 3.9%, on top of RRIP cache replacement running mobile code already optimized using PGO.

Edge computing has enabled a large set of emerging edge applications by exploiting data proximity and offloading computation-intensive workloads to nearby edge servers. However, supporting edge application users at scale poses challenges due to limited point-of-presence edge sites and constrained elasticity. In this paper, we introduce a densely-distributed edge resource model that leverages capacity-constrained volunteer edge nodes to support elastic computation offloading. Our model also enables the use of geo-distributed edge nodes to further support elasticity. Collectively, these features raise the issue of edge selection. We present a distributed edge selection approach that relies on client-centric views of available edge nodes to optimize average end-to-end latency, with considerations of system heterogeneity, resource contention and node churn. Elasticity is achieved by fine-grained performance probing, dynamic load balancing, and proactive multi-edge node connections per client. Evaluations are conducted in both real-world volunteer environments and emulated platforms to show how a common edge application, namely AR-based cognitive assistance, can benefit from our approach and deliver low-latency responses to distributed users at scale.

Federated Learning is a machine learning paradigm where a global model is trained in-situ across a large number of distributed edge devices. While this technique avoids the cost of transferring data to a central location and achieves a strong degree of privacy, it presents additional challenges due to the heterogeneous hardware resources available for training. Furthermore, data is not independent and identically distributed (IID) across all edge devices, resulting in statistical heterogeneity across devices. Due to these constraints, client selection strategies play an important role for timely convergence during model training. Existing strategies ensure that each individual device is included, at least periodically, in the training process. In this work, we propose HACCS, a Heterogeneity-Aware Clustered Client Selection system that identifies and exploits the statistical heterogeneity by representing all distinguishable data distributions instead of individual devices in the training process. HACCS is robust to individual device dropout, provided other devices in the system have similar data distributions. We propose privacy-preserving methods for estimating these client distributions and clustering them. We also propose strategies for leveraging these clusters to make scheduling decisions in a federated learning system. Our evaluation on real-world datasets suggests that our framework can provide 18% −38% reduction in time to convergence compared to the state of the art without any compromise in accuracy.

Edge environments are generating an increasingly large amount of data due to the proliferation of edge devices. Accommodating this large influx of data at edge servers is a challenging issue. While some data can be processed as it is generated, others must be stored for later access. This paper proposes the features that a new edge-native storage system must possess including support for user mobility and node fluctuation. To motivate this, we first describe several emerging edge applications and their data needs. We then describe the challenges in meeting these needs. We then evaluate an out-of-the-box cloud storage system, Cassandra, to assess it's suitability as an edge storage system due to many edge-friendly features. We determined that while a cloud-based storage system can be ported to the edge meeting some of the challenges, other challenges require new solutions. Based on the challenges and the results of Cassandra case study, we propose a set of design principles for a new edge-native storage system.

Parallelizing data mining algorithms has become a necessity as we try to mine ever increasing volumes of data. Spatial data mining algorithms like Dbscan, Optics, Slink, etc. have been parallelized to exploit a cluster infrastructure. The efficiency achieved by existing algorithms can be attributed to spatial locality preservation using spatial indexing structures like k-d-tree, quad-tree, grid files, etc. for distributing data among cluster nodes. However, these indexing structures are static in nature, i.e., they need to scan the entire dataset to determine the partitioning coordinates. This results in high data distribution cost when the data size is large. In this paper, we propose a dynamic distributed data structure, DD-Rtree, which preserves spatial locality while distributing data across compute nodes in a shared nothing environment. Moreover, DD-Rtree is dynamic, i.e., it can be constructed incrementally making it useful for handling big data. We compare the quality of data distribution achieved by DD-Rtree with one of the recent distributed indexing structure, SD-Rtree. We also compare the efficiency of queries supported by these indexing structures along with the overall efficiency of DBSCAN algorithm. Our experimental results show that DD-Rtree achieves better data distribution and thereby resulting in improved overall efficiency.